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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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"""
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All layers just related to the neural network.
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"""

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from __future__ import print_function

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import numpy as np
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import six
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import os
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import inspect
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from ..layer_helper import LayerHelper
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from ..initializer import Normal, Constant, NumpyArrayInitializer
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from ..framework import Variable, OpProtoHolder, in_dygraph_mode
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from ..dygraph import base
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from ..param_attr import ParamAttr
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from .layer_function_generator import autodoc, templatedoc, _generate_doc_string_
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from .tensor import concat, assign
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from . import utils
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from .. import unique_name
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from functools import reduce
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from .. import core
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from ..dygraph import layers
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__all__ = [
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    'fc',
    'embedding',
    'dynamic_lstm',
    'dynamic_lstmp',
    'dynamic_gru',
    'gru_unit',
    'linear_chain_crf',
    'crf_decoding',
    'cos_sim',
    'cross_entropy',
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    'bpr_loss',
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    'square_error_cost',
    'chunk_eval',
    'sequence_conv',
    'conv2d',
    'conv3d',
    'sequence_pool',
    'sequence_softmax',
    'softmax',
    'pool2d',
    'pool3d',
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    'adaptive_pool2d',
    'adaptive_pool3d',
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    'batch_norm',
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    'data_norm',
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    'beam_search_decode',
    'conv2d_transpose',
    'conv3d_transpose',
    'sequence_expand',
    'sequence_expand_as',
    'sequence_pad',
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    'sequence_unpad',
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    'lstm_unit',
    'reduce_sum',
    'reduce_mean',
    'reduce_max',
    'reduce_min',
    'reduce_prod',
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    'reduce_all',
    'reduce_any',
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    'sequence_first_step',
    'sequence_last_step',
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    'sequence_slice',
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    'dropout',
    'split',
    'ctc_greedy_decoder',
    'edit_distance',
    'l2_normalize',
    'matmul',
    'topk',
    'warpctc',
    'sequence_reshape',
    'transpose',
    'im2sequence',
    'nce',
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    'sampled_softmax_with_cross_entropy',
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    'hsigmoid',
    'beam_search',
    'row_conv',
    'multiplex',
    'layer_norm',
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    'group_norm',
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    'spectral_norm',
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    'softmax_with_cross_entropy',
    'smooth_l1',
    'one_hot',
    'autoincreased_step_counter',
    'reshape',
    'squeeze',
    'unsqueeze',
    'lod_reset',
    'lrn',
    'pad',
    'pad_constant_like',
    'label_smooth',
    'roi_pool',
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    'roi_align',
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    'dice_loss',
    'image_resize',
    'image_resize_short',
    'resize_bilinear',
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    'resize_nearest',
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    'gather',
    'scatter',
    'sequence_scatter',
    'random_crop',
    'mean_iou',
    'relu',
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    'selu',
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    'log',
    'crop',
    'rank_loss',
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    'margin_rank_loss',
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    'elu',
    'relu6',
    'pow',
    'stanh',
    'hard_sigmoid',
    'swish',
    'prelu',
    'brelu',
    'leaky_relu',
    'soft_relu',
    'flatten',
    'sequence_mask',
    'stack',
    'pad2d',
    'unstack',
    'sequence_enumerate',
    'expand',
    'sequence_concat',
    'scale',
    'elementwise_add',
    'elementwise_div',
    'elementwise_sub',
    'elementwise_mul',
    'elementwise_max',
    'elementwise_min',
    'elementwise_pow',
    'uniform_random_batch_size_like',
    'gaussian_random',
    'sampling_id',
    'gaussian_random_batch_size_like',
    'sum',
    'slice',
    'shape',
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    'rank',
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    'logical_and',
    'logical_or',
    'logical_xor',
    'logical_not',
    'clip',
    'clip_by_norm',
    'mean',
    'mul',
    'sigmoid_cross_entropy_with_logits',
    'maxout',
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    'space_to_depth',
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    'affine_grid',
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    'sequence_reverse',
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    'affine_channel',
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    'similarity_focus',
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    'hash',
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    'grid_sampler',
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    'log_loss',
    'add_position_encoding',
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    'bilinear_tensor_product',
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    'merge_selected_rows',
    'get_tensor_from_selected_rows',
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    'lstm',
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    'shuffle_channel',
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    'temporal_shift',
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    'py_func',
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    'psroi_pool',
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    'teacher_student_sigmoid_loss',
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    'huber_loss',
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    'kldiv_loss',
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    'tree_conv',
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    'npair_loss',
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    'pixel_shuffle',
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    'fsp_matrix',
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    'continuous_value_model',
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]

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kIgnoreIndex = -100

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def fc(input,
       size,
       num_flatten_dims=1,
       param_attr=None,
       bias_attr=None,
       act=None,
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       is_test=False,
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       name=None):
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    """
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    **Fully Connected Layer**
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    This function creates a fully connected layer in the network. It can take
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    one or multiple tensors as its inputs(input can be a list of Variable, see
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    Args in detail). It creates a variable called weights for each input tensor,
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    which represents a fully connected weight matrix from each input unit to
    each output unit. The fully connected layer multiplies each input tensor
    with its corresponding weight to produce an output Tensor with shape [M, `size`],
    where M is batch size. If multiple input tensors are given, the results of
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    multiple output tensors with shape [M, `size`] will be summed up. If bias_attr
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    is not None, a bias variable will be created and added to the output.
    Finally, if activation is not None, it will be applied to the output as well.
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    When the input is single tensor:
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    .. math::

        Out = Act({XW + b})

    When the input are multiple tensors:
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    .. math::

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        Out = Act({\sum_{i=0}^{N-1}X_iW_i + b})
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    In the above equation:

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    * :math:`N`: Number of the input. N equals to len(input) if input is list of Variable.
    * :math:`X_i`: The i-th input tensor.
    * :math:`W_i`: The i-th weights matrix corresponding i-th input tensor.
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    * :math:`b`: The bias parameter created by this layer (if needed).
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    * :math:`Act`: The activation function.
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    * :math:`Out`: The output tensor.
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    See below for an example.

    .. code-block:: text

        Given:
            data_1.data = [[[0.1, 0.2],
                           [0.3, 0.4]]]
            data_1.shape = (1, 2, 2) # 1 is batch_size

            data_2 = [[[0.1, 0.2, 0.3]]]
            data_2.shape = (1, 1, 3)

            out = fluid.layers.fc(input=[data_1, data_2], size=2)

        Then:
            out.data = [[0.18669507, 0.1893476]]
            out.shape = (1, 2)

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    Args:
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        input (Variable|list of Variable): The input tensor(s) of this layer, and the dimension of
            the input tensor(s) is at least 2.
        size(int): The number of output units in this layer.
        num_flatten_dims (int, default 1): The fc layer can accept an input tensor with more than
            two dimensions. If this happens, the multidimensional tensor will first be flattened
            into a 2-dimensional matrix. The parameter `num_flatten_dims` determines how the input
            tensor is flattened: the first `num_flatten_dims` (inclusive, index starts from 1)
            dimensions will be flatten to form the first dimension of the final matrix (height of
            the matrix), and the rest `rank(X) - num_flatten_dims` dimensions are flattened to
            form the second dimension of the final matrix (width of the matrix). For example, suppose
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            `X` is a 5-dimensional tensor with a shape [2, 3, 4, 5, 6], and `num_flatten_dims` = 3.
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            Then, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30].
        param_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for learnable
            parameters/weights of this layer.
        bias_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for the bias
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            of this layer. If it is set to False, no bias will be added to the output units.
            If it is set to None, the bias is initialized zero. Default: None.
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        act (str, default None): Activation to be applied to the output of this layer.
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        is_test(bool): A flag indicating whether execution is in test phase.
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        name (str, default None): The name of this layer.
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    Returns:
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        Variable: The transformation result.
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    Raises:
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        ValueError: If rank of the input tensor is less than 2.
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    Examples:
        .. code-block:: python

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          # when input is single tensor
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          data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
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          fc = fluid.layers.fc(input=data, size=1000, act="tanh")
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          # when input are multiple tensors
          data_1 = fluid.layers.data(name="data_1", shape=[32, 32], dtype="float32")
          data_2 = fluid.layers.data(name="data_2", shape=[24, 36], dtype="float32")
          fc = fluid.layers.fc(input=[data_1, data_2], size=1000, act="tanh")
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    """
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    helper = LayerHelper("fc", **locals())
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    dtype = helper.input_dtype()

    mul_results = []
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    for input_var, param_attr in helper.iter_inputs_and_params():
        input_shape = input_var.shape
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        param_shape = [
            reduce(lambda a, b: a * b, input_shape[num_flatten_dims:], 1)
        ] + [size]
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        w = helper.create_parameter(
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            attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False)
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        tmp = helper.create_variable_for_type_inference(dtype)
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        helper.append_op(
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            type="mul",
            inputs={"X": input_var,
                    "Y": w},
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            outputs={"Out": tmp},
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            attrs={"x_num_col_dims": num_flatten_dims,
                   "y_num_col_dims": 1})
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        mul_results.append(tmp)

    if len(mul_results) == 1:
        pre_bias = mul_results[0]
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    else:
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        pre_bias = helper.create_variable_for_type_inference(dtype)
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        helper.append_op(
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            type="sum",
            inputs={"X": mul_results},
            outputs={"Out": pre_bias},
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            attrs={"use_mkldnn": False})
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    # add bias
    pre_activation = helper.append_bias_op(pre_bias, dim_start=num_flatten_dims)
    # add activation
    return helper.append_activation(pre_activation)
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def embedding(input,
              size,
              is_sparse=False,
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              is_distributed=False,
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              padding_idx=None,
              param_attr=None,
              dtype='float32'):
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    """
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    **Embedding Layer**

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    This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in
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    a lookup table. The result of this lookup is the embedding of each ID in the
    :attr:`input`.
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    All the input variables are passed in as local variables to the LayerHelper
    constructor.
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    Args:
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        input(Variable): The tensor variable containing the IDs.
        size(tuple|list): The shape of the look up table parameter. It should
            have two elements which indicate the size of the dictionary of
            embeddings and the size of each embedding vector respectively.
        is_sparse(bool): The flag indicating whether to use sparse update.
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        is_distributed(bool): Whether to run lookup table from remote parameter server.
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        padding_idx(int|long|None): If :attr:`None`, it makes no effect to lookup.
            Otherwise the given :attr:`padding_idx` indicates padding the output
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            with zeros whenever lookup encounters it in :attr:`input`. If
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            :math:`padding_idx < 0`, the :attr:`padding_idx` to use in lookup is
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            :math:`size[0] + dim`.
        param_attr(ParamAttr): Parameters for this layer
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        dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc
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    Returns:
        Variable: The tensor variable storing the embeddings of the \
                  supplied inputs.
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    Examples:
        .. code-block:: python
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          dict_size = len(dataset.ids)
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          data = fluid.layers.data(name='ids', shape=[32, 32], dtype='float32')
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          fc = fluid.layers.embedding(input=data, size=[dict_size, 16])
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    """

    helper = LayerHelper('embedding', **locals())
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    remote_prefetch = is_sparse and (not is_distributed)
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    if remote_prefetch:
        assert is_sparse is True and is_distributed is False
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    w = helper.create_parameter(
        attr=helper.param_attr, shape=size, dtype=dtype, is_bias=False)
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    tmp = helper.create_variable_for_type_inference(dtype)
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    padding_idx = -1 if padding_idx is None else padding_idx if padding_idx >= 0 else (
        size[0] + padding_idx)
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    helper.append_op(
        type='lookup_table',
        inputs={'Ids': input,
                'W': w},
        outputs={'Out': tmp},
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        attrs={
            'is_sparse': is_sparse,
            'is_distributed': is_distributed,
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            'remote_prefetch': remote_prefetch,
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            'padding_idx': padding_idx
        })
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    return tmp


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@templatedoc(op_type="lstm")
def dynamic_lstm(input,
                 size,
                 h_0=None,
                 c_0=None,
                 param_attr=None,
                 bias_attr=None,
                 use_peepholes=True,
                 is_reverse=False,
                 gate_activation='sigmoid',
                 cell_activation='tanh',
                 candidate_activation='tanh',
                 dtype='float32',
                 name=None):
    """
    ${comment}
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    Args:
        input (Variable): ${input_comment}
        size (int): 4 * hidden size.
        h_0(Variable): The initial hidden state is an optional input, default is zero.
                       This is a tensor with shape (N x D), where N is the
                       batch size and D is the hidden size.
        c_0(Variable): The initial cell state is an optional input, default is zero.
                       This is a tensor with shape (N x D), where N is the
                       batch size. `h_0` and `c_0` can be NULL but only at the same time.
        param_attr(ParamAttr|None): The parameter attribute for the learnable
                               hidden-hidden weights.
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                               - Weights = {:math:`W_{ch}, W_{ih}, \
                                                W_{fh}, W_{oh}`}
                               - The shape is (D x 4D), where D is the hidden
                                 size.
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                               If it is set to None or one attribute of ParamAttr,
                               dynamic_lstm will create ParamAttr as param_attr.
                               If the Initializer of the param_attr is not set, the
                               parameter is initialized with Xavier. Default: None.
        bias_attr (ParamAttr|None): The bias attribute for the learnable bias
                              weights, which contains two parts, input-hidden
                              bias weights and peephole connections weights if
                              setting `use_peepholes` to `True`.

                              1. `use_peepholes = False`
                                 - Biases = {:math:`b_c, b_i, b_f, b_o`}.
                                 - The shape is (1 x 4D).
                              2. `use_peepholes = True`
                                 - Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic}, \
                                                 W_{fc}, W_{oc}`}.
                                 - The shape is (1 x 7D).

                              If it is set to None or one attribute of ParamAttr,
                              dynamic_lstm will create ParamAttr as bias_attr.
                              If the Initializer of the bias_attr is not set,
                              the bias is initialized zero. Default: None.
        use_peepholes (bool): ${use_peepholes_comment}
        is_reverse (bool): ${is_reverse_comment}
        gate_activation (str): ${gate_activation_comment}
        cell_activation (str): ${cell_activation_comment}
        candidate_activation (str): ${candidate_activation_comment}
        dtype (str): Data type. Choices = ["float32", "float64"], default "float32".
        name (str|None): A name for this layer(optional). If set None, the layer
                         will be named automatically.

    Returns:
        tuple: The hidden state, and cell state of LSTM. The shape of both \
        is (T x D), and lod is the same with the `input`.

    Examples:
        .. code-block:: python

            hidden_dim = 512
            forward_proj = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
                                           bias_attr=False)
            forward, _ = fluid.layers.dynamic_lstm(
                input=forward_proj, size=hidden_dim * 4, use_peepholes=False)
    """
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    assert in_dygraph_mode(
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    ) is not True, "please use lstm instead of dynamic_lstm in dygraph mode!"
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    assert bias_attr is not False, "bias_attr should not be False in dynamic_lstmp."
    helper = LayerHelper('lstm', **locals())
    size = size // 4
    weight = helper.create_parameter(
        attr=helper.param_attr, shape=[size, 4 * size], dtype=dtype)
    bias_size = [1, 7 * size]
    if not use_peepholes:
        bias_size[1] = 4 * size
    bias = helper.create_parameter(
        attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)

    hidden = helper.create_variable_for_type_inference(dtype)
    cell = helper.create_variable_for_type_inference(dtype)
    batch_gate = helper.create_variable_for_type_inference(dtype)
    batch_cell_pre_act = helper.create_variable_for_type_inference(dtype)
    inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
    batch_size = input.shape[0]
    if h_0:
        assert h_0.shape == (batch_size, size), \
            'The shape of h0 should be (batch_size, %d)' % size
        inputs['H0'] = h_0
    if c_0:
        assert c_0.shape == (batch_size, size), \
            'The shape of c0 should be (batch_size, %d)' % size
        inputs['C0'] = c_0

    helper.append_op(
        type='lstm',
        inputs=inputs,
        outputs={
            'Hidden': hidden,
            'Cell': cell,
            'BatchGate': batch_gate,
            'BatchCellPreAct': batch_cell_pre_act
        },
        attrs={
            'use_peepholes': use_peepholes,
            'is_reverse': is_reverse,
            'gate_activation': gate_activation,
            'cell_activation': cell_activation,
            'candidate_activation': candidate_activation
        })
    return hidden, cell
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def lstm(input,
         init_h,
         init_c,
         max_len,
         hidden_size,
         num_layers,
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         dropout_prob=0.0,
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         is_bidirec=False,
         is_test=False,
         name=None,
         default_initializer=None,
         seed=-1):
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    """
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    If Device is GPU, This op will use cudnn LSTM implementation
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    A four-gate Long Short-Term Memory network with no peephole connections.
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    In the forward pass the output ht and cell output ct for a given iteration can be computed from the recurrent input ht-1,
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    the cell input ct-1 and the previous layer input xt given matrices W, R and biases bW, bR from the following equations:

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    .. math::
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       i_t &= \sigma(W_{ix}x_{t} + W_{ih}h_{t-1} + bx_i + bh_i)

       f_t &= \sigma(W_{fx}x_{t} + W_{fh}h_{t-1} + bx_f + bh_f)

       o_t &= \sigma(W_{ox}x_{t} + W_{oh}h_{t-1} + bx_o + bh_o)

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       \\tilde{c_t} &= tanh(W_{cx}x_t + W_{ch}h_{t-1} + bx_c + bh_c)
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       c_t &= f_t \odot c_{t-1} + i_t \odot \\tilde{c_t}

       h_t &= o_t \odot tanh(c_t)
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    - $W$ terms denote weight matrices (e.g. $W_{ix}$ is the matrix
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      of weights from the input gate to the input)
    - The b terms denote bias vectors ($bx_i$ and $bh_i$ are the input gate bias vector).
    - sigmoid is the logistic sigmoid function.
    - $i, f, o$ and $c$ are the input gate, forget gate, output gate,
      and cell activation vectors, respectively, all of which have the same size as
      the cell output activation vector $h$.
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    - The :math:`\odot` is the element-wise product of the vectors.
    - :math:`tanh` is the activation functions.
    - :math:`\\tilde{c_t}` is also called candidate hidden state,
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      which is computed based on the current input and the previous hidden state.
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    Where sigmoid is the sigmoid operator: :math:`sigmoid(x) = 1 / (1 + e^{-x})` , * represents a point-wise multiplication,
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    X represensts a matrix multiplication


    Args:
        input (Variable): LSTM input tensor, shape MUST be ( seq_len x batch_size x input_size )
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        init_h(Variable): The initial hidden state of the LSTM
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                       This is a tensor with shape ( num_layers x batch_size x hidden_size)
                       if is_bidirec = True, shape should be ( num_layers*2 x batch_size x hidden_size)
        init_c(Variable): The initial cell state of the LSTM.
                       This is a tensor with shape ( num_layers x batch_size x hidden_size )
                       if is_bidirec = True, shape should be ( num_layers*2 x batch_size x hidden_size)
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        max_len (int): max length of LSTM. the first dim of input tensor CAN NOT greater than max_len
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        hidden_size (int): hidden size of the LSTM
        num_layers (int): total layers number of the LSTM
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        dropout_prob(float|0.0): dropout prob, dropout ONLY work between rnn layers, NOT between time steps
                             There is NO dropout work on rnn output of the last RNN layers
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        is_bidirec (bool): If it is bidirectional
        is_test (bool): If it is in test phrase
        name (str|None): A name for this layer(optional). If set None, the layer
                         will be named automatically.
        default_initializer(Initialize|None): Where use initializer to initialize the Weight
                         If set None, defaule initializer will be used
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        seed(int): Seed for dropout in LSTM, If it's -1, dropout will use random seed
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    Returns:
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        rnn_out(Tensor),last_h(Tensor),last_c(Tensor):

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                        Three tensors, rnn_out, last_h, last_c:
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                        - rnn_out is result of LSTM hidden, shape is (seq_len x batch_size x hidden_size) \
                          if is_bidirec set to True, shape will be ( seq_len x batch_sze x hidden_size*2)
                        - last_h is the hidden state of the last step of LSTM \
                          shape is ( num_layers x batch_size x hidden_size ) \
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                          if is_bidirec set to True, shape will be ( num_layers*2 x batch_size x hidden_size)
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                        - last_c(Tensor): the cell state of the last step of LSTM \
                          shape is ( num_layers x batch_size x hidden_size ) \
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                          if is_bidirec set to True, shape will be ( num_layers*2 x batch_size x hidden_size)
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    Examples:
        .. code-block:: python

            input = embedding
            batch_size = 20
            max_len = 100
            dropout_prob = 0.2
            input_size = 100
            hidden_size = 150
            num_layers = 1
            init_hidden1 = layers.fill_constant( [num_layers, batch_size, hidden_size], 'float32', 0.0, stop_grad=False)
            init_cell1 = layers.fill_constant( [num_layers, batch_size, hidden_size], 'float32', 0.0, stop_grad=False)

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            rnn_out, last_h, last_c = layers.lstm( input, init_h, init_c, \
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                    max_len, dropout_prob, input_size, hidden_size, \
                    num_layers)
    """

    helper = LayerHelper('cudnn_lstm', **locals())

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    dtype = input.dtype
    input_shape = list(input.shape)
    input_size = input_shape[-1]
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    weight_size = 0
    for i in range(num_layers):
        if i == 0:
            input_weight_size = (input_size * hidden_size) * 4
        else:
            if is_bidirec:
                input_weight_size = (hidden_size * 2 * hidden_size) * 4
            else:
                input_weight_size = (hidden_size * hidden_size) * 4

        hidden_weight_size = (hidden_size * hidden_size) * 4

        if is_bidirec:
            weight_size += (input_weight_size + hidden_weight_size) * 2
            weight_size += hidden_size * 8 * 2
        else:
            weight_size += input_weight_size + hidden_weight_size
            weight_size += hidden_size * 8

    weight = helper.create_parameter(
        attr=helper.param_attr,
        shape=[weight_size],
        dtype=dtype,
        default_initializer=default_initializer)

    out = helper.create_variable_for_type_inference(dtype)
    last_h = helper.create_variable_for_type_inference(dtype)
    last_c = helper.create_variable_for_type_inference(dtype)

    cache = helper.create_variable(
        persistable=True, type=core.VarDesc.VarType.RAW, stop_gradient=True)

    helper.append_op(
        type='cudnn_lstm',
        inputs={
            'Input': input,
            'InitH': init_h,
            'InitC': init_c,
            'W': weight,
            'Cache': cache,
        },
        outputs={
            'Out': out,
            'last_h': last_h,
            'last_c': last_c,
        },
        attrs={
            'max_len': max_len,
            'is_bidirec': is_bidirec,
            'input_size': input_size,
            'hidden_size': hidden_size,
            'num_layers': num_layers,
            'is_test': is_test,
            'dropout_prob': dropout_prob,
            'seed': seed,
        })
    return out, last_h, last_c


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def dynamic_lstmp(input,
                  size,
                  proj_size,
                  param_attr=None,
                  bias_attr=None,
                  use_peepholes=True,
                  is_reverse=False,
                  gate_activation='sigmoid',
                  cell_activation='tanh',
                  candidate_activation='tanh',
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                  proj_activation='tanh',
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                  dtype='float32',
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                  name=None,
                  h_0=None,
                  c_0=None,
                  cell_clip=None,
                  proj_clip=None):
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    """
    **Dynamic LSTMP Layer**

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    LSTMP (LSTM with recurrent projection) layer has a separate projection
    layer after the LSTM layer, projecting the original hidden state to a
    lower-dimensional one, which is proposed to reduce the number of total
    parameters and furthermore computational complexity for the LSTM,
    espeacially for the case that the size of output units is relative
    large (https://research.google.com/pubs/archive/43905.pdf).
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    The formula is as follows:

    .. math::

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        i_t & = \sigma(W_{ix}x_{t} + W_{ir}r_{t-1} + W_{ic}c_{t-1} + b_i)
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        f_t & = \sigma(W_{fx}x_{t} + W_{fr}r_{t-1} + W_{fc}c_{t-1} + b_f)
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        \\tilde{c_t} & = act_g(W_{cx}x_t + W_{cr}r_{t-1} + b_c)
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        o_t & = \sigma(W_{ox}x_{t} + W_{or}r_{t-1} + W_{oc}c_t + b_o)
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        c_t & = f_t \odot c_{t-1} + i_t \odot \\tilde{c_t}
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        h_t & = o_t \odot act_h(c_t)
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        r_t & = \overline{act_h}(W_{rh}h_t)
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    In the above formula:

    * :math:`W`: Denotes weight matrices (e.g. :math:`W_{xi}` is \
          the matrix of weights from the input gate to the input).
    * :math:`W_{ic}`, :math:`W_{fc}`, :math:`W_{oc}`: Diagonal weight \
          matrices for peephole connections. In our implementation, \
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          we use vectors to reprenset these diagonal weight matrices.
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    * :math:`b`: Denotes bias vectors (e.g. :math:`b_i` is the input gate \
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          bias vector).
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    * :math:`\sigma`: The activation, such as logistic sigmoid function.
    * :math:`i, f, o` and :math:`c`: The input gate, forget gate, output \
          gate, and cell activation vectors, respectively, all of which have \
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          the same size as the cell output activation vector :math:`h`.
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    * :math:`h`: The hidden state.
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    * :math:`r`: The recurrent projection of the hidden state.
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    * :math:`\\tilde{c_t}`: The candidate hidden state, whose \
          computation is based on the current input and previous hidden state.
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    * :math:`\odot`: The element-wise product of the vectors.
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    * :math:`act_g` and :math:`act_h`: The cell input and cell output \
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          activation functions and `tanh` is usually used for them.
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    * :math:`\overline{act_h}`: The activation function for the projection \
          output, usually using `identity` or same as :math:`act_h`.
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    Set `use_peepholes` to `False` to disable peephole connection. The formula
    is omitted here, please refer to the paper
    http://www.bioinf.jku.at/publications/older/2604.pdf for details.
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    Note that these :math:`W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}`
    operations on the input :math:`x_{t}` are NOT included in this operator.
    Users can choose to use fully-connected layer before LSTMP layer.

    Args:
        input(Variable): The input of dynamic_lstmp layer, which supports
                         variable-time length input sequence. The underlying
                         tensor in this Variable is a matrix with shape
                         (T X 4D), where T is the total time steps in this
                         mini-batch, D is the hidden size.
        size(int): 4 * hidden size.
        proj_size(int): The size of projection output.
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        param_attr(ParamAttr|None): The parameter attribute for the learnable
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                               hidden-hidden weight and projection weight.

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                               - Hidden-hidden weight = {:math:`W_{ch}, W_{ih}, \
                                                W_{fh}, W_{oh}`}.
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                               - The shape of hidden-hidden weight is (P x 4D),
                                 where P is the projection size and D the hidden
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                                 size.
                               - Projection weight = {:math:`W_{rh}`}.
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                               - The shape of projection weight is (D x P).
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                               If it is set to None or one attribute of ParamAttr,
                               dynamic_lstm will create ParamAttr as param_attr.
                               If the Initializer of the param_attr is not set, the
                               parameter is initialized with Xavier. Default: None.
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        bias_attr(ParamAttr|None): The bias attribute for the learnable bias
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                              weights, which contains two parts, input-hidden
                              bias weights and peephole connections weights if
                              setting `use_peepholes` to `True`.

                              1. `use_peepholes = False`
                                - Biases = {:math:`b_c, b_i, b_f, b_o`}.
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                                - The shape is (1 x 4D).
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                              2. `use_peepholes = True`
                                - Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic}, \
                                                 W_{fc}, W_{oc}`}.
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                                - The shape is (1 x 7D).
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                              If it is set to None or one attribute of ParamAttr,
                              dynamic_lstm will create ParamAttr as bias_attr.
                              If the Initializer of the bias_attr is not set,
                              the bias is initialized zero. Default: None.
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        use_peepholes(bool): Whether to enable diagonal/peephole connections,
                             default `True`.
        is_reverse(bool): Whether to compute reversed LSTM, default `False`.
        gate_activation(str): The activation for input gate, forget gate and
                              output gate. Choices = ["sigmoid", "tanh", "relu",
                              "identity"], default "sigmoid".
        cell_activation(str): The activation for cell output. Choices = ["sigmoid",
                              "tanh", "relu", "identity"], default "tanh".
        candidate_activation(str): The activation for candidate hidden state.
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                              Choices = ["sigmoid", "tanh", "relu", "identity"],
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                              default "tanh".
        proj_activation(str): The activation for projection output.
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                              Choices = ["sigmoid", "tanh", "relu", "identity"],
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                              default "tanh".
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        dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
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        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.
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        h_0(Variable): The initial hidden state is an optional input, default is zero.
                       This is a tensor with shape (N x D), where N is the
                       batch size and D is the projection size.
        c_0(Variable): The initial cell state is an optional input, default is zero.
                       This is a tensor with shape (N x D), where N is the
                       batch size. `h_0` and `c_0` can be NULL but only at the same time.
        cell_clip(float): If provided the cell state is clipped
                             by this value prior to the cell output activation.
        proj_clip(float): If `num_proj > 0` and `proj_clip` is
                            provided, then the projected values are clipped elementwise to within
                            `[-proj_clip, proj_clip]`.
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    Returns:
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        tuple: A tuple of two output variable: the projection of hidden state, \
               and cell state of LSTMP. The shape of projection is (T x P), \
               for the cell state which is (T x D), and both LoD is the same \
               with the `input`.
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    Examples:
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        .. code-block:: python

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            dict_dim, emb_dim = 128, 64
            data = fluid.layers.data(name='sequence', shape=[1],
                                     dtype='int32', lod_level=1)
            emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim])
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            hidden_dim, proj_dim = 512, 256
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            fc_out = fluid.layers.fc(input=emb, size=hidden_dim * 4,
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                                     act=None, bias_attr=None)
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            proj_out, _ = fluid.layers.dynamic_lstmp(input=fc_out,
                                                     size=hidden_dim * 4,
                                                     proj_size=proj_dim,
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                                                     use_peepholes=False,
                                                     is_reverse=True,
                                                     cell_activation="tanh",
                                                     proj_activation="tanh")
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    """
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    assert in_dygraph_mode(
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    ) is not True, "please use lstm instead of dynamic_lstmp in dygraph mode!"

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    assert bias_attr is not False, "bias_attr should not be False in dynamic_lstmp."
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    helper = LayerHelper('lstmp', **locals())
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    size = size // 4
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    weight = helper.create_parameter(
        attr=helper.param_attr, shape=[proj_size, 4 * size], dtype=dtype)
    proj_weight = helper.create_parameter(
        attr=helper.param_attr, shape=[size, proj_size], dtype=dtype)
    bias_size = [1, 7 * size]
    if not use_peepholes:
        bias_size[1] = 4 * size
    bias = helper.create_parameter(
        attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)

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    projection = helper.create_variable_for_type_inference(dtype)
    cell = helper.create_variable_for_type_inference(dtype)
    ordered_proj0 = helper.create_variable_for_type_inference(dtype)
    batch_hidden = helper.create_variable_for_type_inference(dtype)
    batch_gate = helper.create_variable_for_type_inference(dtype)
    batch_cell_pre_act = helper.create_variable_for_type_inference(dtype)
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    inputs = {
        'Input': input,
        'Weight': weight,
        'ProjWeight': proj_weight,
        'Bias': bias
    }
    batch_size = input.shape[0]
    if h_0:
        assert h_0.shape == (batch_size, proj_size), \
            'The shape of h0 should be (batch_size, %d)' % proj_size
        inputs['H0'] = h_0
    if c_0:
        assert c_0.shape == (batch_size, size), \
            'The shape of c0 should be (batch_size, %d)' % size
        inputs['C0'] = c_0
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    if cell_clip:
        assert cell_clip >= 0, "cell_clip should not be negtive."
    if proj_clip:
        assert proj_clip >= 0, "proj_clip should not be negtive."

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    helper.append_op(
        type='lstmp',
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        inputs=inputs,
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        outputs={
            'Projection': projection,
            'Cell': cell,
            'BatchHidden': batch_hidden,
            'BatchGate': batch_gate,
            'BatchCellPreAct': batch_cell_pre_act
        },
        attrs={
            'use_peepholes': use_peepholes,
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            'cell_clip': cell_clip,
            'proj_clip': proj_clip,
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            'is_reverse': is_reverse,
            'gate_activation': gate_activation,
            'cell_activation': cell_activation,
            'candidate_activation': candidate_activation,
            'proj_activation': proj_activation
        })
    return projection, cell


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def dynamic_gru(input,
                size,
                param_attr=None,
                bias_attr=None,
                is_reverse=False,
                gate_activation='sigmoid',
                candidate_activation='tanh',
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                h_0=None,
                origin_mode=False):
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    """
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    **Gated Recurrent Unit (GRU) Layer**
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    if origin_mode is False, then the equation of a gru step is from paper
    `Empirical Evaluation of Gated Recurrent Neural Networks on Sequence
    Modeling <https://arxiv.org/pdf/1412.3555.pdf>`_ .
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    The formula is as follows:

    .. math::

        u_t & = act_g(W_{ux}x_{t} + W_{uh}h_{t-1} + b_u)

        r_t & = act_g(W_{rx}x_{t} + W_{rh}h_{t-1} + b_r)

        \\tilde{h_t} & = act_c(W_{cx}x_{t} + W_{ch}(r_t \odot h_{t-1}) + b_c)
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        h_t & = (1-u_t) \odot h_{t-1} + u_t \odot \\tilde{h_t}
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    if origin_mode is True then the equation is from paper
    Learning Phrase Representations using RNN Encoder-Decoder for Statistical
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    Machine Translation <https://arxiv.org/pdf/1406.1078.pdf>`_

    .. math::

        u_t & = act_g(W_{ux}x_{t} + W_{uh}h_{t-1} + b_u)

        r_t & = act_g(W_{rx}x_{t} + W_{rh}h_{t-1} + b_r)

        \\tilde{h_t} & = act_c(W_{cx}x_{t} + W_{ch}(r_t \odot h_{t-1}) + b_c)

        h_t & = u_t \odot h_{t-1} + (1-u_t) \odot \\tilde{h_t}

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    The :math:`\odot` is the element-wise product of the vectors. :math:`act_g`
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    is the update gate and reset gate activation function and :math:`sigmoid`
    is usually used for it. :math:`act_c` is the activation function for
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    candidate hidden state and :math:`tanh` is usually used for it.

    Note that these :math:`W_{ux}x_{t}, W_{rx}x_{t}, W_{cx}x_{t}` operations on
    the input :math:`x_{t}` are NOT included in this operator. Users can choose
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    to use fully-connect layer before GRU layer.
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    Args:
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        input(Variable): The input of dynamic_gru layer, which supports
            variable-time length input sequence. The underlying tensor in this
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            Variable is a matrix with shape :math:`(T \\times 3D)`, where
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            :math:`T` is the total time steps in this mini-batch, :math:`D`
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            is the hidden size.
        size(int): The dimension of the gru cell.
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        param_attr(ParamAttr|None): The parameter attribute for the learnable
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            hidden-hidden weight matrix. Note:

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            - The shape of the weight matrix is :math:`(T \\times 3D)`, where
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              :math:`D` is the hidden size.
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            - All elements in the weight matrix can be divided into two parts.
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              The first part are weights of the update gate and reset gate with
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              shape :math:`(D \\times 2D)`, and the second part are weights for
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              candidate hidden state with shape :math:`(D \\times D)`.
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            If it is set to None or one attribute of ParamAttr, dynamic_gru will
            create ParamAttr as param_attr. If the Initializer of the param_attr
            is not set, the parameter is initialized with Xavier. Default: None.
        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias
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            of GRU.Note that the bias with :math:`(1 \\times 3D)` concatenates
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            the bias in the update gate, reset gate and candidate calculations.
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            If it is set to False, no bias will be applied to the update gate,
            reset gate and candidate calculations. If it is set to None or one
            attribute of ParamAttr, dynamic_gru will create ParamAttr as
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            bias_attr. If the Initializer of the bias_attr is not set, the bias
            is initialized zero. Default: None.
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        is_reverse(bool): Whether to compute reversed GRU, default
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            :attr:`False`.
        gate_activation(str): The activation for update gate and reset gate.
            Choices = ["sigmoid", "tanh", "relu", "identity"], default "sigmoid".
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        candidate_activation(str): The activation for candidate hidden state.
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            Choices = ["sigmoid", "tanh", "relu", "identity"], default "tanh".
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        h_0 (Variable): This is initial hidden state. If not set, default is
            zero. This is a tensor with shape (N x D), where N is the number of
            total time steps of input mini-batch feature and D is the hidden
            size.
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    Returns:
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        Variable: The hidden state of GRU. The shape is :math:`(T \\times D)`, \
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            and sequence length is the same with the input.
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    Examples:
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        .. code-block:: python

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            dict_dim, emb_dim = 128, 64
            data = fluid.layers.data(name='sequence', shape=[1],
                                     dtype='int32', lod_level=1)
            emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim])
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            hidden_dim = 512
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            x = fluid.layers.fc(input=emb, size=hidden_dim * 3)
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            hidden = fluid.layers.dynamic_gru(input=x, size=hidden_dim)
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    """

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    assert in_dygraph_mode(
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    ) is not True, "please use gru instead of dynamic_gru in dygraph mode!"

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    helper = LayerHelper('gru', **locals())
    dtype = helper.input_dtype()

    weight = helper.create_parameter(
        attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
    bias = helper.create_parameter(
        attr=helper.bias_attr, shape=[1, 3 * size], dtype=dtype, is_bias=True)
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    batch_size = input.shape[0]
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    inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
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    if h_0:
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        assert h_0.shape == (
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            batch_size, size
        ), 'The shape of h0 should be(batch_size, %d)' % size
        inputs['H0'] = h_0
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    hidden = helper.create_variable_for_type_inference(dtype)
    batch_gate = helper.create_variable_for_type_inference(dtype)
    batch_reset_hidden_prev = helper.create_variable_for_type_inference(dtype)
    batch_hidden = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
        type='gru',
        inputs=inputs,
        outputs={
            'Hidden': hidden,
            'BatchGate': batch_gate,
            'BatchResetHiddenPrev': batch_reset_hidden_prev,
            'BatchHidden': batch_hidden
        },
        attrs={
            'is_reverse': is_reverse,
            'gate_activation': gate_activation,
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            'activation': candidate_activation,
            'origin_mode': origin_mode
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        })
    return hidden


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def gru_unit(input,
             hidden,
             size,
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             param_attr=None,
             bias_attr=None,
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             activation='tanh',
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             gate_activation='sigmoid',
             origin_mode=False):
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    """
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    **GRU unit layer**

    if origin_mode is True, then the equation of a gru step is from paper
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    `Learning Phrase Representations using RNN Encoder-Decoder for Statistical
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    Machine Translation <https://arxiv.org/pdf/1406.1078.pdf>`_
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        .. math::
            u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u)
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            r_t & = actGate(xr_{t} + W_r h_{t-1} + b_r)
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            m_t & = actNode(xm_t + W_c dot(r_t, h_{t-1}) + b_m)
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            h_t & = dot(u_t, h_{t-1}) + dot((1-u_t), m_t)

    if origin_mode is False, then the equation of a gru step is from paper
    `Empirical Evaluation of Gated Recurrent Neural Networks on Sequence
    Modeling <https://arxiv.org/pdf/1412.3555.pdf>`_

        .. math::
            u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u)

            r_t & = actGate(xr_{t} + W_r h_{t-1} + b_r)

            m_t & = actNode(xm_t + W_c dot(r_t, h_{t-1}) + b_m)

            h_t & = dot((1-u_t), h_{t-1}) + dot(u_t, m_t)

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    The inputs of gru unit includes :math:`z_t`, :math:`h_{t-1}`. In terms
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    of the equation above, the :math:`z_t` is split into 3 parts -
    :math:`xu_t`, :math:`xr_t` and :math:`xm_t`. This means that in order to
    implement a full GRU unit operator for an input, a fully
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    connected layer has to be applied, such that :math:`z_t = W_{fc}x_t`.

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    The terms :math:`u_t` and :math:`r_t` represent the update and reset gates
    of the GRU cell. Unlike LSTM, GRU has one lesser gate. However, there is
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    an intermediate candidate hidden output, which is denoted by :math:`m_t`.
    This layer has three outputs :math:`h_t`, :math:`dot(r_t, h_{t-1})`
    and concatenation of :math:`u_t`, :math:`r_t` and :math:`m_t`.
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    Args:
        input (Variable): The fc transformed input value of current step.
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        hidden (Variable): The hidden value of gru unit from previous step.
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        size (integer): The input dimension value.
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        param_attr(ParamAttr|None): The parameter attribute for the learnable
            hidden-hidden weight matrix. Note:

            - The shape of the weight matrix is :math:`(T \\times 3D)`, where
              :math:`D` is the hidden size.
            - All elements in the weight matrix can be divided into two parts.
              The first part are weights of the update gate and reset gate with
              shape :math:`(D \\times 2D)`, and the second part are weights for
              candidate hidden state with shape :math:`(D \\times D)`.

            If it is set to None or one attribute of ParamAttr, gru_unit will
            create ParamAttr as param_attr. If the Initializer of the param_attr
            is not set, the parameter is initialized with Xavier. Default: None.
        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias
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            of GRU.Note that the bias with :math:`(1 \\times 3D)` concatenates
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            the bias in the update gate, reset gate and candidate calculations.
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            If it is set to False, no bias will be applied to the update gate,
            reset gate and candidate calculations. If it is set to None or one
            attribute of ParamAttr, gru_unit will create ParamAttr as
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            bias_attr. If the Initializer of the bias_attr is not set, the bias
            is initialized zero. Default: None.
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        activation (string): The activation type for cell (actNode).
                             Default: 'tanh'
        gate_activation (string): The activation type for gates (actGate).
                                  Default: 'sigmoid'
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    Returns:
        tuple: The hidden value, reset-hidden value and gate values.

    Examples:

        .. code-block:: python
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             # assuming we have x_t_data and prev_hidden of size=10
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             x_t = fluid.layers.fc(input=x_t_data, size=30)
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             hidden_val, r_h_val, gate_val = fluid.layers.gru_unit(input=x_t,
                                                    hidden = prev_hidden)
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    """
    activation_dict = dict(
        identity=0,
        sigmoid=1,
        tanh=2,
        relu=3, )
    activation = activation_dict[activation]
    gate_activation = activation_dict[gate_activation]

    helper = LayerHelper('gru_unit', **locals())
    dtype = helper.input_dtype()
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    size = size // 3
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    # create weight
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    weight = helper.create_parameter(
        attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
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    gate = helper.create_variable_for_type_inference(dtype)
    reset_hidden_pre = helper.create_variable_for_type_inference(dtype)
    updated_hidden = helper.create_variable_for_type_inference(dtype)
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    inputs = {'Input': input, 'HiddenPrev': hidden, 'Weight': weight}
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    # create bias
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    if helper.bias_attr:
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        bias_size = [1, 3 * size]
        bias = helper.create_parameter(
            attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
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        inputs['Bias'] = bias
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    helper.append_op(
        type='gru_unit',
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        inputs=inputs,
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        outputs={
            'Gate': gate,
            'ResetHiddenPrev': reset_hidden_pre,
            'Hidden': updated_hidden,
        },
        attrs={
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            'activation': 2,  # tanh
            'gate_activation': 1,  # sigmoid
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        })

    return updated_hidden, reset_hidden_pre, gate


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@templatedoc()
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def linear_chain_crf(input, label, param_attr=None):
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    """
    Linear Chain CRF.

    ${comment}

    Args:
        input(${emission_type}): ${emission_comment}
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        input(${transition_type}): ${transition_comment}
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        label(${label_type}): ${label_comment}
        param_attr(ParamAttr): The attribute of the learnable parameter.

    Returns:
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        output(${emission_exps_type}): ${emission_exps_comment} \n
        output(${transition_exps_type}): ${transition_exps_comment} \n
        output(${log_likelihood_type}): ${log_likelihood_comment}
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    """
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    helper = LayerHelper('linear_chain_crf', **locals())
    size = input.shape[1]
    transition = helper.create_parameter(
        attr=helper.param_attr,
        shape=[size + 2, size],
        dtype=helper.input_dtype())
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    alpha = helper.create_variable_for_type_inference(
        dtype=helper.input_dtype())
    emission_exps = helper.create_variable_for_type_inference(
        dtype=helper.input_dtype())
    transition_exps = helper.create_variable_for_type_inference(
        dtype=helper.input_dtype())
    log_likelihood = helper.create_variable_for_type_inference(
        dtype=helper.input_dtype())
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    helper.append_op(
        type='linear_chain_crf',
        inputs={"Emission": [input],
                "Transition": transition,
                "Label": label},
        outputs={
            "Alpha": [alpha],
            "EmissionExps": [emission_exps],
            "TransitionExps": transition_exps,
            "LogLikelihood": log_likelihood
        })

    return log_likelihood


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@templatedoc()
def crf_decoding(input, param_attr, label=None):
    """
    ${comment}
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    Args:
        input(${emission_type}): ${emission_comment}
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        param_attr(ParamAttr): The parameter attribute for training.
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        label(${label_type}): ${label_comment}
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    Returns:
        Variable: ${viterbi_path_comment}
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    Examples:
        .. code-block:: python
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           crf_decode = layers.crf_decoding(
                input=hidden, param_attr=ParamAttr(name="crfw"))
    """
    helper = LayerHelper('crf_decoding', **locals())
    transition = helper.get_parameter(param_attr.name)
    viterbi_path = helper.create_variable_for_type_inference(
        dtype=helper.input_dtype())
    helper.append_op(
        type='crf_decoding',
        inputs={"Emission": [input],
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                "Transition": transition,
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                "Label": label},
        outputs={"ViterbiPath": [viterbi_path]})
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    return viterbi_path
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@templatedoc()
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def cos_sim(X, Y):
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    """
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    ${comment}

    Args:
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        X (Variable): ${x_comment}.
        Y (Variable): ${y_comment}.
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    Returns:
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        Variable: the output of cosine(X, Y).
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    """
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    helper = LayerHelper('cos_sim', **locals())
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    out = helper.create_variable_for_type_inference(dtype=X.dtype)
    xnorm = helper.create_variable_for_type_inference(dtype=X.dtype)
    ynorm = helper.create_variable_for_type_inference(dtype=X.dtype)
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    helper.append_op(
        type='cos_sim',
        inputs={'X': [X],
                'Y': [Y]},
        outputs={'Out': [out],
                 'XNorm': [xnorm],
                 'YNorm': [ynorm]})
    return out


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def dropout(x,
            dropout_prob,
            is_test=False,
            seed=None,
            name=None,
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            dropout_implementation="downgrade_in_infer"):
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    """
    Computes dropout.

    Drop or keep each element of `x` independently. Dropout is a regularization
    technique for reducing overfitting by preventing neuron co-adaption during
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    training. The dropout operator randomly sets (according to the given dropout
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    probability) the outputs of some units to zero, while others are remain
    unchanged.

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    dropout op can be removed from the program to make the program more efficient.

