nn.py 288.7 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13
# 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.
Y
Yu Yang 已提交
14
"""
15
All layers just related to the neural network.
Y
Yu Yang 已提交
16 17
"""

18 19
from __future__ import print_function

20
import numpy as np
Y
Yu Yang 已提交
21 22
from ..layer_helper import LayerHelper
from ..initializer import Normal, Constant
S
sneaxiy 已提交
23
from ..framework import Variable, OpProtoHolder
Y
yangyaming 已提交
24
from ..param_attr import ParamAttr
S
sneaxiy 已提交
25
from .layer_function_generator import autodoc, templatedoc, _generate_doc_string_
26 27
from .tensor import concat
from . import utils
F
fengjiayi 已提交
28
from .. import unique_name
29
from functools import reduce
Y
Yu Yang 已提交
30 31

__all__ = [
X
Xin Pan 已提交
32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58
    'fc',
    'embedding',
    'dynamic_lstm',
    'dynamic_lstmp',
    'dynamic_gru',
    'gru_unit',
    'linear_chain_crf',
    'crf_decoding',
    'cos_sim',
    'cross_entropy',
    'square_error_cost',
    'chunk_eval',
    'sequence_conv',
    'conv2d',
    'conv3d',
    'sequence_pool',
    'sequence_softmax',
    'softmax',
    'pool2d',
    'pool3d',
    'batch_norm',
    'beam_search_decode',
    'conv2d_transpose',
    'conv3d_transpose',
    'sequence_expand',
    'sequence_expand_as',
    'sequence_pad',
Y
Yibing Liu 已提交
59
    'sequence_unpad',
X
Xin Pan 已提交
60 61 62 63 64 65 66 67
    'lstm_unit',
    'reduce_sum',
    'reduce_mean',
    'reduce_max',
    'reduce_min',
    'reduce_prod',
    'sequence_first_step',
    'sequence_last_step',
Y
Yibing Liu 已提交
68
    'sequence_slice',
X
Xin Pan 已提交
69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98
    'dropout',
    'split',
    'ctc_greedy_decoder',
    'edit_distance',
    'l2_normalize',
    'matmul',
    'topk',
    'warpctc',
    'sequence_reshape',
    'transpose',
    'im2sequence',
    'nce',
    'hsigmoid',
    'beam_search',
    'row_conv',
    'multiplex',
    'layer_norm',
    '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',
J
jerrywgz 已提交
99
    'roi_align',
X
Xin Pan 已提交
100 101 102 103 104 105 106 107 108 109 110 111 112
    'dice_loss',
    'image_resize',
    'image_resize_short',
    'resize_bilinear',
    'gather',
    'scatter',
    'sequence_scatter',
    'random_crop',
    'mean_iou',
    'relu',
    'log',
    'crop',
    'rank_loss',
M
minqiyang 已提交
113
    'margin_rank_loss',
X
Xin Pan 已提交
114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156
    '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',
    'logical_and',
    'logical_or',
    'logical_xor',
    'logical_not',
    'clip',
    'clip_by_norm',
    'mean',
    'mul',
    'sigmoid_cross_entropy_with_logits',
    'maxout',
J
JiabinYang 已提交
157
    'space_to_depth',
W
whs 已提交
158
    'affine_grid',
S
sneaxiy 已提交
159
    'sequence_reverse',
160
    'affine_channel',
M
minqiyang 已提交
161
    'hash',
D
dengkaipeng 已提交
162
    'grid_sampler',
G
gmcather 已提交
163 164
    'log_loss',
    'add_position_encoding',
Q
Qiao Longfei 已提交
165
    'bilinear_tensor_product',
Y
Yu Yang 已提交
166 167 168 169 170 171 172 173 174
]


def fc(input,
       size,
       num_flatten_dims=1,
       param_attr=None,
       bias_attr=None,
       act=None,
J
Jacek Czaja 已提交
175
       is_test=False,
176
       name=None):
Y
Yu Yang 已提交
177
    """
178
    **Fully Connected Layer**
Y
Yu Yang 已提交
179

180 181 182 183 184 185 186 187
    This function creates a fully connected layer in the network. It can take
    multiple tensors as its inputs. It creates a variable called weights for
    each input tensor, 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 coresponding weight to produce an output Tensor.
    If multiple input tensors are given, the results of multiple multiplications
    will be sumed up. If bias_attr is not None, a bias variable will be created
    and added to the output. Finally, if activation is not None, it will be applied
F
fengjiayi 已提交
188
    to the output as well.
C
caoying03 已提交
189