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    Args:
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        x (Variable): The input tensor variable.
        dropout_prob (float): Probability of setting units to zero.
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        is_test (bool): A flag indicating whether it is in test phrase or not.
        seed (int): A Python integer used to create random seeds. If this
                    parameter is set to None, a random seed is used.
                    NOTE: If an integer seed is given, always the same output
                    units will be dropped. DO NOT use a fixed seed in training.
        name (str|None): A name for this layer(optional). If set None, the layer
                         will be named automatically.
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        dropout_implementation(string): ['downgrade_in_infer'(default)|'upscale_in_train']

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                                        1. downgrade_in_infer(default), downgrade the outcome at inference
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                                           - train: out = input * mask
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                                           - inference: out = input * (1.0 - dropout_prob)
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                                           (mask is a tensor same shape with input, value is 0 or 1
                                           ratio of 0 is dropout_prob)
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                                        2. upscale_in_train, upscale the outcome at training time
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                                           - train: out = input * mask / ( 1.0 - dropout_prob )
                                           - inference: out = input
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                                           (mask is a tensor same shape with input, value is 0 or 1
                                           ratio of 0 is dropout_prob)
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    Returns:
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        Variable: A tensor variable is the shape with `x`.
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    Examples:
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        .. code-block:: python

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            x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
            droped = fluid.layers.dropout(x, dropout_prob=0.5)
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    """

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    helper = LayerHelper('dropout', **locals())
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    out = helper.create_variable_for_type_inference(dtype=x.dtype)
    mask = helper.create_variable_for_type_inference(
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        dtype=core.VarDesc.VarType.UINT8, stop_gradient=True)
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    if (seed is None or seed == 0) and helper.main_program.random_seed != 0:
        seed = helper.main_program.random_seed

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    helper.append_op(
        type='dropout',
        inputs={'X': [x]},
        outputs={'Out': [out],
                 'Mask': [mask]},
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        attrs={
            'dropout_prob': dropout_prob,
            'is_test': is_test,
            'fix_seed': seed is not None,
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            'seed': seed if seed is not None else 0,
            'dropout_implementation': dropout_implementation,
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        })
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    return out


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def cross_entropy(input, label, soft_label=False, ignore_index=kIgnoreIndex):
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    """
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    **Cross Entropy Layer**

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    This layer computes the cross entropy between `input` and `label`. It
    supports both standard cross-entropy and soft-label cross-entropy loss
    computation.
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    1) One-hot cross-entropy:
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        `soft_label = False`, `Label[i, 0]` indicates the class index for sample i:
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        .. math::
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            Y[i] = -\log(X[i, Label[i]])

    2) Soft-label cross-entropy:
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        `soft_label = True`, `Label[i, j]` indicates the soft label of class j
        for sample i:
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        .. math::

            Y[i] = \sum_j{-Label[i, j] * log(X[i, j])}

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       Please make sure that in this case the summation of each row of `label`
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       equals one.

    3) One-hot cross-entropy with vecterized `label`:
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         As a special case of 2), when each row of 'label' has only one
         non-zero element which is equal to 1, soft-label cross-entropy degenerates
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         to a one-hot cross-entropy with one-hot label representation.
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    Args:
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        input (Variable|list):  a 2-D tensor with shape [N x D], where N is the
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                                batch size and D is the number of classes. This
                                input is a probability computed by the previous
                                operator, which is almost always the result of
                                a softmax operator.
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        label (Variable|list): the ground truth which is a 2-D tensor. When
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                               `soft_label` is set to `False`, `label` is a
                               tensor<int64> with shape [N x 1]. When
                               `soft_label` is set to `True`, `label` is a
                               tensor<float/double> with shape [N x D].
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        soft_label (bool): a flag indicating whether to
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                                           interpretate the given labels as soft
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                                           labels. Default: `False`.
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        ignore_index (int): Specifies a target value that is ignored and does
                            not contribute to the input gradient. Only valid
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                            if soft_label is set to False. Default: kIgnoreIndex
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    Returns:
         A 2-D tensor with shape [N x 1], the cross entropy loss.

    Raises:
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         ValueError:

                      1. the 1st dimension of ``input`` and ``label`` are not equal.
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                      2. when ``soft_label == True``, and the 2nd dimension of
                         ``input`` and ``label`` are not equal.
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                      3. when ``soft_label == False``, and the 2nd dimension of
                         ``label`` is not 1.
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    Examples:
        .. code-block:: python

          predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
          cost = fluid.layers.cross_entropy(input=predict, label=label)
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    """
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    if not soft_label:
        return cross_entropy2(input, label, ignore_index)
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    helper = LayerHelper('cross_entropy', **locals())
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    out = helper.create_variable_for_type_inference(dtype=input.dtype)
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    helper.append_op(
        type='cross_entropy',
        inputs={'X': [input],
                'Label': [label]},
        outputs={'Y': [out]},
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        attrs={"soft_label": soft_label,
               "ignore_index": ignore_index})
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    return out


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def cross_entropy2(input, label, ignore_index=kIgnoreIndex):
    helper = LayerHelper('cross_entropy2', **locals())
    out = helper.create_variable_for_type_inference(dtype=input.dtype)
    xshape = helper.create_variable_for_type_inference(dtype=input.dtype)
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    match_x = helper.create_variable_for_type_inference(dtype=input.dtype)
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    helper.append_op(
        type='cross_entropy2',
        inputs={'X': [input],
                'Label': [label]},
        outputs={'Y': [out],
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                 'MatchX': [match_x],
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                 'XShape': [xshape]},
        attrs={'ignore_index': ignore_index})
    return out


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def bpr_loss(input, label, name=None):
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    """
    Bayesian Personalized Ranking Loss Operator.

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    This operator belongs to pairwise ranking loss. Label is the desired item.
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    The loss at a given point in one session is defined as:
    $Y[i] = -\frac{1}{N_{i}-1} * \sum_{0\le j<N_{i},~ j\neq Label[i]}\log(\sigma(X[i, Label[i]]-X[i, j]))$

    Learn more details by reading paper <session-based recommendations with recurrent
    neural networks>(https://arxiv.org/abs/1511.06939)

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    Args:
        input (Variable|list):  a 2-D tensor with shape [N x D], where N is the
                                batch size and D is the number of classes.
                                This input is not probability but logits.
        label (Variable|list):  the ground truth which is a 2-D tensor.  `label`
                                is a tensor<int64> with shape [N x 1].
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        name (str|None):        A name for this layer(optional). If set None, the
                                layer will be named automatically. Default: None.
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    Returns:
        A 2-D tensor with shape [N x 1], the bpr loss.

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    Examples:
        .. code-block:: python

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          cost = fluid.layers.bpr_loss(input=predict, label=label)
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    """
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    helper = LayerHelper('bpr_loss', **locals())
    out = helper.create_variable_for_type_inference(dtype=input.dtype)
    helper.append_op(
        type='bpr_loss',
        inputs={'X': [input],
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                'Label': [label]},
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        outputs={'Y': [out]})
    return out


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def square_error_cost(input, label):
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    """
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    **Square error cost layer**

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    This layer accepts input predictions and target label and returns the
    squared error cost.
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    For predictions, :math:`X`, and target labels, :math:`Y`, the equation is:

    .. math::

        Out = (X - Y)^2

    In the above equation:

        * :math:`X`: Input predictions, a tensor.
        * :math:`Y`: Input labels, a tensor.
        * :math:`Out`: Output value, same shape with :math:`X`.

    Args:
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        input (Variable): Input tensor, has predictions.
        label (Variable): Label tensor, has target labels.
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    Returns:
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        Variable: The tensor variable storing the element-wise squared error \
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                  difference of input and label.
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    Examples:
        .. code-block:: python

          y = layers.data(name='y', shape=[1], dtype='float32')
          y_predict = layers.data(name='y_predict', shape=[1], dtype='float32')
          cost = layers.square_error_cost(input=y_predict, label=y)

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    """
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    helper = LayerHelper('square_error_cost', **locals())
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    minus_out = helper.create_variable_for_type_inference(dtype=input.dtype)
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    helper.append_op(
        type='elementwise_sub',
        inputs={'X': [input],
                'Y': [label]},
        outputs={'Out': [minus_out]})

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    square_out = helper.create_variable_for_type_inference(dtype=input.dtype)
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    helper.append_op(
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        type='square', inputs={'X': [minus_out]},
        outputs={'Out': [square_out]})
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    return square_out


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@templatedoc()
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def chunk_eval(input,
               label,
               chunk_scheme,
               num_chunk_types,
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               excluded_chunk_types=None):
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    """
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    **Chunk Evaluator**
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    This function computes and outputs the precision, recall and
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    F1-score of chunk detection.
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    For some basics of chunking, please refer to
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    `Chunking with Support Vector Machines <https://aclanthology.info/pdf/N/N01/N01-1025.pdf>`_ .
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    ChunkEvalOp computes the precision, recall, and F1-score of chunk detection,
    and supports IOB, IOE, IOBES and IO (also known as plain) tagging schemes.
    Here is a NER example of labeling for these tagging schemes:

    .. code-block:: python
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       ====== ====== ======  =====  ==  ============   =====  ===== =====  ==  =========
              Li     Ming    works  at  Agricultural   Bank   of    China  in  Beijing.
       ====== ====== ======  =====  ==  ============   =====  ===== =====  ==  =========
       IO     I-PER  I-PER   O      O   I-ORG          I-ORG  I-ORG I-ORG  O   I-LOC
       IOB    B-PER  I-PER   O      O   B-ORG          I-ORG  I-ORG I-ORG  O   B-LOC
       IOE    I-PER  E-PER   O      O   I-ORG          I-ORG  I-ORG E-ORG  O   E-LOC
       IOBES  B-PER  E-PER   O      O   I-ORG          I-ORG  I-ORG E-ORG  O   S-LOC
       ====== ====== ======  =====  ==  ============   =====  ===== =====  ==  =========

    There are three chunk types(named entity types) including PER(person), ORG(organization)
    and LOC(LOCATION), and we can see that the labels have the form <tag type>-<chunk type>.

    Since the calculations actually use label ids rather than labels, extra attention
    should be paid when mapping labels to ids to make CheckEvalOp work. The key point
    is that the listed equations are satisfied by ids.

    .. code-block:: python

       tag_type = label % num_tag_type
       chunk_type = label / num_tag_type

    where `num_tag_type` is the num of tag types in the tagging scheme, `num_chunk_type`
    is the num of chunk types, and `tag_type` get its value from the following table.

    .. code-block:: python
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       Scheme Begin Inside End   Single
        plain   0     -      -     -
        IOB     0     1      -     -
        IOE     -     0      1     -
        IOBES   0     1      2     3

    Still use NER as example, assuming the tagging scheme is IOB while chunk types are ORG,
    PER and LOC. To satisfy the above equations, the label map can be like this:

    .. code-block:: python

       B-ORG  0
       I-ORG  1
       B-PER  2
       I-PER  3
       B-LOC  4
       I-LOC  5
       O      6

    It's not hard to verify the equations noting that the num of chunk types
    is 3 and the num of tag types in IOB scheme is 2. For example, the label
    id of I-LOC is 5, the tag type id of I-LOC is 1, and the chunk type id of
    I-LOC is 2, which consistent with the results from the equations.

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    Args:
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        input (Variable): prediction output of the network.
        label (Variable): label of the test data set.
        chunk_scheme (str): ${chunk_scheme_comment}
        num_chunk_types (int): ${num_chunk_types_comment}
        excluded_chunk_types (list): ${excluded_chunk_types_comment}
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    Returns:
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        tuple: tuple containing: precision, recall, f1_score,
        num_infer_chunks, num_label_chunks,
        num_correct_chunks
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    Examples:
        .. code-block:: python

            crf = fluid.layers.linear_chain_crf(
                input=hidden, label=label, param_attr=ParamAttr(name="crfw"))
            crf_decode = fluid.layers.crf_decoding(
                input=hidden, param_attr=ParamAttr(name="crfw"))
            fluid.layers.chunk_eval(
                input=crf_decode,
                label=label,
                chunk_scheme="IOB",
                num_chunk_types=(label_dict_len - 1) / 2)
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    """
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    helper = LayerHelper("chunk_eval", **locals())
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    # prepare output
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    precision = helper.create_variable_for_type_inference(dtype="float32")
    recall = helper.create_variable_for_type_inference(dtype="float32")
    f1_score = helper.create_variable_for_type_inference(dtype="float32")
    num_infer_chunks = helper.create_variable_for_type_inference(dtype="int64")
    num_label_chunks = helper.create_variable_for_type_inference(dtype="int64")
    num_correct_chunks = helper.create_variable_for_type_inference(
        dtype="int64")
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    helper.append_op(
        type="chunk_eval",
        inputs={"Inference": [input],
                "Label": [label]},
        outputs={
            "Precision": [precision],
            "Recall": [recall],
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            "F1-Score": [f1_score],
            "NumInferChunks": [num_infer_chunks],
            "NumLabelChunks": [num_label_chunks],
            "NumCorrectChunks": [num_correct_chunks]
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        },
        attrs={
            "num_chunk_types": num_chunk_types,
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            "chunk_scheme": chunk_scheme,
            "excluded_chunk_types": excluded_chunk_types or []
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        })
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    return (precision, recall, f1_score, num_infer_chunks, num_label_chunks,
            num_correct_chunks)
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@templatedoc()
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def sequence_conv(input,
                  num_filters,
                  filter_size=3,
                  filter_stride=1,
                  padding=None,
                  bias_attr=None,
                  param_attr=None,
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                  act=None,
                  name=None):
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    """
    This function creates the op for sequence_conv, using the inputs and
    other convolutional configurations for the filters and stride as given
    in the input parameters to the function.
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    Args:
        input (Variable): ${x_comment}
        num_filters (int): number of filters.
        filter_size (int): the filter size (H and W).
        filter_stride (int): stride of the filter.
        padding (bool): if True, add paddings.
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        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of sequence_conv.
            If it is set to False, no bias will be added to the output units.
            If it is set to None or one attribute of ParamAttr, sequence_conv
            will create ParamAttr as bias_attr. If the Initializer of the bias_attr
            is not set, the bias is initialized zero. Default: None.
        param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
            of sequence_conv. If it is set to None or one attribute of ParamAttr, sequence_conv
            will create ParamAttr as param_attr. If the Initializer of the param_attr
            is not set, the parameter is initialized with Xavier. Default: None.
        act (str): Activation type, if it is set to None, activation is not appended.
            Default: None.
        name (str|None): A name for this layer(optional). If set None, the layer
            will be named automatically. Default: None.
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    Returns:
        Variable: output of sequence_conv
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    """

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    assert not in_dygraph_mode(), (
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        "sequence layer is not supported in dygraph mode yet.")
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    helper = LayerHelper('sequence_conv', **locals())
    dtype = helper.input_dtype()
    filter_shape = [filter_size * input.shape[1], num_filters]
    filter_param = helper.create_parameter(
        attr=helper.param_attr, shape=filter_shape, dtype=dtype)
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    pre_bias = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
        type='sequence_conv',
        inputs={
            'X': [input],
            'Filter': [filter_param],
        },
        outputs={"Out": pre_bias},
        attrs={
            'contextStride': filter_stride,
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            'contextStart': -int(filter_size // 2),
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            'contextLength': filter_size
        })
    pre_act = helper.append_bias_op(pre_bias)
    return helper.append_activation(pre_act)


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def sequence_softmax(input, use_cudnn=False, name=None):
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    """
    This function computes the softmax activation among all time-steps for each
    sequence. The dimension of each time-step should be 1. Thus, the shape of
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    input Tensor can be either :math:`[N, 1]` or :math:`[N]`, where :math:`N`
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    is the sum of the length of all sequences.

    For i-th sequence in a mini-batch:

    .. math::

        Out(X[lod[i]:lod[i+1]], :) = \\frac{\exp(X[lod[i]:lod[i+1], :])}{\sum(\exp(X[lod[i]:lod[i+1], :]))}

    For example, for a mini-batch of 3 sequences with variable-length,
    each containing 2, 3, 2 time-steps, the lod of which is [0, 2, 5, 7],
    then softmax will be computed among :math:`X[0:2, :]`, :math:`X[2:5, :]`,
    :math:`X[5:7, :]`, and :math:`N` turns out to be 7.

    Args:
        input (Variable): The input variable which is a LoDTensor.
        use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn \
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            library is installed. Default: False.
        name (str|None): A name for this layer(optional). If set None, the layer
            will be named automatically. Default: None.
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    Returns:
        Variable: output of sequence_softmax

    Examples:

        .. code-block:: python

             x = fluid.layers.data(name='x', shape=[7, 1],
                              dtype='float32', lod_level=1)
             x_sequence_softmax = fluid.layers.sequence_softmax(input=x)
    """
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    assert not in_dygraph_mode(), (
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        "sequence layer is not supported in dygraph mode yet.")
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    helper = LayerHelper('sequence_softmax', **locals())
    dtype = helper.input_dtype()
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    softmax_out = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
        type="sequence_softmax",
        inputs={"X": input},
        outputs={"Out": softmax_out},
        attrs={"use_cudnn": use_cudnn})
    return softmax_out


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def softmax(input, use_cudnn=False, name=None, axis=-1):
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    """
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    The input of the softmax operator is a tensor of any rank. The output tensor
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    has the same shape as the input.
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    The dimension :attr:`axis` of the input tensor will be permuted to the last.
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    Then the input tensor will be logically flattened to a 2-D matrix. The matrix's
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    second dimension(row length) is the same as the dimension :attr:`axis` of the input
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    tensor, and the first dimension(column length) is the product of all other
    dimensions of the input tensor. For each row of the matrix, the softmax operator
    squashes the K-dimensional(K is the width of the matrix, which is also the size
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    of the input tensor's dimension :attr:`axis`) vector of arbitrary real values to a
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    K-dimensional vector of real values in the range [0, 1] that add up to 1.
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    It computes the exponential of the given dimension and the sum of exponential
    values of all the other dimensions in the K-dimensional vector input.
    Then the ratio of the exponential of the given dimension and the sum of
    exponential values of all the other dimensions is the output of the softmax
    operator.

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    For each row :math:`i` and each column :math:`j` in the matrix, we have:
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    .. math::

        Out[i, j] = \\frac{\exp(X[i, j])}{\sum_j(exp(X[i, j])}

    Args:
        input (Variable): The input variable.
        use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn \
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            library is installed. To improve numerical stablity, set use_cudnn to \
            False by default. Default: False
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        name (str|None): A name for this layer(optional). If set None, the layer
            will be named automatically. Default: None.
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        axis (int): The index of dimension to perform softmax calculations, it should
            be in range :math:`[-1, rank - 1]`, while :math:`rank` is the rank of
            input variable. Default: -1.
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    Returns:
        Variable: output of softmax

    Examples:

        .. code-block:: python

             fc = fluid.layers.fc(input=x, size=10)
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             # perform softmax in the second dimension
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             softmax = fluid.layers.softmax(input=fc, axis=1)
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             # perform softmax in the last dimension
             softmax = fluid.layers.softmax(input=fc, axis=-1)
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    """
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    helper = LayerHelper('softmax', **locals())
    dtype = helper.input_dtype()
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    softmax_out = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
        type="softmax",
        inputs={"X": input},
        outputs={"Out": softmax_out},
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        attrs={"axis": axis,
               "use_cudnn": use_cudnn})
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    return softmax_out


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def conv2d(input,
           num_filters,
           filter_size,
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           stride=1,
           padding=0,
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           dilation=1,
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           groups=None,
           param_attr=None,
           bias_attr=None,
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           use_cudnn=True,
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           act=None,
           name=None):
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    """
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    The convolution2D layer calculates the output based on the input, filter
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    and strides, paddings, dilations, groups parameters. Input and
    Output are in NCHW format, where N is batch size, C is the number of
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    channels, H is the height of the feature, and W is the width of the feature.
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    Filter is in MCHW format, where M is the number of output image channels,
    C is the number of input image channels, H is the height of the filter,
    and W is the width of the filter. If the groups is greater than 1,
    C will equal the number of input image channels divided by the groups.
    Please refer to UFLDL's `convolution
    <http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/>`_
    for more detials.
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    If bias attribution and activation type are provided, bias is added to the
    output of the convolution, and the corresponding activation function is
    applied to the final result.
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    For each input :math:`X`, the equation is:
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    .. math::

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        Out = \sigma (W \\ast X + b)
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    Where:
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    * :math:`X`: Input value, a tensor with NCHW format.
    * :math:`W`: Filter value, a tensor with MCHW format.
    * :math:`\\ast`: Convolution operation.
    * :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
    * :math:`\\sigma`: Activation function.
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    * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
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    Example:

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        - Input:

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          Input shape: :math:`(N, C_{in}, H_{in}, W_{in})`
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          Filter shape: :math:`(C_{out}, C_{in}, H_f, W_f)`
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        - Output:
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          Output shape: :math:`(N, C_{out}, H_{out}, W_{out})`
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        Where
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        .. math::
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            H_{out}&= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\\\
            W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1
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    Args:
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        input (Variable): The input image with [N, C, H, W] format.
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        num_filters(int): The number of filter. It is as same as the output
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            image channel.
        filter_size (int|tuple|None): The filter size. If filter_size is a tuple,
            it must contain two integers, (filter_size_H, filter_size_W).
            Otherwise, the filter will be a square.
        stride (int|tuple): The stride size. If stride is a tuple, it must
            contain two integers, (stride_H, stride_W). Otherwise, the
            stride_H = stride_W = stride. Default: stride = 1.
        padding (int|tuple): The padding size. If padding is a tuple, it must
            contain two integers, (padding_H, padding_W). Otherwise, the
            padding_H = padding_W = padding. Default: padding = 0.
        dilation (int|tuple): The dilation size. If dilation is a tuple, it must
            contain two integers, (dilation_H, dilation_W). Otherwise, the
            dilation_H = dilation_W = dilation. Default: dilation = 1.
        groups (int): The groups number of the Conv2d Layer. According to grouped
            convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
            the first half of the filters is only connected to the first half
            of the input channels, while the second half of the filters is only
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            connected to the second half of the input channels. Default: groups=1.
        param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
            of conv2d. If it is set to None or one attribute of ParamAttr, conv2d
            will create ParamAttr as param_attr. If the Initializer of the param_attr
            is not set, the parameter is initialized with :math:`Normal(0.0, std)`,
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            and the :math:`std` is :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None.
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        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv2d.
            If it is set to False, no bias will be added to the output units.
            If it is set to None or one attribute of ParamAttr, conv2d
            will create ParamAttr as bias_attr. If the Initializer of the bias_attr
            is not set, the bias is initialized zero. Default: None.
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        use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
            library is installed. Default: True
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        act (str): Activation type, if it is set to None, activation is not appended.
            Default: None
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        name (str|None): A name for this layer(optional). If set None, the layer
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            will be named automatically. Default: None
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    Returns:
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        Variable: The tensor variable storing the convolution and \
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                  non-linearity activation result.

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    Raises:
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        ValueError: If the shapes of input, filter_size, stride, padding and
                    groups mismatch.
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    Examples:
        .. code-block:: python

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          data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32')
          conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3, act="relu")
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    """

    num_channels = input.shape[1]
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    assert param_attr is not False, "param_attr should not be False here."
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    l_type = 'conv2d'
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    if (num_channels == groups and num_filters % num_channels == 0 and
            not use_cudnn):
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        l_type = 'depthwise_conv2d'
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    helper = LayerHelper(l_type, **locals())
    dtype = helper.input_dtype()

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    if groups is None:
        num_filter_channels = num_channels
    else:
        if num_channels % groups != 0:
            raise ValueError("num_channels must be divisible by groups.")
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        num_filter_channels = num_channels // groups
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    filter_size = utils.convert_to_list(filter_size, 2, 'filter_size')
    stride = utils.convert_to_list(stride, 2, 'stride')
    padding = utils.convert_to_list(padding, 2, 'padding')
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    dilation = utils.convert_to_list(dilation, 2, 'dilation')
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    if not isinstance(use_cudnn, bool):
        raise ValueError("use_cudnn should be True or False")
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    input_shape = input.shape
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    filter_shape = [num_filters, int(num_filter_channels)] + filter_size
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    def _get_default_param_initializer():
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        filter_elem_num = filter_size[0] * filter_size[1] * num_channels
        std = (2.0 / filter_elem_num)**0.5
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        return Normal(0.0, std, 0)

    filter_param = helper.create_parameter(
        attr=helper.param_attr,
        shape=filter_shape,
        dtype=dtype,
        default_initializer=_get_default_param_initializer())

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    pre_bias = helper.create_variable_for_type_inference(dtype)
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    if use_cudnn:
        helper.create_variable(
            name="kCUDNNFwdAlgoCache",
            persistable=True,
            type=core.VarDesc.VarType.RAW)
        helper.create_variable(
            name="kCUDNNBwdDataAlgoCache",
            persistable=True,
            type=core.VarDesc.VarType.RAW)
        helper.create_variable(
            name="kCUDNNBwdFilterAlgoCache",
            persistable=True,
            type=core.VarDesc.VarType.RAW)

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    helper.append_op(
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        type=l_type,
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        inputs={
            'Input': input,
            'Filter': filter_param,
        },
        outputs={"Output": pre_bias},
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        attrs={
            'strides': stride,
            'paddings': padding,
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            'dilations': dilation,
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            'groups': groups,
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            'use_cudnn': use_cudnn,
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            'use_mkldnn': False,
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            'fuse_relu_before_depthwise_conv': False
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        })
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    pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)

    return helper.append_activation(pre_act)


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def conv3d(input,
           num_filters,
           filter_size,
           stride=1,
           padding=0,
           dilation=1,
           groups=None,
           param_attr=None,
           bias_attr=None,
           use_cudnn=True,
           act=None,
           name=None):
    """
    **Convlution3D Layer**

    The convolution3D layer calculates the output based on the input, filter
    and strides, paddings, dilations, groups parameters. Input(Input) and
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    Output(Output) are in NCDHW format. Where N is batch size C is the number of
    channels, D is the depth of the feature, H is the height of the feature,
    and W is the width of the feature. Convlution3D is similar with Convlution2D
    but adds one dimension(depth). If bias attribution and activation type are
    provided, bias is added to the output of the convolution, and the
    corresponding activation function is applied to the final result.
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    For each input :math:`X`, the equation is:

    .. math::

        Out = \sigma (W \\ast X + b)

    In the above equation:

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    * :math:`X`: Input value, a tensor with NCDHW format.
    * :math:`W`: Filter value, a tensor with MCDHW format.
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    * :math:`\\ast`: Convolution operation.
    * :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
    * :math:`\\sigma`: Activation function.
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    * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
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    Example:

        - Input:

          Input shape: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`

          Filter shape: :math:`(C_{out}, C_{in}, D_f, H_f, W_f)`

        - Output:
          Output shape: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`

        Where

        .. math::

            D_{out}&= \\frac{(D_{in} + 2 * paddings[0] - (dilations[0] * (D_f - 1) + 1))}{strides[0]} + 1 \\\\
            H_{out}&= \\frac{(H_{in} + 2 * paddings[1] - (dilations[1] * (H_f - 1) + 1))}{strides[1]} + 1 \\\\
            W_{out}&= \\frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{strides[2]} + 1

    Args:
        input (Variable): The input image with [N, C, D, H, W] format.
            num_filters(int): The number of filter. It is as same as the output
            image channel.
        filter_size (int|tuple|None): The filter size. If filter_size is a tuple,
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            it must contain three integers, (filter_size_D, filter_size_H, filter_size_W).
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            Otherwise, the filter will be a square.
        stride (int|tuple): The stride size. If stride is a tuple, it must
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            contain three integers, (stride_D, stride_H, stride_W). Otherwise, the
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            stride_D = stride_H = stride_W = stride. Default: stride = 1.
        padding (int|tuple): The padding size. If padding is a tuple, it must
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            contain three integers, (padding_D, padding_H, padding_W). Otherwise, the
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            padding_D = padding_H = padding_W = padding. Default: padding = 0.
        dilation (int|tuple): The dilation size. If dilation is a tuple, it must
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            contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the
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            dilation_D = dilation_H = dilation_W = dilation. Default: dilation = 1.
        groups (int): The groups number of the Conv3d Layer. According to grouped
            convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
            the first half of the filters is only connected to the first half
            of the input channels, while the second half of the filters is only
            connected to the second half of the input channels. Default: groups=1
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        param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
            of conv3d. If it is set to None or one attribute of ParamAttr, conv3d
            will create ParamAttr as param_attr. If it is set to None, the parameter
            is initialized with :math:`Normal(0.0, std)`, and the :math:`std` is
            :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None.
        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv3d.
            If it is set to False, no bias will be added to the output units.
            If it is set to None or one attribute of ParamAttr, conv3d
            will create ParamAttr as bias_attr. If the Initializer of the bias_attr
            is not set, the bias is initialized zero. Default: None.
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        use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
            library is installed. Default: True
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        act (str): Activation type, if it is set to None, activation is not appended.
            Default: None.
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        name (str|None): A name for this layer(optional). If set None, the layer
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            will be named automatically. Default: None.
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    Returns:
        Variable: The tensor variable storing the convolution and \
                  non-linearity activation result.

    Raises:
        ValueError: If the shapes of input, filter_size, stride, padding and
                    groups mismatch.

    Examples:
        .. code-block:: python

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          data = fluid.layers.data(name='data', shape=[3, 12, 32, 32], dtype='float32')
          conv3d = fluid.layers.conv3d(input=data, num_filters=2, filter_size=3, act="relu")
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    """

    l_type = 'conv3d'
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    assert param_attr is not False, "param_attr should not be False here."
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    helper = LayerHelper(l_type, **locals())
    dtype = helper.input_dtype()

    num_channels = input.shape[1]

    if groups is None:
        num_filter_channels = num_channels
    else:
        if num_channels % groups != 0:
            raise ValueError("num_channels must be divisible by groups.")
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        num_filter_channels = num_channels // groups
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    filter_size = utils.convert_to_list(filter_size, 3, 'filter_size')
    stride = utils.convert_to_list(stride, 3, 'stride')
    padding = utils.convert_to_list(padding, 3, 'padding')
    dilation = utils.convert_to_list(dilation, 3, 'dilation')

    if not isinstance(use_cudnn, bool):
        raise ValueError("use_cudnn should be True or False")

    input_shape = input.shape
    filter_shape = [num_filters, num_filter_channels] + filter_size

    def _get_default_param_initializer():
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        filter_elem_num = filter_size[0] * filter_size[1] * filter_size[
            2] * num_channels
        std = (2.0 / filter_elem_num)**0.5
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        return Normal(0.0, std, 0)

    filter_param = helper.create_parameter(
        attr=helper.param_attr,
        shape=filter_shape,
        dtype=dtype,
        default_initializer=_get_default_param_initializer())

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    pre_bias = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
        type=l_type,
        inputs={
            'Input': input,
            'Filter': filter_param,
        },
        outputs={"Output": pre_bias},
        attrs={
            'strides': stride,
            'paddings': padding,
            'dilations': dilation,
            'groups': groups,
            'use_cudnn': use_cudnn,
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            'use_mkldnn': False
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        })

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    pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
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    return helper.append_activation(pre_act)


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def sequence_pool(input, pool_type, is_test=False):
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    """
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    This function add the operator for sequence pooling.
    It pools features of all time-steps of each instance, and is applied
    on top of the input using pool_type mentioned in the parameters.
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    It supports four pool_type:

    - average: :math:`Out[i] = \\frac{\sum_i X_i}{N}`
    - sum:     :math:`Out[i] = \sum_jX_{ij}`
    - sqrt:    :math:`Out[i] = \\frac{\sum_jX_{ij}}{\sqrt{len(X_i)}}`
    - max:     :math:`Out[i] = max(X_i)`

    .. code-block:: text

       x is a 1-level LoDTensor:
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         x.lod = [[2, 3, 2]]
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         x.data = [1, 3, 2, 4, 6, 5, 1]
         x.dims = [7, 1]

       then output is a Tensor:
         out.dim = [3, 1]
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         with condition len(x.lod[-1]) == out.dims[0]
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       for different pool_type:
         average: out.data = [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2
         sum    : out.data = [4, 12, 6], where 4=1+3, 12=2+4+6, 6=5+1
         sqrt   : out.data = [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
                    6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
         max    : out.data = [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
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         last   : out.data = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
         first  : out.data = [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
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    Args:
        input(variable): The input variable which is a LoDTensor.
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        pool_type (string): The pooling type of sequence_pool.
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            It supports average, sum, sqrt and max.
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        is_test(bool, Default False): Used distinguish training from scoring mode.
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    Returns:
        The sequence pooling variable which is a Tensor.

    Examples:

        .. code-block:: python
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             x = fluid.layers.data(name='x', shape=[7, 1],
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                              dtype='float32', lod_level=1)
             avg_x = fluid.layers.sequence_pool(input=x, pool_type='average')
             sum_x = fluid.layers.sequence_pool(input=x, pool_type='sum')
             sqrt_x = fluid.layers.sequence_pool(input=x, pool_type='sqrt')
             max_x = fluid.layers.sequence_pool(input=x, pool_type='max')
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             last_x = fluid.layers.sequence_pool(input=x, pool_type='last')
             first_x = fluid.layers.sequence_pool(input=x, pool_type='first')
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    """
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    assert not in_dygraph_mode(), (
2323
        "sequence layer is not supported in dygraph mode yet.")
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    helper = LayerHelper('sequence_pool', **locals())
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    dtype = helper.input_dtype()
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    pool_out = helper.create_variable_for_type_inference(dtype)
    max_index = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
        type="sequence_pool",
        inputs={"X": input},
        outputs={"Out": pool_out,
                 "MaxIndex": max_index},
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        attrs={"pooltype": pool_type.upper(),
               "is_test": is_test})
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    # when pool_type is max, variable max_index is initialized,
    # so we stop the gradient explicitly here
    if pool_type == 'max':
        max_index.stop_gradient = True

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    return pool_out


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@templatedoc()
def sequence_concat(input, name=None):
    """
    ${comment}

    Args:
        input(list): List of Variables to be concatenated.
        name(str|None): A name for this layer(optional). If set None, the layer
                       will be named automatically.

    Returns:
        Variable: Output variable of the concatenation.

    Examples:
        .. code-block:: python

           out = fluid.layers.sequence_concat(input=[seq1, seq2, seq3])
    """
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    assert not in_dygraph_mode(), (
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        "sequence layer is not supported in dygraph mode yet.")
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    helper = LayerHelper('sequence_concat', **locals())
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    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
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    helper.append_op(
        type='sequence_concat', inputs={'X': input}, outputs={'Out': [out]})
    return out


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def sequence_first_step(input):
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    """
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    This function gets the first step of sequence.
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    .. code-block:: text

       x is a 1-level LoDTensor:
2379
         x.lod = [[2, 3, 2]]
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         x.data = [1, 3, 2, 4, 6, 5, 1]
         x.dims = [7, 1]

       then output is a Tensor:
         out.dim = [3, 1]
2385
         with condition len(x.lod[-1]) == out.dims[0]
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         out.data = [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
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    Args:
        input(variable): The input variable which is a LoDTensor.

    Returns:
        The sequence's first step variable which is a Tensor.

    Examples:

        .. code-block:: python
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             x = fluid.layers.data(name='x', shape=[7, 1],
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                              dtype='float32', lod_level=1)
             x_first_step = fluid.layers.sequence_first_step(input=x)
    """
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    return sequence_pool(input=input, pool_type="first")


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def sequence_last_step(input):
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    """
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    This function gets the last step of sequence.
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    .. code-block:: text

       x is a 1-level LoDTensor:
2412
         x.lod = [[2, 3, 2]]
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         x.data = [1, 3, 2, 4, 6, 5, 1]
         x.dims = [7, 1]

       then output is a Tensor:
         out.dim = [3, 1]
2418
         with condition len(x.lod[-1]) == out.dims[0]
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         out.data = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
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    Args:
        input(variable): The input variable which is a LoDTensor.

    Returns:
        The sequence's last step variable which is a Tensor.

    Examples:

        .. code-block:: python
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             x = fluid.layers.data(name='x', shape=[7, 1],
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                              dtype='float32', lod_level=1)
             x_last_step = fluid.layers.sequence_last_step(input=x)
    """
2435 2436 2437
    return sequence_pool(input=input, pool_type="last")


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def sequence_slice(input, offset, length, name=None):
    """
    **Sequence Slice Layer**

2442
    The layer crops a subsequence from given sequence with given start
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    offset and subsequence length.

    It only supports sequence data (LoDTensor with lod_level equal to 1).

    .. code-block:: text
2448

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              - Case:
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2451
            Given the input Variable **input**:
2452

2453 2454 2455
                input.data = [[a1, a2], [b1, b2], [c1, c2], [d1, d2], [e1, e2]],
                input.lod = [[3, 2]],
                input.dims = (5, 2),
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2457
            with offset.data = [[0], [1]] and length.data = [[2], [1]],
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2459
            the output Variable will be
2460

2461 2462 2463
                out.data = [[a1, a2], [b1, b2], [e1, e2]],
                out.lod = [[2, 1]],
                out.dims = (3, 2).
2464

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    Note:
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          The first dimension size of **input**, **offset** and **length**
2467
          should be equal. The **offset** should start from 0.
2468

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    Args:
2470
        input(Variable): The input Variable which consists of the complete
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                         sequences.
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        offset(Variable): The offset to slice each sequence.
        length(Variable): The length of each subsequence.
        name(str|None): A name for this layer(optional). If set None, the
                        layer will be named automatically.

    Returns:
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        Variable: The output subsequences.
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    Examples:

        .. code-block:: python

             import numpy as np
             seqs = fluid.layers.data(name='x', shape=[10, 5],
                              dtype='float32', lod_level=1)
             offset = fluid.layers.assign(input=np.array([[0, 1]]).astype("int32"))
             length = fluid.layers.assign(input=np.array([[2, 1]]).astype("int32"))
2489
             subseqs = fluid.layers.sequence_slice(input=seqs, offset=offset,
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                                                   length=length)
    """
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    assert not in_dygraph_mode(), (
2493
        "sequence layer is not supported in dygraph mode yet.")
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    helper = LayerHelper("sequence_slice", **locals())
    dtype = helper.input_dtype()
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    out = helper.create_variable_for_type_inference(dtype)
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    offset.stop_gradient = True
    length.stop_gradient = True

    helper.append_op(
        type="sequence_slice",
        inputs={"X": input,
                "Offset": offset,
                "Length": length},
        outputs={"Out": out})

    return out


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@templatedoc()
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def pool2d(input,
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           pool_size=-1,
           pool_type="max",
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           pool_stride=1,
           pool_padding=0,
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           global_pooling=False,
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           use_cudnn=True,
2519
           ceil_mode=False,
2520 2521
           name=None,
           exclusive=True):
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    """
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    ${comment}
2524 2525

    Args:
2526 2527 2528
        input (Variable): The input tensor of pooling operator. The format of
                          input tensor is NCHW, where N is batch size, C is
                          the number of channels, H is the height of the
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                          feature, and W is the width of the feature.
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        pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
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            it must contain two integers, (pool_size_Height, pool_size_Width).
            Otherwise, the pool kernel size will be a square of an int.
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        pool_type: ${pooling_type_comment}
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        pool_stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list,
            it must contain two integers, (pool_stride_Height, pool_stride_Width).
            Otherwise, the pool stride size will be a square of an int.
        pool_padding (int|list|tuple): The pool padding size. If pool padding size is a tuple,
            it must contain two integers, (pool_padding_on_Height, pool_padding_on_Width).
            Otherwise, the pool padding size will be a square of an int.
2540 2541 2542
        global_pooling (bool): ${global_pooling_comment}
        use_cudnn (bool): ${use_cudnn_comment}
        ceil_mode (bool): ${ceil_mode_comment}
2543
        name (str|None): A name for this layer(optional). If set None, the
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                        layer will be named automatically.
2545
        exclusive (bool): Whether to exclude padding points in average pooling
2546
                          mode, default is true
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2548
    Returns:
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        Variable: The pooling result.
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    Raises:
        ValueError: If 'pool_type' is not "max" nor "avg"
        ValueError: If 'global_pooling' is False and 'pool_size' is -1
        ValueError: If 'use_cudnn' is not a bool value.

    Examples:

        .. code-block:: python

          data = fluid.layers.data(
              name='data', shape=[3, 32, 32], dtype='float32')
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          pool2d = fluid.layers.pool2d(
2563 2564 2565 2566
                            input=data,
                            pool_size=2,
                            pool_type='max',
                            pool_stride=1,
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                            global_pooling=False)
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    """
    if pool_type not in ["max", "avg"]:
        raise ValueError(
            "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.",
            str(pool_type))
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    if global_pooling is False and pool_size == -1:
        raise ValueError(
            "When the global_pooling is False, pool_size must be passed "
            "and be a valid value. Received pool_size: " + str(pool_size))

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    pool_size = utils.convert_to_list(pool_size, 2, 'pool_size')
    pool_padding = utils.convert_to_list(pool_padding, 2, 'pool_padding')
    pool_stride = utils.convert_to_list(pool_stride, 2, 'pool_stride')

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    if not isinstance(use_cudnn, bool):
        raise ValueError("use_cudnn should be True or False")
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    l_type = 'pool2d'
2587 2588

    helper = LayerHelper(l_type, **locals())
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    dtype = helper.input_dtype()
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    pool_out = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
2593 2594 2595 2596 2597 2598 2599 2600 2601 2602 2603
        type=l_type,
        inputs={"X": input},
        outputs={"Out": pool_out},
        attrs={
            "pooling_type": pool_type,
            "ksize": pool_size,
            "global_pooling": global_pooling,
            "strides": pool_stride,
            "paddings": pool_padding,
            "use_cudnn": use_cudnn,
            "ceil_mode": ceil_mode,
2604 2605
            "use_mkldnn": False,
            "exclusive": exclusive,
2606 2607 2608 2609 2610
        })

    return pool_out


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@templatedoc()
2612 2613 2614 2615 2616 2617 2618 2619
def pool3d(input,
           pool_size=-1,
           pool_type="max",
           pool_stride=1,
           pool_padding=0,
           global_pooling=False,
           use_cudnn=True,
           ceil_mode=False,
2620 2621
           name=None,
           exclusive=True):
2622
    """
2623
    ${comment}
2624 2625

    Args:
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        input (Variable): The input tensor of pooling operator. The format of
                          input tensor is NCDHW, where N is batch size, C is
                          the number of channels, D is the depth of the feature,
                          H is the height of the feature, and W is the width
                          of the feature.
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        pool_size (int|list|tuple): The pool kernel size. If pool kernel size 
            is a tuple or list, it must contain three integers, 
            (pool_size_Depth, pool_size_Height, pool_size_Width).
            Otherwise, the pool kernel size will be the cube of an int.
        pool_type (string): ${pooling_type_comment}
2636 2637 2638 2639 2640 2641 2642
        pool_stride (int): stride of the pooling layer.
        pool_padding (int): padding size.
        global_pooling (bool): ${global_pooling_comment}
        use_cudnn (bool): ${use_cudnn_comment}
        ceil_mode (bool): ${ceil_mode_comment}
        name (str): A name for this layer(optional). If set None, the layer
            will be named automatically.
2643
        exclusive (bool): Whether to exclude padding points in average pooling
2644
                          mode, default is true
2645

2646
    Returns:
2647
        Variable: output of pool3d layer.
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    Examples:

        .. code-block:: python

          data = fluid.layers.data(
              name='data', shape=[3, 32, 32, 32], dtype='float32')
          pool3d = fluid.layers.pool3d(
                            input=data,
                            pool_size=2,
                            pool_type='max',
                            pool_stride=1,
                            global_pooling=False)
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    """
    if pool_type not in ["max", "avg"]:
        raise ValueError(
            "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.",
            str(pool_type))
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    if global_pooling is False and pool_size == -1:
        raise ValueError(
            "When the global_pooling is False, pool_size must be passed "
            "and be a valid value. Received pool_size: " + str(pool_size))

2672 2673 2674
    pool_size = utils.convert_to_list(pool_size, 3, 'pool_size')
    pool_padding = utils.convert_to_list(pool_padding, 3, 'pool_padding')
    pool_stride = utils.convert_to_list(pool_stride, 3, 'pool_stride')
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    if not isinstance(use_cudnn, bool):
        raise ValueError("use_cudnn should be True or False")
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2679 2680
    l_type = "pool3d"
    helper = LayerHelper(l_type, **locals())
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    dtype = helper.input_dtype()
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    pool_out = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
2685
        type=l_type,
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        inputs={"X": input},
        outputs={"Out": pool_out},
        attrs={
            "pooling_type": pool_type,
            "ksize": pool_size,
            "global_pooling": global_pooling,
            "strides": pool_stride,
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            "paddings": pool_padding,
2694
            "use_cudnn": use_cudnn,
2695
            "ceil_mode": ceil_mode,
2696 2697
            "use_mkldnn": False,
            "exclusive": exclusive,
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        })

    return pool_out


2703 2704 2705 2706 2707 2708 2709
@templatedoc(op_type="pool2d")
def adaptive_pool2d(input,
                    pool_size,
                    pool_type="max",
                    require_index=False,
                    name=None):
    """
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    **Adaptive Pool2d Operator**
    The adaptive_pool2d operation calculates the output based on the input, pool_size,
    pool_type parameters. Input(X) and output(Out) are in NCHW format, where N is batch
    size, C is the number of channels, H is the height of the feature, and W is
    the width of the feature. Parameters(pool_size) should contain two elements which
    represent height and width, respectively. Also the H and W dimensions of output(Out)
    is same as Parameter(pool_size).
2717

2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730
    For average adaptive pool2d:

    ..  math::

       hstart &= floor(i * H_{in} / H_{out})

       hend &= ceil((i + 1) * H_{in} / H_{out})

       wstart &= floor(j * W_{in} / W_{out})

       wend &= ceil((j + 1) * W_{in} / W_{out})

       Output(i ,j) &= \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
2731 2732 2733 2734 2735 2736 2737 2738 2739

    Args:
        input (Variable): The input tensor of pooling operator. The format of
                          input tensor is NCHW, where N is batch size, C is
                          the number of channels, H is the height of the
                          feature, and W is the width of the feature.
        pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
            it must contain two integers, (pool_size_Height, pool_size_Width).
        pool_type: ${pooling_type_comment}
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        require_index (bool): If true, the index of max pooling point will be returned along
            with outputs. It cannot be set in average pooling type.
2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755
        name (str|None): A name for this layer(optional). If set None, the
                        layer will be named automatically.

    Returns:
        Variable: The pooling result.

    Raises:
        ValueError: 'pool_type' is not 'max' nor 'avg'.
        ValueError: invalid setting 'require_index' true when 'pool_type' is 'avg'.
        ValueError: 'pool_size' should be a list or tuple with length as 2.

    Examples:
        .. code-block:: python

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          # suppose input data in shape of [N, C, H, W], `pool_size` is [m, n],
2757
          # output shape is [N, C, m, n], adaptive pool divide H and W dimentions
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          # of input data into m * n grids averagely and performs poolings in each
2759 2760
          # grid to get output.
          # adaptive average pool performs calculations as follow:
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          #
2762 2763 2764 2765 2766 2767 2768 2769
          #     for i in range(m):
          #         for j in range(n):
          #             hstart = floor(i * H / m)
          #             hend = ceil((i + 1) * H / m)
          #             wstart = floor(i * W / n)
          #             wend = ceil((i + 1) * W / n)
          #             output[:, :, i, j] = avg(input[:, :, hstart: hend, wstart: wend])
          #
2770 2771
          data = fluid.layers.data(
              name='data', shape=[3, 32, 32], dtype='float32')
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          pool_out = fluid.layers.adaptive_pool2d(
2773 2774
                            input=data,
                            pool_size=[3, 3],
2775
                            pool_type='avg')
2776 2777 2778 2779 2780 2781 2782 2783 2784 2785
    """
    if pool_type not in ["max", "avg"]:
        raise ValueError(
            "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.",
            str(pool_type))

    if pool_type == "avg" and require_index:
        raise ValueError(
            "invalid setting 'require_index' true when 'pool_type' is 'avg'.")

2786
    pool_size = utils.convert_to_list(pool_size, 2, 'pool_size')
2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811

    if pool_type == "max":
        l_type = 'max_pool2d_with_index'
    else:
        l_type = "pool2d"

    helper = LayerHelper(l_type, **locals())
    dtype = helper.input_dtype()
    pool_out = helper.create_variable_for_type_inference(dtype)

    outputs = {"Out": pool_out}
    if pool_type == "max":
        mask = helper.create_variable_for_type_inference(dtype)
        outputs["Mask"] = mask

    helper.append_op(
        type=l_type,
        inputs={"X": input},
        outputs=outputs,
        attrs={
            "pooling_type": pool_type,
            "ksize": pool_size,
            "adaptive": True,
        })

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    return (pool_out, mask) if require_index else pool_out
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@templatedoc(op_type="pool3d")
def adaptive_pool3d(input,
                    pool_size,
                    pool_type="max",
                    require_index=False,
                    name=None):
    """
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    **Adaptive Pool3d Operator**
    The adaptive_pool3d operation calculates the output based on the input, pool_size,
    pool_type parameters. Input(X) and output(Out) are in NCDHW format, where N is batch
    size, C is the number of channels, D is the depth of the feature, H is the height of
    the feature, and W is the width of the feature. Parameters(pool_size) should contain
    three elements which represent height and width, respectively. Also the D, H and W
    dimensions of output(Out) is same as Parameter(pool_size).
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    For average adaptive pool3d:

    ..  math::

      dstart &= floor(i * D_{in} / D_{out})

      dend &= ceil((i + 1) * D_{in} / D_{out})

      hstart &= floor(j * H_{in} / H_{out})

      hend &= ceil((j + 1) * H_{in} / H_{out})

      wstart &= floor(k * W_{in} / W_{out})

      wend &= ceil((k + 1) * W_{in} / W_{out})

      Output(i ,j, k) &= \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
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    Args:
        input (Variable): The input tensor of pooling operator. The format of
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                          input tensor is NCDHW, where N is batch size, C is
                          the number of channels, D is the depth of the feature,
                          H is the height of the feature, and W is the width of the feature.
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        pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
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            it must contain three integers, (Depth, Height, Width).
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        pool_type: ${pooling_type_comment}
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        require_index (bool): If true, the index of max pooling point will be returned along
            with outputs. It cannot be set in average pooling type.
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        name (str|None): A name for this layer(optional). If set None, the
                        layer will be named automatically.

    Returns:
        Variable: The pooling result.

    Raises:
        ValueError: 'pool_type' is not 'max' nor 'avg'.
        ValueError: invalid setting 'require_index' true when 'pool_type' is 'avg'.
        ValueError: 'pool_size' should be a list or tuple with length as 2.

    Examples:
        .. code-block:: python

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          # suppose input data in shape of [N, C, D, H, W], `pool_size` is [l, m, n],
          # output shape is [N, C, l, m, n], adaptive pool divide D, H and W dimentions
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          # of input data into l * m * n grids averagely and performs poolings in each
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          # grid to get output.
          # adaptive average pool performs calculations as follow:
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          #
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          #     for i in range(l):
          #         for j in range(m):
          #             for k in range(n):
          #                 dstart = floor(i * D / l)
          #                 dend = ceil((i + 1) * D / l)
          #                 hstart = floor(j * H / m)
          #                 hend = ceil((j + 1) * H / m)
          #                 wstart = floor(k * W / n)
          #                 wend = ceil((k + 1) * W / n)
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          #                 output[:, :, i, j, k] =
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          #                     avg(input[:, :, dstart:dend, hstart: hend, wstart: wend])
          #
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          data = fluid.layers.data(
              name='data', shape=[3, 32, 32], dtype='float32')
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          pool_out, mask = fluid.layers.adaptive_pool3d(
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                            input=data,
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                            pool_size=[3, 3, 3],
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                            pool_type='avg')
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    """
    if pool_type not in ["max", "avg"]:
        raise ValueError(
            "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.",
            str(pool_type))

    if pool_type == "avg" and require_index:
        raise ValueError(
            "invalid setting 'require_index' true when 'pool_type' is 'avg'.")

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    pool_size = utils.convert_to_list(pool_size, 3, 'pool_size')
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    if pool_type == "max":
        l_type = 'max_pool3d_with_index'
    else:
        l_type = "pool3d"

    helper = LayerHelper(l_type, **locals())
    dtype = helper.input_dtype()
    pool_out = helper.create_variable_for_type_inference(dtype)

    outputs = {"Out": pool_out}
    if pool_type == "max":
        mask = helper.create_variable_for_type_inference(dtype)
        outputs["Mask"] = mask

    helper.append_op(
        type=l_type,
        inputs={"X": input},
        outputs=outputs,
        attrs={
            "pooling_type": pool_type,
            "ksize": pool_size,
            "adaptive": True,
        })

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    return (pool_out, mask) if require_index else pool_out
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def batch_norm(input,
               act=None,
               is_test=False,
               momentum=0.9,
               epsilon=1e-05,
               param_attr=None,
               bias_attr=None,
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               data_layout='NCHW',
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               in_place=False,
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               name=None,
               moving_mean_name=None,
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               moving_variance_name=None,
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               do_model_average_for_mean_and_var=False,
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               fuse_with_relu=False,
               use_global_stats=False):
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    """
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    **Batch Normalization Layer**

    Can be used as a normalizer function for conv2d and fully_connected operations.
    The required data format for this layer is one of the following:
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    1. NHWC `[batch, in_height, in_width, in_channels]`
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    2. NCHW `[batch, in_channels, in_height, in_width]`

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    Refer to `Batch Normalization: Accelerating Deep Network Training by Reducing
    Internal Covariate Shift <https://arxiv.org/pdf/1502.03167.pdf>`_
    for more details.
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    :math:`input` is the input features over a mini-batch.

    ..  math::

        \\mu_{\\beta} &\\gets \\frac{1}{m} \\sum_{i=1}^{m} x_i \\qquad &//\\
        \ mini-batch\ mean \\\\
        \\sigma_{\\beta}^{2} &\\gets \\frac{1}{m} \\sum_{i=1}^{m}(x_i - \\
        \\mu_{\\beta})^2 \\qquad &//\ mini-batch\ variance \\\\
        \\hat{x_i} &\\gets \\frac{x_i - \\mu_\\beta} {\\sqrt{\\
        \\sigma_{\\beta}^{2} + \\epsilon}} \\qquad &//\ normalize \\\\
        y_i &\\gets \\gamma \\hat{x_i} + \\beta \\qquad &//\ scale\ and\ shift
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    When use_global_stats = True, the :math:`\\mu_{\\beta}`
    and :math:`\\sigma_{\\beta}^{2}` are not the statistics of one mini-batch.
    They are global (or running) statistics. (It usually got from the
    pre-trained model.)
    The training and testing (or inference) have the same behavior:

    ..  math::

        \\hat{x_i} &\\gets \\frac{x_i - \\mu_\\beta} {\\sqrt{\\
        \\sigma_{\\beta}^{2} + \\epsilon}}  \\\\
        y_i &\\gets \\gamma \\hat{x_i} + \\beta

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    Args:
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        input(variable): The rank of input variable can be 2, 3, 4, 5.
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        act(string, Default None): Activation type, linear|relu|prelu|...
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        is_test (bool, Default False): A flag indicating whether it is in
            test phrase or not.
        momentum(float, Default 0.9): The value used for the moving_mean and
            moving_var computation. The updated formula is:
            :math:`moving\_mean = moving\_mean * momentum + new\_mean * (1. - momentum)`
            :math:`moving\_var = moving\_var * momentum + new\_var * (1. - momentum)`
            Default is 0.9.
        epsilon(float, Default 1e-05): A value added to the denominator for
            numerical stability. Default is 1e-5.
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        param_attr(ParamAttr|None): The parameter attribute for Parameter `scale`
             of batch_norm. If it is set to None or one attribute of ParamAttr, batch_norm
             will create ParamAttr as param_attr. If the Initializer of the param_attr
             is not set, the parameter is initialized with Xavier. Default: None.
        bias_attr(ParamAttr|None): The parameter attribute for the bias of batch_norm.
             If it is set to None or one attribute of ParamAttr, batch_norm
             will create ParamAttr as bias_attr. If the Initializer of the bias_attr
             is not set, the bias is initialized zero. Default: None.
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        data_layout(string, default NCHW): NCHW|NHWC
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        in_place(bool, Default False): Make the input and output of batch norm reuse memory.
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        name(string, Default None): A name for this layer(optional). If set None, the layer
            will be named automatically.
        moving_mean_name(string, Default None): The name of moving_mean which store the global Mean.
        moving_variance_name(string, Default None): The name of the moving_variance which store the global Variance.
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        do_model_average_for_mean_and_var(bool, Default False): Do model average for mean and variance or not.
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        fuse_with_relu (bool): if True, this OP performs relu after batch norm.
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        use_global_stats(bool, Default False): Whether to use global mean and
            variance. In inference or test mode, set use_global_stats to true
            or is_test to true, and the behavior is equivalent.
            In train mode, when setting use_global_stats True, the global mean
            and variance are also used during train period.
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    Returns:
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        Variable: A tensor variable which is the result after applying batch normalization on the input.
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    Examples:

        .. code-block:: python

            hidden1 = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
            hidden2 = fluid.layers.batch_norm(input=hidden1)
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    """
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    assert bias_attr is not False, "bias_attr should not be False in batch_norm."
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    helper = LayerHelper('batch_norm', **locals())
    dtype = helper.input_dtype()

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    # use fp32 for bn parameter
    if dtype == core.VarDesc.VarType.FP16:
        dtype = core.VarDesc.VarType.FP32

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    input_shape = input.shape
    if data_layout == 'NCHW':
        channel_num = input_shape[1]
    else:
        if data_layout == 'NHWC':
            channel_num = input_shape[-1]
        else:
            raise ValueError("unsupported data layout:" + data_layout)

    param_shape = [channel_num]

    # create parameter
    scale = helper.create_parameter(
        attr=helper.param_attr,
        shape=param_shape,
        dtype=dtype,
        default_initializer=Constant(1.0))
    bias = helper.create_parameter(
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        attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True)
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    mean = helper.create_parameter(
        attr=ParamAttr(
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            name=moving_mean_name,
            initializer=Constant(0.0),
            trainable=False,
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            do_model_average=do_model_average_for_mean_and_var),
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        shape=param_shape,
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        dtype=dtype)
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    mean.stop_gradient = True

    variance = helper.create_parameter(
        attr=ParamAttr(
            name=moving_variance_name,
            initializer=Constant(1.0),
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            trainable=False,
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            do_model_average=do_model_average_for_mean_and_var),
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        shape=param_shape,
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        dtype=dtype)
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    variance.stop_gradient = True
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    # create output
    # mean and mean_out share the same memory
    mean_out = mean
    # variance and variance out share the same memory
    variance_out = variance
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    saved_mean = helper.create_variable_for_type_inference(
        dtype=dtype, stop_gradient=True)
    saved_variance = helper.create_variable_for_type_inference(
        dtype=dtype, stop_gradient=True)
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    batch_norm_out = input if in_place else helper.create_variable_for_type_inference(
        dtype)
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    helper.append_op(
        type="batch_norm",
        inputs={
            "X": input,
            "Scale": scale,
            "Bias": bias,
            "Mean": mean,
            "Variance": variance
        },
        outputs={
            "Y": batch_norm_out,
            "MeanOut": mean_out,
            "VarianceOut": variance_out,
            "SavedMean": saved_mean,
            "SavedVariance": saved_variance
        },
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        attrs={
            "momentum": momentum,
            "epsilon": epsilon,
            "is_test": is_test,
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            "data_layout": data_layout,
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            "use_mkldnn": False,
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            "fuse_with_relu": fuse_with_relu,
            "use_global_stats": use_global_stats
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        })
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    return helper.append_activation(batch_norm_out)


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def data_norm(input,
              act=None,
              epsilon=1e-05,
              param_attr=None,
              data_layout='NCHW',
              in_place=False,
              name=None,
              moving_mean_name=None,
              moving_variance_name=None,
              do_model_average_for_mean_and_var=False):
    """
    **Data Normalization Layer**

    Can be used as a normalizer function for conv2d and fully_connected operations.
    The required data format for this layer is one of the following:

    1. NHWC `[batch, in_height, in_width, in_channels]`

    2. NCHW `[batch, in_channels, in_height, in_width]`

    :math:`input` is the input features over a mini-batch.

    ..  math::

        \\mu_{\\beta} &\\gets \\frac{1}{m} \\sum_{i=1}^{m} x_i \\qquad &//\\
        \ mini-batch\ mean \\\\
        \\sigma_{\\beta}^{2} &\\gets \\frac{1}{m} \\sum_{i=1}^{m}(x_i - \\
        \\mu_{\\beta})^2 \\qquad &//\ mini-batch\ variance \\\\
        \\hat{x_i} &\\gets \\frac{x_i - \\mu_\\beta} {\\sqrt{\\
        \\sigma_{\\beta}^{2} + \\epsilon}} \\qquad &//\ normalize \\\\
        y_i &\\gets \\gamma \\hat{x_i} + \\beta \\qquad &//\ scale\ and\ shift

    Args:
        input(variable): The input variable which is a LoDTensor.
        act(string, Default None): Activation type, linear|relu|prelu|...
        epsilon(float, Default 1e-05):
        param_attr(ParamAttr): The parameter attribute for Parameter `scale`.
        data_layout(string, default NCHW): NCHW|NHWC
        in_place(bool, Default False): Make the input and output of batch norm reuse memory.
        name(string, Default None): A name for this layer(optional). If set None, the layer
            will be named automatically.
        moving_mean_name(string, Default None): The name of moving_mean which store the global Mean.
        moving_variance_name(string, Default None): The name of the moving_variance which store the global Variance.
        do_model_average_for_mean_and_var(bool, Default False): Do model average for mean and variance or not.

    Returns:
        Variable: A tensor variable which is the result after applying data normalization on the input.

    Examples:

        .. code-block:: python

            data = fluid.layers.data(input=x, size=200, param_attr='fc1.w')
            hidden2 = fluid.layers.data_norm(input=hidden1)
    """
    helper = LayerHelper('data_norm', **locals())
    dtype = helper.input_dtype()

    input_shape = input.shape
    if data_layout == 'NCHW':
        channel_num = input_shape[1]
    else:
        if data_layout == 'NHWC':
            channel_num = input_shape[-1]
        else:
            raise ValueError("unsupported data layout:" + data_layout)

    param_shape = [channel_num]

    batch_size_default = 1e4
    batch_sum_default = 0.0
    batch_square_sum_default = 1e4

    if param_attr and isinstance(param_attr, dict):
        batch_size_default = param_attr.get("batch_size", 1e4)
        batch_sum_default = param_attr.get("batch_sum", 0.0)
        batch_square_sum_default = param_attr.get("batch_square", 1e4)

    # create parameter
    batch_size = helper.create_parameter(
        attr=ParamAttr(
            name=name + '.batch_size',
            initializer=Constant(value=float(batch_size_default)),
            trainable=True),
        shape=param_shape,
        dtype=input.dtype)

    batch_sum = helper.create_parameter(
        attr=ParamAttr(
            name=name + '.batch_sum',
            initializer=Constant(value=float(batch_sum_default)),
            trainable=True),
        shape=param_shape,
        dtype=input.dtype)

    batch_square_sum = helper.create_parameter(
        attr=ParamAttr(
            name=name + '.batch_square_sum',
            initializer=Constant(value=float(batch_square_sum_default)),
            trainable=True),
        shape=param_shape,
        dtype=input.dtype)

    means = helper.create_variable(dtype=dtype, stop_gradient=True)
    scales = helper.create_variable(dtype=dtype, stop_gradient=True)

    data_norm_out = input if in_place else helper.create_variable(dtype=dtype)

    helper.append_op(
        type="data_norm",
        inputs={
            "X": input,
            "BatchSize": batch_size,
            "BatchSum": batch_sum,
            "BatchSquareSum": batch_square_sum
        },
        outputs={"Y": data_norm_out,
                 "Means": means,
                 "Scales": scales},
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        attrs={"epsilon": epsilon})
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    return helper.append_activation(data_norm_out)


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@templatedoc()
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def layer_norm(input,
               scale=True,
               shift=True,
               begin_norm_axis=1,
               epsilon=1e-05,
               param_attr=None,
               bias_attr=None,
               act=None,
               name=None):
    """
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    ${comment}
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    The formula is as follows:

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    ..  math::
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        \\mu & = \\frac{1}{H}\\sum_{i=1}^{H} a_i

        \\sigma & = \\sqrt{\\frac{1}{H}\sum_{i=1}^{H}(a_i - \\mu)^2}

        h & = f(\\frac{g}{\\sigma}(a - \\mu) + b)

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    * :math:`a`: the vector representation of the summed inputs to the neurons
    in that layer.

    * :math:`H`: the number of hidden units in a layers

    * :math:`g`: the trainable scale parameter.

    * :math:`b`: the trainable bias parameter.
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    Args:
        input(Variable): The input tensor variable.
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        scale(bool): Whether to learn the adaptive gain :math:`g` after
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            normalization. Default True.
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        shift(bool): Whether to learn the adaptive bias :math:`b` after
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            normalization. Default True.
        begin_norm_axis(int): The normalization will be performed along
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            dimensions from :attr:`begin_norm_axis` to :attr:`rank(input)`.
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            Default 1.
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        epsilon(float): The small value added to the variance to prevent
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            division by zero. Default 1e-05.
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        param_attr(ParamAttr|None): The parameter attribute for the learnable
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            gain :math:`g`. If :attr:`scale` is False, :attr:`param_attr` is
            omitted. If :attr:`scale` is True and :attr:`param_attr` is None,
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            a default :code:`ParamAttr` would be added as scale. The
            :attr:`param_attr` is initialized as 1 if it is added. Default None.
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        bias_attr(ParamAttr|None): The parameter attribute for the learnable
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            bias :math:`b`. If :attr:`shift` is False, :attr:`bias_attr` is
            omitted. If :attr:`shift` is True and :attr:`param_attr` is None,
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            a default :code:`ParamAttr` would be added as bias. The
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            :attr:`bias_attr` is initialized as 0 if it is added. Default None.
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        act(str): Activation to be applied to the output of layer normalizaiton.
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                  Default None.
        name(str): The name of this layer. It is optional. Default None, and a
                   unique name would be generated automatically.
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    Returns:
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        ${y_comment}
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    Examples:

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        >>> data = fluid.layers.data(name='data', shape=[3, 32, 32],
        >>>                          dtype='float32')
        >>> x = fluid.layers.layer_norm(input=data, begin_norm_axis=1)
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    """
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    assert in_dygraph_mode(
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    ) is not True, "please use FC instead of fc in dygraph mode!"
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    helper = LayerHelper('layer_norm', **locals())
    dtype = helper.input_dtype()

    # create intput and parameters
    inputs = {'X': input}
    input_shape = input.shape
    param_shape = [reduce(lambda x, y: x * y, input_shape[begin_norm_axis:])]
    if scale:
        scale = helper.create_parameter(
            attr=helper.param_attr,
            shape=param_shape,
            dtype=dtype,
            default_initializer=Constant(1.0))
        inputs['Scale'] = scale
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    if shift:
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        assert bias_attr is not False
        bias = helper.create_parameter(
            attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True)
        inputs['Bias'] = bias

    # create output
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    mean_out = helper.create_variable_for_type_inference(
        dtype=dtype, stop_gradient=True)
    variance_out = helper.create_variable_for_type_inference(
        dtype=dtype, stop_gradient=True)
    layer_norm_out = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
        type="layer_norm",
        inputs=inputs,
        outputs={
            "Y": layer_norm_out,
            "Mean": mean_out,
            "Variance": variance_out,
        },
        attrs={"epsilon": epsilon,
               "begin_norm_axis": begin_norm_axis})

    return helper.append_activation(layer_norm_out)


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@templatedoc()
def group_norm(input,
               groups,
               epsilon=1e-05,
               param_attr=None,
               bias_attr=None,
               act=None,
               data_layout='NCHW',
               name=None):
    """
    **Group Normalization Layer**

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    Refer to `Group Normalization <https://arxiv.org/abs/1803.08494>`_ .
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    Args:
        input(Variable): The input tensor variable.
        groups(int): The number of groups that divided from channels.
        epsilon(float): The small value added to the variance to prevent
            division by zero.
        param_attr(ParamAttr|None): The parameter attribute for the learnable
            scale :math:`g`. If it is set to False, no scale will be added to the output units.
            If it is set to None, the bias is initialized one. Default: None.
        bias_attr(ParamAttr|None): The parameter attribute for the learnable
            bias :math:`b`. If it is set to False, no bias will be added to the output units.
            If it is set to None, the bias is initialized zero. Default: None.
        act(str): Activation to be applied to the output of group normalizaiton.
        data_layout(string|NCHW): Only NCHW is supported.
        name (str): The name of this layer. It is optional.

    Returns:
        Variable: A tensor variable which is the result after applying group normalization on the input.

    Examples:

        >>> data = fluid.layers.data(name='data', shape=[8, 32, 32],
        >>>                          dtype='float32')
        >>> x = fluid.layers.group_norm(input=data, groups=4)
    """
    helper = LayerHelper('group_norm', **locals())
    dtype = helper.input_dtype()

    # create intput and parameters
    inputs = {'X': input}
    input_shape = input.shape
    if data_layout != 'NCHW':
        raise ValueError("unsupported data layout:" + data_layout)
    param_shape = [input_shape[1]]
    if param_attr:
        scale = helper.create_parameter(
            attr=helper.param_attr,
            shape=param_shape,
            dtype=dtype,
            default_initializer=Constant(1.0))
        inputs['Scale'] = scale
    if bias_attr:
        bias = helper.create_parameter(
            attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True)
        inputs['Bias'] = bias

    # create output
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    mean_out = helper.create_variable(dtype=dtype, stop_gradient=True)
    variance_out = helper.create_variable(dtype=dtype, stop_gradient=True)
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    group_norm_out = helper.create_variable(dtype=dtype)

    helper.append_op(
        type="group_norm",
        inputs=inputs,
        outputs={
            "Y": group_norm_out,
            "Mean": mean_out,
            "Variance": variance_out,
        },
        attrs={"epsilon": epsilon,
               "groups": groups})

    return helper.append_activation(group_norm_out)


@templatedoc()
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def spectral_norm(weight, dim=0, power_iters=1, eps=1e-12, name=None):
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    """
    **Spectral Normalization Layer**

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    This layer calculates the spectral normalization value of weight parameters of
3441
    fc, conv1d, conv2d, conv3d layers which should be 2-D, 3-D, 4-D, 5-D
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    Parameters. Calculations are showed as follows.
3443

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    Step 1:
    Generate vector U in shape of [H], and V in shape of [W].
    While H is the :attr:`dim` th dimension of the input weights,
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    and W is the product result of remaining dimensions.
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    Step 2:
    :attr:`power_iters` shoule be a positive interger, do following
    calculations with U and V for :attr:`power_iters` rounds.

    .. math:: 

        \mathbf{v} := \\frac{\mathbf{W}^{T} \mathbf{u}}{\|\mathbf{W}^{T} \mathbf{u}\|_2}

        \mathbf{u} := \\frac{\mathbf{W}^{T} \mathbf{v}}{\|\mathbf{W}^{T} \mathbf{v}\|_2}

    Step 3:
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    Calculate :math:`\sigma(\mathbf{W})` and normalize weight values.
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    .. math::

        \sigma(\mathbf{W}) = \mathbf{u}^{T} \mathbf{W} \mathbf{v}
3465

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        \mathbf{W} = \\frac{\mathbf{W}}{\sigma(\mathbf{W})}
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    Refer to `Spectral Normalization <https://arxiv.org/abs/1802.05957>`_ .

    Args:
        weight(${weight_type}): ${weight_comment}
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        dim(int): ${dim_comment}
        power_iters(int): ${power_iters_comment}
        eps(float): ${eps_comment}
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        name (str): The name of this layer. It is optional.

    Returns:
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        Variable: A tensor variable of weight parameters after spectral normalization.
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    Examples:

        >>> weight = fluid.layers.data(name='weight', shape=[8, 32, 32],
        >>>                          dtype='float32')
        >>> x = fluid.layers.spectral_norm(weight=data, dim=1, power_iters=2)
    """
    helper = LayerHelper('spectral_norm', **locals())
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    dtype = weight.dtype
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    # create intput and parameters
    inputs = {'Weight': weight}
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    input_shape = weight.shape
    h = input_shape[dim]
    w = np.prod(input_shape) // h

    u = helper.create_parameter(
        attr=ParamAttr(),
        shape=[h],
        dtype=dtype,
        default_initializer=Normal(0., 1.))
    u.stop_gradient = True
    inputs['U'] = u
    v = helper.create_parameter(
        attr=ParamAttr(),
        shape=[w],
        dtype=dtype,
        default_initializer=Normal(0., 1.))
    inputs['V'] = v
    v.stop_gradient = True
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    # create output
3512
    out = helper.create_variable(dtype=dtype)
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    helper.append_op(
3515
        type="spectral_norm",
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        inputs=inputs,
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        outputs={"Out": out, },
        attrs={
            "dim": dim,
            "power_iters": power_iters,
            "eps": eps,
        })
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3524
    return out
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def conv2d_transpose(input,
                     num_filters,
                     output_size=None,
                     filter_size=None,
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                     padding=0,
                     stride=1,
                     dilation=1,
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                     groups=None,
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                     param_attr=None,
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                     bias_attr=None,
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                     use_cudnn=True,
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                     act=None,
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                     name=None):
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    """
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    **Convlution2D transpose layer**

    The convolution2D transpose layer calculates the output based on the input,
    filter, and dilations, strides, paddings. Input(Input) and output(Output)
    are in NCHW format. Where N is batch size, C is the number of channels,
    H is the height of the feature, and W is the width of the feature.
    Parameters(dilations, strides, paddings) are two elements. These two elements
    represent height and width, respectively. The details of convolution transpose
3549 3550
    layer, please refer to the following explanation and references
    `therein <http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf>`_.
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    If bias attribution and activation type are provided, bias is added to
    the output of the convolution, and the corresponding activation function
    is applied to the final result.
3554 3555 3556 3557 3558

    For each input :math:`X`, the equation is:

    .. math::

3559
        Out = \sigma (W \\ast X + b)
3560

3561
    Where:
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    * :math:`X`: Input value, a tensor with NCHW format.
    * :math:`W`: Filter value, a tensor with MCHW format.
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    * :math:`\\ast`: Convolution operation.
    * :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
    * :math:`\\sigma`: Activation function.
    * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
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    Example:

        - Input:

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          Input shape: :math:`(N, C_{in}, H_{in}, W_{in})`
3575

3576
          Filter shape: :math:`(C_{in}, C_{out}, H_f, W_f)`
3577 3578 3579

        - Output:

3580
          Output shape: :math:`(N, C_{out}, H_{out}, W_{out})`
3581 3582

        Where
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3584 3585
        .. math::

3586 3587
           H^\prime_{out} &= (H_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\\\
           W^\prime_{out} &= (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1 \\\\
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           H_{out} &\in [ H^\prime_{out}, H^\prime_{out} + strides[0] ) \\\\
           W_{out} &\in [ W^\prime_{out}, W^\prime_{out} + strides[1] )
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    Args:
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        input(Variable): The input image with [N, C, H, W] format.
        num_filters(int): The number of the filter. It is as same as the output
            image channel.
        output_size(int|tuple|None): The output image size. If output size is a
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            tuple, it must contain two integers, (image_H, image_W). None if use
            filter_size, padding, and stride to calculate output_size.
            if output_size and filter_size are specified at the same time, They
            should follow the formula above.
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        filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
            it must contain two integers, (filter_size_H, filter_size_W).
            Otherwise, the filter will be a square. None if use output size to
            calculate filter_size.
        padding(int|tuple): The padding size. If padding is a tuple, it must
            contain two integers, (padding_H, padding_W). Otherwise, the
            padding_H = padding_W = padding. Default: padding = 0.
        stride(int|tuple): The stride size. If stride is a tuple, it must
            contain two integers, (stride_H, stride_W). Otherwise, the
            stride_H = stride_W = stride. Default: stride = 1.
        dilation(int|tuple): The dilation size. If dilation is a tuple, it must
            contain two integers, (dilation_H, dilation_W). Otherwise, the
            dilation_H = dilation_W = dilation. Default: dilation = 1.
        groups(int): The groups number of the Conv2d transpose layer. Inspired by
            grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
            when group=2, the first half of the filters is only connected to the
            first half of the input channels, while the second half of the
            filters is only connected to the second half of the input channels.
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            Default: groups = 1.
        param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
            of conv2d_transpose. If it is set to None or one attribute of ParamAttr, conv2d_transpose
            will create ParamAttr as param_attr. If the Initializer of the param_attr
            is not set, the parameter is initialized with Xavier. Default: None.
        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv2d_transpose.
            If it is set to False, no bias will be added to the output units.
            If it is set to None or one attribute of ParamAttr, conv2d_transpose
            will create ParamAttr as bias_attr. If the Initializer of the bias_attr
            is not set, the bias is initialized zero. Default: None.
3628
        use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn
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            library is installed. Default: True.
        act (str): Activation type, if it is set to None, activation is not appended.
            Default: None.
3632
        name(str|None): A name for this layer(optional). If set None, the layer
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            will be named automatically. Default: True.
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    Returns:
3636
        Variable: The tensor variable storing the convolution transpose result.
3637 3638

    Raises:
3639 3640
        ValueError: If the shapes of input, filter_size, stride, padding and
                    groups mismatch.
3641 3642 3643 3644

    Examples:
       .. code-block:: python

3645 3646
          data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32')
          conv2d_transpose = fluid.layers.conv2d_transpose(input=data, num_filters=2, filter_size=3)
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    """
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    assert param_attr is not False, "param_attr should not be False in conv2d_transpose."
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    input_channel = input.shape[1]

    op_type = 'conv2d_transpose'
    if (input_channel == groups and num_filters == input_channel and
            not use_cudnn):
        op_type = 'depthwise_conv2d_transpose'

    helper = LayerHelper(op_type, **locals())
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    if not isinstance(input, Variable):
        raise TypeError("Input of conv2d_transpose must be Variable")

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    padding = utils.convert_to_list(padding, 2, 'padding')
    stride = utils.convert_to_list(stride, 2, 'stride')
    dilation = utils.convert_to_list(dilation, 2, 'dilation')
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    if not isinstance(use_cudnn, bool):
        raise ValueError("use_cudnn should be True or False")
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    if filter_size is None:
        if output_size is None:
            raise ValueError("output_size must be set when filter_size is None")
        if isinstance(output_size, int):
            output_size = [output_size, output_size]
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        h_in = input.shape[2]
        w_in = input.shape[3]
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        filter_size_h = (output_size[0] - (h_in - 1) * stride[0] + 2 *
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                         padding[0] - 1) // dilation[0] + 1
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        filter_size_w = (output_size[1] - (w_in - 1) * stride[1] + 2 *
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                         padding[1] - 1) // dilation[1] + 1
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        filter_size = [filter_size_h, filter_size_w]
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    else:
        filter_size = utils.convert_to_list(filter_size, 2,
                                            'conv2d_transpose.filter_size')
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    if output_size is None:
        output_size = []
    elif isinstance(output_size, list) or isinstance(output_size, int):
        output_size = utils.convert_to_list(output_size, 2, 'output_size')
    else:
        raise ValueError("output_size should be list or int")
    padding = utils.convert_to_list(padding, 2, 'padding')
3692
    groups = 1 if groups is None else groups
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    filter_shape = [input_channel, num_filters // groups] + filter_size
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    img_filter = helper.create_parameter(
        dtype=input.dtype, shape=filter_shape, attr=helper.param_attr)

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    pre_bias = helper.create_variable_for_type_inference(dtype=input.dtype)
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    helper.append_op(
3700
        type=op_type,
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        inputs={'Input': [input],
                'Filter': [img_filter]},
3703
        outputs={'Output': pre_bias},
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        attrs={
3705
            'output_size': output_size,
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            'strides': stride,
            'paddings': padding,
            'dilations': dilation,
            'groups': groups,
            'use_cudnn': use_cudnn
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        })

3713 3714 3715
    pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
    out = helper.append_activation(pre_act)
    return out
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3718
def conv3d_transpose(input,
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                     num_filters,
                     output_size=None,
                     filter_size=None,
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                     padding=0,
                     stride=1,
                     dilation=1,
3725
                     groups=None,
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                     param_attr=None,
3727
                     bias_attr=None,
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                     use_cudnn=True,
3729
                     act=None,
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                     name=None):
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    """
3732
    **Convlution3D transpose layer**
3733

3734
    The convolution3D transpose layer calculates the output based on the input,
3735
    filter, and dilations, strides, paddings. Input(Input) and output(Output)
3736 3737 3738 3739 3740 3741
    are in NCDHW format. Where N is batch size, C is the number of channels,
    D is the depth of the feature, H is the height of the feature, and W
    is the width of the feature. Parameters(dilations, strides, paddings) are
    two elements. These two elements represent height and width, respectively.
    The details of convolution transpose layer, please refer to the following
    explanation and references `therein <http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf>`_.
3742 3743 3744
    If bias attribution and activation type are provided, bias is added to
    the output of the convolution, and the corresponding activation function
    is applied to the final result.
3745 3746 3747 3748 3749

    For each input :math:`X`, the equation is:

    .. math::

3750
        Out = \sigma (W \\ast X + b)
3751 3752 3753

    In the above equation:

3754 3755
    * :math:`X`: Input value, a tensor with NCDHW format.
    * :math:`W`: Filter value, a tensor with MCDHW format.
3756 3757 3758 3759
    * :math:`\\ast`: Convolution operation.
    * :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
    * :math:`\\sigma`: Activation function.
    * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
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3761 3762 3763 3764
    Example:

        - Input:

3765
          Input shape: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`
3766

3767
          Filter shape: :math:`(C_{in}, C_{out}, D_f, H_f, W_f)`
3768 3769 3770

        - Output:

3771
          Output shape: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`
3772 3773

        Where
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3775 3776
        .. math::

3777 3778 3779
           D_{out} &= (D_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (D_f - 1) + 1 \\\\
           H_{out} &= (H_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (H_f - 1) + 1 \\\\
           W_{out} &= (W_{in} - 1) * strides[2] - 2 * paddings[2] + dilations[2] * (W_f - 1) + 1
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    Args:
3782
        input(Variable): The input image with [N, C, D, H, W] format.
3783 3784 3785
        num_filters(int): The number of the filter. It is as same as the output
            image channel.
        output_size(int|tuple|None): The output image size. If output size is a
3786
            tuple, it must contain three integers, (image_D, image_H, image_W). This
3787 3788
            parameter only works when filter_size is None.
        filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
3789
            it must contain three integers, (filter_size_D, filter_size_H, filter_size_W).
3790 3791 3792
            Otherwise, the filter will be a square. None if use output size to
            calculate filter_size.
        padding(int|tuple): The padding size. If padding is a tuple, it must
3793 3794
            contain three integers, (padding_D, padding_H, padding_W). Otherwise, the
            padding_D = padding_H = padding_W = padding. Default: padding = 0.
3795
        stride(int|tuple): The stride size. If stride is a tuple, it must
3796 3797
            contain three integers, (stride_D, stride_H, stride_W). Otherwise, the
            stride_D = stride_H = stride_W = stride. Default: stride = 1.
3798
        dilation(int|tuple): The dilation size. If dilation is a tuple, it must
3799 3800 3801
            contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the
            dilation_D = dilation_H = dilation_W = dilation. Default: dilation = 1.
        groups(int): The groups number of the Conv3d transpose layer. Inspired by
3802 3803 3804 3805 3806
            grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
            when group=2, the first half of the filters is only connected to the
            first half of the input channels, while the second half of the
            filters is only connected to the second half of the input channels.
            Default: groups=1
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        param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
            of conv3d_transpose. If it is set to None or one attribute of ParamAttr, conv3d_transpose
            will create ParamAttr as param_attr. If the Initializer of the param_attr
            is not set, the parameter is initialized with Xavier. Default: None.
        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv3d_transpose.
            If it is set to False, no bias will be added to the output units.
            If it is set to None or one attribute of ParamAttr, conv3d_transpose
            will create ParamAttr as bias_attr. If the Initializer of the bias_attr
            is not set, the bias is initialized zero. Default: None.
3816 3817
        use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn
            library is installed. Default: True
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        act (str): Activation type, if it is set to None, activation is not appended.
            Default: None.
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        name(str|None): A name for this layer(optional). If set None, the layer
            will be named automatically.
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    Returns:
3824
        Variable: The tensor variable storing the convolution transpose result.
3825 3826

    Raises:
3827 3828
        ValueError: If the shapes of input, filter_size, stride, padding and
                    groups mismatch.
3829 3830 3831 3832

    Examples:
       .. code-block:: python

3833 3834
          data = fluid.layers.data(name='data', shape=[3, 12, 32, 32], dtype='float32')
          conv3d_transpose = fluid.layers.conv3d_transpose(input=data, num_filters=2, filter_size=3)
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    """
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    assert param_attr is not False, "param_attr should not be False in conv3d_transpose."
3837 3838
    l_type = "conv3d_transpose"
    helper = LayerHelper(l_type, **locals())
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    if not isinstance(input, Variable):
3840
        raise TypeError("Input of conv3d_transpose must be Variable")
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    input_channel = input.shape[1]

3843 3844 3845
    padding = utils.convert_to_list(padding, 3, 'padding')
    stride = utils.convert_to_list(stride, 3, 'stride')
    dilation = utils.convert_to_list(dilation, 3, 'dilation')
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    if not isinstance(use_cudnn, bool):
        raise ValueError("use_cudnn should be True or False")

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    if filter_size is None:
        if output_size is None:
            raise ValueError("output_size must be set when filter_size is None")
        if isinstance(output_size, int):
            output_size = [output_size, output_size]

3856 3857 3858
        d_in = input.shape[2]
        h_in = input.shape[3]
        w_in = input.shape[4]
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3860
        filter_size_d = (output_size[0] - (d_in - 1) * stride[0] + 2 *
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                         padding[0] - 1) // dilation[0] + 1
3862
        filter_size_h = (output_size[1] - (h_in - 1) * stride[1] + 2 *
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                         padding[1] - 1) // dilation[1] + 1
3864
        filter_size_w = (output_size[2] - (w_in - 1) * stride[2] + 2 *
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                         padding[2] - 1) // dilation[2] + 1
3866
        filter_size = [filter_size_d, filter_size_h, filter_size_w]
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    else:
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        filter_size = utils.convert_to_list(filter_size, 3,
                                            'conv3d_transpose.filter_size')
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3871
    groups = 1 if groups is None else groups
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    filter_shape = [input_channel, num_filters // groups] + filter_size
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    img_filter = helper.create_parameter(
        dtype=input.dtype, shape=filter_shape, attr=helper.param_attr)

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    pre_bias = helper.create_variable_for_type_inference(dtype=input.dtype)
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    helper.append_op(
3878
        type=l_type,
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        inputs={'Input': [input],
                'Filter': [img_filter]},
3881
        outputs={'Output': pre_bias},
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        attrs={
            'strides': stride,
            'paddings': padding,
            'dilations': dilation,
3886
            'groups': groups,
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            'use_cudnn': use_cudnn
        })
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3890 3891
    pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
    out = helper.append_activation(pre_act)
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    return out
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def sequence_expand(x, y, ref_level=-1, name=None):
3896
    """Sequence Expand Layer. This layer will expand the input variable **x**
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    according to specified level lod of **y**. Please note that lod level of
    **x** is at most 1 and rank of **x** is at least 2. When rank of **x**
    is greater than 2, then it would be viewed as a 2-D tensor.
    Following examples will explain how sequence_expand works:
3901 3902 3903 3904 3905

    .. code-block:: text

        * Case 1
            x is a LoDTensor:
3906
                x.lod  = [[2,        2]]
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                x.data = [[a], [b], [c], [d]]
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                x.dims = [4, 1]

            y is a LoDTensor:
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                y.lod = [[2,    2],
                         [3, 3, 1, 1]]
3913

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            ref_level: 0
3915

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            then output is a 1-level LoDTensor:
3917
                out.lod =  [[2,        2,        2,        2]]
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                out.data = [[a], [b], [a], [b], [c], [d], [c], [d]]
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                out.dims = [8, 1]

        * Case 2
            x is a Tensor:
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                x.data = [[a], [b], [c]]
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                x.dims = [3, 1]

            y is a LoDTensor:
3927
                y.lod = [[2, 0, 3]]
3928

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            ref_level: -1
3930

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            then output is a Tensor:
                out.data = [[a], [a], [c], [c], [c]]
                out.dims = [5, 1]
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    Args:
        x (Variable): The input variable which is a Tensor or LoDTensor.
        y (Variable): The input variable which is a LoDTensor.
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        ref_level (int): Lod level of `y` to be referred by `x`. If set to -1,
                         refer the last level of lod.
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        name(str|None): A name for this layer(optional). If set None, the layer
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                        will be named automatically.
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    Returns:
        Variable: The expanded variable which is a LoDTensor.

    Examples:
        .. code-block:: python

            x = fluid.layers.data(name='x', shape=[10], dtype='float32')
            y = fluid.layers.data(name='y', shape=[10, 20],
                             dtype='float32', lod_level=1)
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            out = layers.sequence_expand(x=x, y=y, ref_level=0)
3952
    """
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    assert not in_dygraph_mode(), (
3954
        "sequence layer is not supported in dygraph mode yet.")
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    helper = LayerHelper('sequence_expand', input=x, **locals())
3956
    dtype = helper.input_dtype()
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    tmp = helper.create_variable_for_type_inference(dtype)
3958
    helper.append_op(
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        type='sequence_expand',
        inputs={'X': x,
                'Y': y},
        outputs={'Out': tmp},
        attrs={'ref_level': ref_level})
3964
    return tmp
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def sequence_expand_as(x, y, name=None):
    """Sequence Expand As Layer. This layer will expand the input variable **x**
    according to the zeroth level lod of **y**. Current implementation requires
    the level number of Input(Y)'s lod must be 1, and the first dimension of
    Input(X) should be equal to the size of Input(Y)'s zeroth level lod, and
    lod of Input(X) is not considered.

    Following examples will explain how sequence_expand_as works:

    .. code-block:: text

        * Case 1:

            Given a 1-level LoDTensor input(X)
                X.data = [[a], [b], [c], [d]]
                X.dims = [4, 1]
            and input(Y)
                Y.lod = [[0, 3, 6, 7, 8]]
            ref_level: 0
            then we get 1-level LoDTensor
                Out.lod =  [[0,            3,              6,  7,  8]]
                Out.data = [[a], [a], [a], [b], [b], [b], [c], [d]]
                Out.dims = [8, 1]

        * Case 2:

            Given a common Tensor input(X)
                X.data = [[a, b], [c, d], [e, f]]
                X.dims = [3, 2]
            and input(Y)
                Y.lod = [[0, 2, 3, 6]]
            ref_level: 0
            then we get a common LoDTensor
                Out.lod =  [[0,             2,     3,                    6]]
                Out.data = [[a, b], [a, b] [c, d], [e, f], [e, f], [e, f]]
                Out.dims = [6, 2]

    Args:
        x (Variable): The input variable which is a Tensor or LoDTensor.
        y (Variable): The input variable which is a LoDTensor.
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.

    Returns:
        Variable: The expanded variable which is a LoDTensor.

    Examples:
        .. code-block:: python

            x = fluid.layers.data(name='x', shape=[10], dtype='float32')
            y = fluid.layers.data(name='y', shape=[10, 20],
                             dtype='float32', lod_level=1)
            out = layers.sequence_expand_as(x=x, y=y)
    """
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    assert not in_dygraph_mode(), (
4022
        "sequence layer is not supported in dygraph mode yet.")
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    helper = LayerHelper('sequence_expand_as', input=x, **locals())
    dtype = helper.input_dtype()
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    tmp = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
        type='sequence_expand_as',
        inputs={'X': x,
                'Y': y},
        outputs={'Out': tmp})
    return tmp


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@templatedoc()
4035
def sequence_pad(x, pad_value, maxlen=None, name=None):
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    """
    ${comment}

    Args:
        x(Variable): Input variable which should contain lod information.
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        pad_value(Variable): The Variable that holds values that will be fill
            into padded steps. It can be a scalar or a tensor whose shape
            equals to time steps in sequences. If it's a scalar, it will be
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            automatically broadcasted to the shape of time step.
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        maxlen(int, default None): The length of padded sequences. It can be
            None or any positive int. When it is None, all sequences will be
            padded up to the length of the longest one among them; when it a
            certain positive value, it must be greater than the length of the
4049 4050 4051
            longest original sequence.
        name(str|None): A name for this layer(optional). If set None, the layer
            will be named automatically.
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    Returns:
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        Variable: The padded sequence batch and the original lengths before
4055
                  padding. All sequences has the same length.
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    Examples:
        .. code-block:: python

            import numpy

            x = fluid.layers.data(name='y', shape=[10, 5],
                             dtype='float32', lod_level=1)
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            pad_value = fluid.layers.assign(
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                input=numpy.array([0.0], dtype=numpy.float32))
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            out = fluid.layers.sequence_pad(x=x, pad_value=pad_value)
    """

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    assert not in_dygraph_mode(), (
4070
        "sequence layer is not supported in dygraph mode yet.")
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    helper = LayerHelper('sequence_pad', input=x, **locals())
    dtype = helper.input_dtype()
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    out = helper.create_variable_for_type_inference(dtype)
    length = helper.create_variable_for_type_inference(dtype)
4075 4076 4077 4078

    pad_value.stop_gradient = True
    length.stop_gradient = True

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    if maxlen is None:
        maxlen = -1
    helper.append_op(
        type='sequence_pad',
        inputs={'X': x,
                'PadValue': pad_value},
4085 4086
        outputs={'Out': out,
                 'Length': length},
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        attrs={'padded_length': maxlen})
4088
    return out, length
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4091
def sequence_unpad(x, length, name=None):
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    """
4093
    **Sequence Unpad Layer**
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4094

4095 4096
    This layer removes the padding data in the input sequences and convert
    them into sequences with actual length as output, identitied by lod
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    information.