C
caoying03 已提交
190
    This process can be formulated as follows:
191 192 193

    .. math::

194
        Out = Act({\sum_{i=0}^{N-1}X_iW_i + b})
195 196 197

    In the above equation:

C
caoying03 已提交
198 199 200 201
    * :math:`N`: Number of the input.
    * :math:`X_i`: The input tensor.
    * :math:`W`: The weights created by this layer.
    * :math:`b`: The bias parameter created by this layer (if needed).
202
    * :math:`Act`: The activation function.
C
caoying03 已提交
203
    * :math:`Out`: The output tensor.
Y
Yu Yang 已提交
204 205

    Args:
R
ranqiu 已提交
206 207 208 209 210 211 212 213 214 215 216 217 218 219 220
        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
            `X` is a 6-dimensional tensor with a shape [2, 3, 4, 5, 6], and `num_flatten_dims` = 3.
            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
221 222
            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.
R
ranqiu 已提交
223
        act (str, default None): Activation to be applied to the output of this layer.
J
Jacek Czaja 已提交
224
        is_test(bool): A flag indicating whether execution is in test phase.
R
ranqiu 已提交
225
        name (str, default None): The name of this layer.
Y
Yu Yang 已提交
226

227
    Returns:
F
fengjiayi 已提交
228
        Variable: The transformation result.
229 230

    Raises:
C
caoying03 已提交
231
        ValueError: If rank of the input tensor is less than 2.
232 233 234 235

    Examples:
        .. code-block:: python

F
fengjiayi 已提交
236
          data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
237
          fc = fluid.layers.fc(input=data, size=1000, act="tanh")
Y
Yu Yang 已提交
238
    """
C
caoying03 已提交
239

C
caoying03 已提交
240
    helper = LayerHelper("fc", **locals())
Y
Yu Yang 已提交
241 242 243 244

    dtype = helper.input_dtype()

    mul_results = []
245 246
    for input_var, param_attr in helper.iter_inputs_and_params():
        input_shape = input_var.shape
Y
Yu Yang 已提交
247 248 249
        param_shape = [
            reduce(lambda a, b: a * b, input_shape[num_flatten_dims:], 1)
        ] + [size]
Y
ying 已提交
250

Y
Yu Yang 已提交
251
        w = helper.create_parameter(
252
            attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False)
X
Xin Pan 已提交
253
        tmp = helper.create_variable_for_type_inference(dtype)
254
        helper.append_op(
255 256 257
            type="mul",
            inputs={"X": input_var,
                    "Y": w},
258
            outputs={"Out": tmp},
M
mozga-intel 已提交
259 260
            attrs={"x_num_col_dims": num_flatten_dims,
                   "y_num_col_dims": 1})
261 262 263 264
        mul_results.append(tmp)

    if len(mul_results) == 1:
        pre_bias = mul_results[0]
265
    else:
X
Xin Pan 已提交
266
        pre_bias = helper.create_variable_for_type_inference(dtype)
267
        helper.append_op(
268 269 270
            type="sum",
            inputs={"X": mul_results},
            outputs={"Out": pre_bias},
X
Xin Pan 已提交
271
            attrs={"use_mkldnn": False})
272 273 274 275
    # add bias
    pre_activation = helper.append_bias_op(pre_bias, dim_start=num_flatten_dims)
    # add activation
    return helper.append_activation(pre_activation)
Y
Yu Yang 已提交
276 277


278 279 280
def embedding(input,
              size,
              is_sparse=False,
281
              is_distributed=False,
282 283 284
              padding_idx=None,
              param_attr=None,
              dtype='float32'):
Y
Yu Yang 已提交
285
    """
286 287
    **Embedding Layer**

288
    This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in
289 290
    a lookup table. The result of this lookup is the embedding of each ID in the
    :attr:`input`.
291 292 293

    All the input variables are passed in as local variables to the LayerHelper
    constructor.
Y
Yu Yang 已提交
294 295

    Args:
296 297 298 299 300
        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.
301
        is_distributed(bool): Whether to run lookup table from remote parameter server.
302 303
        padding_idx(int|long|None): If :attr:`None`, it makes no effect to lookup.
            Otherwise the given :attr:`padding_idx` indicates padding the output
304
            with zeros whenever lookup encounters it in :attr:`input`. If
305
            :math:`padding_idx < 0`, the :attr:`padding_idx` to use in lookup is
306 307
            :math:`size[0] + dim`.
        param_attr(ParamAttr): Parameters for this layer
308
        dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc
Y
Yu Yang 已提交
309

310 311 312
    Returns:
        Variable: The tensor variable storing the embeddings of the \
                  supplied inputs.
Y
Yu Yang 已提交
313

314 315
    Examples:
        .. code-block:: python
Y
Yu Yang 已提交
316

C
chengduoZH 已提交
317
          dict_size = len(dataset.ids)
318
          data = fluid.layers.data(name='ids', shape=[32, 32], dtype='float32')
C
chengduoZH 已提交
319
          fc = fluid.layers.embedding(input=data, size=[dict_size, 16])
Y
Yu Yang 已提交
320 321 322 323 324
    """

    helper = LayerHelper('embedding', **locals())
    w = helper.create_parameter(
        attr=helper.param_attr, shape=size, dtype=dtype, is_bias=False)
X
Xin Pan 已提交
325
    tmp = helper.create_variable_for_type_inference(dtype)
326 327
    padding_idx = -1 if padding_idx is None else padding_idx if padding_idx >= 0 else (
        size[0] + padding_idx)
Y
Yu Yang 已提交
328 329 330 331 332
    helper.append_op(
        type='lookup_table',
        inputs={'Ids': input,
                'W': w},
        outputs={'Out': tmp},
333 334 335 336 337
        attrs={
            'is_sparse': is_sparse,
            'is_distributed': is_distributed,
            'padding_idx': padding_idx
        })
Y
Yu Yang 已提交
338 339 340
    return tmp


Y
yi.wu 已提交
341
@templatedoc(op_type="lstm")
Y
Yu Yang 已提交
342 343
def dynamic_lstm(input,
                 size,
Y
Yancey 已提交
344 345
                 h_0=None,
                 c_0=None,
Y
Yu Yang 已提交
346 347 348 349 350 351 352
                 param_attr=None,
                 bias_attr=None,
                 use_peepholes=True,
                 is_reverse=False,
                 gate_activation='sigmoid',
                 cell_activation='tanh',
                 candidate_activation='tanh',
353 354
                 dtype='float32',
                 name=None):
Y
Yibing Liu 已提交
355
    """
Y
yi.wu 已提交
356
    ${comment}
Y
Yibing Liu 已提交
357 358

    Args:
Y
yi.wu 已提交
359 360
        input (Variable): ${input_comment}
        size (int): 4 * hidden size.
Y
Yancey 已提交
361 362 363 364 365 366
        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.
367
        param_attr(ParamAttr|None): The parameter attribute for the learnable
368
                               hidden-hidden weights.
Y
Yibing Liu 已提交
369 370 371

                               - Weights = {:math:`W_{ch}, W_{ih}, \
                                                W_{fh}, W_{oh}`}
372 373
                               - The shape is (D x 4D), where D is the hidden
                                 size.
C
chengduo 已提交
374 375 376 377 378

                               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.
Y
yi.wu 已提交
379
        bias_attr (ParamAttr|None): The bias attribute for the learnable bias
380 381 382
                              weights, which contains two parts, input-hidden
                              bias weights and peephole connections weights if
                              setting `use_peepholes` to `True`.
Y
Yibing Liu 已提交
383

384
                              1. `use_peepholes = False`
Y
yi.wu 已提交
385 386
                                 - Biases = {:math:`b_c, b_i, b_f, b_o`}.
                                 - The shape is (1 x 4D).
387
                              2. `use_peepholes = True`
Y
yi.wu 已提交
388
                                 - Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic}, \
Y
Yibing Liu 已提交
389
                                                 W_{fc}, W_{oc}`}.
Y
yi.wu 已提交
390
                                 - The shape is (1 x 7D).
C
chengduo 已提交
391 392 393 394 395

                              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.
Y
yi.wu 已提交
396 397 398 399 400 401 402 403
        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.
Y
Yibing Liu 已提交
404 405

    Returns:
Y
Yibing Liu 已提交
406 407
        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`.
Y
Yibing Liu 已提交
408