    .. code-block:: text

	Example:

	Given input Variable **x**:
	    x.data = [[ 1.0,  2.0,  3.0,  4.0,  5.0],
		      [ 6.0,  7.0,  8.0,  9.0, 10.0],
4106 4107 4108
		      [11.0, 12.0, 13.0, 14.0, 15.0]],

	in which there are 3 sequences padded to length 5, and the acutal length
4109
	specified by input Variable **length**:
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	    length.data = [[2], [3], [4]],

	after unpadding, the output Variable will be:

	    out.data = [[1.0, 2.0, 6.0, 7.0, 8.0, 11.0, 12.0, 13.0, 14.0]]
4116
	    out.lod = [[2, 3, 4]]
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    Args:
        x(Variable): Input Variable which contains the padded sequences with
            equal length.
        length(Variable): The Variable that specifies the actual ength of
            sequences after unpadding.
4123 4124
        name(str|None): A name for this layer(optional). If set None, the layer
            will be named automatically.
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    Returns:
        Variable: The Variable contains the unpadded sequences.

    Examples:
        .. code-block:: python

            x = fluid.layers.data(name='x', shape=[10, 5], dtype='float32')
            len = fluid.layers.data(name='length', shape=[1], dtype='int64')
            out = fluid.layers.sequence_unpad(x=x, length=len)
    """

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    assert not in_dygraph_mode(), (
4138
        "sequence layer is not supported in dygraph mode yet.")
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4139 4140
    helper = LayerHelper('sequence_unpad', input=x, **locals())
    dtype = helper.input_dtype()
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    out = helper.create_variable_for_type_inference(dtype)
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    length.stop_gradient = True

    helper.append_op(
        type='sequence_unpad',
        inputs={'X': x,
                'Length': length},
        outputs={'Out': out})
    return out


4153 4154 4155 4156 4157 4158 4159
def beam_search(pre_ids,
                pre_scores,
                ids,
                scores,
                beam_size,
                end_id,
                level=0,
4160
                is_accumulated=True,
4161 4162
                name=None,
                return_parent_idx=False):
4163
    """
4164 4165
    Beam search is a classical algorithm for selecting candidate words in a
    machine translation task.
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    Refer to `Beam search <https://en.wikipedia.org/wiki/Beam_search>`_
    for more details.
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    This layer does the search in beams for one time step. Specifically, it
4171 4172 4173
    selects the top-K candidate word ids of current step from :attr:`ids`
    according to their :attr:`scores` for all source sentences, where K is
    :attr:`beam_size` and :attr:`ids, scores` are predicted results from the
4174 4175 4176 4177 4178 4179 4180 4181 4182 4183 4184
    computation cell. If :attr:`ids` is not set, it will be calculated out
    according to :attr:`scores`. Additionally, :attr:`pre_ids` and
    :attr:`pre_scores` are the output of beam_search at previous step, they
    are needed for special use to handle ended candidate translations.

    Note that if :attr:`is_accumulated` is :attr:`True`, the :attr:`scores`
    passed in should be accumulated scores. Else, the :attr:`scores` are
    considered as the straightforward scores and will be transformed to the
    log field and accumulated the :attr:`pre_scores` in this operator.
    Length penalty should be done with extra operators before calculating the
    accumulated scores if needed.
4185 4186 4187 4188

    Please see the following demo for a fully beam search usage example:

        fluid/tests/book/test_machine_translation.py
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4190
    Args:
4191 4192 4193 4194 4195 4196 4197 4198 4199 4200 4201 4202 4203 4204 4205 4206 4207 4208 4209 4210 4211 4212 4213
        pre_ids(Variable): The LodTensor variable which is the output of
            beam_search at previous step. It should be a LodTensor with shape
            :math:`(batch_size, 1)` and lod
            :math:`[[0, 1, ... , batch_size], [0, 1, ..., batch_size]]` at the
            first step.
        pre_scores(Variable): The LodTensor variable which is the output of
            beam_search at previous step.
        ids(Variable): The LodTensor variable containing the candidates ids.
            Its shape should be :math:`(batch_size \\times beam_size, K)`,
            where :math:`K` supposed to be :attr:`beam_size`.
        scores(Variable): The LodTensor variable containing the accumulated
            scores corresponding to :attr:`ids` and its shape is the same as
            the shape of :attr:`ids`.
        beam_size(int): The beam width used in beam search.
        end_id(int): The id of end token.
        level(int, default 0): It can be ignored and mustn't change currently.
            It means the source level of lod, which is explained as following.
            The lod level of :attr:`ids` should be 2. The first level is source
            level which describes how many prefixes (branchs) for each source
            sentece (beam), and the second level is sentence level which
            describes how these candidates belong to the prefix. The paths
            linking prefixes and selected candidates are organized and reserved
            in lod.
4214 4215
        is_accumulated(bool, default True): Whether the input :attr:`score` is
             accumulated scores.
4216 4217
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.
4218 4219 4220 4221
        return_parent_idx(bool): Whether to return an extra Tensor variable 
                        preserving the selected_ids' parent indice in pre_ids
                        in output, which can be used to gather cell states at
                        the next time step.
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4223
    Returns:
4224 4225 4226 4227
        Variable: The LodTensor tuple containing the selected ids and the \
            corresponding scores. If :attr:`return_parent_idx` is :attr:`True`, \
            an extra Tensor variable preserving the selected_ids' parent indice \
            is included.
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    Examples:
        .. code-block:: python

4232 4233 4234 4235 4236 4237 4238 4239 4240 4241 4242 4243 4244 4245 4246 4247 4248
            # Suppose `probs` contains predicted results from the computation
            # cell and `pre_ids` and `pre_scores` is the output of beam_search
            # at previous step.
            topk_scores, topk_indices = layers.topk(probs, k=beam_size)
            accu_scores = layers.elementwise_add(
                x=layers.log(x=topk_scores)),
                y=layers.reshape(
                    pre_scores, shape=[-1]),
                axis=0)
            selected_ids, selected_scores = layers.beam_search(
                pre_ids=pre_ids,
                pre_scores=pre_scores,
                ids=topk_indices,
                scores=accu_scores,
                beam_size=beam_size,
                end_id=end_id)
    """
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    helper = LayerHelper('beam_search', **locals())
4250 4251 4252 4253 4254 4255
    score_type = pre_scores.dtype
    id_type = pre_ids.dtype

    inputs = {"pre_ids": pre_ids, "pre_scores": pre_scores, "scores": scores}
    if ids is not None:
        inputs["ids"] = ids
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    selected_scores = helper.create_variable_for_type_inference(
        dtype=score_type)
    selected_ids = helper.create_variable_for_type_inference(dtype=id_type)
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    # parent_idx is a tensor used to gather cell states at the next time
    # step. Though lod in selected_ids can also be used to gather by
    # sequence_expand, it is not efficient.
    # gather_op's index input only supports int32 dtype currently
    parent_idx = helper.create_variable_for_type_inference(dtype="int32")
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    helper.append_op(
        type='beam_search',
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        inputs=inputs,
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        outputs={
            'selected_ids': selected_ids,
            'selected_scores': selected_scores,
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            'parent_idx': parent_idx
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        },
        attrs={
            # TODO(ChunweiYan) to assure other value support
            'level': level,
            'beam_size': beam_size,
            'end_id': end_id,
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            'is_accumulated': is_accumulated,
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        })
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    if return_parent_idx:
        return selected_ids, selected_scores, parent_idx
    else:
        return selected_ids, selected_scores
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def beam_search_decode(ids, scores, beam_size, end_id, name=None):
    """
    Beam Search Decode Layer. This layer constructs the full hypotheses for
    each source sentence by walking back along the LoDTensorArray :attr:`ids`
    whose lods can be used to restore the path in the beam search tree.
    Please see the following demo for a fully beam search usage example:
        fluid/tests/book/test_machine_translation.py
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    Args:
        ids(Variable): The LodTensorArray variable containing the selected ids
            of all steps.
        scores(Variable): The LodTensorArray variable containing the selected
            scores of all steps.
        beam_size(int): The beam width used in beam search.
        end_id(int): The id of end token.
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.
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    Returns:
        Variable: The LodTensor pair containing the generated id sequences \
            and the corresponding scores. The shapes and lods of the two \
            LodTensor are same. The lod level is 2 and the two levels \
            separately indicate how many hypotheses each source sentence has \
            and how many ids each hypothesis has.
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    Examples:
        .. code-block:: python
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            # Suppose `ids` and `scores` are LodTensorArray variables reserving
            # the selected ids and scores of all steps
            finished_ids, finished_scores = layers.beam_search_decode(
                ids, scores, beam_size=5, end_id=0)
    """
    helper = LayerHelper('beam_search_decode', **locals())
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    sentence_ids = helper.create_variable_for_type_inference(dtype=ids.dtype)
    sentence_scores = helper.create_variable_for_type_inference(dtype=ids.dtype)
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    helper.append_op(
        type="beam_search_decode",
        inputs={"Ids": ids,
                "Scores": scores},
        outputs={
            "SentenceIds": sentence_ids,
            "SentenceScores": sentence_scores
        },
        attrs={"beam_size": beam_size,
               "end_id": end_id})

    return sentence_ids, sentence_scores


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def lstm_unit(x_t,
              hidden_t_prev,
              cell_t_prev,
              forget_bias=0.0,
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              param_attr=None,
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              bias_attr=None,
              name=None):
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    """Lstm unit layer. The equation of a lstm step is:

        .. math::

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            i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i)
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            f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + b_f)
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            c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t + W_{h_c}h_{t-1} + b_c)
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            o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + b_o)
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            h_t & = o_t tanh(c_t)

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    The inputs of lstm unit include :math:`x_t`, :math:`h_{t-1}` and
    :math:`c_{t-1}`. The 2nd dimensions of :math:`h_{t-1}` and :math:`c_{t-1}`
    should be same. The implementation separates the linear transformation and
    non-linear transformation apart. Here, we take :math:`i_t` as an example.
    The linear transformation is applied by calling a `fc` layer and the
    equation is:
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        .. math::

4368
            L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i
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    The non-linear transformation is applied by calling `lstm_unit_op` and the
    equation is:

        .. math::

            i_t = \sigma(L_{i_t})

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    This layer has two outputs including :math:`h_t` and :math:`o_t`.
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    Args:
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        x_t (Variable): The input value of current step, a 2-D tensor with shape
            M x N, M for batch size and N for input size.
        hidden_t_prev (Variable): The hidden value of lstm unit, a 2-D tensor
            with shape M x S, M for batch size and S for size of lstm unit.
        cell_t_prev (Variable): The cell value of lstm unit, a 2-D tensor with
            shape M x S, M for batch size and S for size of lstm unit.
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        forget_bias (float): The forget bias of lstm unit.
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        param_attr(ParamAttr|None): The parameter attribute for the learnable
                               hidden-hidden weights.
                               If it is set to None or one attribute of ParamAttr,
                               lstm_unit will create ParamAttr as param_attr.
                               If the Initializer of the param_attr is not set, the
                               parameter is initialized with Xavier. Default: None.
        bias_attr (ParamAttr|None): The bias attribute for the learnable bias
                              weights. If it is set to False, no bias will be added
                              to the output units. If it is set to None or one attribute of ParamAttr,
                              lstm_unit will create ParamAttr as bias_attr.
                              If the Initializer of the bias_attr is not set,
                              the bias is initialized zero. Default: None.
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        name(str|None): A name for this layer(optional). If set None, the layer
                       will be named automatically.
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    Returns:
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        tuple: The hidden value and cell value of lstm unit.
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    Raises:
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        ValueError: The ranks of **x_t**, **hidden_t_prev** and **cell_t_prev**
                    not be 2 or the 1st dimensions of **x_t**, **hidden_t_prev**
                    and **cell_t_prev** not be the same or the 2nd dimensions of
                    **hidden_t_prev** and **cell_t_prev** not be the same.
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    Examples:

        .. code-block:: python

             x_t = fluid.layers.fc(input=x_t_data, size=10)
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             prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=30)
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             prev_cell = fluid.layers.fc(input=prev_cell_data, size=30)
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             hidden_value, cell_value = fluid.layers.lstm_unit(x_t=x_t,
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                                                    hidden_t_prev=prev_hidden,
                                                    cell_t_prev=prev_cell)
    """
    helper = LayerHelper('lstm_unit', **locals())

    if len(x_t.shape) != 2:
        raise ValueError("Rank of x_t must be 2.")

    if len(hidden_t_prev.shape) != 2:
        raise ValueError("Rank of hidden_t_prev must be 2.")

    if len(cell_t_prev.shape) != 2:
        raise ValueError("Rank of cell_t_prev must be 2.")

    if x_t.shape[0] != hidden_t_prev.shape[0] or x_t.shape[
            0] != cell_t_prev.shape[0]:
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        raise ValueError("The 1st dimensions of x_t, hidden_t_prev and "
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                         "cell_t_prev must be the same.")

    if hidden_t_prev.shape[1] != cell_t_prev.shape[1]:
        raise ValueError("The 2nd dimensions of hidden_t_prev and "
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                         "cell_t_prev must be the same.")

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    if bias_attr is None:
        bias_attr = ParamAttr()

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    size = cell_t_prev.shape[1]
4446
    concat_out = concat(input=[x_t, hidden_t_prev], axis=1)
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    fc_out = fc(input=concat_out,
                size=4 * size,
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                param_attr=param_attr,
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                bias_attr=bias_attr)
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    dtype = x_t.dtype
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    c = helper.create_variable_for_type_inference(dtype)
    h = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
        type='lstm_unit',
        inputs={"X": fc_out,
                "C_prev": cell_t_prev},
        outputs={"C": c,
                 "H": h},
        attrs={"forget_bias": forget_bias})

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    return h, c
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def reduce_sum(input, dim=None, keep_dim=False, name=None):
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    """
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    Computes the sum of tensor elements over the given dimension.
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    Args:
        input (Variable): The input variable which is a Tensor or LoDTensor.
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        dim (list|int|None): The dimensions along which the sum is performed. If
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            :attr:`None`, sum all elements of :attr:`input` and return a
            Tensor variable with a single element, otherwise must be in the
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            range :math:`[-rank(input), rank(input))`. If :math:`dim[i] < 0`,
            the dimension to reduce is :math:`rank + dim[i]`.
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        keep_dim (bool|False): Whether to reserve the reduced dimension in the
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            output Tensor. The result tensor will have one fewer dimension
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            than the :attr:`input` unless :attr:`keep_dim` is true.
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        name(str|None): A name for this layer(optional). If set None, the layer
                       will be named automatically.
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    Returns:
        Variable: The reduced Tensor variable.
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    Examples:
        .. code-block:: python

            # x is a Tensor variable with following elements:
            #    [[0.2, 0.3, 0.5, 0.9]
            #     [0.1, 0.2, 0.6, 0.7]]
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            # Each example is followed by the corresponding output tensor.
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            fluid.layers.reduce_sum(x)  # [3.5]
            fluid.layers.reduce_sum(x, dim=0)  # [0.3, 0.5, 1.1, 1.6]
            fluid.layers.reduce_sum(x, dim=-1)  # [1.9, 1.6]
            fluid.layers.reduce_sum(x, dim=1, keep_dim=True)  # [[1.9], [1.6]]
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            # x is a Tensor variable with shape [2, 2, 2] and elements as below:
            #      [[[1, 2], [3, 4]],
            #      [[5, 6], [7, 8]]]
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            # Each example is followed by the corresponding output tensor.
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            fluid.layers.reduce_sum(x, dim=[1, 2]) # [10, 26]
            fluid.layers.reduce_sum(x, dim=[0, 1]) # [16, 20]

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    """
    helper = LayerHelper('reduce_sum', **locals())
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    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
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    if dim is not None and not isinstance(dim, list):
        dim = [dim]
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    helper.append_op(
        type='reduce_sum',
        inputs={'X': input},
        outputs={'Out': out},
        attrs={
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            'dim': dim if dim != None else [0],
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            'keep_dim': keep_dim,
            'reduce_all': True if dim == None else False
        })
    return out
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def reduce_mean(input, dim=None, keep_dim=False, name=None):
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    """
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    Computes the mean of the input tensor's elements along the given dimension.
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    Args:
        input (Variable): The input variable which is a Tensor or LoDTensor.
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        dim (list|int|None): The dimension along which the mean is computed. If
            `None`, compute the mean over all elements of :attr:`input`
            and return a variable with a single element, otherwise it
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            must be in the range :math:`[-rank(input), rank(input))`. If
4532
            :math:`dim[i] < 0`, the dimension to reduce is
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            :math:`rank(input) + dim[i]`.
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        keep_dim (bool): Whether to reserve the reduced dimension in the
            output Tensor. The result tensor will have one fewer dimension
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            than the :attr:`input` unless :attr:`keep_dim` is true.
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        name(str|None): A name for this layer(optional). If set `None`, the layer
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                       will be named automatically.
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    Returns:
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        Variable: The reduced mean Variable.
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    Examples:
        .. code-block:: python

            # x is a Tensor variable with following elements:
            #    [[0.2, 0.3, 0.5, 0.9]
            #     [0.1, 0.2, 0.6, 0.7]]
            # Each example is followed by the correspending output tensor.
            fluid.layers.reduce_mean(x)  # [0.4375]
            fluid.layers.reduce_mean(x, dim=0)  # [0.15, 0.25, 0.55, 0.8]
            fluid.layers.reduce_mean(x, dim=-1)  # [0.475, 0.4]
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            fluid.layers.reduce_mean(
                x, dim=1, keep_dim=True)  # [[0.475], [0.4]]
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            # x is a Tensor variable with shape [2, 2, 2] and elements as below:
            #      [[[1.0, 2.0], [3.0, 4.0]],
            #      [[5.0, 6.0], [7.0, 8.0]]]
            # Each example is followed by the correspending output tensor.
            fluid.layers.reduce_mean(x, dim=[1, 2]) # [2.5, 6.5]
            fluid.layers.reduce_mean(x, dim=[0, 1]) # [4.0, 5.0]
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    """
    helper = LayerHelper('reduce_mean', **locals())
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    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
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    if dim is not None and not isinstance(dim, list):
        dim = [dim]
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    helper.append_op(
        type='reduce_mean',
        inputs={'X': input},
        outputs={'Out': out},
        attrs={
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            'dim': dim if dim != None else [0],
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            'keep_dim': keep_dim,
            'reduce_all': True if dim == None else False
        })
    return out
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def reduce_max(input, dim=None, keep_dim=False, name=None):
4580
    """
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    Computes the maximum of tensor elements over the given dimension.
4582 4583 4584

    Args:
        input (Variable): The input variable which is a Tensor or LoDTensor.
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        dim (list|int|None): The dimension along which the maximum is computed.
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            If :attr:`None`, compute the maximum over all elements of
            :attr:`input` and return a Tensor variable with a single element,
            otherwise must be in the range :math:`[-rank(input), rank(input))`.
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            If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
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        keep_dim (bool): Whether to reserve the reduced dimension in the
            output Tensor. The result tensor will have one fewer dimension
4592
            than the :attr:`input` unless :attr:`keep_dim` is true.
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        name(str|None): A name for this layer(optional). If set None, the layer
                       will be named automatically.
4595 4596 4597

    Returns:
        Variable: The reduced Tensor variable.
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4599 4600 4601 4602 4603 4604 4605 4606 4607 4608 4609
    Examples:
        .. code-block:: python

            # x is a Tensor variable with following elements:
            #    [[0.2, 0.3, 0.5, 0.9]
            #     [0.1, 0.2, 0.6, 0.7]]
            # Each example is followed by the correspending output tensor.
            fluid.layers.reduce_max(x)  # [0.9]
            fluid.layers.reduce_max(x, dim=0)  # [0.2, 0.3, 0.6, 0.9]
            fluid.layers.reduce_max(x, dim=-1)  # [0.9, 0.7]
            fluid.layers.reduce_max(x, dim=1, keep_dim=True)  # [[0.9], [0.7]]
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            # x is a Tensor variable with shape [2, 2, 2] and elements as below:
            #      [[[1.0, 2.0], [3.0, 4.0]],
            #      [[5.0, 6.0], [7.0, 8.0]]]
            # Each example is followed by the correspending output tensor.
            fluid.layers.reduce_max(x, dim=[1, 2]) # [4.0, 8.0]
            fluid.layers.reduce_max(x, dim=[0, 1]) # [7.0, 8.0]
4617 4618
    """
    helper = LayerHelper('reduce_max', **locals())
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    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
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    if dim is not None and not isinstance(dim, list):
        dim = [dim]
4622 4623 4624 4625 4626
    helper.append_op(
        type='reduce_max',
        inputs={'X': input},
        outputs={'Out': out},
        attrs={
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            'dim': dim if dim != None else [0],
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            'keep_dim': keep_dim,
            'reduce_all': True if dim == None else False
        })
    return out


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def reduce_min(input, dim=None, keep_dim=False, name=None):
4635
    """
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    Computes the minimum of tensor elements over the given dimension.
4637 4638 4639

    Args:
        input (Variable): The input variable which is a Tensor or LoDTensor.
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        dim (list|int|None): The dimensions along which the minimum is computed.
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            If :attr:`None`, compute the minimum over all elements of
            :attr:`input` and return a Tensor variable with a single element,
            otherwise must be in the range :math:`[-rank(input), rank(input))`.
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            If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
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        keep_dim (bool): Whether to reserve the reduced dimension in the
            output Tensor. The result tensor will have one fewer dimension
4647
            than the :attr:`input` unless :attr:`keep_dim` is true.
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        name(str|None): A name for this layer(optional). If set None, the layer
                       will be named automatically.
4650 4651 4652

    Returns:
        Variable: The reduced Tensor variable.
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4654 4655 4656 4657 4658 4659 4660 4661 4662 4663 4664
    Examples:
        .. code-block:: python

            # x is a Tensor variable with following elements:
            #    [[0.2, 0.3, 0.5, 0.9]
            #     [0.1, 0.2, 0.6, 0.7]]
            # Each example is followed by the correspending output tensor.
            fluid.layers.reduce_min(x)  # [0.1]
            fluid.layers.reduce_min(x, dim=0)  # [0.1, 0.2, 0.5, 0.7]
            fluid.layers.reduce_min(x, dim=-1)  # [0.2, 0.1]
            fluid.layers.reduce_min(x, dim=1, keep_dim=True)  # [[0.2], [0.1]]
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            # x is a Tensor variable with shape [2, 2, 2] and elements as below:
            #      [[[1.0, 2.0], [3.0, 4.0]],
            #      [[5.0, 6.0], [7.0, 8.0]]]
            # Each example is followed by the correspending output tensor.
            fluid.layers.reduce_min(x, dim=[1, 2]) # [1.0, 5.0]
            fluid.layers.reduce_min(x, dim=[0, 1]) # [1.0, 2.0]
4672 4673
    """
    helper = LayerHelper('reduce_min', **locals())
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    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
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    if dim is not None and not isinstance(dim, list):
        dim = [dim]
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    helper.append_op(
        type='reduce_min',
        inputs={'X': input},
        outputs={'Out': out},
        attrs={
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            'dim': dim if dim != None else [0],
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            'keep_dim': keep_dim,
            'reduce_all': True if dim == None else False
        })
    return out
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def reduce_prod(input, dim=None, keep_dim=False, name=None):
    """
    Computes the product of tensor elements over the given dimension.

    Args:
        input (Variable): The input variable which is a Tensor or LoDTensor.
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        dim (list|int|None): The dimensions along which the product is performed. If
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            :attr:`None`, multipy all elements of :attr:`input` and return a
            Tensor variable with a single element, otherwise must be in the
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            range :math:`[-rank(input), rank(input))`. If :math:`dim[i] < 0`,
            the dimension to reduce is :math:`rank + dim[i]`.
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        keep_dim (bool|False): Whether to reserve the reduced dimension in the
            output Tensor. The result tensor will have one fewer dimension
            than the :attr:`input` unless :attr:`keep_dim` is true.
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        name(str|None): A name for this layer(optional). If set None, the
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            layer will be named automatically.
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    Returns:
        Variable: The reduced Tensor variable.

    Examples:
        .. code-block:: python

            # x is a Tensor variable with following elements:
            #    [[0.2, 0.3, 0.5, 0.9]
            #     [0.1, 0.2, 0.6, 0.7]]
            # Each example is followed by the correspending output tensor.
            fluid.layers.reduce_prod(x)  # [0.0002268]
            fluid.layers.reduce_prod(x, dim=0)  # [0.02, 0.06, 0.3, 0.63]
            fluid.layers.reduce_prod(x, dim=-1)  # [0.027, 0.0084]
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            fluid.layers.reduce_prod(x, dim=1,
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                                     keep_dim=True)  # [[0.027], [0.0084]]
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            # x is a Tensor variable with shape [2, 2, 2] and elements as below:
            #      [[[1.0, 2.0], [3.0, 4.0]],
            #      [[5.0, 6.0], [7.0, 8.0]]]
            # Each example is followed by the correspending output tensor.
            fluid.layers.reduce_prod(x, dim=[1, 2]) # [24.0, 1680.0]
            fluid.layers.reduce_prod(x, dim=[0, 1]) # [105.0, 384.0]
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    """
    helper = LayerHelper('reduce_prod', **locals())
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    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
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    if dim is not None and not isinstance(dim, list):
        dim = [dim]
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    helper.append_op(
        type='reduce_prod',
        inputs={'X': input},
        outputs={'Out': out},
        attrs={
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            'dim': dim if dim != None else [0],
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            'keep_dim': keep_dim,
            'reduce_all': True if dim == None else False
        })
    return out


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def reduce_all(input, dim=None, keep_dim=False, name=None):
    """
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    Computes the ``logical and`` of tensor elements over the given dimension.
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    Args:
        input (Variable): The input variable which is a Tensor or LoDTensor.
        dim (list|int|None): The dimension along which the logical and is computed.
            If :attr:`None`, compute the logical and over all elements of
            :attr:`input` and return a Tensor variable with a single element,
            otherwise must be in the range :math:`[-rank(input), rank(input))`.
            If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
        keep_dim (bool): Whether to reserve the reduced dimension in the
            output Tensor. The result tensor will have one fewer dimension
            than the :attr:`input` unless :attr:`keep_dim` is true.
        name(str|None): A name for this layer(optional). If set None, the layer
                       will be named automatically.

    Returns:
        Variable: The reduced Tensor variable.

    Examples:
        .. code-block:: python
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            # x is a bool Tensor variable with following elements:
            #    [[True, False]
            #     [True, True]]
            # Each example is followed by the correspending output tensor.
            fluid.layers.reduce_all(x)  # False 
            fluid.layers.reduce_all(x, dim=0)  # [True, False]
            fluid.layers.reduce_all(x, dim=-1)  # [False, True]
            fluid.layers.reduce_all(x, dim=1,
                                     keep_dim=True)  # [[False], [True]]

    """
    helper = LayerHelper('reduce_all', **locals())
    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
    if dim is not None and not isinstance(dim, list):
        dim = [dim]
    helper.append_op(
        type='reduce_all',
        inputs={'X': input},
        outputs={'Out': out},
        attrs={
            'dim': dim if dim != None else [0],
            'keep_dim': keep_dim,
            'reduce_all': True if dim == None else False
        })
    return out


def reduce_any(input, dim=None, keep_dim=False, name=None):
    """
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    Computes the ``logical or`` of tensor elements over the given dimension.
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    Args:
        input (Variable): The input variable which is a Tensor or LoDTensor.
        dim (list|int|None): The dimension along which the logical or is computed.
            If :attr:`None`, compute the logical or over all elements of
            :attr:`input` and return a Tensor variable with a single element,
            otherwise must be in the range :math:`[-rank(input), rank(input))`.
            If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
        keep_dim (bool): Whether to reserve the reduced dimension in the
            output Tensor. The result tensor will have one fewer dimension
            than the :attr:`input` unless :attr:`keep_dim` is true.
        name(str|None): A name for this layer(optional). If set None, the layer
                       will be named automatically.

    Returns:
        Variable: The reduced Tensor variable.

    Examples:
        .. code-block:: python
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            # x is a bool Tensor variable with following elements:
            #    [[True, False]
            #     [False, False]]
            # Each example is followed by the correspending output tensor.
            fluid.layers.reduce_any(x)  # True
            fluid.layers.reduce_any(x, dim=0)  # [True, False]
            fluid.layers.reduce_any(x, dim=-1)  # [True, False]
            fluid.layers.reduce_any(x, dim=1,
                                     keep_dim=True)  # [[True], [False]]

    """
    helper = LayerHelper('reduce_any', **locals())
    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
    if dim is not None and not isinstance(dim, list):
        dim = [dim]
    helper.append_op(
        type='reduce_any',
        inputs={'X': input},
        outputs={'Out': out},
        attrs={
            'dim': dim if dim != None else [0],
            'keep_dim': keep_dim,
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            'reduce_all': True if dim == None else False
        })
    return out


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def split(input, num_or_sections, dim=-1, name=None):
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    """
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    Split the input tensor into multiple sub-tensors.
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    Args:
        input (Variable): The input variable which is a Tensor or LoDTensor.
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        num_or_sections (int|list): If :attr:`num_or_sections` is an integer,
            then the integer indicates the number of equal sized sub-tensors
            that the tensor will be divided into. If :attr:`num_or_sections`
            is a list of integers, the length of list indicates the number of
            sub-tensors and the integers indicate the sizes of sub-tensors'
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            :attr:`dim` dimension orderly.
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        dim (int): The dimension along which to split. If :math:`dim < 0`, the
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            dimension to split along is :math:`rank(input) + dim`.
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        name(str|None): A name for this layer(optional). If set None, the layer
                       will be named automatically.
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    Returns:
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        list(Variable): The list of segmented tensor variables.
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    Examples:
        .. code-block:: python

            # x is a Tensor variable with shape [3, 9, 5]:
            x0, x1, x2 = fluid.layers.split(x, num_or_sections=3, dim=1)
            x0.shape  # [3, 3, 5]
            x1.shape  # [3, 3, 5]
            x2.shape  # [3, 3, 5]
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            x0, x1, x2 = fluid.layers.split(
                x, num_or_sections=[2, 3, 4], dim=1)
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            x0.shape  # [3, 2, 5]
            x1.shape  # [3, 3, 5]
            x2.shape  # [3, 4, 5]
    """
    helper = LayerHelper('split', **locals())
    input_shape = input.shape
    dim = (len(input_shape) + dim) if dim < 0 else dim
    if isinstance(num_or_sections, int):
        assert num_or_sections > 1, 'num_or_sections must be more than 1.'
        num = num_or_sections
    else:
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        assert len(num_or_sections) <= input_shape[
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            dim], 'len(num_or_sections) must not be more than input.shape[dim].'
        num = len(num_or_sections)
    outs = [
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        helper.create_variable_for_type_inference(dtype=helper.input_dtype())
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        for i in range(num)
    ]
    helper.append_op(
        type='split',
        inputs={'X': input},
        outputs={'Out': outs},
        attrs={
            'num': num_or_sections if isinstance(num_or_sections, int) else 0,
            'sections': num_or_sections
            if isinstance(num_or_sections, list) else [],
            'axis': dim
        })
    return outs
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def l2_normalize(x, axis, epsilon=1e-12, name=None):
    """
    **L2 normalize Layer**

    The l2 normalize layer normalizes `x` along dimension `axis` using an L2
    norm. For a 1-D tensor (`dim` is fixed to 0), this layer computes

4913
    .. math::
4914 4915

        y = \\frac{x}{ \sqrt{\sum {x^2} + epsion }}
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    For `x` with more dimensions, this layer independently normalizes each 1-D
    slice along dimension `axis`.

    Args:
4921
        x(Variable|list): The input tensor to l2_normalize layer.
4922
        axis(int): The axis on which to apply normalization. If `axis < 0`, \
4923 4924
            the dimension to normalization is rank(X) + axis. -1 is the
            last dimension.
4925
        epsilon(float): The epsilon value is used to avoid division by zero, \
4926
            the defalut value is 1e-12.
4927
        name(str|None): A name for this layer(optional). If set None, the layer \
4928
            will be named automatically.
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    Returns:
4931
        Variable: The output tensor variable is the same shape with `x`.
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    Examples:
4934

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        .. code-block:: python

4937 4938 4939 4940
            data = fluid.layers.data(name="data",
                                     shape=(3, 17, 13),
                                     dtype="float32")
            normed = fluid.layers.l2_normalize(x=data, axis=1)
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    """

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    if len(x.shape) == 1:
        axis = 0
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    helper = LayerHelper("l2_normalize", **locals())

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    out = helper.create_variable_for_type_inference(dtype=x.dtype)
    norm = helper.create_variable_for_type_inference(dtype=x.dtype)
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    helper.append_op(
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        type="norm",
        inputs={"X": x},
        outputs={"Out": out,
                 "Norm": norm},
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        attrs={
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            "axis": 1 if axis is None else axis,
            "epsilon": epsilon,
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        })
    return out
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def matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None):
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    """
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    Applies matrix multiplication to two tensors.

    Currently, the input tensors' rank can be any, but when the rank of any
    inputs is bigger than 3, this two inputs' rank should be equal.
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    The actual behavior depends on the shapes of :math:`x`, :math:`y` and the
4969
    flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:
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    - If a transpose flag is specified, the last two dimensions of the tensor
      are transposed. If the tensor is rank-1 of shape :math:`[D]`, then for
      :math:`x` it is treated as :math:`[1, D]` in nontransposed form and as
      :math:`[D, 1]` in transposed form, whereas for :math:`y` it is the
      opposite: It is treated as :math:`[D, 1]` in nontransposed form and as
4976
      :math:`[1, D]` in transposed form.
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    - After transpose, the two tensors are 2-D or n-D and matrix multiplication
4979
      performs in the following way.
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4981
      - If both are 2-D, they are multiplied like conventional matrices.
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      - If either is n-D, it is treated as a stack of matrices residing in the
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        last two dimensions and a batched matrix multiply supporting broadcast
4984
        applies on the two tensors.
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    Also note that if the raw tensor :math:`x` or :math:`y` is rank-1 and
    nontransposed, the prepended or appended dimension :math:`1` will be
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    removed after matrix multiplication.
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    Args:
        x (Variable): The input variable which is a Tensor or LoDTensor.
4992 4993 4994
        y (Variable): The input variable which is a Tensor or LoDTensor.
        transpose_x (bool): Whether to transpose :math:`x` before multiplication.
        transpose_y (bool): Whether to transpose :math:`y` before multiplication.
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        alpha (float): The scale of output. Default 1.0.
4996
        name(str|None): A name for this layer(optional). If set None, the layer
4997
            will be named automatically.
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    Returns:
5000
        Variable: The product Tensor variable.
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    Examples:
        .. code-block:: python

5005
            # Examples to clarify shapes of the inputs and output
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            # x: [B, ..., M, K], y: [B, ..., K, N]
            fluid.layers.matmul(x, y)  # out: [B, ..., M, N]
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5009 5010
            # x: [B, M, K], y: [B, K, N]
            fluid.layers.matmul(x, y)  # out: [B, M, N]
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5012 5013
            # x: [B, M, K], y: [K, N]
            fluid.layers.matmul(x, y)  # out: [B, M, N]
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5015 5016
            # x: [M, K], y: [K, N]
            fluid.layers.matmul(x, y)  # out: [M, N]
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            # x: [B, M, K], y: [K]
            fluid.layers.matmul(x, y)  # out: [B, M]

5021 5022
            # x: [K], y: [K]
            fluid.layers.matmul(x, y)  # out: [1]
5023

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            # x: [M], y: [N]
5025
            fluid.layers.matmul(x, y, True, True)  # out: [M, N]
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    """
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    def __check_input(x, y):
        x_shape = list(x.shape)
        y_shape = list(y.shape)
        if len(x_shape) == 1:
            x_shape = [1] + x_shape
        if len(y_shape) == 1:
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            y_shape = y_shape + [1]
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        # check the inner 2 dimensions
        if transpose_x:
            x_shape[-2], x_shape[-1] = x_shape[-1], x_shape[-2]
        if transpose_y:
            y_shape[-2], y_shape[-1] = y_shape[-1], y_shape[-2]
        if x_shape[-1] != y_shape[-2]:
5042 5043
            raise ValueError("Invalid inputs for matmul. x: %s, y: %s\n" %
                             (x_shape, y_shape))
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        if len(y_shape) > 2 and len(x_shape) > 2:
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            for i, dim_x in enumerate(x_shape[:-2]):
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                # don't check neg shape
                if dim_x < 0 or y_shape[i] < 0:
                    continue
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                if dim_x != y_shape[i]:
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                    raise ValueError("Invalid inputs for matmul. x(%s), y(%s)" %
                                     (x.shape, y.shape))
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    __check_input(x, y)

5056
    helper = LayerHelper('matmul', **locals())
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    out = helper.create_variable_for_type_inference(dtype=x.dtype)
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    helper.append_op(
5059 5060 5061 5062
        type='matmul',
        inputs={'X': x,
                'Y': y},
        outputs={'Out': out},
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        attrs={
            'transpose_X': transpose_x,
            'transpose_Y': transpose_y,
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            'alpha': float(alpha),
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        })
5068
    return out
5069 5070


5071
def topk(input, k, name=None):
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    """
    This operator is used to find values and indices of the k largest entries
    for the last dimension.

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    If the input is a vector (1-D Tensor), finds the k largest entries in the vector
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    and outputs their values and indices as vectors. Thus values[j] is the j-th
    largest entry in input, and its index is indices[j].

    If the input is a Tensor with higher rank, this operator computes the top k
    entries along the last dimension.

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    For example:

    .. code-block:: text

        If:
            input = [[5, 4, 2, 3],
                     [9, 7, 10, 25],
                     [6, 2, 10, 1]]
            k = 2

        Then:
            The first output:
            values = [[5, 4],
                      [10, 25],
                      [6, 10]]

            The second output:
            indices = [[0, 1],
                       [2, 3],
                       [0, 2]]

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    Args:
        input(Variable): The input variable which can be a vector or Tensor with
            higher rank.
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        k(int | Variable):  The number of top elements to look for along the last dimension
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                 of input.
5109
        name(str|None): A name for this layer(optional). If set None, the layer
5110
                       will be named automatically.
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                       Default: None
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    Returns:
5114 5115 5116
        Tuple[Variable]: A tuple with two elements. Each element is a Variable.
        The first one is k largest elements along each last
        dimensional slice. The second one is indices of values
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        within the last dimension of input.
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    Raises:
        ValueError: If k < 1 or k is not less than the last dimension of input
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    Examples:
        .. code-block:: python

            top5_values, top5_indices = layers.topk(input, k=5)
    """
    helper = LayerHelper("top_k", **locals())
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    values = helper.create_variable_for_type_inference(dtype=input.dtype)
    indices = helper.create_variable_for_type_inference(dtype="int64")
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    inputs = {"X": [input]}
    attrs = None
    if isinstance(k, Variable):
        inputs['K'] = k
    else:
        attrs = {'k': k}
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    helper.append_op(
        type="top_k",
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        inputs=inputs,
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        outputs={"Out": [values],
                 "Indices": [indices]},
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        attrs=attrs)
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    values.stop_gradient = True
    indices.stop_gradient = True
    return values, indices


5147
def edit_distance(input, label, normalized=True, ignored_tokens=None):
5148
    """
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    EditDistance operator computes the edit distances between a batch of
    hypothesis strings and their references. Edit distance, also called
    Levenshtein distance, measures how dissimilar two strings are by counting
    the minimum number of operations to transform one string into anthor.
    Here the operations include insertion, deletion, and substitution.

    For example, given hypothesis string A = "kitten" and reference
    B = "sitting", the edit distance is 3 for A will be transformed into B
    at least after two substitutions and one insertion:
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    "kitten" -> "sitten" -> "sittin" -> "sitting"
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5160

5161
    The input is a LoDTensor consisting of all the hypothesis strings with
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    the total number denoted by `batch_size`, and the separation is specified
    by the LoD information. And the `batch_size` reference strings are arranged
5164
    in order in the same way in the input LoDTensor.
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5166
    The output contains the `batch_size` results and each stands for the edit
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    distance for a pair of strings respectively. If Attr(normalized) is true,
    the edit distance will be divided by the length of reference string.
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5170 5171 5172
    Args:
        input(Variable): The indices for hypothesis strings.
        label(Variable): The indices for reference strings.
5173
        normalized(bool, default True): Indicated whether to normalize the edit distance by
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                          the length of reference string.
5175
        ignored_tokens(list<int>, default None): Tokens that should be removed before
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                                     calculating edit distance.
5177
        name (str): The name of this layer. It is optional.
5178

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    Returns:
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        Variable: sequence-to-sequence edit distance in shape [batch_size, 1].
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    Examples:
        .. code-block:: python

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            x = fluid.layers.data(name='x', shape=[1], dtype='float32')
            y = fluid.layers.data(name='y', shape=[1], dtype='float32')
5187
            cost = fluid.layers.edit_distance(input=x,label=y)
5188
    """
5189
    helper = LayerHelper("edit_distance", **locals())
5190

5191
    # remove some tokens from input and labels
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    if ignored_tokens is not None and len(ignored_tokens) > 0:
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        erased_input = helper.create_variable_for_type_inference(dtype="int64")
        erased_label = helper.create_variable_for_type_inference(dtype="int64")
5195 5196 5197 5198 5199

        helper.append_op(
            type="sequence_erase",
            inputs={"X": [input]},
            outputs={"Out": [erased_input]},
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            attrs={"tokens": ignored_tokens})
5201 5202 5203 5204 5205
        input = erased_input

        helper.append_op(
            type="sequence_erase",
            inputs={"X": [label]},
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            outputs={"Out": [erased_label]},
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            attrs={"tokens": ignored_tokens})
5208 5209
        label = erased_label

5210
    # edit distance op
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    edit_distance_out = helper.create_variable_for_type_inference(dtype="int64")
    sequence_num = helper.create_variable_for_type_inference(dtype="int64")
5213 5214 5215 5216
    helper.append_op(
        type="edit_distance",
        inputs={"Hyps": [input],
                "Refs": [label]},
5217 5218
        outputs={"Out": [edit_distance_out],
                 "SequenceNum": [sequence_num]},
5219 5220
        attrs={"normalized": normalized})

5221
    return edit_distance_out, sequence_num
5222 5223 5224 5225 5226


def ctc_greedy_decoder(input, blank, name=None):
    """
    This op is used to decode sequences by greedy policy by below steps:
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    1. Get the indexes of max value for each row in input. a.k.a.
       numpy.argmax(input, axis=0).
    2. For each sequence in result of step1, merge repeated tokens between two
       blanks and delete all blanks.
5232 5233 5234 5235 5236 5237 5238 5239 5240 5241 5242 5243 5244 5245 5246 5247 5248

    A simple example as below:

    .. code-block:: text

        Given:

        input.data = [[0.6, 0.1, 0.3, 0.1],
                      [0.3, 0.2, 0.4, 0.1],
                      [0.1, 0.5, 0.1, 0.3],
                      [0.5, 0.1, 0.3, 0.1],

                      [0.5, 0.1, 0.3, 0.1],
                      [0.2, 0.2, 0.2, 0.4],
                      [0.2, 0.2, 0.1, 0.5],
                      [0.5, 0.1, 0.3, 0.1]]

5249
        input.lod = [[4, 4]]
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        Computation:
5252

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        step1: Apply argmax to first input sequence which is input.data[0:4]. Then we get:
               [[0], [2], [1], [0]]
        step2: merge repeated tokens and remove blank which is 0. Then we get first output sequence:
               [[2], [1]]

        Finally:
5259 5260 5261 5262 5263

        output.data = [[2],
                       [1],
                       [3]]

5264
        output.lod = [[2, 1]]
5265

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5267 5268
    Args:

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        input(Variable): (LoDTensor<float>), the probabilities of
                         variable-length sequences, which is a 2-D Tensor with
                         LoD information. It's shape is [Lp, num_classes + 1],
                         where Lp is the sum of all input sequences' length and
                         num_classes is the true number of classes. (not
                         including the blank label).
        blank(int): the blank label index of Connectionist Temporal
                    Classification (CTC) loss, which is in thehalf-opened
                    interval [0, num_classes + 1).
5278
        name (str): The name of this layer. It is optional.
5279 5280

    Returns:
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        Variable: CTC greedy decode result which is a 2-D tensor with shape [Lp, 1]. \
                  'Lp' is the sum if all output sequences' length. If all the sequences \
                  in result were empty, the result LoDTensor will be [-1] with  \
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                  LoD [[]] and dims [1, 1].
5285 5286 5287 5288 5289

    Examples:
        .. code-block:: python

            x = fluid.layers.data(name='x', shape=[8], dtype='float32')
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5291
            cost = fluid.layers.ctc_greedy_decoder(input=x, blank=0)
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    """
5293
    helper = LayerHelper("ctc_greedy_decoder", **locals())
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    _, topk_indices = topk(input, k=1)
5295 5296

    # ctc align op
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    ctc_out = helper.create_variable_for_type_inference(dtype="int64")
5298 5299 5300
    helper.append_op(
        type="ctc_align",
        inputs={"Input": [topk_indices]},
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        outputs={"Output": [ctc_out]},
5302 5303
        attrs={"merge_repeated": True,
               "blank": blank})
5304
    return ctc_out
5305 5306


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def warpctc(input, label, blank=0, norm_by_times=False, use_cudnn=False):
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    """
5309 5310
    An operator integrating the open source Warp-CTC library
    (https://github.com/baidu-research/warp-ctc)
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    to compute Connectionist Temporal Classification (CTC) loss.
5312 5313
    It can be aliased as softmax with CTC, since a native softmax activation is
    interated to the Warp-CTC library, to to normlize values for each row of the
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    input tensor.

    Args:
5317
       input (Variable): The unscaled probabilities of variable-length sequences,
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         which is a 2-D Tensor with LoD information.
         It's shape is [Lp, num_classes + 1], where Lp is the sum of all input
         sequences' length and num_classes is the true number of classes.
         (not including the blank label).
5322
       label (Variable): The ground truth of variable-length sequence,
5323 5324 5325
         which is a 2-D Tensor with LoD information. It is of the shape [Lg, 1],
         where Lg is th sum of all labels' length.
       blank (int, default 0): The blank label index of Connectionist
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         Temporal Classification (CTC) loss, which is in the
         half-opened interval [0, num_classes + 1).
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       norm_by_times(bool, default false): Whether to normalize the gradients
         by the number of time-step, which is also the sequence's length.
         There is no need to normalize the gradients if warpctc layer was
5331
         follewed by a mean_op.
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       use_cudnn (bool, default false): Whether to use cudnn.
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    Returns:
5335 5336
        Variable: The Connectionist Temporal Classification (CTC) loss,
        which is a 2-D Tensor of the shape [batch_size, 1].
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    Examples:
5339

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        .. code-block:: python
5341

5342 5343 5344
            label = fluid.layers.data(shape=[11, 8], dtype='float32', lod_level=1)
            predict = fluid.layers.data(shape=[11, 1], dtype='float32')
            cost = fluid.layers.warpctc(input=predict, label=label)
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    """
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    helper = LayerHelper('warpctc', **locals())
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    loss_out = helper.create_variable_for_type_inference(dtype=input.dtype)
    grad_out = helper.create_variable_for_type_inference(dtype=input.dtype)
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    helper.append_op(
        type='warpctc',
        inputs={'Logits': [input],
                'Label': [label]},
        outputs={'WarpCTCGrad': [grad_out],
                 'Loss': [loss_out]},
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        attrs={
            'blank': blank,
            'norm_by_times': norm_by_times,
            'use_cudnn': use_cudnn
        })
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    return loss_out
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def sequence_reshape(input, new_dim):
    """
    **Sequence Reshape Layer**

    This layer will rearrange the input sequences. The new dimension is set by
    user. Length of each sequence is computed according to original length,
    original dimension and new dimension. The following example will help to
    illustrate the function of this layer:

    .. code-block:: text

        x is a LoDTensor:
            x.lod  = [[0, 2, 6]]
5377 5378 5379
            x.data = [[1,  2], [3,  4],
                      [5,  6], [7,  8],
                      [9, 10], [11, 12]]
5380 5381 5382 5383 5384
            x.dims = [6, 2]

        set new_dim = 4

        then out is a LoDTensor:
5385

5386
            out.lod  = [[0, 1, 3]]
5387 5388 5389 5390

            out.data = [[1,  2,  3,  4],
                        [5,  6,  7,  8],
                        [9, 10, 11, 12]]
5391 5392 5393 5394 5395 5396 5397
            out.dims = [3, 4]

    Currently, only 1-level LoDTensor is supported and please make sure
    (original length * original dimension) can be divided by new dimension with
    no remainder for each sequence.

    Args:
5398 5399 5400

       input (Variable): A 2-D LoDTensor with shape being [N, M] where M for dimension.
       new_dim (int): New dimension that the input LoDTensor is reshaped to.
5401 5402

    Returns:
5403

5404 5405 5406 5407 5408
        Variable: Reshaped LoDTensor according to new dimension.

    Examples:
        .. code-block:: python

5409
            x = fluid.layers.data(shape=[5, 20], dtype='float32', lod_level=1)
5410
            x_reshaped = fluid.layers.sequence_reshape(input=x, new_dim=10)
5411
    """
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    assert not in_dygraph_mode(), (
5413
        "sequence layer is not supported in dygraph mode yet.")
5414
    helper = LayerHelper('sequence_reshape', **locals())
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    out = helper.create_variable_for_type_inference(helper.input_dtype())
5416 5417 5418 5419 5420 5421
    helper.append_op(
        type='sequence_reshape',
        inputs={'X': [input]},
        outputs={'Out': [out]},
        attrs={'new_dim': new_dim})
    return out
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5424 5425 5426 5427
# FIXME(wuyi): let docstring_checker.py understand @autodoc.
# For now, the comments in c++ use types like Tensor, but in python side
# the type is often "Variable", and arguments may vary.
@templatedoc(op_type="nce")
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def nce(input,
        label,
        num_total_classes,
        sample_weight=None,
        param_attr=None,
        bias_attr=None,
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        num_neg_samples=None,
5435 5436 5437
        name=None,
        sampler="uniform",
        custom_dist=None,
5438 5439
        seed=0,
        is_sparse=False):
5440 5441 5442 5443 5444 5445 5446
    """
    ${comment}

    Args:
        input (Variable): input variable.
        label (Variable): label.
        num_total_classes (int):${num_total_classes_comment}
5447 5448
        sample_weight (Variable|None): A Variable of shape [batch_size, 1]
            storing a weight for each sample. The default weight for each
5449
            sample is 1.0.
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        param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
             of nce. If it is set to None or one attribute of ParamAttr, nce
             will create ParamAttr as param_attr. If the Initializer of the param_attr
             is not set, the parameter is initialized with Xavier. Default: None.
        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of nce.
             If it is set to False, no bias will be added to the output units.
             If it is set to None or one attribute of ParamAttr, nce
             will create ParamAttr as bias_attr. If the Initializer of the bias_attr
             is not set, the bias is initialized zero. Default: None.
5459
        num_neg_samples (int): ${num_neg_samples_comment}
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        name (str|None): A name for this layer(optional). If set None, the layer
             will be named automatically. Default: None.
5462 5463 5464
        sampler (str): The sampler used to sample class from negtive classes.
                       It can be 'uniform', 'log_uniform' or 'custom_dist'.
                       default: 'uniform'.
5465
        custom_dist (float[]): A float[] with size=num_total_classes.
5466 5467 5468 5469
                       It is used when sampler is set to 'custom_dist'.
                       custom_dist[i] is the probsbility of i-th class to be sampled.
                       default: None.
        seed (int): The seed used in sampler. default: 0.
5470
        is_sparse(bool): The flag indicating whether to use sparse update, the weight@GRAD and bias@GRAD will be changed to SelectedRows.
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5472
    Returns:
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        Variable: The output nce loss.

    Examples:
        .. code-block:: python

            window_size = 5
            words = []
            for i in xrange(window_size):
                words.append(layers.data(
                    name='word_{0}'.format(i), shape=[1], dtype='int64'))

            dict_size = 10000
            label_word = int(window_size / 2) + 1

            embs = []
            for i in xrange(window_size):
                if i == label_word:
                    continue

                emb = layers.embedding(input=words[i], size=[dict_size, 32],
                                       param_attr='emb.w', is_sparse=True)
                embs.append(emb)

            embs = layers.concat(input=embs, axis=1)
            loss = layers.nce(input=embs, label=words[label_word],
                          num_total_classes=dict_size, param_attr='nce.w',
                          bias_attr='nce.b')
5500 5501 5502 5503 5504 5505 5506 5507 5508

            #or use custom distribution
            dist = fluid.layers.assign(input=np.array([0.05,0.5,0.1,0.3,0.05]).astype("float32"))
            loss = layers.nce(input=embs, label=words[label_word],
                          num_total_classes=5, param_attr='nce.w',
                          bias_attr='nce.b',
                          num_neg_samples=3,
                          sampler="custom_dist",
                          custom_dist=dist)
5509

5510
    """
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    helper = LayerHelper('nce', **locals())
    assert isinstance(input, Variable)
    assert isinstance(label, Variable)
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    dim = input.shape[1]
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    num_true_class = label.shape[1]
    w = helper.create_parameter(
        attr=helper.param_attr,
        shape=[num_total_classes, dim],
        is_bias=False,
        dtype=input.dtype)
5522
    inputs = {}
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    if helper.bias_attr:
        b = helper.create_parameter(
            attr=helper.bias_attr,
            shape=[num_total_classes, 1],
            is_bias=True,
            dtype=input.dtype)
        inputs['Bias'] = b
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    cost = helper.create_variable_for_type_inference(dtype=input.dtype)
    sample_logits = helper.create_variable_for_type_inference(dtype=input.dtype)
    sample_labels = helper.create_variable_for_type_inference(dtype=label.dtype)
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5534 5535 5536 5537
    inputs['Input'] = input
    inputs['Label'] = label
    inputs['Weight'] = w
    inputs['SampleWeight'] = sample_weight if sample_weight is not None else []
5538 5539 5540 5541 5542 5543 5544

    if sampler == "uniform":
        sampler = 0
    elif sampler == "log_uniform":
        sampler = 1
    elif sampler == "custom_dist":
        assert custom_dist is not None
5545 5546 5547 5548 5549 5550 5551 5552 5553
        # assert isinstance(custom_dist, Variable)

        custom_dist_len = len(custom_dist)
        alias_probs_ = [0] * custom_dist_len
        alias_ = [0] * custom_dist_len
        bigs = []
        littles = []
        for i in range(custom_dist_len):
            normal_prob = custom_dist[i] * custom_dist_len
5554
            if normal_prob - 1.0 > 0:
5555
                bigs.append((i, normal_prob))
5556
            elif 1.0 - normal_prob > 0:
5557 5558 5559 5560 5561 5562 5563 5564 5565 5566 5567 5568 5569 5570 5571
                littles.append((i, normal_prob))
            else:
                alias_probs_[i] = normal_prob
                alias_[i] = -1

        while len(bigs) and len(littles):
            big = bigs.pop(0)
            little = littles.pop(0)

            big_idx = big[0]
            big_prob = big[1]

            alias_probs_[little[0]] = little[1]
            alias_[little[0]] = big_idx
            big_left = big[1] + little[1] - 1
5572
            if big_left - 1.0 > 0:
5573
                bigs.append((big_idx, big_left))
5574
            elif 1.0 - big_left > 0:
5575 5576 5577 5578 5579 5580 5581 5582 5583 5584 5585 5586 5587 5588
                littles.append((big_idx, big_left))
            else:
                alias_probs_[big_idx] = big_left
                alias_[big_idx] = -1

        if len(bigs):
            big = bigs.pop(0)
            alias_probs_[big[0]] = 1.0
            alias_[big[0]] = -1
        if len(littles):
            little = littles.pop(0)
            alias_probs_[little[0]] = 1.0
            alias_[little[0]] = -1

5589 5590 5591 5592 5593 5594 5595 5596 5597 5598 5599 5600 5601 5602 5603
        def _init_by_numpy_array(numpy_array):
            ret = helper.create_parameter(
                attr=ParamAttr(),
                shape=numpy_array.shape,
                dtype=numpy_array.dtype,
                default_initializer=NumpyArrayInitializer(numpy_array))
            ret.stop_gradient = True
            return ret

        inputs['CustomDistProbs'] = _init_by_numpy_array(
            np.array(custom_dist).astype('float32'))
        inputs['CustomDistAlias'] = _init_by_numpy_array(
            np.array(alias_).astype('int32'))
        inputs['CustomDistAliasProbs'] = _init_by_numpy_array(
            np.array(alias_probs_).astype('float32'))
5604 5605 5606 5607
        sampler = 2
    else:
        raise Exception("Unsupported sampler type.")

5608 5609 5610 5611 5612
    if num_neg_samples is None:
        num_neg_samples = 10
    else:
        num_neg_samples = int(num_neg_samples)

5613 5614 5615 5616
    remote_prefetch = is_sparse
    print(
        "With sparse mode, if your models has only small parameter prefetch may cause speed down"
    )
5617

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5618 5619
    attrs = {
        'num_total_classes': int(num_total_classes),
5620 5621
        'num_neg_samples': num_neg_samples,
        'seed': seed,
5622
        'sampler': sampler,
5623 5624
        'is_sparse': is_sparse,
        'remote_prefetch': remote_prefetch
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    }
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5626 5627 5628

    helper.append_op(
        type='nce',
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        inputs=inputs,
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        outputs={
            'Cost': cost,
            'SampleLogits': sample_logits,
            'SampleLabels': sample_labels
        },
        attrs=attrs)
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    return cost / (num_neg_samples + 1)
5637 5638


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def hsigmoid(input,
             label,
5641
             num_classes,
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5642 5643
             param_attr=None,
             bias_attr=None,
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             name=None,
5645 5646 5647
             path_table=None,
             path_code=None,
             is_custom=False,
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             is_sparse=False):
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    """
    The hierarchical sigmoid operator is used to accelerate the training
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    process of language model. This operator organizes the classes into a
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    complete binary tree, or you can use is_custom to pass your own tree to
5653
    implement hierarchical. Each leaf node represents a class(a word) and each
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    internal node acts as a binary classifier. For each word there's a unique
    path from root to it's leaf node, hsigmoid calculate the cost for each
    internal node on the path, and sum them to get a total cost. hsigmoid can
    achive a acceleration from :math:`O(N)` to :math:`O(logN)`, where :math:`N`
    represents the size of word dict.

5660
    Using default tree you can Refer to `Hierarchical Probabilistic Neural Network Language Model
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    <http://www.iro.umontreal.ca/~lisa/pointeurs/hierarchical-nnlm-aistats05.pdf>`_
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5663 5664
    And if you want to use the costumed tree by set 'is_custom' as true you may need to do following things first:

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    1. using your word dict to build a binary tree, each leaf node should be an word of your word dict
    2. build a dict to store word_id -> word's leaf to root path, we call it path_table.
    3. build a dict to store word_id -> code of word's leaf to root path, we call it path_code. Code
       means label of each binary classification, using 1 indicate true, 0 indicate false.
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    4. now, each word should has its path and code along the path, you can pass a batch of path and code
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       related to the same batch of inputs.
5671

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    Args:
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        input (Variable): The input tensor variable with shape
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            :math:`[N \\times D]`, where :math:`N` is the size of mini-batch,
            and :math:`D` is the feature size.
        label (Variable): The tensor variable contains labels of training data.
            It's a tensor with shape is :math:`[N \\times 1]`.
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        num_classes: (int), The number of classes, must not be less than 2. with default tree this has to be set,
            it should never be None under is_custom=False, but while is_custom is true, it should be non leaf num
5680
            which indicates the num of classes using by binary classify.
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        param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
             of hsigmoid. If it is set to None or one attribute of ParamAttr, hsigmoid
             will create ParamAttr as param_attr. If the Initializer of the param_attr
             is not set, the parameter is initialized with Xavier. Default: None.
        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of hsigmoid.
             If it is set to False, no bias will be added to the output units.
             If it is set to None or one attribute of ParamAttr, hsigmoid
             will create ParamAttr as bias_attr. If the Initializer of the bias_attr
             is not set, the bias is initialized zero. Default: None.
        name (str|None): A name for this layer(optional). If set None, the layer
             will be named automatically. Default: None.
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        path_table: (Variable|None) this variable can store each batch of samples' path to root,
5693
            it should be in leaf -> root order
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            path_table should have the same shape with path_code, and for each sample i path_table[i] indicates a np.array like
            structure and each element in this array is indexes in parent nodes' Weight Matrix.
        path_code:  (Variable|None) this variable can store each batch of samples' code,
5697
            each code consist with every code of parent nodes. it should be in leaf -> root order
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        is_custom: (bool|False)using user defined binary tree instead of default complete binary tree, if costum is
5699
             set you need to set path_table/path_code/num_classes, otherwise num_classes should be set
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        is_sparse: (bool|False)using sparse update instead of dense update, if set, the gradient
5701
             of W and input will be sparse.
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    Returns:
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        Out: (LodTensor) The cost of hierarchical sigmoid operator. the shape is [N, 1]
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    Examples:

        .. code-block:: python

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            x = fluid.layers.data(name='x', shape=[2], dtype='float32')
            y = fluid.layers.data(name='y', shape=[1], dtype='int64')
            out = fluid.layers.hsigmoid(input=x, label=y, num_classes=6)
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    """

    helper = LayerHelper('hierarchical_sigmoid', **locals())
    dtype = helper.input_dtype()
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    out = helper.create_variable_for_type_inference(dtype)
    pre_out = helper.create_variable_for_type_inference(dtype)
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    dim = input.shape[1]
5720
    if ((num_classes is None) or (num_classes < 2)) and (not is_custom):
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        raise ValueError(
            "num_classes must not be less than 2 with default tree")

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    if (not is_custom) and (is_sparse):
        print("Sparse mode should not be used without custom tree")
        is_sparse = False

    if (not is_custom) and ((path_table is not None) or
                            (path_code is not None)):
        raise ValueError(
            "only num_classes should be passed without custom tree")

5733
    if (is_custom) and (path_code is None):
5734
        raise ValueError("path_code should not be None with custom tree")
5735
    elif (is_custom) and (path_table is None):
5736
        raise ValueError("path_table should not be None with custom tree")
5737
    elif (is_custom) and (num_classes is None):
5738
        raise ValueError("num_classes should not be None with custom tree")
5739 5740 5741
    else:
        pass

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    weights = None
5743 5744 5745 5746
    remote_prefetch = is_sparse
    print(
        "With sparse mode, if your models has only small parameter prefetch may cause speed down"
    )
5747
    if not is_custom:
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        weights = helper.create_parameter(
            attr=helper.param_attr,
            shape=[num_classes - 1, dim],
            is_bias=False,
            dtype=input.dtype)
    else:
        weights = helper.create_parameter(
            attr=helper.param_attr,
5756
            shape=[num_classes, dim],
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            is_bias=False,
            dtype=input.dtype)
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    inputs = {
        "X": input,
        "W": weights,
5762
        "PathTable": path_table,
5763
        "PathCode": path_code,
5764 5765
        "Label": label
    }
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    if helper.bias_attr:
5767
        if not is_custom:
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            bias = helper.create_parameter(
                attr=helper.bias_attr,
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                shape=[num_classes - 1, 1],
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                is_bias=True,
                dtype=input.dtype)
            inputs['Bias'] = bias
        else:
            bias = helper.create_parameter(
                attr=helper.bias_attr,
5777
                shape=[num_classes, 1],
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                is_bias=True,
                dtype=input.dtype)
            inputs['Bias'] = bias
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    helper.append_op(
        type="hierarchical_sigmoid",
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        inputs=inputs,
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        outputs={"Out": out,
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                 "PreOut": pre_out,
                 "W_Out": weights},
        attrs={
            "num_classes": num_classes,
            "is_sparse": is_sparse,
            "remote_prefetch": remote_prefetch
        })
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    return out


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def transpose(x, perm, name=None):
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    """
    Permute the dimensions of `input` according to `perm`.

    The `i`-th dimension  of the returned tensor will correspond to the
    perm[i]-th dimension of `input`.

    Args:
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        x (Variable): The input Tensor.
        perm (list): A permutation of the dimensions of `input`.
        name (str): The name of this layer. It is optional.
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    Returns:
        Variable: A transposed Tensor.

    Examples:
        .. code-block:: python

5813
            # use append_batch_size=False to avoid prepending extra
5814
            # batch size in shape
5815
            x = fluid.layers.data(name='x', shape=[5, 10, 15],
5816
                            dtype='float32', append_batch_size=False)
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            x_transposed = layers.transpose(x, perm=[1, 0, 2])
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    """

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    if len(perm) != len(x.shape):
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        raise ValueError(
            "Input(perm) is the permutation of dimensions of Input(input). "
            "It's length shoud be equal to Input(input)'s rank.")
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    for idx, dim in enumerate(perm):
        if dim >= len(x.shape):
            raise ValueError(
                "Each element in perm should be less than x's rank. "
                "%d-th element in perm is %d which accesses x's rank %d." %
                (idx, perm[idx], len(x.shape)))
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    helper = LayerHelper('transpose', **locals())
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    out = helper.create_variable_for_type_inference(x.dtype)
    x_shape = helper.create_variable_for_type_inference(x.dtype)
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    helper.append_op(
5835
        type='transpose2',
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        inputs={'X': [x]},
5837 5838
        outputs={'Out': [out],
                 'XShape': [x_shape]},
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        attrs={'axis': perm})
    return out
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5843 5844 5845 5846 5847 5848 5849
def im2sequence(input,
                filter_size=1,
                stride=1,
                padding=0,
                input_image_size=None,
                out_stride=1,
                name=None):
5850
    """
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    Extracts image patches from the input tensor to form a tensor of shape
    {input.batch_size * output_height * output_width, filter_size_H *
    filter_size_W * input.channels} which is similar with im2col.
    This op use filter / kernel to scan images and convert these images to
    sequences. After expanding, the number of time step are
    output_height * output_width for an image, in which output_height and
    output_width are calculated by below equation:
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    .. math::

        output\_size = 1 + \
            (2 * padding + img\_size - block\_size + stride - 1) / stride

    And the dimension of each time step is block_y * block_x * input.channels.

    Args:
        input (Variable): The input should be a tensor in NCHW format.
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        filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
            it must contain two integers, (filter_size_H, filter_size_W).
            Otherwise, the filter will be a square.

        stride(int|tuple): The stride size. If stride is a tuple, it must
            contain two integers, (stride_H, stride_W). Otherwise, the
            stride_H = stride_W = stride. Default: stride = 1.

        padding(int|tuple): The padding size. If padding is a tuple, it can
            contain two integers like (padding_H, padding_W) which means
            padding_up = padding_down = padding_H and
            padding_left = padding_right = padding_W. Or it can use
            (padding_up, padding_left, padding_down, padding_right) to indicate
            paddings of four direction. Otherwise, a scalar padding means
            padding_up = padding_down = padding_left = padding_right = padding
            Default: padding = 0.

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        input_image_size(Variable): the input contains image real size.It's dim
            is [batchsize, 2]. It is dispensable.It is just for batch inference.

        out_stride(int|tuple): The scaling of image through CNN. It is
            dispensable. It is valid only when input_image_size is not null.
            If out_stride is tuple,  it must contain two intergers,
            (out_stride_H, out_stride_W). Otherwise,
            the out_stride_H = out_stride_W = out_stride.

5895 5896 5897
        name (int): The name of this layer. It is optional.

    Returns:
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        output: The output is a LoDTensor with shape
        {input.batch_size * output_height * output_width,
        filter_size_H * filter_size_W * input.channels}.
        If we regard output as a matrix, each row of this matrix is
        a step of a sequence.
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    Examples:

        .. code-block:: text

            Given:

            x = [[[[ 6.  2.  1.]
                   [ 8.  3.  5.]
                   [ 0.  2.  6.]]

                  [[ 2.  4.  4.]
                   [ 6.  3.  0.]
                   [ 6.  4.  7.]]]

                 [[[ 6.  7.  1.]
                   [ 5.  7.  9.]
                   [ 2.  4.  8.]]

                  [[ 1.  2.  1.]
                   [ 1.  3.  5.]
                   [ 9.  0.  8.]]]]

            x.dims = {2, 2, 3, 3}

            And:

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            filter = [2, 2]
            stride = [1, 1]
            padding = [0, 0]
5933 5934 5935 5936 5937 5938 5939 5940 5941 5942 5943 5944

            Then:

            output.data = [[ 6.  2.  8.  3.  2.  4.  6.  3.]
                           [ 2.  1.  3.  5.  4.  4.  3.  0.]
                           [ 8.  3.  0.  2.  6.  3.  6.  4.]
                           [ 3.  5.  2.  6.  3.  0.  4.  7.]
                           [ 6.  7.  5.  7.  1.  2.  1.  3.]
                           [ 7.  1.  7.  9.  2.  1.  3.  5.]
                           [ 5.  7.  2.  4.  1.  3.  9.  0.]
                           [ 7.  9.  4.  8.  3.  5.  0.  8.]]

5945
            output.dims = {8, 8}
5946

5947
            output.lod = [[4, 4]]
5948

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    Examples:
5950 5951 5952

        .. code-block:: python

5953 5954
            output = fluid.layers.im2sequence(
                input=layer, stride=[1, 1], filter_size=[2, 2])
5955 5956

    """
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    assert not in_dygraph_mode(), (
5958
        "sequence layer is not supported in dygraph mode yet.")
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    if isinstance(filter_size, int):
        filter_size = [filter_size, filter_size]
    if isinstance(stride, int):
        stride = [stride, stride]
    if isinstance(padding, int):
        padding = [padding, padding]
    if len(padding) == 2:
        padding.append(padding[0])
        padding.append(padding[1])
5969 5970 5971 5972 5973 5974 5975
    inputs = {"X": input}
    attrs = {"kernels": filter_size, "strides": stride, "padding": padding}
    if input_image_size:
        if isinstance(out_stride, int):
            out_stride = [out_stride, out_stride]
        inputs["Y"] = input_image_size
        attrs["out_stride"] = out_stride
5976
    helper = LayerHelper('im2sequence', **locals())
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    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
5978
    helper.append_op(
5979
        type='im2sequence', inputs=inputs, outputs={'Out': out}, attrs=attrs)
5980
    return out
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@templatedoc()
5984
def row_conv(input, future_context_size, param_attr=None, act=None):
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    """
    ${comment}
5987 5988

    Args:
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        input (${x_type}): ${x_comment}.
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        future_context_size (int): Future context size. Please note, the shape
            of convolution kernel is [future_context_size + 1, D].
5992 5993 5994 5995 5996
        param_attr (ParamAttr): Attributes of parameters, including
            name, initializer etc.
        act (str): Non-linear activation to be applied to output variable.

    Returns:
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        ${out_comment}.
5998 5999

    Examples:
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        >>> import paddle.fluid as fluid
        >>> x = fluid.layers.data(name='x', shape=[16],
        >>>                        dtype='float32', lod_level=1)
        >>> out = fluid.layers.row_conv(input=x, future_context_size=2)
6004 6005 6006 6007 6008 6009
    """
    helper = LayerHelper('row_conv', **locals())
    dtype = helper.input_dtype()
    filter_shape = [future_context_size + 1, input.shape[1]]
    filter_param = helper.create_parameter(
        attr=helper.param_attr, shape=filter_shape, dtype=dtype)
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    out = helper.create_variable_for_type_inference(dtype)
6011 6012 6013 6014 6015
    helper.append_op(
        type='row_conv',
        inputs={'X': [input],
                'Filter': [filter_param]},
        outputs={'Out': [out]})
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    return helper.append_activation(out)
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@templatedoc()
6020 6021
def multiplex(inputs, index):
    """
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    ${comment}

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    For Example:

    .. code-block:: text

        case 1:

        Given:

        X = [[[0,0,3,4], [0,1,3,4], [0,2,4,4], [0,3,3,4]],
             [[1,0,3,4], [1,1,7,8], [1,2,4,2], [1,3,3,4]],
             [[2,0,3,4], [2,1,7,8], [2,2,4,2], [2,3,3,4]],
             [[3,0,3,4], [3,1,7,8], [3,2,4,2], [3,3,3,4]]]

        index = [3,0,1,2]

        out:[[3 0 3 4]    // X[3,0] (3 = index[i], 0 = i); i=0
             [0 1 3 4]    // X[0,1] (0 = index[i], 1 = i); i=1
             [1 2 4 2]    // X[1,2] (0 = index[i], 2 = i); i=2
             [2 3 3 4]]   // X[2,3] (0 = index[i], 3 = i); i=3

        case 2:

        Given:

        X = [[[0,0,3,4], [0,1,3,4], [0,2,4,4], [0,3,3,4]],
             [[1,0,3,4], [1,1,7,8], [1,2,4,2], [1,3,3,4]]]

        index = [1,0]

        out:[[1 0 3 4]    // X[1,0] (3 = index[0], 0 = i); i=1
             [0 1 3 4]    // X[0,1] (0 = index[1], 1 = i); i=2
             [0 2 4 4]    // X[0,2] (0 = 0, 2 = i); i=3
             [0 3 3 4]]   // X[0,3] (0 = 0, 3 = i); i=4

    Examples:

    .. code-block:: python

        import paddle.fluid as fluid
        x1 = fluid.layers.data(name='x1', shape=[4], dtype='float32')
        x2 = fluid.layers.data(name='x2', shape=[4], dtype='float32')
        index = fluid.layers.data(name='index', shape=[1], dtype='int32')
        out = fluid.layers.multiplex(inputs=[x1, x2], index=index)
6067 6068

    Args:
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       inputs (list): ${x_comment}.
       index (${ids_type}): ${ids_comment}.
6071 6072

    Returns:
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        ${out_comment}.
6074 6075
    """
    helper = LayerHelper('multiplex', **locals())
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    if not isinstance(inputs, list) and len(inputs) < 2:
        raise ValueError("inputs should be a list object and contains at least "
                         "2 elements.")

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    out = helper.create_variable_for_type_inference(inputs[0].dtype)
6082 6083 6084 6085 6086 6087
    helper.append_op(
        type='multiplex',
        inputs={'X': inputs,
                'Ids': index},
        outputs={'Out': [out]})
    return out
6088 6089


6090 6091 6092
def softmax_with_cross_entropy(logits,
                               label,
                               soft_label=False,
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                               ignore_index=kIgnoreIndex,
6094
                               numeric_stable_mode=True,
6095
                               return_softmax=False):
6096 6097
    """
    **Softmax With Cross Entropy Operator.**
6098

6099 6100 6101 6102
    Cross entropy loss with softmax is used as the output layer extensively. This
    operator computes the softmax normalized values for each row of the input
    tensor, after which cross-entropy loss is computed. This provides a more
    numerically stable gradient.
6103

6104 6105 6106
    Because this operator performs a softmax on logits internally, it expects
    unscaled logits. This operator should not be used with the output of
    softmax operator since that would produce incorrect results.
6107

6108 6109 6110
    When the attribute soft_label is set false, this operators expects mutually
    exclusive hard labels, each sample in a batch is in exactly one class with a
    probability of 1.0. Each sample in the batch will have a single label.
6111

6112
    The equation is as follows:
6113

6114
    1) Hard label (one-hot label, so every sample has exactly one class)
6115

6116 6117 6118 6119
    .. math::

        loss_j =  -\\text{logit}_{label_j} +
        \\log\\left(\\sum_{i=0}^{K}\\exp(\\text{logit}_i)\\right), j = 1,..., K
6120

6121 6122 6123
    2) Soft label (each sample can have a distribution over all classes)

    .. math::
6124

6125 6126 6127 6128
        loss_j =  -\\sum_{i=0}^{K}\\text{label}_i
        \\left(\\text{logit}_i - \\log\\left(\\sum_{i=0}^{K}
        \\exp(\\text{logit}_i)\\right)\\right), j = 1,...,K

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    3) If numeric_stable_mode is True, softmax is calculated first by:

    .. math::
6132

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        max_j &= \\max_{i=0}^{K}{\\text{logit}_i}
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        log\\_max\\_sum_j &= \\log\\sum_{i=0}^{K}\\exp(logit_i - max_j)
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        softmax_j &= \\exp(logit_j - max_j - {log\\_max\\_sum}_j)
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    and then cross entropy loss is calculated by softmax and label.

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    Args:
        logits (Variable): The unscaled log probabilities, which is a 2-D tensor
            with shape [N x K]. N is the batch_size, and K is the class number.
        label (Variable): The ground truth which is a 2-D tensor. If soft_label
            is set to false, Label is a Tensor<int64> with shape [N x 1]. If
            soft_label is set to true, Label is a Tensor<float/double> with
        soft_label (bool): A flag to indicate whether to interpretate the given
            labels as soft labels. By default, `soft_label` is set to False.
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        ignore_index (int): Specifies a target value that is ignored and does
                            not contribute to the input gradient. Only valid
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                            if soft_label is set to False. Default: kIgnoreIndex
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        numeric_stable_mode (bool): A flag to indicate whether to use a more
                                    numerically stable algorithm. Only valid
                                    when soft_label is False and GPU is used.
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                                    When soft_label is True or CPU is used,
                                    the algorithm is always numerically stable.
                                    Note that the speed may be slower when use
6158
                                    stable algorithm. Default: True
6159
        return_softmax (bool): A flag indicating whether to return the softmax
6160
                               along with the cross entropy loss. Default: False
6161

6162
    Returns:
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        Variable or Tuple of two Variables: Return the cross entropy loss if \
                                            `return_softmax` is False, otherwise the tuple \
                                            (loss, softmax), where the cross entropy loss is \
                                            a 2-D tensor with shape [N x 1], and softmax is a \
                                            2-D tensor with shape [N x K].
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    Examples:
        .. code-block:: python

            data = fluid.layers.data(name='data', shape=[128], dtype='float32')
            label = fluid.layers.data(name='label', shape=[1], dtype='int64')
            fc = fluid.layers.fc(input=data, size=100)
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            out = fluid.layers.softmax_with_cross_entropy(
                logits=fc, label=label)
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    """
    helper = LayerHelper('softmax_with_cross_entropy', **locals())
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    softmax = helper.create_variable_for_type_inference(dtype=logits.dtype)
    loss = helper.create_variable_for_type_inference(dtype=logits.dtype)
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    helper.append_op(
        type='softmax_with_cross_entropy',
        inputs={'Logits': logits,
                'Label': label},
        outputs={'Softmax': softmax,
                 'Loss': loss},
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        attrs={
            'soft_label': soft_label,
            'ignore_index': ignore_index,
            'numeric_stable_mode': numeric_stable_mode
        })
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    if return_softmax:
        return loss, softmax

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    return loss


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def sampled_softmax_with_cross_entropy(logits,
                                       label,
                                       num_samples,
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                                       num_true=1,
6203
                                       remove_accidental_hits=True,
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                                       use_customized_samples=False,
                                       customized_samples=None,
                                       customized_probabilities=None,
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                                       seed=0):
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    """
    **Sampled Softmax With Cross Entropy Operator.**

    Cross entropy loss with sampled softmax is used as the output layer for 
    larger output classes extensively. This operator samples a number of samples
6213
    for all examples, and computes the softmax normalized values for each 
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    row of the sampled tensor, after which cross-entropy loss is computed. 

    Because this operator performs a softmax on logits internally, it expects
    unscaled logits. This operator should not be used with the output of
    softmax operator since that would produce incorrect results.
    
    For examples with T true labels (T >= 1), we assume that each true label has
    a probability of 1/T. For each sample, S samples are generated using a
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    log uniform distribution. True labels are concatenated with these samples to
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    form T + S samples for each example. So, assume the shape of logits is
    [N x K], the shape for samples is [N x (T+S)]. For each sampled label, a 
    probability is calculated, which corresponds to the Q(y|x) in 
    [Jean et al., 2014](http://arxiv.org/abs/1412.2007).
    
    Logits are sampled according to the sampled labels. Then if 
    remove_accidental_hits is True, if a sample[i, j] accidentally hits true 
    labels, then the corresponding sampled_logits[i, j] is minus by 1e20 to 
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    make its softmax result close to zero. Then sampled logits are subtracted by
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    logQ(y|x), these sampled logits and re-indexed labels are used to compute 
    a softmax with cross entropy.

    Args:
        logits (Variable): The unscaled log probabilities, which is a 2-D tensor
            with shape [N x K]. N is the batch_size, and K is the class number.
        label (Variable): The ground truth which is a 2-D tensor. Label is a 
            Tensor<int64> with shape [N x T], where T is the number of true 
            labels per example. 
        num_samples (int): The number for each example, num_samples should be 
            less than the number of class.
6243
        num_true(int): The number of target classes per training example.
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        remove_accidental_hits (bool): A flag indicating whether to remove 
            accidental hits when sampling. If True and if a sample[i, j] 
            accidentally hits true labels, then the corresponding 
            sampled_logits[i, j] is minus by 1e20 to make its softmax result 
            close to zero. Default is True.
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        use_customized_samples (bool): Whether to use custom samples and probabities to sample
6250
            logits.
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        customized_samples (Variable): User defined samples, which is a 2-D tensor
            with shape [N, T + S]. S is the num_samples, and T is the number of true 
            labels per example. 
        customized_probabilities (Variable): User defined probabilities of samples, 
            a 2-D tensor which has the same shape with customized_samples.
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        seed (int): The random seed for generating random number, which is used
            in the process of sampling. Default is 0.

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    Returns:
        Variable: Return the cross entropy loss which is a 2-D tensor with shape
                  [N x 1].

    Examples:
        .. code-block:: python

            logits = fluid.layers.data(name='data', shape=[256], dtype='float32')
            label = fluid.layers.data(name='label', shape=[5], dtype='int64')
            fc = fluid.layers.fc(input=data, size=100)
            out = fluid.layers.sampled_softmax_with_cross_entropy(
                logits=fc, label=label, num_samples=25)
    """
    helper = LayerHelper('sample_logits', **locals())
    samples = helper.create_variable_for_type_inference(dtype='int64')
    probabilities = helper.create_variable_for_type_inference(
        dtype=logits.dtype)
    sampled_logits \
        = helper.create_variable_for_type_inference(dtype=logits.dtype)
    sampled_label = helper.create_variable_for_type_inference(dtype='int64')
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    sampled_softlabel = helper.create_variable_for_type_inference(
        dtype=logits.dtype)
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    logits_dim = helper.create_variable_for_type_inference(dtype=logits.dtype)
    labels_dim = helper.create_variable_for_type_inference(dtype=label.type)
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    helper.append_op(
        type='sample_logits',
        inputs={
            'Logits': logits,
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            'Labels': label,
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            'CustomizedSamples': customized_samples,
            'CustomizedProbabilities': customized_probabilities
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        },
        outputs={
            'Samples': samples,
            'Probabilities': probabilities,
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            'SampledLabels': sampled_label,
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            'SampledLogits': sampled_logits,
            'LogitsDim': logits_dim,
            'LabelsDim': labels_dim
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        },
        attrs={
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            'use_customized_samples': use_customized_samples,
6302
            'uniq': True,
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            'remove_accidental_hits': remove_accidental_hits,
            'num_samples': num_samples,
            'seed': seed
        })
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    loss = helper.create_variable_for_type_inference(dtype=logits.dtype)
    softmax = helper.create_variable_for_type_inference(dtype=logits.dtype)
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    helper.append_op(
        type='one_hot',
        inputs={'X': sampled_label},
        attrs={'depth': num_samples + 1},
        outputs={'Out': sampled_softlabel})

6315 6316
    helper.append_op(
        type='softmax_with_cross_entropy',
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        inputs={'Logits': sampled_logits,
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                'Label': sampled_softlabel},
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        outputs={'Softmax': softmax,
                 'Loss': loss},
        attrs={
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            'soft_label': True,
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            'ignore_index': False,
            'numeric_stable_mode': False
        })
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    return loss / num_true
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6329 6330
def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
    """
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    This layer computes the smooth L1 loss for Variable :attr:`x` and :attr:`y`.
    It takes the first dimension of :attr:`x` and :attr:`y` as batch size.
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    For each instance, it computes the smooth L1 loss element by element first
6334
    and then sums all the losses. So the shape of ouput Variable is
6335
    [batch_size, 1].
6336

6337 6338
    Args:
        x (Variable): A tensor with rank at least 2. The input value of smooth
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            L1 loss op with shape [batch_size, dim1, ..., dimN].
6340
        y (Variable): A tensor with rank at least 2. The target value of smooth
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            L1 loss op with same shape as :attr:`x`.
6342
        inside_weight (Variable|None):  A tensor with rank at least 2. This
6343 6344
            input is optional and should have same shape with :attr:`x`. If
            provided, the result of (:attr:`x` - :attr:`y`) will be multiplied
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            by this tensor element by element.
6346
        outside_weight (Variable|None): A tensor with rank at least 2. This
6347 6348
            input is optional and should have same shape with :attr:`x`. If
            provided, the out smooth L1 loss will be multiplied by this tensor
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            element by element.
6350
        sigma (float|None): Hyper parameter of smooth L1 loss layer. A float
6351 6352
           scalar with default value 1.0.

6353
    Returns:
6354
        Variable: The output smooth L1 loss with shape [batch_size, 1].
6355 6356 6357 6358 6359

    Examples:
        .. code-block:: python

            data = fluid.layers.data(name='data', shape=[128], dtype='float32')
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            label = fluid.layers.data(
                name='label', shape=[100], dtype='float32')
6362
            fc = fluid.layers.fc(input=data, size=100)
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            out = fluid.layers.smooth_l1(x=fc, y=label)
6364
    """
6365

6366
    helper = LayerHelper('smooth_l1_loss', **locals())
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    diff = helper.create_variable_for_type_inference(dtype=x.dtype)
    loss = helper.create_variable_for_type_inference(dtype=x.dtype)
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    helper.append_op(
        type='smooth_l1_loss',
        inputs={
            'X': x,
            'Y': y,
            'InsideWeight': inside_weight,
            'OutsideWeight': outside_weight
        },
        outputs={'Diff': diff,
                 'Out': loss},
6379
        attrs={'sigma': sigma if sigma is not None else 1.0})
6380
    return loss
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def one_hot(input, depth):
    """
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    This layer creates the one-hot representations for input indices.
6386 6387

    Args:
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        input(Variable): Input indices, last dimension must be 1.
        depth(scalar): An interger defining the depth of the one-hot dimension.
6390 6391

    Returns:
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        Variable: The one-hot representations of input.
6393 6394

    Examples:
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        .. code-block:: python
6396

6397
            label = layers.data(name="label", shape=[1], dtype="int64")
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            one_hot_label = layers.one_hot(input=label, depth=10)
6399 6400
    """
    helper = LayerHelper("one_hot", **locals())
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    one_hot_out = helper.create_variable_for_type_inference(dtype='float32')
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    helper.append_op(
        type="one_hot",
        inputs={'X': input},
        attrs={'depth': depth},
6406 6407
        outputs={'Out': one_hot_out},
        stop_gradient=True)
6408
    return one_hot_out
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def autoincreased_step_counter(counter_name=None, begin=1, step=1):
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    """
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    Create an auto-increase variable
    which will be automatically increased by 1 every mini-batch
    Return the run counter of the main program, default is started from 1.
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    Args:
        counter_name(str): The counter name, default is '@STEP_COUNTER@'.
        begin(int): The first value of this counter.
        step(int): The increment step between each execution.

6422 6423
    Returns:
        Variable: The global run counter.
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    Examples:
        .. code-block:: python

           global_step = fluid.layers.autoincreased_step_counter(
               counter_name='@LR_DECAY_COUNTER@', begin=begin, step=1)
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    """
    helper = LayerHelper('global_step_counter')
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    if counter_name is None:
        counter_name = '@STEP_COUNTER@'
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    counter, is_new_var = helper.create_or_get_global_variable(
        name=counter_name, dtype='int64', shape=[1], persistable=True)
    if is_new_var:
        helper.set_variable_initializer(
            counter, initializer=Constant(
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                value=begin - 1, force_cpu=True))
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        helper.main_program.global_block()._prepend_op(
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            type='increment',
            inputs={'X': [counter]},
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            outputs={'Out': [counter]},
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            attrs={'step': float(step)},
            stop_gradient=True)
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        counter.stop_gradient = True

    return counter
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6451
def reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None):
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    """
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    Gives a new shape to the input Tensor without changing its data.

6455 6456 6457 6458 6459
    The target shape can be given by :attr:`shape` or :attr:`actual_shape`.
    :attr:`shape` is a list of integer while :attr:`actual_shape` is a tensor
    variable. :attr:`actual_shape` has a higher priority than :attr:`shape`
    if it is provided, while :attr:`shape` still should be set correctly to
    gurantee shape inference in compile-time.
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6461
    Some tricks exist when specifying the target shape.
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6463 6464 6465 6466
    1. -1 means the value of this dimension is inferred from the total element
    number of x and remaining dimensions. Thus one and only one dimension can
    be set -1.

6467
    2. 0 means the actual dimension value is going to be copied from the
6468 6469 6470 6471
    corresponding dimension of x. The indice of 0s in shape can not exceed
    Rank(X).

    Here are some examples to explain it.
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    1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
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    is [6, 8], the reshape operator will transform x into a 2-D tensor with
6475
    shape [6, 8] and leaving x's data unchanged.
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6477
    2. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
6478 6479
    specified is [2, 3, -1, 2], the reshape operator will transform x into a
    4-D tensor with shape [2, 3, 4, 2] and leaving x's data unchanged. In this
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    case, one dimension of the target shape is set to -1, the value of this
    dimension is inferred from the total element number of x and remaining
6482
    dimensions.
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6484
    3. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
6485 6486 6487 6488
    is [-1, 0, 3, 2], the reshape operator will transform x into a 4-D tensor
    with shape [2, 4, 3, 2] and leaving x's data unchanged. In this case,
    besides -1, 0 means the actual dimension value is going to be copied from
    the corresponding dimension of x.
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    Args:
6491
        x(variable): The input tensor.
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        shape(list): The new shape. At most one dimension of the new shape can
                     be -1.
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        actual_shape(variable): An optional input. If provided, reshape
                                according to this given shape rather than
                                :attr:`shape` specifying shape. That is to
                                say :attr:`actual_shape` has a higher priority
                                than :attr:`shape`.
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        act (str): The non-linear activation to be applied to the reshaped tensor
                   variable.
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        inplace(bool): If ``inplace`` is `True`, the input and output of ``layers.reshape``
                       are the same variable, otherwise, the input and output of
                       ``layers.reshape`` are different variables. Note that if :attr:`x`
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                       is more than one layer's input, ``inplace`` must be :attr:`False`.
6505
        name (str): The name of this layer. It is optional.
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6507
    Returns:
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        Variable: The reshaped tensor variable if :attr:`act` is None. It is a \
                  new tensor variable if :attr:`inplace` is :attr:`False`, \
                  otherwise it is :attr:`x`. If :attr:`act` is not None, return \
                  the activated tensor variable.
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    Raises:
        TypeError: if actual_shape is neither Variable nor None.

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    Examples:
        .. code-block:: python
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6519
            data = fluid.layers.data(
6520
                name='data', shape=[2, 4, 6], dtype='float32')
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            reshaped = fluid.layers.reshape(
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                x=data, shape=[-1, 0, 3, 2], inplace=True)
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    """

    if not (isinstance(shape, list) or isinstance(shape, tuple)):
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        raise ValueError("Input shape must be a python list or tuple.")
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    inputs = {"X": x}
    if isinstance(actual_shape, Variable):
        inputs["Shape"] = actual_shape
    elif actual_shape is not None:
        raise TypeError("actual_shape should either be Variable or None")
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6533 6534 6535 6536 6537 6538 6539 6540 6541 6542 6543 6544 6545 6546 6547
    # Validate the shape
    unk_dim_idx = -1
    for dim_idx, dim_size in enumerate(shape):
        if dim_size == -1:
            assert unk_dim_idx == -1, (
                "Only one dimension in shape can be unknown.")
            unk_dim_idx = dim_idx
        elif dim_size == 0:
            assert dim_idx < len(x.shape), (
                "The indice of 0s in shape can not exceed Rank(X).")
        else:
            assert dim_size > 0, (
                "Each dimension size given in shape must not be negtive "
                "except one unknown dimension.")

6548
    helper = LayerHelper("reshape2", **locals())
6549 6550
    out = x if inplace else helper.create_variable_for_type_inference(
        dtype=x.dtype)
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    x_shape = helper.create_variable_for_type_inference(dtype=x.dtype)
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    helper.append_op(
6553
        type="reshape2",
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        inputs=inputs,
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        attrs={"shape": shape},
6556 6557
        outputs={"Out": out,
                 "XShape": x_shape})
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    return helper.append_activation(out)
6560

6561

6562
def squeeze(input, axes, name=None):
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    """
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    Remove single-dimensional entries from the shape of a tensor. Takes a
    parameter axes with a list of axes to squeeze. If axes is not provided, all
    the single dimensions will be removed from the shape. If an axis is
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    selected with shape entry not equal to one, an error is raised.
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    For example:

    .. code-block:: text

        Case 1:

          Given
            X.shape = (1, 3, 1, 5)
          and
            axes = [0]
          we get:
            Out.shape = (3, 1, 5)

        Case 2:

          Given
            X.shape = (1, 3, 1, 5)
          and
            axes = []
          we get:
            Out.shape = (3, 5)
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    Args:
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        input (Variable): The input variable to be squeezed.
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        axes (list): List of integers, indicating the dimensions to be squeezed.
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        name (str|None): Name for this layer.
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    Returns:
        Variable: Output squeezed variable.

    Examples:
        .. code-block:: python

            x = layers.data(name='x', shape=[5, 1, 10])
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            y = layers.sequeeze(input=x, axes=[1])
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    """
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    assert not in_dygraph_mode(), (
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        "squeeze layer is not supported in dygraph mode yet.")
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    helper = LayerHelper("squeeze", **locals())
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    out = helper.create_variable_for_type_inference(dtype=input.dtype)
    x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)
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    helper.append_op(
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        type="squeeze2",
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        inputs={"X": input},
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        attrs={"axes": axes},
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        outputs={"Out": out,
                 "XShape": x_shape})
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    return out


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def unsqueeze(input, axes, name=None):
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    """
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    Insert single-dimensional entries to the shape of a tensor. Takes one
    required argument axes, a list of dimensions that will be inserted.
    Dimension indices in axes are as seen in the output tensor.
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    For example:
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    .. code-block:: text

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      Given a tensor such that tensor with shape [3, 4, 5],
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      then Unsqueezed tensor with axes=[0, 4] has shape [1, 3, 4, 5, 1].
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    Args:
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        input (Variable): The input variable to be unsqueezed.
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        axes (list): List of integers, indicating the dimensions to be inserted.
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        name (str|None): Name for this layer.
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    Returns:
        Variable: Output unsqueezed variable.

    Examples:
        .. code-block:: python

            x = layers.data(name='x', shape=[5, 10])
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            y = layers.unsequeeze(input=x, axes=[1])
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    """
    helper = LayerHelper("unsqueeze", **locals())
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    out = helper.create_variable_for_type_inference(dtype=input.dtype)
    x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)
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    helper.append_op(
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        type="unsqueeze2",
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        inputs={"X": input},
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        attrs={"axes": axes},
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        outputs={"Out": out,
                 "XShape": x_shape})
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    return out

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def lod_reset(x, y=None, target_lod=None):
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    """
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    Set LoD of :attr:`x` to a new one specified by :attr:`y` or
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    :attr:`target_lod`. When :attr:`y` provided, :attr:`y.lod` would be
    considered as target LoD first, otherwise :attr:`y.data` would be
    considered as target LoD. If :attr:`y` is not provided, target LoD should
    be specified by :attr:`target_lod`. If target LoD is specified by
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    :attr:`Y.data` or :attr:`target_lod`, only one level LoD is supported.
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    .. code-block:: text

        * Example 1:

            Given a 1-level LoDTensor x:
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                x.lod =  [[ 2,           3,                   1 ]]
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                x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
                x.dims = [6, 1]

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            target_lod: [4, 2]
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            then we get a 1-level LoDTensor:
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                out.lod =  [[4,                          2]]
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                out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
                out.dims = [6, 1]

        * Example 2:

            Given a 1-level LoDTensor x:
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                x.lod =  [[2,            3,                   1]]
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                x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
                x.dims = [6, 1]

            y is a Tensor:
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                y.data = [[2, 4]]
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                y.dims = [1, 3]

            then we get a 1-level LoDTensor:
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                out.lod =  [[2,            4]]
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                out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
                out.dims = [6, 1]

        * Example 3:

            Given a 1-level LoDTensor x:
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                x.lod =  [[2,            3,                   1]]
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                x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
                x.dims = [6, 1]

            y is a 2-level LoDTensor:
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                y.lod =  [[2, 2], [2, 2, 1, 1]]
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                y.data = [[1.1], [2.1], [3.1], [4.1], [5.1], [6.1]]
                y.dims = [6, 1]

            then we get a 2-level LoDTensor:
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                out.lod =  [[2, 2], [2, 2, 1, 1]]
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                out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
                out.dims = [6, 1]

    Args:
        x (Variable): Input variable which could be a Tensor or LodTensor.
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        y (Variable|None): If provided, output's LoD would be derived
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                           from :attr:`y`.
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        target_lod (list|tuple|None): One level LoD which should be considered
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                                      as target LoD when :attr:`y` not provided.
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    Returns:
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        Variable: Output variable with LoD specified by this layer.
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    Raises:
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        ValueError: If :attr:`y` and :attr:`target_lod` are both None.
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    Examples:
        .. code-block:: python

            x = layers.data(name='x', shape=[10])
            y = layers.data(name='y', shape=[10, 20], lod_level=2)
            out = layers.lod_reset(x=x, y=y)
    """
    helper = LayerHelper("lod_reset", **locals())
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    out = helper.create_variable_for_type_inference(dtype=x.dtype)
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    if y is not None:
        helper.append_op(
            type="lod_reset", inputs={'X': x,
                                      'Y': y}, outputs={'Out': out})
    elif target_lod is not None:
        helper.append_op(
            type="lod_reset",
            inputs={'X': x},
            attrs={'target_lod': target_lod},
            outputs={'Out': out})
    else:
        raise ValueError("y and target_lod should not be both None.")

    return out
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def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None):
    """
    Local Response Normalization Layer. This layer performs a type of
    "lateral inhibition" by normalizing over local input regions.

    The formula is as follows:

    .. math::

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      Output(i, x, y) = Input(i, x, y) / \\left(k + \\alpha \\sum\\limits^{\\min(C, c + n/2)}_{j = \\max(0, c - n/2)}(Input(j, x, y))^2\\right)^{\\beta}
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    In the above equation:

    * :math:`n`: The number of channels to sum over.
    * :math:`k`: The offset (avoid being divided by 0).
    * :math:`alpha`: The scaling parameter.
    * :math:`beta`: The exponent parameter.

    Refer to `ImageNet Classification with Deep Convolutional Neural Networks
    <https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf>`_

    Args:
        input (Variable): The input tensor of this layer, and the dimension of input tensor must be 4.
        n (int, default 5): The number of channels to sum over.
        k (float, default 1.0): An offset (usually positive to avoid dividing by 0).
        alpha (float, default 1e-4): The scaling parameter.
        beta (float, default 0.75): The exponent.
        name (str, default None): A name for this operation.

    Raises:
        ValueError: If rank of the input tensor is not 4.

    Returns:
        A tensor variable storing the transformation result.

    Examples:
        .. code-block:: python

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          data = fluid.layers.data(
              name="data", shape=[3, 112, 112], dtype="float32")
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          lrn = fluid.layers.lrn(input=data)
    """
    helper = LayerHelper('lrn', **locals())
    dtype = helper.input_dtype()
    input_shape = input.shape
    dims = len(input_shape)

    if dims != 4:
        raise ValueError(
            "dims of input must be 4(not %d), and it's order must be NCHW" %
            (dims))

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    mid_out = helper.create_variable_for_type_inference(
        dtype=dtype, stop_gradient=True)
    lrn_out = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
        type="lrn",
        inputs={"X": input},
        outputs={
            "Out": lrn_out,
            "MidOut": mid_out,
        },
        attrs={"n": n,
               "k": k,
               "alpha": alpha,
               "beta": beta})

    return lrn_out
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def pad(x, paddings, pad_value=0., name=None):
    """
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    Pads a tensor with a constant value given by :attr:`pad_value`, and the
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    padded width is specified by :attr:`paddings`.
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    Specifically, the number of values padded before the contents of :attr:`x`
    in dimension :attr:`i` is indicated by :attr:`paddings[i]`, and the number
    of values padded after the contents of :attr:`x` in dimension :attr:`i` is
    indicated by :attr:`paddings[i+1]`.
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    See below for an example.

    .. code-block:: text

        Given:
            x = [[1, 2], [3, 4]]

            paddings = [0, 1, 1, 2]

            pad_value = 0

        Return:

            out = [[0, 1, 2, 0, 0]
                   [0, 3, 4, 0, 0]
                   [0, 0, 0, 0, 0]]

    Args:
        x (Variable): The input tensor variable.
        paddings (list): A list of integers. Its elements specify the padded
                         width before and after for each dimension in turn.
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                         The length of :attr:paddings must be
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                         :math:`rank(x) \\times 2`.
        pad_value (float): The constant value used to pad.
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.

    Returns:
        Variable: The padded tensor variable.

    Examples:
        .. code-block:: python
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            # x is a rank 2 tensor variable.
            out = fluid.layers.pad(
                x=x, paddings=[0, 1, 1, 2], pad_value=0.)
    """
    helper = LayerHelper('pad', input=x, **locals())
    dtype = helper.input_dtype()
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    out = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
        type='pad',
        inputs={'X': x},
        outputs={'Out': out},
        attrs={'paddings': paddings,
               'pad_value': float(pad_value)})
    return out
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def pad_constant_like(x, y, pad_value=0., name=None):
    """
    Pad input(Y) with :attr:`pad_value`, the number of values padded to
    the edges of each axis is specified by the difference of the shape
    of X and Y. ((0, shape_x_0 - shape_y_0), ... (0, shape_x_n - shape_y_n))
    unique pad widths for each axis. The input should be a k-D
    tensor(k > 0 and k < 7).

    See below for an example.

    .. code-block:: text

        Given:
            X = [[[[ 0,  1,  2],
                   [ 3,  4,  5]],
                  [[ 6,  7,  8],
                   [ 9, 10, 11]],
                  [[12, 13, 14],
                   [15, 16, 17]]],
                 [[[18, 19, 20],
                   [21, 22, 23]],
                  [[24, 25, 26],
                   [27, 28, 29]],
                  [[30, 31, 32],
                   [33, 34, 35]]]]
            X.shape = (2, 3, 2, 3)

            Y = [[[[35, 36, 37]],
                  [[38, 39, 40]],
                  [[41, 42, 43]]]]
            Y.shape = (1, 3, 1, 3)
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		And
            pad_value = -1,
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        Return:
            Out = [[[[35, 36, 37],
                     [-1, -1, -1]],
                    [[38, 39, 40],
                     [-1, -1, -1]],
                    [[41, 42, 43],
                     [-1, -1, -1]]],
                  [[[-1, -1, -1],
                    [-1, -1, -1]],
                   [[-1, -1, -1],
                    [-1, -1, -1]],
                   [[-1, -1, -1],
                    [-1, -1, -1]]]]
            Out.shape = (2, 3, 2, 3)
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    Args:
        x (Variable): The input tensor variable.
        y (Variable): The input tensor variable.
        pad_value (float): The constant value used to pad.
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.

    Returns:
        Variable: The padded tensor variable.

    Examples:
        .. code-block:: python

            # x is a rank 4 tensor variable, x.shape = (2, 3, 2, 3)
            # y is a rank 4 tensor variable, y.shape = (1, 3, 1, 3)
            out = fluid.layers.pad_constant_like(x=x, y=y, pad_value=0.)
            # out is a rank 4 tensor variable, and out.shape = [2, 3 ,2 , 3]
    """
    helper = LayerHelper('pad_constant_like', input=x, **locals())
    dtype = helper.input_dtype()
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    out = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
        type='pad_constant_like',
        inputs={'X': x,
                'Y': y},
        outputs={'Out': out},
        attrs={'pad_value': float(pad_value)})
    return out


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def label_smooth(label,
                 prior_dist=None,
                 epsilon=0.1,
                 dtype="float32",
                 name=None):
    """
    Label smoothing is a mechanism to regularize the classifier layer and is
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    called label-smoothing regularization (LSR).

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    Label smoothing is proposed to encourage the model to be less confident,
    since optimizing the log-likelihood of the correct label directly may
    cause overfitting and reduce the ability of the model to adapt. Label
    smoothing replaces the ground-truth label :math:`y` with the weighted sum
    of itself and some fixed distribution :math:`\mu`. For class :math:`k`,
    i.e.

    .. math::

        \\tilde{y_k} = (1 - \epsilon) * y_k + \epsilon * \mu_k,

    where :math:`1 - \epsilon` and :math:`\epsilon` are the weights
    respectively, and :math:`\\tilde{y}_k` is the smoothed label. Usually
    uniform distribution is used for :math:`\mu`.

    See more details about label smoothing in https://arxiv.org/abs/1512.00567.

    Args:
        label(Variable): The input variable containing the label data. The
                          label data should use one-hot representation.
        prior_dist(Variable): The prior distribution to be used to smooth
                              labels. If not provided, an uniform distribution
                              is used. The shape of :attr:`prior_dist` should
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                              be :math:`(1, class\_num)`.
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        epsilon(float): The weight used to mix up the original ground-truth
                        distribution and the fixed distribution.
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        dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32,
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                                                  float_64, int etc.
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.

    Returns:
        Variable: The tensor variable containing the smoothed labels.

    Examples:
        .. code-block:: python

            label = layers.data(name="label", shape=[1], dtype="float32")
            one_hot_label = layers.one_hot(input=label, depth=10)
            smooth_label = layers.label_smooth(
                label=one_hot_label, epsilon=0.1, dtype="float32")
    """
    if epsilon > 1. or epsilon < 0.:
        raise ValueError("The value of epsilon must be between 0 and 1.")
    helper = LayerHelper("label_smooth", **locals())
    label.stop_gradient = True
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    smooth_label = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
        type="label_smooth",
        inputs={"X": label,
                "PriorDist": prior_dist} if prior_dist else {"X": label},
        outputs={"Out": smooth_label},
        attrs={"epsilon": float(epsilon)})
    return smooth_label
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@templatedoc()
def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
    """
    ${comment}

    Args:
        input (Variable): ${x_comment}
        rois (Variable): ROIs (Regions of Interest) to pool over.
        pooled_height (integer): ${pooled_height_comment} Default: 1
        pooled_width (integer): ${pooled_width_comment} Default: 1
        spatial_scale (float): ${spatial_scale_comment} Default: 1.0

    Returns:
        Variable: ${out_comment}.

    Examples:
        .. code-block:: python

            pool_out = fluid.layers.roi_pool(input=x, rois=rois, 7, 7, 1.0)
    """
    helper = LayerHelper('roi_pool', **locals())
    dtype = helper.input_dtype()
    pool_out = helper.create_variable_for_type_inference(dtype)
    argmaxes = helper.create_variable_for_type_inference(dtype='int32')
    helper.append_op(
        type="roi_pool",
        inputs={"X": input,
                "ROIs": rois},
        outputs={"Out": pool_out,
                 "Argmax": argmaxes},
        attrs={
            "pooled_height": pooled_height,
            "pooled_width": pooled_width,
            "spatial_scale": spatial_scale
        })
    return pool_out
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@templatedoc()
def roi_align(input,
              rois,
              pooled_height=1,
              pooled_width=1,
              spatial_scale=1.0,
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              sampling_ratio=-1,
              name=None):
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    """
    ${comment}

    Args:
        input (Variable): ${x_comment}
        rois (Variable): ROIs (Regions of Interest) to pool over.
        pooled_height (integer): ${pooled_height_comment} Default: 1
        pooled_width (integer): ${pooled_width_comment} Default: 1
        spatial_scale (float): ${spatial_scale_comment} Default: 1.0
        sampling_ratio(intger): ${sampling_ratio_comment} Default: -1

    Returns:
        Variable: ${out_comment}.
    Examples:
        .. code-block:: python

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            align_out = fluid.layers.roi_align(input=x,
                                               rois=rois,
                                               pooled_height=7,
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                                               pooled_width=7,
                                               spatial_scale=0.5,
                                               sampling_ratio=-1)
    """
    helper = LayerHelper('roi_align', **locals())
    dtype = helper.input_dtype()
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    align_out = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
        type="roi_align",
        inputs={"X": input,
                "ROIs": rois},
        outputs={"Out": align_out},
        attrs={
            "pooled_height": pooled_height,
            "pooled_width": pooled_width,
            "spatial_scale": spatial_scale,
            "sampling_ratio": sampling_ratio
        })
    return align_out


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7115 7116 7117 7118 7119 7120 7121 7122 7123 7124 7125 7126 7127 7128 7129 7130 7131 7132 7133 7134 7135 7136 7137 7138 7139 7140
def dice_loss(input, label, epsilon=0.00001):
    """
    Dice loss for comparing the similarity of two batch of data,
    usually is used for binary image segmentation i.e. labels are binary.
    The dice loss can be defined as below equation:

    .. math::

        dice\_loss &= 1 - \\frac{2 * intersection\_area}{total\_area} \\\\
                  &= \\frac{(total\_area - intersection\_area) - intersection\_area}{total\_area} \\\\
                  &= \\frac{(union\_area - intersection\_area)}{total\_area}


    Args:
        input (Variable): The predictions with rank>=2. The first dimension is batch size,
                          and the last dimension is class number.
        label (Variable): The groud truth with the same rank with input. The first dimension
                          is batch size, and the last dimension is 1.
        epsilon (float): The epsilon will be added to the numerator and denominator.
                         If both input and label are empty, it makes sure dice is 1.
                         Default: 0.00001

    Returns:
        dice_loss (Variable): The dice loss with shape [1].

    Examples:
7141 7142
        .. code-block:: python

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7143 7144 7145 7146
            predictions = fluid.layers.softmax(x)
            loss = fluid.layers.dice_loss(input=predictions, label=label, 2)
    """
    label = one_hot(label, depth=input.shape[-1])
7147
    reduce_dim = list(range(1, len(input.shape)))
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    inse = reduce_sum(input * label, dim=reduce_dim)
    dice_denominator = reduce_sum(
        input, dim=reduce_dim) + reduce_sum(
            label, dim=reduce_dim)
    dice_score = 1 - inse * 2 / (dice_denominator + epsilon)
    return reduce_mean(dice_score)
7154 7155


7156 7157 7158 7159
def image_resize(input,
                 out_shape=None,
                 scale=None,
                 name=None,
7160
                 resample='BILINEAR',
7161 7162
                 actual_shape=None,
                 align_corners=True,
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                 align_mode=1):
7164
    """
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    **Resize a Batch of Images**
F
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7166

7167
    The input must be a tensor of the shape (num_batches, channels, in_h, in_w),
7168 7169 7170
    and the resizing only applies on the last two dimensions(hight and width).

    Supporting resample methods:
Q
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7172
        'BILINEAR' : Bilinear interpolation
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7173

7174
        'NEAREST' : Nearest neighbor interpolation
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7176 7177 7178 7179 7180 7181 7182 7183 7184 7185
    Nearest neighbor interpolation is to perform nearest neighbor interpolation
    in both the 3rd dimention(in height direction) and the 4th dimention(in width 
    direction) on input tensor.
            
    Bilinear interpolation is an extension of linear interpolation for 
    interpolating functions of two variables (e.g. H-direction and 
    W-direction in this op) on a rectilinear 2D grid. The key idea is 
    to perform linear interpolation first in one direction, and then 
    again in the other direction.

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    Align_corners and align_mode are optinal parameters,the calculation method 
7187 7188 7189 7190
    of interpolation can be selected by them.

    Example:

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7191
    .. code-block:: text
7192

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7193
        For scale:
7194
          
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7195
            if align_corners = True && out_size > 1 :
7196

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7197 7198 7199 7200 7201 7202 7203 7204 7205 7206 7207
              scale_factor = (in_size-1.0)/(out_size-1.0)
            
            else:
              
              scale_factor = float(in_size/out_size)
            
          
        Nearest neighbor interpolation:
          
          if:
              align_corners = False
7208

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7209 7210
              input : (N,C,H_in,W_in)
              output: (N,C,H_out,W_out) where:
7211

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7212 7213
              H_out = floor (H_{in} * scale_{factor})
              W_out = floor (W_{in} * scale_{factor})
7214

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7215 7216
          else:
              align_corners = True
7217

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              input : (N,C,H_in,W_in)
              output: (N,C,H_out,W_out) where:
7220

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7221 7222
              H_out = round(H_{in} * scale_{factor})
              W_out = round(W_{in} * scale_{factor})
7223

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7224 7225 7226 7227 7228 7229 7230 7231 7232 7233
        Bilinear interpolation:

          if:
              align_corners = False , align_mode = 0
              
              input : (N,C,H_in,W_in)
              output: (N,C,H_out,W_out) where:
              
              H_out = (H_{in}+0.5) * scale_{factor} - 0.5
              W_out = (W_{in}+0.5) * scale_{factor} - 0.5
7234

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          else:
           
              input : (N,C,H_in,W_in)
              output: (N,C,H_out,W_out) where:
7239

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7240 7241
              H_out = H_{in} * scale_{factor}
              W_out = W_{in} * scale_{factor}
7242 7243 7244 7245 7246 7247 7248 7249 7250

    For details of nearest neighbor interpolation, please refer to Wikipedia: 
    https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation.

    For details of bilinear interpolation, please refer to Wikipedia: 
    https://en.wikipedia.org/wiki/Bilinear_interpolation.



7251
    Args:
7252
        input (Variable): The input tensor of image resize layer,
7253 7254
                          This is a 4-D tensor of the shape
                          (num_batches, channels, in_h, in_w).
7255
        out_shape(list|tuple|Variable|None): Output shape of image resize
7256 7257
                                    layer, the shape is (out_h, out_w).
                                    Default: None
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        scale(float|None): The multiplier for the input height or width. At
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             least one of :attr:`out_shape` or :attr:`scale` must be set. 
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             And :attr:`out_shape` has a higher priority than :attr:`scale`. 
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             Default: None.
7262 7263
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.
7264
        resample(str): The resample method. It supports 'BILINEAR' and 'NEAREST'
7265
                       currently.
7266
                       Default: 'BILINEAR'
7267 7268 7269
        actual_shape(Variable): An optional input to specify output shape
                                dynamically. If provided, image resize
                                according to this given shape rather than
7270
                                :attr:`out_shape` and :attr:`scale` specifying
7271 7272 7273 7274 7275 7276 7277
                                shape. That is to say actual_shape has the
                                highest priority. It is recommended to use
                                actual_shape instead of :attr:`out_shape` if you
                                want to specify output shape dynamically. When
                                using actual_shape to specify output shape, one of
                                :attr:`out_shape` and :attr:`scale` should also be
                                set, otherwise errors would be occured in graph
7278 7279
                                constructing stage.
                                Default: None
7280 7281 7282 7283
        align_corners(bool) :  An optional bool, If True, the centers of the 4 corner pixels of the 
                               input and output tensors are aligned, preserving the values at the 
                               corner pixels.
                               Default: True
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        align_mode(int)  :  An optional for bilinear interpolation. can be \'0\' 
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                            for src_idx = scale*(dst_indx+0.5)-0.5 , can be \'1\' for 
                            src_idx = scale*dst_index .
7287 7288

    Returns:
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        Variable: The output is a 4-D tensor of the shape
        (num_batches, channls, out_h, out_w).
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7292 7293 7294
    Raises:
        TypeError: out_shape should be a list or tuple or Variable.
        TypeError: actual_shape should either be Variable or None.
7295
        ValueError: The 'resample' of image_resize can only be 'BILINEAR'
7296 7297 7298
                    or 'NEAREST' currently.
        ValueError: One of out_shape and scale must not be None.
        ValueError: out_shape length should be 2.
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        ValueError: scale should be greater than zero.
7300 7301
        TypeError: align_corners shoule be a bool value
        ValueError: align_mode can only be '0' or '1'
7302

7303 7304 7305
    Examples:
        .. code-block:: python

7306
            out = fluid.layers.image_resize(input, out_shape=[12, 12], resample="NEAREST")
7307
    """
7308 7309 7310 7311
    resample_methods = {
        'BILINEAR': 'bilinear',
        'NEAREST': 'nearest',
    }
7312 7313
    if resample not in resample_methods:
        raise ValueError(
7314
            "The 'resample' of image_resize can only be 'BILINEAR' or 'NEAREST' currently."
7315
        )
7316
    resample_type = resample_methods[resample]
7317 7318 7319 7320 7321 7322

    if not isinstance(align_corners, bool):
        raise TypeError("Attr align_corners should be a bool value")
    if align_mode != 0 and align_mode != 1:
        raise ValueError("align_mode can only be 0 or 1")

7323
    if out_shape is None and scale is None:
7324
        raise ValueError("One of out_shape and scale must not be None.")
7325
    helper = LayerHelper('{}_interp'.format(resample_type), **locals())
7326
    dtype = helper.input_dtype()
7327 7328 7329 7330

    def _is_list_or_turple_(data):
        return (isinstance(data, list) or isinstance(data, tuple))

7331
    inputs = {"X": input}
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    attrs = {
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        "out_h": 0,
        "out_w": 0,
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        "interp_method": resample_type,
        "align_corners": align_corners,
        "align_mode": align_mode
    }

7340
    if out_shape is not None:
7341 7342 7343 7344
        if isinstance(out_shape, Variable):
            warnings.warn("out_shape as Variable type is deprecated, \
                    it is recommended to use actual_shape instead of \
                    out_shape to specify output shape dynamically.")
7345
            inputs['OutSize'] = out_shape
7346 7347
        else:
            if not (_is_list_or_turple_(out_shape)):
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7348 7349
                raise TypeError(
                    "out_shape should be a list or tuple or Variable.")
7350 7351 7352 7353 7354 7355 7356
            if len(out_shape) != 2:
                raise ValueError("out_shape length should be 2.")

            out_shape = list(map(int, out_shape))
            attrs['out_h'] = out_shape[0]
            attrs['out_w'] = out_shape[1]

7357
    else:
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7358 7359
        if scale <= 0:
            raise ValueError("scale should be greater than zero.")
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        attrs['scale'] = float(scale)
7361

7362 7363 7364 7365 7366
    if isinstance(actual_shape, Variable):
        inputs["OutSize"] = actual_shape
    elif actual_shape is not None:
        raise TypeError("actual_shape should either be Variable or None.")

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7367
    out = helper.create_variable_for_type_inference(dtype)
7368
    helper.append_op(
7369
        type='{}_interp'.format(resample_type),
7370
        inputs=inputs,
7371
        outputs={"Out": out},
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        attrs=attrs)
7373
    return out
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7374 7375


7376
@templatedoc(op_type="bilinear_interp")
7377 7378 7379 7380
def resize_bilinear(input,
                    out_shape=None,
                    scale=None,
                    name=None,
7381 7382
                    actual_shape=None,
                    align_corners=True,
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7383
                    align_mode=1):
7384
    """
7385 7386
    Resize input by performing bilinear interpolation based on given
    output shape which specified by actual_shape, out_shape and scale
7387 7388
    in priority order.

7389 7390 7391 7392
    Bilinear interpolation is an extension of linear interpolation for
    interpolating functions of two variables (e.g. H-direction and
    W-direction in this op) on a rectilinear 2D grid. The key idea is
    to perform linear interpolation first in one direction, and then
7393 7394
    again in the other direction.

7395
    For details of bilinear interpolation, please refer to Wikipedia:
7396
    https://en.wikipedia.org/wiki/Bilinear_interpolation
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7397

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7398
    Align_corners and align_mode are optinal parameters,the calculation 
7399 7400 7401 7402
    method of interpolation can be selected by them.

    Example:

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7403
    .. code-block:: text
7404

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7405
        For scale:
7406
          
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7407
            if align_corners = True && out_size > 1 :
7408

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7409 7410 7411 7412 7413
              scale_factor = (in_size-1.0)/(out_size-1.0)
            
            else:
              
              scale_factor = float(in_size/out_size)     
7414

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        Bilinear interpolation:

          if:
              align_corners = False , align_mode = 0
              
              input : (N,C,H_in,W_in)
              output: (N,C,H_out,W_out) where:
              
              H_out = (H_{in}+0.5) * scale_{factor} - 0.5
              W_out = (W_{in}+0.5) * scale_{factor} - 0.5
7425 7426


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          else:
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7428

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7429 7430
              input : (N,C,H_in,W_in)
              output: (N,C,H_out,W_out) where:
7431

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7432 7433
              H_out = H_{in} * scale_{factor}
              W_out = W_{in} * scale_{factor}
7434 7435 7436



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7437 7438 7439
    Args:
        input(${x_type}): ${x_comment}.

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        out_shape(list|tuple|Variable|None): Output shape of resize bilinear
                                    layer, the shape is (out_h, out_w).
                                    Default: None
7443

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7444
        scale(float|None): The multiplier for the input height or width. At
D
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             least one of :attr:`out_shape` or :attr:`scale` must be set. 
D
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7446
             And :attr:`out_shape` has a higher priority than :attr:`scale`. 
D
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7447
             Default: None.
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7448 7449

        name(str|None): The output variable name.
7450 7451 7452
        actual_shape(Variable): An optional input to specify output shape
                                dynamically. If provided, image resize
                                according to this given shape rather than
7453
                                :attr:`out_shape` and :attr:`scale` specifying
7454 7455 7456 7457 7458 7459 7460
                                shape. That is to say actual_shape has the
                                highest priority. It is recommended to use
                                actual_shape instead of :attr:`out_shape` if you
                                want to specify output shape dynamically. When
                                using actual_shape to specify output shape, one of
                                :attr:`out_shape` and :attr:`scale` should also be
                                set, otherwise errors would be occured in graph
7461 7462
                                constructing stage.
                                Default: None
7463 7464
        align_corners(bool): ${align_corners_comment}
        align_mode(bool): ${align_mode_comment}
Y
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7465 7466 7467

    Returns:
        ${out_comment}.
7468 7469 7470 7471 7472

    Examples:
        .. code-block:: python

            out = fluid.layers.resize_bilinear(input, out_shape=[12, 12])
7473 7474
    """

7475 7476
    return image_resize(input, out_shape, scale, name, 'BILINEAR', actual_shape,
                        align_corners, align_mode)
7477 7478


7479
@templatedoc(op_type="nearest_interp")
7480 7481 7482 7483
def resize_nearest(input,
                   out_shape=None,
                   scale=None,
                   name=None,
7484 7485
                   actual_shape=None,
                   align_corners=True):
7486
    """
7487
    Resize input by performing nearest neighbor interpolation in both the
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7488 7489
    3rd dimension(in height direction) and the 4th dimension(in width
    direction) based on given output shape which is specified by actual_shape,
7490 7491
    out_shape and scale in priority order.

7492 7493
    Example:

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7494 7495 7496 7497 7498
    .. code-block:: text

        For scale:
          
            if align_corners = True && out_size > 1 :
7499

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7500 7501 7502 7503 7504 7505 7506 7507
              scale_factor = (in_size-1.0)/(out_size-1.0)
            
            else:
              
              scale_factor = float(in_size/out_size)
            
          
        Nearest neighbor interpolation:
7508
          
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7509 7510
          if:
              align_corners = False
7511

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7512 7513
              input : (N,C,H_in,W_in)
              output: (N,C,H_out,W_out) where:
7514

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7515 7516
              H_out = floor(H_{in} * scale_{factor})
              W_out = floor(W_{in} * scale_{factor})
7517

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7518 7519
          else:
              align_corners = True
7520

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7521 7522
              input : (N,C,H_in,W_in)
              output: (N,C,H_out,W_out) where:
7523

T
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7524 7525
              H_out = round(H_{in} * scale_{factor})
              W_out = round(W_{in} * scale_{factor})
7526 7527


7528
    For details of nearest neighbor interpolation, please refer to Wikipedia:
7529
    https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation
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7530 7531 7532 7533

    Args:
        input(${x_type}): ${x_comment}.

D
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7534 7535 7536
        out_shape(list|tuple|Variable|None): Output shape of resize nearest
                                    layer, the shape is (out_h, out_w).
                                    Default: None
7537

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7538
        scale(float|None): The multiplier for the input height or width. At
D
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7539
             least one of :attr:`out_shape` or :attr:`scale` must be set. 
D
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7540
             And :attr:`out_shape` has a higher priority than :attr:`scale`. 
D
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7541
             Default: None.
Y
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7542 7543

        name(str|None): The output variable name.
7544 7545 7546
        actual_shape(Variable): An optional input to specify output shape
                                dynamically. If provided, image resize
                                according to this given shape rather than
7547
                                :attr:`out_shape` and :attr:`scale` specifying
7548 7549 7550 7551 7552 7553 7554
                                shape. That is to say actual_shape has the
                                highest priority. It is recommended to use
                                actual_shape instead of :attr:`out_shape` if you
                                want to specify output shape dynamically. When
                                using actual_shape to specify output shape, one of
                                :attr:`out_shape` and :attr:`scale` should also be
                                set, otherwise errors would be occured in graph
7555 7556
                                constructing stage.
                                Default: None
7557
        align_corners(bool): ${align_corners_comment}
Y
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7558 7559 7560

    Returns:
        ${out_comment}.
7561 7562 7563 7564 7565

    Examples:
        .. code-block:: python

            out = fluid.layers.resize_nearest(input, out_shape=[12, 12])
7566 7567
    """

7568 7569
    return image_resize(input, out_shape, scale, name, 'NEAREST', actual_shape,
                        align_corners)
7570 7571 7572 7573


def image_resize_short(input, out_short_len, resample='BILINEAR'):
    """
7574 7575 7576
    Resize a batch of images. The short edge of input images will be
    resized to the given 'out_short_len'. The long edge of input images
    will be resized proportionately to make images' length-width ratio
7577 7578 7579 7580 7581 7582 7583
    constant.

    Args:
        input (Variable): The input tensor of image resize layer,
                          This is a 4-D tensor of the shape
                          (num_batches, channels, in_h, in_w).
        out_short_len(int): The length of output images' short edge.
7584
        resample (str): resample method, default: BILINEAR.
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7585

7586
    Returns:
Q
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7587
        Variable: The output is a 4-D tensor of the shape
7588
        (num_batches, channls, out_h, out_w).
7589 7590 7591 7592 7593 7594 7595 7596 7597 7598
    """
    in_shape = input.shape
    if len(in_shape) != 4:
        raise ValueError(
            "The rank of input must be 4 (num_batches, channels, in_h, in_w).")
    hw = in_shape[2:4]
    short_idx = hw.index(min(hw))
    long_idx = 1 - short_idx
    out_shape = list(hw)
    out_shape[short_idx] = out_short_len
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7599 7600 7601
    out_shape[long_idx] = int(
        float(out_shape[long_idx]) * (float(out_short_len) / float(hw[
            short_idx])) + 0.5)
7602 7603 7604
    return image_resize(input=input, out_shape=out_shape, resample=resample)


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7605 7606
def gather(input, index):
    """
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7607 7608
    **Gather Layer**

7609
    Output is obtained by gathering entries of the outer-most dimension
W
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7610 7611 7612 7613
    of X indexed by `index` and concatenate them together.

    .. math::

7614
        Out = X[Index]
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7615 7616 7617 7618 7619 7620 7621


    .. code-block:: text


                Given:

7622 7623
                X = [[1, 2],
                     [3, 4],
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7624 7625 7626 7627 7628 7629 7630 7631 7632 7633
                     [5, 6]]

                Index = [1, 2]

                Then:

                Out = [[3, 4],
                       [5, 6]]

    Args:
7634
        input (Variable): The source input with rank>=1.
W
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7635 7636 7637 7638 7639 7640
        index (Variable): The index input with rank=1.

    Returns:
        output (Variable): The output is a tensor with the same rank as input.

    Examples:
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        .. code-block:: python

            output = fluid.layers.gather(x, index)
    """
    helper = LayerHelper('gather', **locals())
    dtype = helper.input_dtype()
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    out = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
        type="gather",
        inputs={"X": input,
                "Index": index},
        outputs={"Out": out})
    return out


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def scatter(input, index, updates, name=None):
    """
    **Scatter Layer**

    Output is obtained by updating the input on selected indices on the first
    axis.

    .. math::

        Out = X
        Out[Ids] = Updates

    Args:
        input (Variable): The source input with rank>=1.
        index (Variable): The index input with rank=1. Its dtype should be
                          int32 or int64 as it is used as indexes.
        updates (Variable): The updated value of scatter op.
        name (str|None): The output variable name. Default None.

    Returns:
        output (Variable): The output is a tensor with the same shape as input.

    Examples:

        .. code-block:: python

            output = fluid.layers.scatter(input, index, updates)

    """
    helper = LayerHelper('scatter', **locals())
    dtype = helper.input_dtype()
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    out = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
        type="scatter",
        inputs={"X": input,
                "Ids": index,
                "Updates": updates},
        outputs={"Out": out})
    return out


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def sequence_scatter(input, index, updates, name=None):
    """
    **Sequence Scatter Layer**

    This operator scatters the Updates tensor to the input X. It uses the LoD
    information of Ids to select the rows to update, and use the values in Ids as
    the columns to update in each row of X.

    Here is an example:
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    Given the following input:
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    .. code-block:: text
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        input.data = [[1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
                      [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
                      [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]]
        input.dims = [3, 6]

        index.data = [[0], [1], [2], [5], [4], [3], [2], [1], [3], [2], [5], [4]]
        index.lod =  [[0,        3,                       8,                 12]]

        updates.data = [[0.3], [0.3], [0.4], [0.1], [0.2], [0.3], [0.4], [0.0], [0.2], [0.3], [0.1], [0.4]]
        updates.lod =  [[  0,            3,                                 8,                         12]]

    Then we have the output:
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    .. code-block:: text
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        out.data = [[1.3, 1.3, 1.4, 1.0, 1.0, 1.0],
                    [1.0, 1.0, 1.4, 1.3, 1.2, 1.1],
                    [1.0, 1.0, 1.3, 1.2, 1.4, 1.1]]
        out.dims = X.dims = [3, 6]

    Args:
        input (Variable): The source input with rank>=1.
        index (Variable): A LoD Tensor. The index input of sequence scatter op
            where input will be  updated. The index input with rank=1. Its dtype
            should be int32 or int64 as it is used as indexes.
        updates (Variable): A LoD Tensor. The values to scatter to the input
            tensor X, must be a LoDTensor with the same LoD information as index.
        name (str|None): The output variable name. Default None.

    Returns:
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        Variable: The output is a tensor with the same shape as input.
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    Examples:

        .. code-block:: python

            output = fluid.layers.sequence_scatter(input, index, updates)

    """
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    assert not in_dygraph_mode(), (
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        "sequence layer is not supported in dygraph mode yet.")
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    helper = LayerHelper('sequence_scatter', **locals())
    dtype = helper.input_dtype()
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    out = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
        type="sequence_scatter",
        inputs={"X": input,
                "Ids": index,
                "Updates": updates},
        outputs={"Out": out})
    return out


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@templatedoc()
def random_crop(x, shape, seed=None):
    """
    ${comment}

    Args:
        x(${x_type}): ${x_comment}
        shape(${shape_type}): ${shape_comment}
        seed(int|${seed_type}|None): ${seed_comment} By default, the seed will
            get from `random.randint(-65536, 65535)`.

    Returns:
        ${out_comment}
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    Examples:
        >>> img = fluid.layers.data("img", [3, 256, 256])
        >>> cropped_img = fluid.layers.random_crop(img, shape=[3, 224, 224])
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    """
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    helper = LayerHelper("random_crop", **locals())
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    dtype = x.dtype
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    out = helper.create_variable_for_type_inference(dtype)
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    if seed is None:
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        seed = np.random.randint(-65536, 65536)
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    op_attrs = {"shape": shape}
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    if isinstance(seed, int):
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        op_attrs["startup_seed"] = seed
        seed = helper.create_variable(
            name=unique_name.generate("random_crop_seed"),
            dtype="int64",
            persistable=True)
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    elif not isinstance(seed, Variable):
        raise ValueError("'seed' must be a Variable or an int.")
    helper.append_op(
        type="random_crop",
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        inputs={"X": x,
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                "Seed": seed},
        outputs={"Out": out,
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                 "SeedOut": seed},
        attrs=op_attrs)
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    return out
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def log(x, name=None):
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    """
    Calculates the natural log of the given input tensor, element-wise.

    .. math::

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        Out = \\ln(x)
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    Args:
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        x (Variable): Input tensor.
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        name (str|None, default None): A name for this layer If set None,
            the layer will be named automatically.
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    Returns:
        Variable: The natural log of the input tensor computed element-wise.

    Examples:

        .. code-block:: python

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            output = fluid.layers.log(x)
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    """
    helper = LayerHelper('log', **locals())
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    dtype = helper.input_dtype(input_param_name='x')
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    out = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(type="log", inputs={"X": x}, outputs={"Out": out})
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    return out


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def relu(x, name=None):
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    """
    Relu takes one input data (Tensor) and produces one output data (Tensor)
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    where the rectified linear function, y = max(0, x), is applied to
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    the tensor elementwise.

    .. math::

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        Out = \\max(0, x)
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    Args:
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        x (Variable): The input tensor.
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        name (str|None, default None): A name for this layer If set None,
            the layer will be named automatically.
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    Returns:
        Variable: The output tensor with the same shape as input.

    Examples:

        .. code-block:: python

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            output = fluid.layers.relu(x)
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    """
    helper = LayerHelper('relu', **locals())
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    dtype = helper.input_dtype(input_param_name='x')
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    out = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
        type="relu", inputs={"X": helper.input('x')}, outputs={"Out": out})
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    return out
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@templatedoc()
def selu(x, scale=None, alpha=None, name=None):
    """
    ${comment}

    Args:
        x (Variable): The input tensor.
        scale(float, None): If the scale is not set,
            the default value is 1.0507009873554804934193349852946.
            For more information about this value, please refer
            to: https://arxiv.org/abs/1706.02515.
        alpha(float, None): If the alpha is not set,
            the default value is 1.6732632423543772848170429916717.
            For more information about this value, please refer
            to: https://arxiv.org/abs/1706.02515.
        name (str|None, default None): A name for this layer If set None,
            the layer will be named automatically.

    Returns:
        Variable: The output tensor with the same shape as input.

    Examples:

        .. code-block:: python

            output = fluid.layers.selu(x)
    """
    helper = LayerHelper('selu', **locals())
    dtype = helper.input_dtype(input_param_name='x')
    out = helper.create_variable_for_type_inference(dtype)
    attrs = {}
    if scale is not None:
        attrs["scale"] = scale
    if alpha is not None:
        attrs["alpha"] = alpha

    helper.append_op(
        type="selu", inputs={"X": x}, outputs={"Out": out}, attrs=attrs)
    return out


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def mean_iou(input, label, num_classes):
    """
    Mean Intersection-Over-Union is a common evaluation metric for
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    semantic image segmentation, which first computes the IOU for each
    semantic class and then computes the average over classes.
    IOU is defined as follows:

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    .. math::
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        IOU = \\frac{true\_positive}{(true\_positive + false\_positive + false\_negative)}.
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    The predictions are accumulated in a confusion matrix and mean-IOU
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    is then calculated from it.


    Args:
        input (Variable): A Tensor of prediction results for semantic labels with type int32 or int64.
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        label (Variable): A Tensor of ground truth labels with type int32 or int64.
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                           Its shape should be the same as input.
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        num_classes (int): The possible number of labels.
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    Returns:
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        mean_iou (Variable),out_wrong(Variable),out_correct(Variable):

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                     Three variables:
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                     - mean_iou : A Tensor representing the mean intersection-over-union with shape [1].
                     - out_wrong: A Tensor with shape [num_classes]. The wrong numbers of each class.
                     - out_correct: A Tensor with shape [num_classes]. The correct numbers of each class.
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    Examples:

        .. code-block:: python
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            iou, wrongs, corrects = fluid.layers.mean_iou(predict, label, num_classes)
    """
    helper = LayerHelper('mean_iou', **locals())
    dtype = helper.input_dtype()
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    out_mean_iou = helper.create_variable_for_type_inference(dtype='float32')
    out_wrong = helper.create_variable_for_type_inference(dtype='int32')
    out_correct = helper.create_variable_for_type_inference(dtype='int32')
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    helper.append_op(
        type="mean_iou",
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        inputs={"Predictions": input,
                "Labels": label},
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        outputs={
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            "OutMeanIou": out_mean_iou,
            "OutWrong": out_wrong,
            "OutCorrect": out_correct
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        },
        attrs={"num_classes": num_classes})
    return out_mean_iou, out_wrong, out_correct
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def crop(x, shape=None, offsets=None, name=None):
    """
    Crop input into output, as specified by offsets and shape.

    .. code-block:: text

        * Case 1:
            Given
                X = [[0, 1, 2, 0, 0]
                     [0, 3, 4, 0, 0]
                     [0, 0, 0, 0, 0]],
            and
                shape = [2, 2],
                offsets = [0, 1],
            output is:
                Out = [[1, 2],
                       [3, 4]].
        * Case 2:
            Given
                X = [[0, 1, 2, 5, 0]
                     [0, 3, 4, 6, 0]
                     [0, 0, 0, 0, 0]],
            and shape is tensor
                shape = [[0, 0, 0]
                         [0, 0, 0]]
            and
                offsets = [0, 1],

            output is:
                Out = [[1, 2, 5],
                       [3, 4, 6]].

    Args:
        x (Variable): The input tensor variable.
        shape (Variable|list/tuple of integer): The output shape is specified
            by `shape`, which can a Variable or a list/tupe of integer.
            If a tensor Variable, it's rank must be the same as `x`. This way
            is suitable for the case that the output shape may be changed each
            iteration. If a list/tupe of integer, it's length must be the same
            as the rank of `x`
        offsets (Variable|list/tuple of integer|None): Specifies the copping
            offsets at each dimension. It can be a Variable or or a list/tupe
            of integer. If a tensor Variable, it's rank must be the same as `x`.
            This way is suitable for the case that the offsets may be changed
            each iteration. If a list/tupe of integer, it's length must be the
            same as the rank of `x`. If None, the offsets are 0 at each
            dimension.
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.

    Returns:
        Variable: The cropped tensor variable.

    Raises:
        ValueError: If shape is not a list, tuple or Variable.

    Examples:

        .. code-block:: python

            x = fluid.layers.data(name="x", shape=[3, 5], dtype="float32")
            y = fluid.layers.data(name="y", shape=[2, 3], dtype="float32")
            crop = fluid.layers.crop(x, shape=y)

            # or
            z = fluid.layers.data(name="z", shape=[3, 5], dtype="float32")
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            crop = fluid.layers.crop(z, shape=[-1, 2, 3])
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    """
    helper = LayerHelper('crop', **locals())

    if not (isinstance(shape, list) or isinstance(shape, tuple) or \
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            isinstance(shape, Variable)):
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        raise ValueError("The shape should be a list, tuple or Variable.")

    if offsets is None:
        offsets = [0] * len(x.shape)

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    out = helper.create_variable_for_type_inference(x.dtype)
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    ipts = {'X': x}
    attrs = {}
    if isinstance(shape, Variable):
        ipts['Y'] = shape
    else:
        attrs['shape'] = shape
    if isinstance(offsets, Variable):
        ipts['Offsets'] = offsets
    else:
        attrs['offsets'] = offsets

    helper.append_op(
        type='crop',
        inputs=ipts,
        outputs={'Out': out},
        attrs=None if len(attrs) == 0 else attrs)
    return out
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def affine_grid(theta, out_shape, name=None):
    """
    It generates a grid of (x,y) coordinates using the parameters of
    the affine transformation that correspond to a set of points where
    the input feature map should be sampled to produce the transformed
    output feature map.

    .. code-block:: text

        * Case 1:

          Given:

              theta = [[[x_11, x_12, x_13]
                        [x_14, x_15, x_16]]
                       [[x_21, x_22, x_23]
                        [x_24, x_25, x_26]]]
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              out_shape = [2, 3, 5, 5]
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          Step 1:
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              Generate normalized coordinates according to out_shape.
              The values of the normalized coordinates are in the interval between -1 and 1.
              The shape of the normalized coordinates is [2, H, W] as below:
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              C = [[[-1.  -1.  -1.  -1.  -1. ]
                    [-0.5 -0.5 -0.5 -0.5 -0.5]
                    [ 0.   0.   0.   0.   0. ]
                    [ 0.5  0.5  0.5  0.5  0.5]
                    [ 1.   1.   1.   1.   1. ]]
                   [[-1.  -0.5  0.   0.5  1. ]
                    [-1.  -0.5  0.   0.5  1. ]
                    [-1.  -0.5  0.   0.5  1. ]
                    [-1.  -0.5  0.   0.5  1. ]
                    [-1.  -0.5  0.   0.5  1. ]]]
              C[0] is the coordinates in height axis and  C[1] is the coordinates in width axis.

          Step2:

              Tanspose and reshape C to shape [H * W, 2] and append ones to last dimension. The we get:
              C_ = [[-1.  -1.   1. ]
                    [-0.5 -1.   1. ]
                    [ 0.  -1.   1. ]
                    [ 0.5 -1.   1. ]
                    [ 1.  -1.   1. ]
                    [-1.  -0.5  1. ]
                    [-0.5 -0.5  1. ]
                    [ 0.  -0.5  1. ]
                    [ 0.5 -0.5  1. ]
                    [ 1.  -0.5  1. ]
                    [-1.   0.   1. ]
                    [-0.5  0.   1. ]
                    [ 0.   0.   1. ]
                    [ 0.5  0.   1. ]
                    [ 1.   0.   1. ]
                    [-1.   0.5  1. ]
                    [-0.5  0.5  1. ]
                    [ 0.   0.5  1. ]
                    [ 0.5  0.5  1. ]
                    [ 1.   0.5  1. ]
                    [-1.   1.   1. ]
                    [-0.5  1.   1. ]
                    [ 0.   1.   1. ]
                    [ 0.5  1.   1. ]
                    [ 1.   1.   1. ]]
          Step3:
              Compute output by equation $$Output[i] = C_ * Theta[i]^T$$

    Args:
        theta (Variable): A batch of affine transform parameters with shape [N, 2, 3].
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        out_shape (Variable | list | tuple): The shape of target output with format [N, C, H, W].
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                                             ``out_shape`` can be a Variable or a list or tuple.
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        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.

    Returns:
        Variable: The output with shape [N, H, W, 2].

    Raises:
        ValueError: If the type of arguments is not supported.

    Examples:

        .. code-block:: python
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            theta = fluid.layers.data(name="x", shape=[2, 3], dtype="float32")
            out_shape = fluid.layers.data(name="y", shape=[-1], dtype="float32")
            data = fluid.layers.affine_grid(theta, out_shape)

            # or
            data = fluid.layers.affine_grid(theta, [5, 3, 28, 28])

    """
    helper = LayerHelper('affine_grid')

    if not (isinstance(out_shape, list) or isinstance(out_shape, tuple) or \
8158
            isinstance(out_shape, Variable)):
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        raise ValueError("The out_shape should be a list, tuple or Variable.")

    if not isinstance(theta, Variable):
        raise ValueError("The theta should be a Variable.")

    out = helper.create_variable_for_type_inference(theta.dtype)
    ipts = {'Theta': theta}
    attrs = {}
    if isinstance(out_shape, Variable):
        ipts['OutputShape'] = out_shape
    else:
        attrs['output_shape'] = out_shape

    helper.append_op(
        type='affine_grid',
        inputs=ipts,
        outputs={'Output': out},
        attrs=None if len(attrs) == 0 else attrs)
    return out


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def rank_loss(label, left, right, name=None):
    """
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    **Rank loss layer for RankNet**

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    `RankNet <http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf>`_
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    is a pairwise ranking model with a training sample consisting of a pair
    of documents, A and B. Label P indicates whether A is ranked higher than B
    or not:
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    P = {0, 1} or {0, 0.5, 1}, where 0.5 means that there is no information
    about the rank of the input pair.
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    Rank loss layer takes three inputs: left ( :math:`o_i` ), right ( :math:`o_j` ) and
    label ( :math:`P_{i,j}` ). The inputs respectively represent RankNet's output scores
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    for documents A and B and the value of label P. The following equation
    computes rank loss C_{i,j} from the inputs:
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    .. math::

      C_{i,j} &= -\\tilde{P_{ij}} * o_{i,j} + \log(1 + e^{o_{i,j}}) \\\\

      o_{i,j} &=  o_i - o_j  \\\\

      \\tilde{P_{i,j}} &= \\left \{0, 0.5, 1 \\right \} \ or \ \\left \{0, 1 \\right \}

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    Rank loss layer takes batch inputs with size batch_size (batch_size >= 1).

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    Args:
        label (Variable): Indicats whether A ranked higher than B or not.
        left (Variable): RankNet's output score for doc A.
        right (Variable): RankNet's output score for doc B.
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.

    Returns:
        list: The value of rank loss.

    Raises:
        ValueError: Any of label, left, and right is not a variable.

    Examples:

        .. code-block:: python

            label = fluid.layers.data(name="label", shape=[4, 1], dtype="float32")
            left = fluid.layers.data(name="left", shape=[4, 1], dtype="float32")
            right = fluid.layers.data(name="right", shape=[4, 1], dtype="float32")
            out = fluid.layers.rank_loss(label, left, right)

    """
    helper = LayerHelper('rank_loss', **locals())

    if not (isinstance(label, Variable)):
        raise ValueError("The label should be a Variable")

    if not (isinstance(left, Variable)):
        raise ValueError("The left should be a Variable")

    if not (isinstance(right, Variable)):
        raise ValueError("The right should be a Variable")

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    out = helper.create_variable_for_type_inference("float32")
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    helper.append_op(
        type='rank_loss',
        inputs={"Label": label,
                "Left": left,
                "Right": right},
        outputs={'Out': out})
    return out
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def margin_rank_loss(label, left, right, margin=0.1, name=None):
    """
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    Margin Ranking Loss Layer for ranking problem,
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    which compares left score and right score passed in.
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    The ranking loss can be defined as following equation:
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    .. math::

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        rank\_loss = max(0, -label * (left - right) + margin)
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    Args:
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       label (Variable): Indicates whether the left is ranked higher than the right or not.
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       left (Variable): Ranking score for left.
       right (Variable): Ranking score for right.
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       margin (float): Indicates the given margin.
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       name (str|None): A name for this layer (optional). If set None, the layer
                       will be named automatically.
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    Returns:
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       Variable: The ranking loss.
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    Raises:
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       ValueError: Any of label, left, and right is not a Variable.
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    Examples:
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        .. code-block:: python
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           label = fluid.layers.data(name="label", shape=[4, 1], dtype="float32")
           left = fluid.layers.data(name="left", shape=[4, 1], dtype="float32")
           right = fluid.layers.data(name="right", shape=[4, 1], dtype="float32")
           out = fluid.layers.margin_rank_loss(label, left, right)
    """
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    helper = LayerHelper('margin_rank_loss', **locals())
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    if not isinstance(label, Variable):
        raise ValueError("The label should be a Variable.")
    if not isinstance(left, Variable):
        raise ValueError("The left should be a Variable.")
    if not isinstance(right, Variable):
        raise ValueError("The right should be a Variable.")
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    out = helper.create_variable_for_type_inference(left.dtype)
    act = helper.create_variable_for_type_inference(left.dtype)
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    helper.append_op(
        type='margin_rank_loss',
        inputs={"Label": label,
                "X1": left,
                "X2": right},
        outputs={'Out': out,
                 'Activated': act},
        attrs={'margin': margin})
    return out


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def pad2d(input,
          paddings=[0, 0, 0, 0],
          mode='constant',
          pad_value=0.0,
          data_format="NCHW",
          name=None):
    """
    Pad 2-d images accordding to 'paddings' and 'mode'.
    If mode is 'reflect', paddings[0] and paddings[1] must be no greater
    than height-1. And the width dimension has the same condition.

    Example:
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        .. code-block:: text
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	      Given that X is a channel of image from input:
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	      X = [[1, 2, 3],
		   [4, 5, 6]]
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	      Case 0:
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		paddings = [0, 1, 2, 3],
		mode = 'constant'
		pad_value = 0
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		Out = [[0, 0, 1, 2, 3, 0, 0, 0]
		       [0, 0, 4, 5, 6, 0, 0, 0]
		       [0, 0, 0, 0, 0, 0, 0, 0]]
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	      Case 1:
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		paddings = [0, 1, 2, 1],
		mode = 'reflect'
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		Out = [[3, 2, 1, 2, 3, 2]
		       [6, 5, 4, 5, 6, 5]
		       [3, 2, 1, 2, 3, 2]]
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	      Case 2:
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		paddings = [0, 1, 2, 1],
		mode = 'edge'
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		Out = [[1, 1, 1, 2, 3, 3]
		       [4, 4, 4, 5, 6, 6]
		       [4, 4, 4, 5, 6, 6]]
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    Args:
        input (Variable): The input image with [N, C, H, W] format or [N, H, W, C] format.
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        paddings (tuple|list|Variable): The padding size. If padding is a tuple, it must
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            contain four integers, (padding_top, padding_bottom, padding_left, padding_right).
            Default: padding = [0, 0, 0, 0].
        mode (str): Three modes: constant(default), reflect, edge. Default: constant
        pad_value (float32): The value to fill the padded areas in constant mode. Default: 0
        data_format (str): An optional string from: "NHWC", "NCHW". Specify the data format of
                           the input data.
                           Default: "NCHW"
        name (str|None): A name for this layer(optional). If set None, the layer
            will be named automatically.

    Returns:
        Variable: The tensor variable padded accordding to paddings and mode.


    Examples:
        .. code-block:: python

          data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32')
          result = fluid.layers.pad2d(input=data, padding=[1,2,3,4], mode='reflect')
    """

    helper = LayerHelper('pad2d', **locals())
    dtype = helper.input_dtype(input_param_name='input')
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    out = helper.create_variable_for_type_inference(dtype)
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    inputs = {'X': input}
    attrs = {'mode': mode, 'pad_value': pad_value, 'data_format': data_format}

    if isinstance(paddings, Variable):
        inputs['Paddings'] = paddings
        attrs['paddings'] = []
    else:
        attrs['paddings'] = paddings

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    helper.append_op(
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        type='pad2d', inputs=inputs, outputs={"Out": out}, attrs=attrs)
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    return out


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@templatedoc()
def elu(x, alpha=1.0, name=None):
    """
    ${comment}
    Args:
        x(${x_type}): ${x_comment}
        alpha(${alpha_type}|1.0): ${alpha_comment}
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.

    Returns:
        output(${out_type}): ${out_comment}
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    Examples:

        .. code-block:: python

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            x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
            y = fluid.layers.elu(x, alpha=0.2)
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    """
    helper = LayerHelper('elu', **locals())
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    out = helper.create_variable_for_type_inference(dtype=x.dtype)
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    helper.append_op(
        type='elu',
        inputs={'X': x},
        outputs={'Out': out},
        attrs={'alpha': alpha})
    return out


@templatedoc()
def relu6(x, threshold=6.0, name=None):
    """
    ${comment}
    Args:
        x(${x_type}): ${x_comment}
        threshold(${threshold_type}|6.0): ${threshold_comment}
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.

    Returns:
        output(${out_type}): ${out_comment}
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    Examples:

        .. code-block:: python

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            x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
            y = fluid.layers.relu6(x, threshold=6.0)
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    """
    helper = LayerHelper('relu6', **locals())
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    out = helper.create_variable_for_type_inference(dtype=x.dtype)
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    helper.append_op(
        type='relu6',
        inputs={'X': x},
        outputs={'Out': out},
        attrs={'threshold': threshold})
    return out


@templatedoc()
def pow(x, factor=1.0, name=None):
    """
    ${comment}
    Args:
        x(${x_type}): ${x_comment}
        factor(${factor_type}|1.0): ${factor_comment}
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.

    Returns:
        output(${out_type}): ${out_comment}
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    Examples:

        .. code-block:: python

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            x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
            y = fluid.layers.pow(x, factor=2.0)
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    """
    helper = LayerHelper('pow', **locals())
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    out = helper.create_variable_for_type_inference(dtype=x.dtype)
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    helper.append_op(
        type='pow',
        inputs={'X': x},
        outputs={'Out': out},
        attrs={'factor': factor})
    return out


@templatedoc()
def stanh(x, scale_a=2.0 / 3.0, scale_b=1.7159, name=None):
    """
    ${comment}
    Args:
        x(${x_type}): ${x_comment}
        scale_a(${scale_a_type}|2.0 / 3.0): ${scale_a_comment}
        scale_b(${scale_b_type}|1.7159): ${scale_b_comment}
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.

    Returns:
        output(${out_type}): ${out_comment}
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    Examples:

        .. code-block:: python

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            x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
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            y = fluid.layers.stanh(x, scale_a=0.67, scale_b=1.72)
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    """
    helper = LayerHelper('stanh', **locals())
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    out = helper.create_variable_for_type_inference(dtype=x.dtype)
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    helper.append_op(
        type='stanh',
        inputs={'X': x},
        outputs={'Out': out},
        attrs={'scale_a': scale_a,
               'scale_b': scale_b})
    return out


@templatedoc()
def hard_sigmoid(x, slope=0.2, offset=0.5, name=None):
    """
    ${comment}
    Args:
        x(${x_type}): ${x_comment}
        slope(${slope_type}|0.2): ${slope_comment}
        offset(${offset_type}|0.5): ${offset_comment}
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.

    Returns:
        output(${out_type}): ${out_comment}
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    Examples:

        .. code-block:: python

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            x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
            y = fluid.layers.hard_sigmoid(x, slope=0.3, offset=0.8)
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    """
    helper = LayerHelper('hard_sigmoid', **locals())
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    out = helper.create_variable_for_type_inference(dtype=x.dtype)
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    helper.append_op(
        type='hard_sigmoid',
        inputs={'X': x},
        outputs={'Out': out},
        attrs={'slope': slope,
               'offset': offset})
    return out


@templatedoc()
def swish(x, beta=1.0, name=None):
    """
    ${comment}
    Args:
        x(${x_type}): ${x_comment}
        beta(${beta_type}|1.0): ${beta_comment}
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.

    Returns:
        output(${out_type}): ${out_comment}
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    Examples:

        .. code-block:: python

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            x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
            y = fluid.layers.swish(x, beta=2.0)
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    """
    helper = LayerHelper('swish', **locals())
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    out = helper.create_variable_for_type_inference(dtype=x.dtype)
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    helper.append_op(
        type='swish',
        inputs={'X': x},
        outputs={'Out': out},
        attrs={'slope': beta})
    return out


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def prelu(x, mode, param_attr=None, name=None):
    """
    Equation:

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    .. math::
        y = \max(0, x) + \\alpha * \min(0, x)
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    Args:
        x (Variable): The input tensor.
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        param_attr(ParamAttr|None): The parameter attribute for the learnable
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          weight (alpha).
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        mode (string): The mode for weight sharing. It supports all, channel
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          and element. all: all elements share same weight
          channel:elements in a channel share same weight
          element:each element has a weight
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        name(str|None): A name for this layer(optional). If set None, the layer
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          will be named automatically.
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    Returns:
        Variable: The output tensor with the same shape as input.

    Examples:

        .. code-block:: python

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            x = fluid.layers.data(name="x", shape=[10,10], dtype="float32")
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            mode = 'channel'
            output = fluid.layers.prelu(x,mode)
    """
    helper = LayerHelper('prelu', **locals())
    if mode not in ['all', 'channel', 'element']:
        raise ValueError('mode should be one of all, channel, element.')
    alpha_shape = [1]
    if mode == 'channel':
        alpha_shape = [1, x.shape[1], 1, 1]
    elif mode == 'element':
        alpha_shape = x.shape
    dtype = helper.input_dtype(input_param_name='x')
    alpha = helper.create_parameter(
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        attr=helper.param_attr,
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        shape=alpha_shape,
        dtype='float32',
        is_bias=False,
        default_initializer=Constant(1.0))
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    out = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
        type="prelu",
        inputs={"X": x,
                'Alpha': alpha},
        attrs={"mode": mode},
        outputs={"Out": out})
    return out


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@templatedoc()
def brelu(x, t_min=0.0, t_max=24.0, name=None):
    """
    ${comment}
    Args:
        x(${x_type}): ${x_comment}
        t_min(${t_min_type}|0.0): ${t_min_comment}
        t_max(${t_max_type}|24.0): ${t_max_comment}
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.
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    Returns:
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        output(${out_type}): ${out_comment}
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    Examples:

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    .. code-block:: python
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            x = fluid.layers.data(name="x", shape=[2,3,16,16], dtype="float32")
            y = fluid.layers.brelu(x, t_min=1.0, t_max=20.0)
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    """
    helper = LayerHelper('brelu', **locals())
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    out = helper.create_variable_for_type_inference(dtype=x.dtype)
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    helper.append_op(
        type='brelu',
        inputs={'X': x},
        outputs={'Out': out},
        attrs={'t_min': t_min,
               't_max': t_max})
    return out


@templatedoc()
def leaky_relu(x, alpha=0.02, name=None):
    """
    ${comment}
    Args:
        x(${x_type}): ${x_comment}
        alpha(${alpha_type}|0.02): ${alpha_comment}
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.
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    Returns:
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        output(${out_type}): ${out_comment}
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    Examples:

        .. code-block:: python

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            x = fluid.layers.data(name="x", shape=[2,3,16,16], dtype="float32")
            y = fluid.layers.leaky_relu(x, alpha=0.01)
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    """
    helper = LayerHelper('leaky_relu', **locals())
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    out = helper.create_variable_for_type_inference(dtype=x.dtype)
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    helper.append_op(
        type='leaky_relu',
        inputs={'X': x},
        outputs={'Out': out},
        attrs={'alpha': alpha})
    return out


@templatedoc()
def soft_relu(x, threshold=40.0, name=None):
    """
    ${comment}
    Args:
        x(${x_type}): ${x_comment}
        threshold(${threshold_type}|40.0): ${threshold_comment}
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.
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    Returns:
8705
        output(${out_type}): ${out_comment}
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    Examples:

        .. code-block:: python

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            x = fluid.layers.data(name="x", shape=[2,3,16,16], dtype="float32")
            y = fluid.layers.soft_relu(x, threshold=20.0)
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    """
    helper = LayerHelper('soft_relu', **locals())
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    out = helper.create_variable_for_type_inference(dtype=x.dtype)
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    helper.append_op(
        type='soft_relu',
        inputs={'X': x},
        outputs={'Out': out},
        attrs={'threshold': threshold})
    return out


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def flatten(x, axis=1, name=None):
    """
    **Flatten layer**
    Flattens the input tensor into a 2D matrix.
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    For Example:
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    .. code-block:: text
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        Case 1:

          Given
            X.shape = (3, 100, 100, 4)

          and
            axis = 2

          We get:
            Out.shape = (3 * 100, 4 * 100)

        Case 2:

          Given
            X.shape = (3, 100, 100, 4)

          and
            axis = 0

          We get:
            Out.shape = (1, 3 * 100 * 100 * 4)
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    Args:
        x (Variable): A tensor of rank >= axis.
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        axis (int): Indicate up to which input dimensions (exclusive) should
                    be flattened to the outer dimension of the output.
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                    The value for axis must be in the range [0, R], where R
                    is the rank of the input tensor. When axis = 0, the shape
                    of the output tensor is (1, (d_0 X d_1 ... d_n), where the
                    shape of the input tensor is (d_0, d_1, ... d_n).
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.

    Returns:
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        Variable: A 2D tensor with the contents of the input tensor, with input \
                  dimensions up to axis flattened to the outer dimension of \
                  the output and remaining input dimensions flattened into the \
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                  inner dimension of the output.

    Raises:
        ValueError: If x is not a variable.
8774
        ValueError: If axis is not in range [0, rank(x)].
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    Examples:

        .. code-block:: python

            x = fluid.layers.data(name="x", shape=[4, 4, 3], dtype="float32")
            out = fluid.layers.flatten(x=x, axis=2)
    """
    helper = LayerHelper('flatten', **locals())

    if not (isinstance(x, Variable)):
        raise ValueError("The input x should be a Variable")

    if not (isinstance(axis, int)) or axis > len(x.shape) or axis < 0:
        raise ValueError("The axis should be a int, and in range [0, rank(x)]")

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    out = helper.create_variable_for_type_inference(x.dtype)
    x_shape = helper.create_variable_for_type_inference(x.dtype)
8793
    helper.append_op(
8794
        type='flatten2',
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        inputs={"X": x},
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        outputs={'Out': out,
                 'XShape': x_shape},
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        attrs={"axis": axis})
    return out
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def sequence_enumerate(input, win_size, pad_value=0, name=None):
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    """
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    Generate a new sequence for the input index sequence, which enumerates all the
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    sub-sequences with length `win_size` of the input.
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    The enumerated sequence has the same 1st dimension with variable `input`, and
    the 2nd dimension is `win_size`, padded by `pad_value` if necessary in generation.
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    .. code-block:: text

        Case 1:

          Input:
            X.lod = [[0, 3, 5]]
            X.data = [[1], [2], [3], [4], [5]]
            X.dims = [5, 1]

          Attrs:
            win_size = 2
            pad_value = 0

          Output:
            Out.lod = [[0, 3, 5]]
            Out.data = [[1, 2], [2, 3], [3, 0], [4, 5], [5, 0]]
            Out.dims = [5, 2]
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    Args:
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        input (Variable): The input variable which is a index sequence.
        win_size (int): The window size for enumerating all sub-sequences.
        pad_value (int): The padding value, default 0.
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    Returns:
        Variable: The enumerate sequence variable which is a LoDTensor.

    Examples:
        .. code-block:: python

            x = fluid.layers.data(shape[30, 1], dtype='int32', lod_level=1)
            out = fluid.layers.sequence_enumerate(input=x, win_size=3, pad_value=0)
    """
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    assert not in_dygraph_mode(), (
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        "sequence layer is not supported in dygraph mode yet.")
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    helper = LayerHelper('sequence_enumerate', **locals())
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    out = helper.create_variable_for_type_inference(
        helper.input_dtype(), stop_gradient=True)
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    helper.append_op(
        type='sequence_enumerate',
        inputs={'X': input},
        outputs={'Out': out},
        attrs={'win_size': win_size,
               'pad_value': pad_value})
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    return out
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def sequence_mask(x, maxlen=None, dtype='int64', name=None):
    """
    **SequenceMask Layer**

    This layer outputs a mask according to the input :code:`x` and
    :code:`maxlen` with data type of :code:`dtype`.

    Supposing :code:`x` is a Tensor with shape [d_1, d_2, ..., d_n], the
    :code:`y` is a mask with shape [d_1, d_2, ..., d_n, maxlen], where:
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    .. math::
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        y(i_1, i_2,..., i_n, j) = (j < x(i_1, i_2,..., i_n))

    Args:
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        x (Variable): Input tensor of sequence_mask layer,
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                      whose elements are integers less than :code:`maxlen`.
        maxlen (int|None): Maximum length of the sequence. If :code:`maxlen`
                           is None, it would be replace with :math:`max(x)`.
        dtype (np.dtype|core.VarDesc.VarType|str): Data type of the output.
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        name (str|None): A name for this layer(optional). If set None, the
                         layer will be named automatically.

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    Returns:
        Variable: The output sequence mask.
8880

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    """
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    assert not in_dygraph_mode(), (
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        "sequence layer is not supported in dygraph mode yet.")
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    helper = LayerHelper('sequence_mask', **locals())
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    if name is None:
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        out = helper.create_variable_for_type_inference(dtype=dtype)
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    else:
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        out = helper.create_variable_for_type_inference(dtype=dtype, name=name)
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    helper.append_op(
        type='sequence_mask',
        inputs={'X': [x]},
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        outputs={'Y': out},
        attrs={
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            'maxlen': maxlen if maxlen is not None else -1,
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            'out_dtype': out.dtype
        })
    return out
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def stack(x, axis=0):
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    """
    **Stack Layer**

    This layer stacks all of the input :code:`x` along axis.
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    Input :code:`x` can be a single variable, a :code:`list` of variables,
    or a :code:`tuple` of variables. If :code:`x` is a :code:`list` or
    :code:`tuple`, the shapes of all these variables must be the same.
    Supposing the shape of each input is :math:`[d_0, d_1, ..., d_{n-1}]`,
    the shape of the output variable would be
    :math:`[d_0, d_1, ..., d_{axis}=len(x), ..., d_{n-1}]`.
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    If :code:`axis` < 0, it would be replaced with :code:`axis+rank(x[0])+1`.
8915
    If :code:`axis` is None, it would be replaced with 0.
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    For Example:

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    .. code-block:: text

        Case 1:
          Input:
            x[0].data = [ [1.0 , 2.0 ] ]
            x[0].dims = [1, 2]
            x[1].data = [ [3.0 , 4.0 ] ]
            x[1].dims = [1, 2]
            x[2].data = [ [5.0 , 6.0 ] ]
            x[2].dims = [1, 2]

          Attrs:
            axis = 0

          Output:
            Out.data =[ [ [1.0, 2.0] ],
                        [ [3.0, 4.0] ],
                        [ [5.0, 6.0] ] ]
            Out.dims = [3, 1, 2]

        Case 2:
          Given
            x[0].data = [ [1.0 , 2.0 ] ]
            x[0].dims = [1, 2]
            x[1].data = [ [3.0 , 4.0 ] ]
            x[1].dims = [1, 2]
            x[2].data = [ [5.0 , 6.0 ] ]
            x[2].dims = [1, 2]

          Attrs:
            axis = 1 or axis = -2

          Output:
            Out.data =[ [ [1.0, 2.0]
                          [3.0, 4.0]
                          [5.0, 6.0] ] ]
            Out.dims = [1, 3, 2]

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    Args:
8958
        x (Variable|list(Variable)|tuple(Variable)): Input variables.
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        axis (int|None): The axis along which all inputs are stacked.
8960

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    Returns:
        Variable: The stacked variable.
8963

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    """

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    helper = LayerHelper('stack', **locals())
    axis = 0 if axis is None else axis

    if not isinstance(x, list) and not isinstance(x, tuple):
        x = [x]

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    out = helper.create_variable_for_type_inference(x[0].dtype)
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    helper.append_op(
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        type='stack', inputs={'X': x}, outputs={'Y': out},
        attrs={'axis': axis})
8976

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    return out
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def unstack(x, axis=0, num=None):
    """
    **UnStack Layer**

    This layer unstacks input :code:`x` into several tensors along axis.
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    If :code:`axis` < 0, it would be replaced with :code:`axis+rank(x)`.
    If :code:`num` is None, it would be inferred from :code:`x.shape[axis]`,
    and if :code:`x.shape[axis]` <= 0 or is unknown, :code:`ValueError` is
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    raised.
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    Args:
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        x (Variable): Input variable.
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        axis (int): The axis along which the input is unstacked.
        num (int|None): The number of output variables.
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    Returns:
        list(Variable): The unstacked variables.
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    """

    helper = LayerHelper('unstack', **locals())
    if num is None:
        if axis is None or x.shape[axis] <= 0:
            raise ValueError('unknown unstack number')
        else:
            num = x.shape[axis]

    outs = []
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    for _ in range(num):
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        outs.append(helper.create_variable_for_type_inference(x.dtype))
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    helper.append_op(
        type='unstack',
        inputs={'X': [x]},
        outputs={'Y': outs},
        attrs={'axis': axis,
               'num': num})
    return outs
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def expand(x, expand_times, name=None):
    """Expand operator tiles the input by given times number. You should set times
    number for each dimension by providing attribute 'expand_times'. The rank of X
    should be in [1, 6]. Please note that size of 'expand_times' must be the same
    with X's rank. Following is a using case:


    .. code-block:: text

        Input(X) is a 3-D tensor with shape [2, 3, 1]:
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                [
                   [[1], [2], [3]],
                   [[4], [5], [6]]
                ]
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        Attr(expand_times):  [1, 2, 2]
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        Output(Out) is a 3-D tensor with shape [2, 6, 2]:
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                [
                    [[1, 1], [2, 2], [3, 3], [1, 1], [2, 2], [3, 3]],
                    [[4, 4], [5, 5], [6, 6], [4, 4], [5, 5], [6, 6]]
                ]
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    Args:
        x (Variable): A tensor with rank in [1, 6].
        expand_times (list|tuple): Expand times number for each dimension.

    Returns:
        Variable: The expanded variable which is a LoDTensor. After expanding, size of each dimension of Output(Out) is equal to ithe size of the corresponding dimension of Input(X) multiplying the corresponding value given by expand_times.


    Examples:
        .. code-block:: python

            x = fluid.layers.data(name='x', shape=[10], dtype='float32')
            out = fluid.layers.expand(x=x, expand_times=[1, 2, 2])
    """
    helper = LayerHelper('expand', input=x, **locals())
    dtype = helper.input_dtype(input_param_name='x')
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    out = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
        type='expand',
        inputs={'X': x},
        outputs={'Out': out},
        attrs={'expand_times': expand_times})
    return out
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from paddle.fluid.framework import convert_np_dtype_to_dtype_


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@templatedoc()
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def uniform_random_batch_size_like(input,
                                   shape,
                                   dtype='float32',
                                   input_dim_idx=0,
                                   output_dim_idx=0,
                                   min=-1.0,
                                   max=1.0,
                                   seed=0):
    """
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    ${comment}
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    Args:
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        input (Variable): ${input_comment}
        shape (tuple|list): ${shape_comment}
        input_dim_idx (Int): ${input_dim_idx_comment}
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        output_dim_idx (Int): ${output_dim_idx_comment}
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        min (Float): ${min_comment}
        max (Float): ${max_comment}
        seed (Int): ${seed_comment}
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        dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc
    Returns:
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        out (Variable): ${out_comment}
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9098 9099 9100 9101 9102
    Examples:
        .. code-block:: python

            input = layers.data(name="input", shape=[13, 11], dtype='float32')
            out = layers.uniform_random_batch_size_like(input, [-1, 11])
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    """

    helper = LayerHelper('uniform_random_batch_size_like', **locals())
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    out = helper.create_variable_for_type_inference(dtype)
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    c_dtype = convert_np_dtype_to_dtype_(dtype)
    helper.append_op(
        type='uniform_random_batch_size_like',
        inputs={'Input': input},
        outputs={'Out': out},
        attrs={
            'shape': shape,
            'input_dim_idx': input_dim_idx,
            'output_dim_idx': output_dim_idx,
            'min': min,
            'max': max,
            'seed': seed,
            'dtype': c_dtype
        })

    return out
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@templatedoc()
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def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
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    """
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    ${comment}
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    Args:
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        shape (tuple|list): ${shape_comment}
        mean (Float): ${mean_comment}
        std (Float): ${std_comment}
        seed (Int): ${seed_comment}
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        dtype(np.dtype|core.VarDesc.VarType|str): Output data type.

    Returns:
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        out (Variable): ${out_comment}
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9140 9141 9142 9143
    Examples:
        .. code-block:: python

            out = layers.gaussian_random(shape=[20, 30])
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    """

    helper = LayerHelper('gaussian_random', **locals())
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    out = helper.create_variable_for_type_inference(dtype)
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    c_dtype = convert_np_dtype_to_dtype_(dtype)
    helper.append_op(
        type='gaussian_random',
        outputs={'Out': out},
        attrs={
            'shape': shape,
            'mean': mean,
            'std': std,
            'seed': seed,
            'dtype': c_dtype,
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            'use_mkldnn': False
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        })

    return out


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@templatedoc()
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def sampling_id(x, min=0.0, max=1.0, seed=0, dtype='float32'):
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    """
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    ${comment}
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    Args:
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        x (Variable): ${x_comment}
        min (Float): ${min_comment}
        max (Float): ${max_comment}
        seed (Float): ${seed_comment}
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        dtype(np.dtype|core.VarDesc.VarType|str): The type of output data : float32, float_16, int etc
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    Returns:
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        out (Variable): ${out_comment}
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    Examples:
        .. code-block:: python

            x = layers.data(
                name="X",
                shape=[13, 11],
                dtype='float32',
                append_batch_size=False)

            out = layers.sampling_id(x)
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    """

    helper = LayerHelper('sampling_id', **locals())
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    out = helper.create_variable_for_type_inference(dtype)
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    helper.append_op(
        type='sampling_id',
        inputs={'X': x},
        outputs={'Out': out},
        attrs={'min': min,
               'max': max,
               'seed': seed})

    return out


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@templatedoc()
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def gaussian_random_batch_size_like(input,
                                    shape,
                                    input_dim_idx=0,
                                    output_dim_idx=0,
                                    mean=0.0,
                                    std=1.0,
                                    seed=0,
                                    dtype='float32'):
    """
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    ${comment}
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    Args:
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        input (Variable): ${input_comment}
        shape (tuple|list): ${shape_comment}
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        input_dim_idx (Int): ${input_dim_idx_comment}
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        output_dim_idx (Int): ${output_dim_idx_comment}
        mean (Float): ${mean_comment}
        std (Float): ${std_comment}
        seed (Int): ${seed_comment}
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        dtype(np.dtype|core.VarDesc.VarType|str): The type of output data : float32, float_16, int etc
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    Returns:
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        out (Variable): ${out_comment}
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    Examples:
        .. code-block:: python

            input = layers.data(name="input", shape=[13, 11], dtype='float32')

            out = layers.gaussian_random_batch_size_like(
                input, shape=[-1, 11], mean=1.0, std=2.0)
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    """

    helper = LayerHelper('gaussian_random_batch_size_like', **locals())
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    out = helper.create_variable_for_type_inference(dtype)
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    c_dtype = convert_np_dtype_to_dtype_(dtype)
    helper.append_op(
        type='gaussian_random_batch_size_like',
        inputs={'Input': input},
        outputs={'Out': out},
        attrs={
            'shape': shape,
            'input_dim_idx': input_dim_idx,
            'output_dim_idx': output_dim_idx,
            'mean': mean,
            'std': std,
            'seed': seed,
            'dtype': c_dtype
        })

    return out


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@templatedoc()
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def sum(x):
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    """
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    ${comment}
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    Args:
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        x (Variable): ${x_comment}
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    Returns:
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        out (Variable): ${out_comment}
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    Examples:
        .. code-block:: python

            input = layers.data(name="input", shape=[13, 11], dtype='float32')
            out = layers.sum(input)
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    """

    helper = LayerHelper('sum', **locals())
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    out = helper.create_variable_for_type_inference(
        dtype=helper.input_dtype('x'))
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    helper.append_op(
        type='sum',
        inputs={'X': x},
        outputs={'Out': out},
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        attrs={'use_mkldnn': False})
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    return out


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@templatedoc()
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def slice(input, axes, starts, ends):
    """
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    ${comment}
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    Args:
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        input (Variable): ${input_comment}.
        axes (List): ${axes_comment}
        starts (List): ${starts_comment}
        ends (List): ${ends_comment}
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    Returns:
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        out (Variable): ${out_comment}
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    Examples:
        .. code-block:: python

            starts = [1, 0, 2]
            ends = [3, 3, 4]
            axes = [0, 1, 2]

            input = layers.data(
                name="input", shape=[3, 4, 5, 6], dtype='float32')

            out = layers.slice(input, axes=axes, starts=starts, ends=ends)
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    """

    helper = LayerHelper('slice', **locals())
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    out = helper.create_variable_for_type_inference(
        dtype=helper.input_dtype('input'))
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    helper.append_op(
        type='slice',
        inputs={'Input': input},
        outputs={'Out': out},
        attrs={'axes': axes,
               'starts': starts,
               'ends': ends})

    return out


def shape(input):
    """
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    **Shape Layer**

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    Get the shape of the input.
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    Args:
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        input (Variable): The input variable.
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    Returns:
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        Variable: The shape of the input variable.
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    Examples:
        .. code-block:: python

            input = layers.data(
                name="input", shape=[3, 100, 100], dtype="float32")
            out = layers.shape(input)
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    """

    helper = LayerHelper('shape', **locals())
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    out = helper.create_variable_for_type_inference(dtype='int32')
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    helper.append_op(
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        type='shape', inputs={'Input': input}, outputs={'Out': out})
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    return out
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def rank(input):
    """
    **Rank Layer**

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    Returns the number of dimensions for a tensor, which is a 0-D int32 Tensor.
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    Args:
        input (Variable): The input variable.

    Returns:
        Variable: The rank of the input variable.

    Examples:
        .. code-block:: python

            input = layers.data(
                name="input", shape=[3, 100, 100], dtype="float32")
            rank = layers.rank(input) # 4
    """

    ndims = len(input.shape)
    out = assign(np.array(ndims, 'int32'))

    return out


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def _elementwise_op(helper):
    op_type = helper.layer_type
    x = helper.kwargs.get('x', None)
    y = helper.kwargs.get('y', None)
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    if in_dygraph_mode():
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        x = base.to_variable(x)
        y = base.to_variable(y)

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    assert x is not None, 'x cannot be None in {}'.format(op_type)
    assert y is not None, 'y cannot be None in {}'.format(op_type)
    axis = helper.kwargs.get('axis', -1)
    use_mkldnn = helper.kwargs.get('use_mkldnn', False)
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    name = helper.kwargs.get('name', None)
    if name is None:
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        out = helper.create_variable_for_type_inference(dtype=x.dtype)
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    else:
        out = helper.create_variable(
            name=name, dtype=x.dtype, persistable=False)
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    helper.append_op(
        type=op_type,
        inputs={'X': x,
                'Y': y},
        outputs={'Out': out},
        attrs={'axis': axis,
               'use_mkldnn': use_mkldnn})
    return helper.append_activation(out)


@templatedoc()
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def scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None):
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    """
    ${comment}

    Args:
        x(${x_type}): ${x_comment}
        scale(${scale_type}): ${scale_comment}
        bias(${bias_type}): ${bias_comment}
        bias_after_scale(${bias_after_scale_type}): ${bias_after_scale_comment}
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        act(basestring|None): Activation applied to the output.
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        name(basestring|None): Name of the output.
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    Returns:
        out(${out_type}): ${out_comment}
    """

    helper = LayerHelper('scale', **locals())
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    if name is None:
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        out = helper.create_variable_for_type_inference(dtype=x.dtype)
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    else:
        out = helper.create_variable(
            name=name, dtype=x.dtype, persistable=False)
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    helper.append_op(
        type='scale',
        inputs={'X': x},
        outputs={'Out': out},
        attrs={
            'scale': float(scale),
            'bias': float(bias),
            'bias_after_scale': bias_after_scale
        })
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    return helper.append_activation(out)
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def elementwise_add(x, y, axis=-1, act=None, name=None):
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    return _elementwise_op(LayerHelper('elementwise_add', **locals()))


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def elementwise_div(x, y, axis=-1, act=None, name=None):
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    return _elementwise_op(LayerHelper('elementwise_div', **locals()))


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def elementwise_sub(x, y, axis=-1, act=None, name=None):
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    return _elementwise_op(LayerHelper('elementwise_sub', **locals()))


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def elementwise_mul(x, y, axis=-1, act=None, name=None):
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    return _elementwise_op(LayerHelper('elementwise_mul', **locals()))


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def elementwise_max(x, y, axis=-1, act=None, name=None):
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    return _elementwise_op(LayerHelper('elementwise_max', **locals()))


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def elementwise_min(x, y, axis=-1, act=None, name=None):
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    return _elementwise_op(LayerHelper('elementwise_min', **locals()))


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def elementwise_pow(x, y, axis=-1, act=None, name=None):
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    return _elementwise_op(LayerHelper('elementwise_pow', **locals()))


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def elementwise_mod(x, y, axis=-1, act=None, name=None):
    return _elementwise_op(LayerHelper('elementwise_mod', **locals()))


def elementwise_floordiv(x, y, axis=-1, act=None, name=None):
    return _elementwise_op(LayerHelper('elementwise_floordiv', **locals()))


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for func in [
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        elementwise_add,
        elementwise_div,
        elementwise_sub,
        elementwise_mul,
        elementwise_max,
        elementwise_min,
        elementwise_pow,
        elementwise_mod,
        elementwise_floordiv,
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]:
    op_proto = OpProtoHolder.instance().get_op_proto(func.__name__)
    func.__doc__ = _generate_doc_string_(
        op_proto,
        additional_args_lines=[
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            "act (basestring|None): Activation applied to the output.",
            "name (basestring|None): Name of the output."
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        ])
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def _logical_op(op_name, x, y, out=None, name=None, binary_op=True):
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    helper = LayerHelper(op_name, **locals())

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    if binary_op:
        assert x.dtype == y.dtype
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    if out is None:
        if name is None:
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            out = helper.create_variable_for_type_inference(dtype=x.dtype)
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        else:
            out = helper.create_variable(
                name=name, dtype=x.dtype, persistable=False)

    if binary_op:
        helper.append_op(
            type=op_name, inputs={"X": x,
                                  "Y": y}, outputs={"Out": out})
    else:
        helper.append_op(type=op_name, inputs={"X": x}, outputs={"Out": out})

    return out


@templatedoc()
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def logical_and(x, y, out=None, name=None):
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    """
    ${comment}

    Args:
        x(${x_type}): ${x_comment}
        y(${y_type}): ${y_comment}
        out(Tensor): Output tensor of logical operation.
        name(basestring|None): Name of the output.

    Returns:
        out(${out_type}): ${out_comment}
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    Examples:
        .. code-block:: python

            left = fluid.layers.data(
                name='left', shape=[1], dtype='int32')
            right = fluid.layers.data(
                name='right', shape=[1], dtype='int32')
            result = fluid.layers.logical_and(x=left, y=right)
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    """

    return _logical_op(
        op_name="logical_and", x=x, y=y, name=name, out=out, binary_op=True)


@templatedoc()
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def logical_or(x, y, out=None, name=None):
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    """
    ${comment}

    Args:
        x(${x_type}): ${x_comment}
        y(${y_type}): ${y_comment}
        out(Tensor): Output tensor of logical operation.
        name(basestring|None): Name of the output.

    Returns:
        out(${out_type}): ${out_comment}
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    Examples:
        .. code-block:: python

            left = fluid.layers.data(
                name='left', shape=[1], dtype='int32')
            right = fluid.layers.data(
                name='right', shape=[1], dtype='int32')
            result = fluid.layers.logical_or(x=left, y=right)
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    """

    return _logical_op(
        op_name="logical_or", x=x, y=y, name=name, out=out, binary_op=True)


@templatedoc()
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def logical_xor(x, y, out=None, name=None):
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    """
    ${comment}

    Args:
        x(${x_type}): ${x_comment}
        y(${y_type}): ${y_comment}
        out(Tensor): Output tensor of logical operation.
        name(basestring|None): Name of the output.

    Returns:
        out(${out_type}): ${out_comment}
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    Examples:
        .. code-block:: python

            left = fluid.layers.data(
                name='left', shape=[1], dtype='int32')
            right = fluid.layers.data(
                name='right', shape=[1], dtype='int32')
            result = fluid.layers.logical_xor(x=left, y=right)
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    """

    return _logical_op(
        op_name="logical_xor", x=x, y=y, name=name, out=out, binary_op=True)


@templatedoc()
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def logical_not(x, out=None, name=None):
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    """
    ${comment}

    Args:
        x(${x_type}): ${x_comment}
        out(Tensor): Output tensor of logical operation.
        name(basestring|None): Name of the output.

    Returns:
        out(${out_type}): ${out_comment}
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    Examples:
        .. code-block:: python

            left = fluid.layers.data(
                name='left', shape=[1], dtype='int32')
            result = fluid.layers.logical_not(x=left)
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    """

    return _logical_op(
        op_name="logical_not", x=x, y=None, name=name, out=out, binary_op=False)
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@templatedoc()
def clip(x, min, max, name=None):
    """
    ${comment}

    Args:
        x(${x_type}): ${x_comment}
        min(${min_type}): ${min_comment}
        max(${max_type}): ${max_comment}
        name(basestring|None): Name of the output.

    Returns:
        out(${out_type}): ${out_comment}
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    Examples:
        .. code-block:: python

            input = fluid.layers.data(
                name='data', shape=[1], dtype='float32')
            reward = fluid.layers.clip(x=input, min=-1.0, max=1.0)
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    """

    helper = LayerHelper("clip", **locals())

    if name is None:
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        name = unique_name.generate(".".join([helper.name, 'tmp']))

    out = helper.create_variable(
        type=x.type, name=name, dtype=x.dtype, persistable=False)
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    helper.append_op(
        type="clip",
        inputs={"X": x},
        attrs={"min": min,
               "max": max},
        outputs={"Out": out})

    return out


@templatedoc()
def clip_by_norm(x, max_norm, name=None):
    """
    ${comment}

    Args:
        x(${x_type}): ${x_comment}
        max_norm(${max_norm_type}): ${max_norm_comment}
        name(basestring|None): Name of the output.

    Returns:
        out(${out_type}): ${out_comment}
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    Examples:
        .. code-block:: python

            input = fluid.layers.data(
                name='data', shape=[1], dtype='float32')
            reward = fluid.layers.clip_by_norm(x=input, max_norm=1.0)
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    """

    helper = LayerHelper("clip_by_norm", **locals())

    if name is None:
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        name = unique_name.generate(".".join([helper.name, 'tmp']))

    out = helper.create_variable(
        type=x.type, name=name, dtype=x.dtype, persistable=False)
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    helper.append_op(
        type="clip_by_norm",
        inputs={"X": x},
        attrs={"max_norm": max_norm},
        outputs={"Out": out})

    return out
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@templatedoc()
def mean(x, name=None):
    """
    ${comment}

    Args:
        x(${x_type}): ${x_comment}
        name(basestring|None): Name of the output.

    Returns:
        out(${out_type}): ${out_comment}
    """

    helper = LayerHelper("mean", **locals())

    if name is None:
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        out = helper.create_variable_for_type_inference(dtype=x.dtype)
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    else:
        out = helper.create_variable(
            name=name, dtype=x.dtype, persistable=False)

    helper.append_op(
        type="mean", inputs={"X": x}, attrs={}, outputs={"Out": out})

    return out


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@templatedoc()
def merge_selected_rows(x, name=None):
    """
    ${comment}

    Args:
        x(${x_type}): ${x_comment}
        name(basestring|None): Name of the output.

    Returns:
        out(${out_type}): ${out_comment}
    """

    helper = LayerHelper("merge_selected_rows", **locals())
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
    helper.append_op(
        type="merge_selected_rows",
        inputs={"X": x},
        attrs={},
        outputs={"Out": out})
    return out


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@templatedoc()
def mul(x, y, x_num_col_dims=1, y_num_col_dims=1, name=None):
    """
    ${comment}

    Args:
        x(${x_type}): ${x_comment}
        y(${y_type}): ${y_comment}
        x_num_col_dims(${x_num_col_dims_type}): ${x_num_col_dims_comment}
        y_num_col_dims(${y_num_col_dims_type}): ${y_num_col_dims_comment}
        name(basestring|None): Name of the output.

    Returns:
        out(${out_type}): ${out_comment}
    """

    helper = LayerHelper("mul", **locals())

    if name is None:
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        out = helper.create_variable_for_type_inference(dtype=x.dtype)
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    else:
        out = helper.create_variable(
            name=name, dtype=x.dtype, persistable=False)

    helper.append_op(
        type="mul",
        inputs={"X": x,
                "Y": y},
        attrs={
X
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            "x_num_col_dims": x_num_col_dims,
            "y_num_col_dims": y_num_col_dims
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        },
        outputs={"Out": out})
    return out


@templatedoc()
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def sigmoid_cross_entropy_with_logits(x,
                                      label,
                                      ignore_index=kIgnoreIndex,
9804 9805
                                      name=None,
                                      normalize=False):
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    """
    ${comment}

    Args:
        x(${x_type}): ${x_comment}
        label(${label_type}): ${label_comment}
9812
        ignore_index(&{ignore_index}): ${ignore_index_comment}
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        name(basestring|None): Name of the output.
9814 9815
        normalize(bool): If true, divide the output by the number of
            targets != ignore_index.
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    Returns:
        out(${out_type}): ${out_comment}
9819 9820 9821 9822 9823 9824 9825 9826 9827 9828 9829 9830 9831 9832

    Examples:
        .. code-block:: python

            input = fluid.layers.data(
                name='data', shape=[10], dtype='float32')
            label = fluid.layers.data(
                name='data', shape=[10], dtype='float32')
            loss = fluid.layers.sigmoid_cross_entropy_with_logits(
                x=input,
                label=label,
                ignore_index=-1,
                normalize=True) # or False
            # loss = fluid.layers.reduce_sum(loss) # summation of loss
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    """

    helper = LayerHelper("sigmoid_cross_entropy_with_logits", **locals())

    if name is None:
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        out = helper.create_variable_for_type_inference(dtype=x.dtype)
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    else:
        out = helper.create_variable(
            name=name, dtype=x.dtype, persistable=False)

    helper.append_op(
        type="sigmoid_cross_entropy_with_logits",
        inputs={"X": x,
                "Label": label},
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        attrs={"ignore_index": ignore_index,
               'normalize': normalize},
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        outputs={"Out": out})
    return out


@templatedoc()
def maxout(x, groups, name=None):
    """
    ${comment}

    Args:
        x(${x_type}): ${x_comment}
        groups(${groups_type}): ${groups_comment}
        name(basestring|None): Name of the output.

    Returns:
        out(${out_type}): ${out_comment}
    """
    helper = LayerHelper("maxout", **locals())

    if name is None:
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        out = helper.create_variable_for_type_inference(dtype=x.dtype)
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    else:
        out = helper.create_variable(
            name=name, dtype=x.dtype, persistable=False)

    helper.append_op(
        type="maxout",
        inputs={"X": x},
        attrs={"groups": groups},
        outputs={"Out": out})
    return out
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def space_to_depth(x, blocksize, name=None):
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    """
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    Gives a blocksize to space_to_depth the input LoDtensor with Layout: [batch, channel, height, width]
9885 9886 9887

    This op rearranges blocks of spatial data, into depth. More specifically, this op outputs a copy of the
    input LoDtensor where values from the height and width dimensions are moved to the channel dimension.
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    The attr blocksize indicates the input block size.
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    space_to_depth will reorgnize the elements of input with shape[batch, channel, height, width] according
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    to blocksize to construct output with shape [batch, channel * blocksize * blocksize, height/blocksize, width/blocksize]:
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    space_to_depth is used to This operation is useful for resizing the activations between convolutions
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    (but keeping all data)
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    - Non-overlapping blocks of size block_size x block size are rearranged into depth at each location.
9897
    - The depth of the output tensor is block_size * block_size * input channel
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    - The Y, X coordinates within each block of the input become the high order component of the output channel index
    - channel should be divisible by square of blocksize
    - height, width should be divsible by blocksize


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    Args:
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        x(variable): The input LoDtensor.
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        blocksize(variable): The blocksize to select the element on each feature map should be > 2
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    Returns:
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        Variable: The output LoDtensor.
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    Raises:
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        TypeError: blocksize type must be a long.
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    Examples:
        .. code-block:: python

            data = fluid.layers.data(
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                name='data', shape=[1, 4, 2, 2], dtype='float32', append_batch_size=False)
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            space_to_depthed = fluid.layers.space_to_depth(
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                x=data, blocksize=2)
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            exe = fluid.Executor(fluid.CUDAPlace(0))
            data_np = np.arange(0,16).reshape((1,4,2,2)).astype('float32')
            out_main = exe.run(fluid.default_main_program(),
                          feed={'data': data_np},
                          fetch_list=[space_to_depthed])
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    """

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    helper = LayerHelper("space_to_depth", **locals())
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    if not (isinstance(blocksize, int)):
        raise ValueError("blocksize must be a python Int")
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    if name is None:
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        out = helper.create_variable_for_type_inference(
            dtype=x.dtype)  #fix create
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    else:
        out = helper.create_variable(
            name=name, dtype=x.dtype, persistable=False)

    helper.append_op(
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        type="space_to_depth",
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        inputs={"X": x},
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        attrs={"blocksize": blocksize},
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        outputs={"Out": out})
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    return out

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@templatedoc()
def sequence_reverse(x, name=None):
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    """
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    ${comment}

    Args:
        x(${x_type}): ${x_comment}
        name(basestring|None): Name of the output.

    Returns:
        out(${y_type}): ${y_comment}
    """
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    assert not in_dygraph_mode(), (
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        "sequence layer is not supported in dygraph mode yet.")
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    helper = LayerHelper("sequence_reverse", **locals())
    if name is None:
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        out = helper.create_variable_for_type_inference(dtype=x.dtype)
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    else:
        out = helper.create_variable(
            name=name, dtype=x.dtype, persistable=False)

    helper.append_op(
        type="sequence_reverse",
        inputs={"X": x},
        outputs={"Y": out},
        attrs=dict())
    return out
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def affine_channel(x,
                   scale=None,
                   bias=None,
                   data_layout='NCHW',
                   name=None,
                   act=None):
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    """
    Applies a separate affine transformation to each channel of the input.
    Useful for replacing spatial batch norm with its equivalent fixed
    transformation. The input also can be 2D tensor and applies a affine
    transformation in second dimension.
9988

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    Args:
        x (Variable): Feature map input can be a 4D tensor with order NCHW
            or NHWC. It also can be a 2D tensor and the affine transformation
            is applied in the second dimension.
        scale (Variable): 1D input of shape (C), the c-th element is the scale
            factor of the affine transformation for the c-th channel of
            the input.
        bias (Variable): 1D input of shape (C), the c-th element is the bias
            of the affine transformation for the c-th channel of the input.
        data_layout (string, default NCHW): NCHW or NHWC. If input is 2D
            tensor, you can ignore data_layout.
        name (str, default None): The name of this layer.
10001
        act (str, default None): Activation to be applied to the output of this layer.
10002 10003 10004 10005 10006 10007 10008

    Returns:
        out (Variable): A tensor of the same shape and data layout with x.
    """
    helper = LayerHelper("affine_channel", **locals())

    if name is None:
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        out = helper.create_variable_for_type_inference(dtype=x.dtype)
10010 10011 10012 10013 10014 10015 10016 10017 10018 10019 10020
    else:
        out = helper.create_variable(
            name=name, dtype=x.dtype, persistable=False)

    helper.append_op(
        type="affine_channel",
        inputs={"X": x,
                'Scale': scale,
                'Bias': bias},
        attrs={"data_layout": data_layout},
        outputs={"Out": out})
10021
    return helper.append_activation(out)
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def similarity_focus(input, axis, indexes, name=None):
10025
    """
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    SimilarityFocus Operator
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    Generate a similarity focus mask with the same shape of input using the following method:
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10030 10031 10032
    1. Extract the 3-D tensor(here the first dimension is BatchSize) corresponding
       to the axis according to the indexes. For example, if axis=1 and indexes=[a],
       it will get the matrix T=X[:, a, :, :]. In this case, if the shape of input X
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       is (BatchSize, A, B, C), the shape of tensor T is (BatchSize, B, C).
10034 10035 10036 10037 10038 10039 10040
    2. For each index, find the largest numbers in the tensor T, so that the same
       row and same column has at most one number(what it means is that if the
       largest number has been found in the i-th row and the j-th column, then
       the numbers in the i-th row or j-th column will be skipped. And then the
       next largest number will be selected from the remaining numbers. Obviously
       there will be min(B, C) numbers), and mark the corresponding position of the
       3-D similarity focus mask as 1, otherwise as 0. Do elementwise-or for
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       each index.
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    3. Broadcast the 3-D similarity focus mask to the same shape of input X.

    Refer to `Similarity Focus Layer <http://www.aclweb.org/anthology/N16-1108>`_

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    .. code-block:: text

        * Example :

            Given a 4-D tensor x with the shape (BatchSize, C, A, B), where C is
            the number of channels and the shape of feature map is (A, B):
                x.shape = (2, 3, 2, 2)
                x.data = [[[[0.8, 0.1],
                            [0.4, 0.5]],

                           [[0.9, 0.7],
                            [0.9, 0.9]],

                           [[0.8, 0.9],
                            [0.1, 0.2]]],


                          [[[0.2, 0.5],
                            [0.3, 0.4]],

                           [[0.9, 0.7],
                            [0.8, 0.4]],

                           [[0.0, 0.2],
                            [0.4, 0.7]]]]

            Given axis: 1 (the axis of the channel)
            Given indexes: [0]

            then we get a 4-D tensor out with the same shape of input x:
                out.shape = (2, 3, 2, 2)
                out.data = [[[[1.0, 0.0],
                              [0.0, 1.0]],

                             [[1.0, 0.0],
                              [0.0, 1.0]],

                             [[1.0, 0.0],
                              [0.0, 1.0]]],

                            [[[0.0, 1.0],
                              [1.0, 0.0]],

                             [[0.0, 1.0],
                              [1.0, 0.0]],

                             [[0.0, 1.0],
                              [1.0, 0.0]]]]

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    Args:
10096
        input(Variable): The input tensor variable(default float). It should
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            be a 4-D tensor with shape [BatchSize, A, B, C].
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        axis(int): Indicating the dimension to be selected. It can only be
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            1, 2 or 3.
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        indexes(list): Indicating the indexes of the selected dimension.
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    Returns:
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        Variable: A tensor variable with the same shape and same type \
                  as the input.
10105

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    Examples:
        .. code-block:: python
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            data = fluid.layers.data(
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              name='data', shape=[2, 3, 2, 2], dtype='float32')
            x = fluid.layers.layer_norm(input=data, axis=1, indexes=[0])
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    """
    helper = LayerHelper('similarity_focus', **locals())
    # check attrs
    if isinstance(axis, int) is False:
        raise TypeError("axis must be int type.")
    if isinstance(indexes, list) is False:
        raise TypeError("indexes must be list type.")
    if axis != 1 and axis != 2 and axis != 3:
        raise ValueError("axis must be 1, 2 or 3.")
    if len(indexes) == 0:
        raise ValueError("indexes can not be empty.")

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    if name is None:
        out = helper.create_variable_for_type_inference(dtype=input.dtype)
    else:
        out = helper.create_variable(
            name=name, dtype=input.dtype, persistable=False)
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    helper.append_op(
        type='similarity_focus',
        inputs={'X': input},
        outputs={'Out': out},
        attrs={"axis": axis,
               "indexes": indexes})
    return out
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def hash(input, hash_size, num_hash=1, name=None):
    """
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    Hash the input to an integer whose value is less than the given hash size.

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    The hash algorithm we used was xxHash - Extremely fast hash algorithm
    (https://github.com/Cyan4973/xxHash/tree/v0.6.5)
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    A simple example as below:

    .. code-block:: text

        Given:

        # shape [2, 2]
        input.data = [
            [[1], [2]],
            [[3], [4]],
        ]

        input.lod = [[0, 2]]

        hash_size = 10000

        num_hash = 4

        Then:

        Hash op will take all number in input's 2nd dimension as hash algorithm's
        input for each time. Each input will be hashed for 4 times, and get an
        array whose length is 4. Each value in the array ranges from 0 to 9999.

        # shape [2, 4]
        output.data = [
            [[9662], [9217], [1129], [8487]],
            [[8310], [1327], [1654], [4567]],
        ]

        output.lod = [[0, 2]]

    Args:
        input (Variable): The input variable which is a one-hot word. The
            dimensions of the input variable must be 2.
        hash_size (int): The space size for hash algorithm. The output value
            will keep in the range:math:`[0, hash_size - 1]`.
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        num_hash (int): The times of hash, default 1.
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        name (str, default None): The name of this layer.
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    Returns:
       Variable: The hash result variable which is a LoDTensor.

    Examples:
       .. code-block:: python
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           word_dict = paddle.dataset.imdb.word_dict()
           x = fluid.layers.data(shape[1], dtype='int32', lod_level=1)
           out = fluid.layers.hash(input=x, num_hash=4, hash_size=1000)
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    """
    helper = LayerHelper('hash', **locals())
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    out = helper.create_variable_for_type_inference(
        helper.input_dtype(), stop_gradient=True)
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    helper.append_op(
        type='hash',
        inputs={'X': input},
        outputs={'Out': out},
        attrs={'num_hash': num_hash,
               'mod_by': hash_size})
    return out
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@templatedoc()
10209 10210
def grid_sampler(x, grid, name=None):
    """
10211
    This operation samples input X by using bilinear interpolation based on
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    flow field grid, which is usually gennerated by :code:`affine_grid` . The grid of
10213 10214 10215 10216
    shape [N, H, W, 2] is the concatenation of (grid_x, grid_y) coordinates
    with shape [N, H, W] each, where grid_x is indexing the 4th dimension
    (in width dimension) of input data x and grid_y is indexng the 3rd
    dimention (in height dimension), finally results is the bilinear
10217
    interpolation value of 4 nearest corner points.
10218

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    .. code-block:: text
10220

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        Step 1:
        Get (x, y) grid coordinates and scale to [0, H-1/W-1].
10223

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        grid_x = 0.5 * (grid[:, :, :, 0] + 1) * (W - 1)
        grid_y = 0.5 * (grid[:, :, :, 1] + 1) * (H - 1)
10226

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        Step 2:
        Indices input data X with grid (x, y) in each [H, W] area, and bilinear
        interpolate point value by 4 nearest points.
10230

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          wn ------- y_n ------- en
          |           |           |
          |          d_n          |
          |           |           |
         x_w --d_w-- grid--d_e-- x_e
          |           |           |
          |          d_s          |
          |           |           |
          ws ------- y_s ------- wn
10240

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        x_w = floor(x)              // west side x coord
        x_e = x_w + 1               // east side x coord
        y_n = floor(y)              // north side y coord
        y_s = y_s + 1               // south side y coord
10245

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        d_w = grid_x - x_w          // distance to west side
        d_e = x_e - grid_x          // distance to east side
        d_n = grid_y - y_n          // distance to north side
        d_s = y_s - grid_y          // distance to south side
10250

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        wn = X[:, :, y_n, x_w]      // north-west point value
        en = X[:, :, y_n, x_e]      // north-east point value
        ws = X[:, :, y_s, x_w]      // south-east point value
        es = X[:, :, y_s, x_w]      // north-east point value
10255

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        output = wn * d_e * d_s + en * d_w * d_s
               + ws * d_e * d_n + es * d_w * d_n
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    Args:
10260 10261 10262
        x(Variable): Input data of shape [N, C, H, W].
        grid(Variable): Input grid tensor of shape [N, H, W, 2].
        name (str, default None): The name of this layer.
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    Returns:
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        Variable: Output of shape [N, C, H, W] data samples input X
10266 10267
        using bilnear interpolation based on input grid.

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    Examples:

        .. code-block:: python

            x = fluid.layers.data(name='x', shape=[3, 10, 32, 32], dtype='float32')
            theta = fluid.layers.data(name='theta', shape=[3, 2, 3], dtype='float32')
            grid = fluid.layers.affine_grid(input=theta, size=[3, 10, 32, 32]})
            out = fluid.layers.grid_sampler(x=x, grid=grid)
10276

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    """
    helper = LayerHelper("grid_sampler", **locals())

    if not isinstance(x, Variable):
        return ValueError("The x should be a Variable")

    if not isinstance(grid, Variable):
        return ValueError("The grid should be a Variable")

10286
    out = helper.create_variable_for_type_inference(x.dtype)
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    ipts = {'X': x, 'Grid': grid}

10289
    helper.append_op(type='grid_sampler', inputs=ipts, outputs={'Output': out})
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    return out


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def log_loss(input, label, epsilon=1e-4, name=None):
    """
    **Negative Log Loss Layer**

    This layer accepts input predictions and target label and returns the
    negative log loss.

    .. math::

        Out = -label * \\log{(input + \\epsilon)}
              - (1 - label) * \\log{(1 - input + \\epsilon)}

    Args:
        input (Variable|list):  a 2-D tensor with shape [N x 1], where N is the
                                batch size. This input is a probability computed
                                by the previous operator.
        label (Variable|list):  the ground truth which is a 2-D tensor with
                                shape [N x 1], where N is the batch size.
        epsilon (float): epsilon
        name (string): the name of log_loss

    Returns:
        Variable: A 2-D tensor with shape [N x 1], the negative log loss.

    Examples:
        .. code-block:: python

          prob = fluid.layers.sigmoid(net)
          cost = fluid.layers.log_loss(input=prob, label=label)
    """
    helper = LayerHelper('log_loss', **locals())

    if name is None:
        loss = helper.create_variable_for_type_inference(dtype=input.dtype)
    else:
        loss = helper.create_variable(
            name=name, dtype=input.dtype, persistable=False)

    helper.append_op(
        type='log_loss',
        inputs={'Predicted': [input],
                'Labels': [label]},
        outputs={'Loss': [loss]},
        attrs={'epsilon': epsilon})
    return loss


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def teacher_student_sigmoid_loss(input,
                                 label,
                                 soft_max_up_bound=15.0,
                                 soft_max_lower_bound=-15.0):
    """
    **Teacher Student Log Loss Layer**

    This layer accepts input predictions and target label and returns the
    teacher_student loss.

    .. math::
        loss = max(x, 0) - x * z + log(1 + exp(-abs(x))) + max(x, 0) - x * z' + log(1 + exp(-abs(x)))

    Args:
        input (Variable|list):  a 2-D tensor with shape [N x 1], where N is the
                                batch size. This input is a probability computed
                                by the previous operator.
        label (Variable|list):  the ground truth which is a 2-D tensor with
                                shape [N x 1], where N is the batch size.
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        soft_max_up_bound  (float):  if input > soft_max_up_bound, will be bound
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        soft_max_lower_bound (float): if input < soft_max_lower_bound, will be bound

    Returns:
        Variable: A 2-D tensor with shape [N x 1], the teacher_student_sigmoid_loss.

    Examples:
        .. code-block:: python
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          cost = fluid.layers.teacher_student_sigmoid_loss(input=similarity, label=label)
    """
    helper = LayerHelper('teacher_student_sigmoid_loss', **locals())
    out = helper.create_variable(dtype=input.dtype)
    helper.append_op(
        type='teacher_student_sigmoid_loss',
        inputs={'X': [input],
                'Label': [label]},
        outputs={'Y': [out]},
        attrs={"soft_max_lower_bound": float(soft_max_lower_bound), \
                "soft_max_up_bound": float(soft_max_up_bound)})
    return out


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def add_position_encoding(input, alpha, beta, name=None):
    """
    **Add Position Encoding Layer**

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    This layer accepts an input 3D-Tensor of shape [N x M x P], and returns an
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    output Tensor of shape [N x M x P] with positional encoding value.

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    Refer to `Attention Is All You Need <http://arxiv.org/pdf/1706.03762.pdf>`_ .
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    .. math::
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        PE(pos, 2i) &= \\sin{(pos / 10000^{2i / P})}   \\\\
        PE(pos, 2i + 1) &= \\cos{(pos / 10000^{2i / P})}  \\\\
        Out(:, pos, i) &= \\alpha * input(:, pos, i) + \\beta * PE(pos, i)
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    Where:
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      - :math:`PE(pos, 2i)` : the increment for the number at even position
      - :math:`PE(pos, 2i + 1)` : the increment for the number at odd position
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    Args:
        input (Variable): 3-D input tensor with shape [N x M x P]
        alpha (float): multiple of Input Tensor
        beta (float): multiple of Positional Encoding Tensor
        name (string): the name of position encoding layer

    Returns:
        Variable: A 3-D Tensor of shape [N x M x P] with positional encoding.

    Examples:
        .. code-block:: python

          position_tensor = fluid.layers.add_position_encoding(input=tensor)
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    """
    helper = LayerHelper('add_position_encoding', **locals())
    dtype = helper.input_dtype()

    if name is None:
        out = helper.create_variable_for_type_inference(dtype=dtype)
    else:
        out = helper.create_variable(name=name, dtype=dtype, persistable=False)

    helper.append_op(
        type="add_position_encoding",
        inputs={"X": input},
        outputs={"Out": out},
        attrs={"alpha": alpha,
               "beta": beta})
    return out
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def bilinear_tensor_product(x,
                            y,
                            size,
                            act=None,
                            name=None,
                            param_attr=None,
                            bias_attr=None):
    """
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    **Add Bilinear Tensor Product Layer**
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    This layer performs bilinear tensor product on two inputs.
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    For example:

    .. math::
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       out_{i} = x * W_{i} * {y^\mathrm{T}}, i=0,1,...,size-1
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    In this formula:
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      - :math:`x`: the first input contains M elements, shape is [batch_size, M].
      - :math:`y`: the second input contains N elements, shape is [batch_size, N].
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      - :math:`W_{i}`: the i-th learned weight, shape is [M, N]
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      - :math:`out_{i}`: the i-th element of out, shape is [batch_size, size].
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      - :math:`y^\mathrm{T}`: the transpose of :math:`y_{2}`.

    Args:
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        x (Variable): 2-D input tensor with shape [batch_size, M]
        y (Variable): 2-D input tensor with shape [batch_size, N]
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        size (int): The dimension of this layer.
        act (str, default None): Activation to be applied to the output of this layer.
        name (str, default None): The name of this layer.
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        param_attr (ParamAttr, default None): The parameter attribute for the learnable w.
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            parameters/weights of this layer.
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        bias_attr (ParamAttr, default None): The parameter attribute for the bias
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            of this layer. If it is set to False, no bias will be added to the output units.
            If it is set to None, the bias is initialized zero. Default: None.

    Returns:
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        Variable: A 2-D Tensor of shape [batch_size, size].
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    Examples:
        .. code-block:: python

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          tensor = bilinear_tensor_product(x=layer1, y=layer2, size=1000)
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    """
    helper = LayerHelper('bilinear_tensor_product', **locals())
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    dtype = helper.input_dtype('x')
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    param_shape = [size, x.shape[1], y.shape[1]]

    w = helper.create_parameter(
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        attr=helper.param_attr, shape=param_shape, dtype=dtype, is_bias=False)
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    if name is None:
        out = helper.create_variable_for_type_inference(dtype=dtype)
    else:
        out = helper.create_variable(name=name, dtype=dtype, persistable=False)

    inputs = {"X": x, "Y": y, "Weight": w}
    if helper.bias_attr:
        bias_size = [1, size]
        bias = helper.create_parameter(
            attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
        inputs["Bias"] = bias
    helper.append_op(
        type="bilinear_tensor_product", inputs=inputs, outputs={"Out": out})

    # add activation
    return helper.append_activation(out)
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@templatedoc()
def get_tensor_from_selected_rows(x, name=None):
    """
    ${comment}

    Args:
        x(${x_type}): ${x_comment}
        name(basestring|None): Name of the output.

    Returns:
        out(${out_type}): ${out_comment}
    """

    helper = LayerHelper('get_tensor_from_selected_rows', **locals())
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
    helper.append_op(
        type='get_tensor_from_selected_rows',
        inputs={'X': x},
        outputs={'Out': out},
        attrs={})
    return out
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def shuffle_channel(x, group, name=None):
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    """
    **Shuffle Channel Operator**
10527

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    This operator shuffles the channels of input x.
    It divide the input channels in each group into :attr:`group` subgroups,
    and obtain a new order by selecting element from every subgroup one by one.

    Please refer to the paper
    https://arxiv.org/pdf/1707.01083.pdf
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    .. code-block:: text
10536

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        Given a 4-D tensor input with the shape (N, C, H, W):
            input.shape = (1, 4, 2, 2)
            input.data =[[[[0.1, 0.2],
                           [0.2, 0.3]],

                          [[0.3, 0.4],
                           [0.4, 0.5]],

                          [[0.5, 0.6],
                           [0.6, 0.7]],

                          [[0.7, 0.8],
                           [0.8, 0.9]]]]
            Given group: 2
            then we get a 4-D tensor out whth the same shape of input:
            out.shape = (1, 4, 2, 2)
            out.data = [[[[0.1, 0.2],
                          [0.2, 0.3]],
                          
                         [[0.5, 0.6],
                          [0.6, 0.7]],
                          
                         [[0.3, 0.4],
                          [0.4, 0.5]],
                          
                         [[0.7, 0.8],
                          [0.8, 0.9]]]]
                        
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    Args: 
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        x(Variable): The input tensor variable. It should be a 4-D tensor with shape [N, C, H, W]
        group(int): Indicating the conuts of subgroups, It should divide the number of channels.
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    Returns:
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        out(Variable): the channels shuffling result is a tensor variable with the 
        same shape and same type as the input.
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    Raises:
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        ValueError: If group is not an int type variable.
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    Examples:
        .. code-block:: python
10578 10579

            input = fluid.layers.data(name='input', shape=[4,2,2], dtype='float32')
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            out = fluid.layers.shuffle_channel(x=input, group=2)
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    """
    helper = LayerHelper("shuffle_channel", **locals())

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    out = helper.create_variable_for_type_inference(dtype=x.dtype)
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    if not isinstance(group, int):
        raise TypeError("group must be int type")

    helper.append_op(
        type="shuffle_channel",
        inputs={"X": x},
        outputs={"Out": out},
        attrs={"group": group})
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    return out
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10597
@templatedoc()
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def temporal_shift(x, seg_num, shift_ratio=0.25, name=None):
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    """
    **Temporal Shift Operator**
    
    ${comment}
                        
    Args: 
        x(Variable): ${x_comment}
        seg_num(int): ${seg_num_comment}
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        shift_ratio(float): ${shift_ratio_comment}
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        name (str, default None): The name of this layer.
10609 10610 10611 10612 10613 10614 10615 10616 10617 10618 10619 10620

    Returns:
        out(Variable): The temporal shifting result is a tensor variable with the 
        same shape and same type as the input.

    Raises:
        TypeError: seg_num must be int type.

    Examples:
        .. code-block:: python

            input = fluid.layers.data(name='input', shape=[4,2,2], dtype='float32')
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            out = fluid.layers.temporal_shift(x=input, seg_num=2, shift_ratio=0.2)
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    """
    helper = LayerHelper("temporal_shift", **locals())

    out = helper.create_variable_for_type_inference(dtype=x.dtype)

    if not isinstance(seg_num, int):
        raise TypeError("seg_num must be int type.")

    helper.append_op(
        type="temporal_shift",
        inputs={"X": x},
        outputs={"Out": out},
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        attrs={"seg_num": seg_num,
               "shift_ratio": shift_ratio})
10636 10637 10638
    return out


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class PyFuncRegistry(object):
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    _register_funcs = []

    def __init__(self, func):
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        if func is None or not callable(func):
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            raise TypeError('func must be a Python function')

        self._func = func
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        # find named args using reflection
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        args = inspect.getargspec(self._func)
        if len(args[0]) == 0 and args[1] is None and args[2] is None:
            # Function with no inputs
            self._named_args = None
        else:
            self._named_args = args[0]
        self._id = core._append_python_callable_object_and_return_id(self)
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        '''
        Why record self here?

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        1. For debug usage. Users can call
           :code:`py_func.registered_func(idx)` method
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           to find the registered function corresponding
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           to :code:`idx`.
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        2. For increasing reference count of self.
           It seems that to release Python object
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           whose reference count is 1 would cause
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           segmentation fault error in C++ side.
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           May be lack of Python GC in C++ side?
        '''
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        PyFuncRegistry._register_funcs.append(self)
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    @classmethod
    def registered_func(cls, idx):
        return cls._register_funcs[idx]._func

    @classmethod
    def registered_func_num(cls):
        return len(cls._register_funcs)

    @property
    def id(self):
        return self._id

    def __call__(self, *args):
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        if self._named_args is None:
            func_ret = self._func()
        else:
            kwargs = dict()
            idx = 0
            for arg in self._named_args:
                kwargs[arg] = args[idx]
                idx += 1
            func_ret = self._func(*args[idx:], **kwargs)
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        if not isinstance(func_ret, (list, tuple)):
            func_ret = (func_ret, )
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        ret = []
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        for each_ret in func_ret:
            if each_ret is None or isinstance(each_ret, core.LoDTensor):
                ret.append(each_ret)
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                continue

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            if not isinstance(each_ret, np.ndarray):
                each_ret = np.array(each_ret)
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            tensor = core.LoDTensor()
            tensor.set(each_ret, core.CPUPlace())
            ret.append(tensor)
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        return tuple(ret)
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@templatedoc()
def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
    """
    PyFunc Operator.
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    User can use :code:`py_func` to register operators in Python side.
    The inputs of :code:`func` is :code:`LoDTensor` and outputs can be
    numpy array or :code:`LoDTensor`. Paddle would call the registered
    :code:`func` in forward part, and call :code:`backward_func` in
    backward part (if :code:`backward_func` is not None).

    User should set the right data type and shape of :code:`out` before
    calling this function. However, data types and shapes of gradients of
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    :code:`out` and :code:`x` would be inferred automatically.
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    Input orders of :code:`backward_func` would be: forward inputs
    :code:`x`, forward outputs :code:`out` and backward input gradients of
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    :code:`out`. If some variables of :code:`out` have no gradient, the input
    tensor would be None in Python side. If some variables of :code:`in` have
    no gradient, users should return None.

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    This function can also be used to debug the running network. User can
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    add a :code:`py_func` operator without output, and print input
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    :code:`x` inside :code:`func`.

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    Args:
        func (callable): forward Python function.
        x (Variable|list(Variable)|tuple(Variable)): inputs of :code:`func`.
        out (Variable|list(Variable)|tuple(Variable)): outputs of :code:`func`.
            Paddle cannot infer shapes and data types of :code:`out`. Users
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            should create :code:`out` beforehand.
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        backward_func (callable|None): backward Python function.
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                                       None means no backward. Default None.
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        skip_vars_in_backward_input (Variable|list(Variable)|tuple(Variable)):
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            Variables that are not needed in :code:`backward_func` inputs.
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            These variables must be any of :code:`x` and :code:`out`.
            If set, these vars would not be inputs of :code:`backward_func`,
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            Only useful when :code:`backward_func` is not None. Default None.
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    Returns:
        out (Variable|list(Variable)|tuple(Variable)): input :code:`out`
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    Examples:
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        >>> import paddle.fluid as fluid
        >>> import six
        >>>
        >>> def create_tmp_var(name, dtype, shape):
        >>>     return fluid.default_main_program().current_block().create_var(
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        >>>         name=name, dtype=dtype, shape=shape)
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        >>>
        >>> # tanh activation has been provided by Paddle C++ op
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        >>> # Here, we only use tanh to be an example to show the usage
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        >>> # of py_func
        >>> def tanh(x):
        >>>     return np.tanh(x)
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        >>>
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        >>> # forward input x is skipped
        >>> def tanh_grad(y, dy):
        >>>     return np.array(dy) * (1 - np.square(np.array(y)))
        >>>
        >>> def debug_func(x):
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        >>>     print(x)
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        >>>
        >>> def simple_net(img, label):
        >>>     hidden = img
        >>>     for idx in six.moves.range(4):
        >>>         hidden = fluid.layers.fc(hidden, size=200)
        >>>         new_hidden = create_tmp_var(name='hidden_{}'.format(idx),
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        >>>             dtype=hidden.dtype, shape=hidden.shape)
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        >>>
        >>>         # user-defined layers with forward and backward
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        >>>         hidden = fluid.layers.py_func(func=tanh, x=hidden,
        >>>             out=new_hidden, backward_func=tanh_grad,
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        >>>             skip_vars_in_backward_input=hidden)
        >>>
        >>>         # user-defined debug layers to print variables
        >>>         fluid.layers.py_func(func=debug_func, x=hidden, out=None)
        >>>
        >>>     prediction = fluid.layers.fc(hidden, size=10, act='softmax')
        >>>     loss = fluid.layers.cross_entropy(input=prediction, label=label)
        >>>     return fluid.layers.mean(loss)
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    """
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    helper = LayerHelper('py_func', **locals())
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    if x is None:
        x = []
    elif isinstance(x, Variable):
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        x = [x]
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    elif not isinstance(x, (list, tuple)):
        raise TypeError('Input must be Variable/list(Variable)/tuple(Variable)')
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    if out is None:
        out_list = []
    elif isinstance(out, Variable):
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        out_list = [out]
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    elif isinstance(out, (list, tuple)):
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        out_list = out
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    else:
        raise TypeError(
            'Output must be Variable/list(Variable)/tuple(Variable)')
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    fwd_func_id = PyFuncRegistry(func).id
    bwd_func_id = PyFuncRegistry(
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        backward_func).id if backward_func is not None else -1
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    for each_out in out_list:
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        if len(each_out.shape) == 0:
            raise ValueError(
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                'Output shapes of py_func op should be provided by users manually'
            )
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    backward_skip_vars = set()
    if backward_func is not None and skip_vars_in_backward_input is not None:
        if isinstance(skip_vars_in_backward_input, Variable):
            skip_vars_in_backward_input = [skip_vars_in_backward_input]

        fwd_in_out = [v.name for v in x]
        fwd_in_out.extend([v.name for v in out_list])
        fwd_in_out = set(fwd_in_out)
        backward_skip_vars = set()
        for v in skip_vars_in_backward_input:
            if not v.name in fwd_in_out:
                raise ValueError(
                    'Variable {} is not found in forward inputs and outputs'
                    .format(v.name))
            backward_skip_vars.add(v.name)
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    helper.append_op(
        type='py_func',
        inputs={'X': x},
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        outputs={'Out': out_list},
        attrs={
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            'forward_callable_id': fwd_func_id,
            'backward_callable_id': bwd_func_id,
            'backward_skip_vars': list(backward_skip_vars)
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        })
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    return out
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# For debug usage
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py_func.registered_func = PyFuncRegistry.registered_func
py_func.registered_func_num = PyFuncRegistry.registered_func_num


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@templatedoc()
def psroi_pool(input,
               rois,
               output_channels,
               spatial_scale,
               pooled_height,
               pooled_width,
               name=None):
    """
    ${comment}

    Args:
        input (Variable): ${x_comment}
        rois (Variable): ROIs (Regions of Interest) to pool over.
        output_channels (integer): ${output_channels_comment}
        spatial_scale (float): ${spatial_scale_comment} Default: 1.0
        pooled_height (integer): ${pooled_height_comment} Default: 1
        pooled_width (integer): ${pooled_width_comment} Default: 1
        name (str, default None): The name of this layer.

    Returns:
        Variable: ${out_comment}.

    Examples:
        .. code-block:: python

            pool_out = fluid.layers.psroi_pool(input=x, rois=rois, 490, 1.0, 7, 7)
    """
    helper = LayerHelper('psroi_pool', **locals())
    # check attrs
    if not isinstance(output_channels, int):
        raise TypeError("output_channels must be int type")
    if not isinstance(spatial_scale, float):
        raise TypeError("spatial_scale must be float type")
    if not isinstance(pooled_height, int):
        raise TypeError("pooled_height must be int type")
    if not isinstance(pooled_width, int):
        raise TypeError("pooled_width must be int type")
    dtype = helper.input_dtype()
    out = helper.create_variable_for_type_inference(dtype)
    helper.append_op(
        type='psroi_pool',
        inputs={'X': input,
                'ROIs': rois},
        outputs={'Out': out},
        attrs={
            'output_channels': output_channels,
            'spatial_scale': spatial_scale,
            'pooled_height': pooled_height,
            'pooled_width': pooled_width
        })
    return out
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def huber_loss(input, label, delta):
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    """
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    Huber loss is a loss function used in robust.
    Huber loss can evaluate the fitness of input to label.
    Different from MSE loss, Huber loss is more robust for outliers.
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    When the difference between input and label is large than delta
    .. math::

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        huber\_loss = delta * (label - input) - 0.5 * delta * delta
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    When the difference between input and label is less than delta
    .. math::

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        huber\_loss = 0.5 * (label - input) * (label - input)
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    Args:
        input (Variable): This input is a probability computed by the previous operator.
                          The first dimension is batch size, and the last dimension is 1.
        label (Variable): The groud truth whose first dimension is batch size
                          and last dimension is 1.
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        delta (float): The parameter of huber loss, which controls
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                       the range of outliers

    Returns:
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        huber\_loss (Variable): The huber loss with shape [batch_size, 1].
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    Examples:
        .. code-block:: python

            predictions = fluid.layers.softmax(x)
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            loss = fluid.layers.huber_loss(input=predictions, label=label, 1.0)
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    """
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    helper = LayerHelper('huber_loss', **locals())
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    residual = helper.create_variable_for_type_inference(
        dtype=helper.input_dtype())
    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
    helper.append_op(
        type='huber_loss',
        inputs={'X': input,
                'Y': label},
        outputs={'Out': out,
                 'Residual': residual},
        attrs={'delta': delta})
    return out
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@templatedoc()
def kldiv_loss(x, target, reduction='mean', name=None):
    """
    ${comment}

    Args:
        x (Variable): ${x_comment}
        target (Variable): ${target_comment}
        reduction (Variable): ${reduction_comment}
        name (str, default None): The name of this layer.

    Returns:
        kldiv\_loss (Variable): The KL divergence loss.

    Examples:
        .. code-block:: python

            x = fluid.layers.data(name='x', shape=[4,2,2], dtype='float32')
            target = fluid.layers.data(name='target', shape=[4,2,2], dtype='float32')
            loss = fluid.layers.kldiv_loss(x=x, target=target, reduction='batchmean')
    """
    helper = LayerHelper('kldiv_loss', **locals())
    loss = helper.create_variable_for_type_inference(dtype=x.dtype)
    helper.append_op(
        type='kldiv_loss',
        inputs={'X': x,
                'Target': target},
        outputs={'Loss': loss},
        attrs={'reduction': reduction})
    return loss


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@templatedoc()
def tree_conv(nodes_vector,
              edge_set,
              output_size,
              num_filters=1,
              max_depth=2,
              act='tanh',
              param_attr=None,
              bias_attr=None,
              name=None):
    """ 
    ${comment}
    		
    Args:
        nodes_vector(${nodes_vector_type}): ${nodes_vector_comment}
        edge_set(${edge_set_type}): ${edge_set_comment}
        output_size(int): output feature width
        num_filters(int): number of filters, Default 1
        max_depth(int): max depth of filters, Default 2
        act(str): activation function, Default tanh
        param_attr(ParamAttr): the parameter attribute for the filters, Default None
        bias_attr(ParamAttr): the parameter attribute for the bias of this layer, Default None
        name(str): a name of this layer(optional). If set None, the layer will be named automatically, Default None

    Returns:
        out(${out_type}): ${out_comment}

    Examples:
        .. code-block:: python

          nodes_vector = layers.data(name='vectors', shape=[None, 10, 5], dtype='float32)
          # None for batch size, 10 for max_node_size of dataset, 5 for vector width
          edge_set = layers.data(name='edge_set', shape=[None, 10, 2], dtype='float32')
          # None for batch size, 10 for max_node_size of dataset, 2 for every edge has two nodes
          # edges must be directional
          out_vector = layers.tree_conv(nodes_vector, edge_set, 6, 1, 2, 'tanh',
              ParamAttr(initializer=Constant(1.0), ParamAttr(initializer=Constant(1.0))
          # the shape of output will be [None, 10, 6, 1],
          # None for batch size, 10 for max_node_size of dataset, 6 for output size, 1 for 1 filter
          out_vector = layers.reshape(out_vector, shape=[None, 10, 6])
          # After reshape, output tensor could be nodes_vector for next tree convolution
          out_vector_2 = layers.tree_conv(out_vector, edge_set, 3, 4, 2, 'tanh',
              ParamAttr(initializer=Constant(1.0), ParamAttr(initializer=Constant(1.0))
          # also output tensor could be pooling(the pooling in paper called global pooling)
          pooled = layers.reduce_max(out_vector, dims=2) # global pooling
    """
    helper = LayerHelper("tree_conv", **locals())
    dtype = helper.input_dtype('nodes_vector')
    feature_size = nodes_vector.shape[2]
    W_shape = [feature_size, 3, output_size, num_filters]
    W = helper.create_parameter(
        attr=param_attr, shape=W_shape, dtype=dtype, is_bias=False)
    if name == None:
        out = helper.create_variable_for_type_inference(dtype=dtype)
    else:
        out = helper.create_variable(name=name, dtype=dtype, persistable=False)
    helper.append_op(
        type='tree_conv',
        inputs={'NodesVector': nodes_vector,
                'EdgeSet': edge_set,
                'Filter': W},
        outputs={'Out': out, },
        attrs={'max_depth': max_depth})
    if helper.bias_attr:
        pre_activation = helper.append_bias_op(out)
    else:
        pre_activation = out
    return helper.append_activation(pre_activation)
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from .ops import square
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from .control_flow import equal
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def npair_loss(anchor, positive, labels, l2_reg=0.002):
    '''
  **Npair Loss Layer**
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  Read `Improved Deep Metric Learning with Multi class N pair Loss Objective <http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf>`_ .
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  Npair loss requires paired data. Npair loss has two parts: the first part is L2
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  regularizer on the embedding vector; the second part is cross entropy loss which
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  takes the similarity matrix of anchor and positive as logits.

  Args:
    anchor(Variable): embedding vector for the anchor image. shape=[batch_size, embedding_dims]
    positive(Variable): embedding vector for the positive image. shape=[batch_size, embedding_dims]
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    labels(Variable): 1-D tensor. shape=[batch_size]
    l2_reg(float32): L2 regularization term on embedding vector, default: 0.002
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  Returns:
    npair loss(Variable): return npair loss, shape=[1]

  Examples:
    .. code-block:: python

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       anchor = fluid.layers.data(
                     name = 'anchor', shape = [18, 6], dtype = 'float32', append_batch_size=False)
       positive = fluid.layers.data(
                     name = 'positive', shape = [18, 6], dtype = 'float32', append_batch_size=False)
       labels = fluid.layers.data(
                     name = 'labels', shape = [18], dtype = 'float32', append_batch_size=False)

       npair_loss = fluid.layers.npair_loss(anchor, positive, labels, l2_reg = 0.002)
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  '''
    Beta = 0.25
    batch_size = labels.shape[0]

    labels = reshape(labels, shape=[batch_size, 1], inplace=True)
    labels = expand(labels, expand_times=[1, batch_size])

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    labels = equal(labels, transpose(labels, perm=[1, 0])).astype('float32')
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    labels = labels / reduce_sum(labels, dim=1, keep_dim=True)

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    l2loss = reduce_mean(reduce_sum(square(anchor), 1)) \
             + reduce_mean(reduce_sum(square(positive), 1))
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    l2loss = l2loss * Beta * l2_reg

    similarity_matrix = matmul(
        anchor, positive, transpose_x=False, transpose_y=True)
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    softmax_ce = softmax_with_cross_entropy(
        logits=similarity_matrix, label=labels, soft_label=True)
    cross_entropy = reduce_sum(labels * softmax_ce, 0)
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    celoss = reduce_mean(cross_entropy)

    return l2loss + celoss
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def pixel_shuffle(x, upscale_factor):
    """

    **Pixel Shuffle Layer**

    This layer rearranges elements in a tensor of shape [N, C, H, W]
    to a tensor of shape [N, C/r**2, H*r, W*r].
    This is useful for implementing efficient sub-pixel convolution
    with a stride of 1/r.
    Please refer to the paper: `Real-Time Single Image and Video Super-Resolution 
    Using an Efficient Sub-Pixel Convolutional Neural Network <https://arxiv.org/abs/1609.05158v2>`_ .
    by Shi et. al (2016) for more details.

        .. code-block:: text
        
            Given a 4-D tensor with the shape:
                x.shape = [1, 9, 4, 4]
            Given upscale_factor:
                upscale_factor= 3
            output shape is:
                [1, 1, 12, 12]
    
    Args:

        x(Variable): The input tensor variable.
        upscale_factor(int): factor to increase spatial resolution

    Returns:

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        Out(Variable): Reshaped tensor according to the new dimension.
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    Raises:

        ValueError: If the square of upscale_factor cannot divide the channels of input.

    Examples:

        .. code-block:: python

            input = fluid.layers.data(shape=[9,4,4])
            output = fluid.layers.pixel_shuffle(x=input, upscale_factor=3)

    """

    helper = LayerHelper("pixel_shuffle", **locals())

    out = helper.create_variable_for_type_inference(dtype=x.dtype)

    if not isinstance(upscale_factor, int):
        raise TypeError("upscale factor must be int type")

    helper.append_op(
        type="pixel_shuffle",
        inputs={"X": x},
        outputs={"Out": out},
        attrs={"upscale_factor": upscale_factor})
    return out


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def fsp_matrix(x, y):
    """

    **FSP matrix op**

    This op is used to calculate the flow of solution procedure (FSP) matrix of two feature maps.
    Given feature map x with shape [x_channel, h, w] and feature map y with shape
    [y_channel, h, w], we can get the fsp matrix of x and y in two steps:

    1. reshape x into matrix with shape [x_channel, h * w] and reshape and
       transpose y into matrix with shape [h * w, y_channel].
    2. multiply x and y to get fsp matrix with shape [x_channel, y_channel].

    The output is a batch of fsp matrices.

    Args:

        x (Variable): A feature map with shape [batch_size, x_channel, height, width].
        y (Variable): A feature map with shape [batch_size, y_channel, height, width].
                      The y_channel can be different with the x_channel of Input(X)
                      while the other dimensions must be the same with Input(X)'s.

    Returns:

        fsp matrix (Variable): The output of FSP op with shape [batch_size, x_channel, y_channel].
        The x_channel is the channel of x and the y_channel is the channel of y.

    Examples:

        .. code-block:: python

            feature_map_0 = fluid.layers.conv2d(x)
            feature_map_1 = fluid.layers.conv2d(feature_map_0)
            loss = fluid.layers.fsp_matrix(feature_map_0, feature_map_1)

    """
    helper = LayerHelper('fsp_matrix', **locals())
    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype(
        input_param_name='x'))
    helper.append_op(type='fsp', inputs={'X': x, 'Y': y}, outputs={'Out': out})
    return out
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def continuous_value_model(input, cvm, use_cvm=True):
    """
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    **continuous_value_model layers**
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    continuous value model(cvm). Now, it only considers show and click value in CTR project.
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    We assume that input is an embedding vector with cvm_feature, whose shape is [N * D] (D is 2 + embedding dim).
    If use_cvm is True, it will log(cvm_feature), and output shape is [N * D].
    If use_cvm is False, it will remove cvm_feature from input, and output shape is [N * (D - 2)].
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    This layer accepts a tensor named input which is ID after embedded(lod level is 1), cvm is a show_click info.
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    Args:
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        input (Variable): a 2-D LodTensor with shape [N x D], where N is the batch size, D is 2 + the embedding dim. lod level = 1.
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        cvm (Variable):   a 2-D Tensor with shape [N x 2], where N is the batch size, 2 is show and click.
        use_cvm  (bool):  use cvm or not. if use cvm, the output dim is the same as input
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                          if don't use cvm, the output dim is input dim - 2(remove show and click)
                          (cvm op is a customized op, which input is a sequence has embedd_with_cvm default, so we need an op named cvm to decided whever use it or not.)
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    Returns:
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        Variable: A 2-D LodTensor with shape [N x D], if use cvm, D is equal to input dim, if don't use cvm, D is equal to input dim - 2. 

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    Examples:
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        .. code-block:: python
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          input = fluid.layers.data(name="input", shape=[-1, 1], lod_level=1, append_batch_size=False, dtype="int64")#, stop_gradient=False)
          label = fluid.layers.data(name="label", shape=[-1, 1], append_batch_size=False, dtype="int64")
          embed = fluid.layers.embedding(
                            input=input,
                            size=[100, 11],
                            dtype='float32')
          ones = fluid.layers.fill_constant_batch_size_like(input=label, shape=[-1, 1], dtype="int64", value=1)
          show_clk = fluid.layers.cast(fluid.layers.concat([ones, label], axis=1), dtype='float32')
          show_clk.stop_gradient = True
          input_with_cvm = fluid.layers.continuous_value_model(embed, show_clk, True)
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    """
    helper = LayerHelper('cvm', **locals())
    out = helper.create_variable(dtype=input.dtype)
    helper.append_op(
        type='cvm',
        inputs={'X': [input],
                'CVM': [cvm]},
        outputs={'Y': [out]},
        attrs={"use_cvm": use_cvm})
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    return out