Y
Yibing Liu 已提交
409
    Examples:
Y
Yibing Liu 已提交
410 411
        .. code-block:: python

Y
Yibing Liu 已提交
412 413
            hidden_dim = 512
            forward_proj = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
C
chengduo 已提交
414
                                           bias_attr=False)
Y
Yibing Liu 已提交
415 416
            forward, _ = fluid.layers.dynamic_lstm(
                input=forward_proj, size=hidden_dim * 4, use_peepholes=False)
Y
Yibing Liu 已提交
417
    """
C
chengduo 已提交
418
    assert bias_attr is not False, "bias_attr should not be False in dynamic_lstmp."
Y
Yu Yang 已提交
419
    helper = LayerHelper('lstm', **locals())
M
minqiyang 已提交
420
    size = size // 4
Y
Yu Yang 已提交
421 422 423 424 425 426 427 428
    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)

X
Xin Pan 已提交
429 430 431 432
    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)
Y
Yancey 已提交
433 434 435 436 437 438 439 440 441 442
    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
Y
Yu Yang 已提交
443 444 445

    helper.append_op(
        type='lstm',
Y
Yancey 已提交
446
        inputs=inputs,
Y
Yu Yang 已提交
447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462
        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


Y
Yibing Liu 已提交
463 464 465 466 467 468 469 470 471 472 473
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',
                  proj_activation='tanh',
474 475
                  dtype='float32',
                  name=None):
Y
Yibing Liu 已提交
476 477 478
    """
    **Dynamic LSTMP Layer**

479 480 481 482 483 484
    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).
Y
Yibing Liu 已提交
485 486 487 488 489

    The formula is as follows:

    .. math::

490
        i_t & = \sigma(W_{ix}x_{t} + W_{ir}r_{t-1} + W_{ic}c_{t-1} + b_i)
Y
Yibing Liu 已提交
491

492
        f_t & = \sigma(W_{fx}x_{t} + W_{fr}r_{t-1} + W_{fc}c_{t-1} + b_f)
Y
Yibing Liu 已提交
493

494
        \\tilde{c_t} & = act_g(W_{cx}x_t + W_{cr}r_{t-1} + b_c)
Y
Yibing Liu 已提交
495

496
        o_t & = \sigma(W_{ox}x_{t} + W_{or}r_{t-1} + W_{oc}c_t + b_o)
Y
Yibing Liu 已提交
497

498
        c_t & = f_t \odot c_{t-1} + i_t \odot \\tilde{c_t}
Y
Yibing Liu 已提交
499

500
        h_t & = o_t \odot act_h(c_t)
Y
Yibing Liu 已提交
501

502
        r_t & = \overline{act_h}(W_{rh}h_t)
Y
Yibing Liu 已提交
503

Y
Yibing Liu 已提交
504 505 506 507 508 509
    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, \
510
          we use vectors to reprenset these diagonal weight matrices.
Y
Yibing Liu 已提交
511
    * :math:`b`: Denotes bias vectors (e.g. :math:`b_i` is the input gate \
512
          bias vector).
Y
Yibing Liu 已提交
513 514 515
    * :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 \
516
          the same size as the cell output activation vector :math:`h`.
Y
Yibing Liu 已提交
517
    * :math:`h`: The hidden state.
518
    * :math:`r`: The recurrent projection of the hidden state.
Y
Yibing Liu 已提交
519 520
    * :math:`\\tilde{c_t}`: The candidate hidden state, whose \
          computation is based on the current input and previous hidden state.
521
    * :math:`\odot`: The element-wise product of the vectors.
Y
Yibing Liu 已提交
522
    * :math:`act_g` and :math:`act_h`: The cell input and cell output \
523
          activation functions and `tanh` is usually used for them.
Y
Yibing Liu 已提交
524 525
    * :math:`\overline{act_h}`: The activation function for the projection \
          output, usually using `identity` or same as :math:`act_h`.
Y
Yibing Liu 已提交
526 527 528 529

    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.
530

Y
Yibing Liu 已提交
531 532 533 534 535 536 537 538 539 540 541 542
    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.
543
        param_attr(ParamAttr|None): The parameter attribute for the learnable
Y
Yibing Liu 已提交
544 545
                               hidden-hidden weight and projection weight.

546 547
                               - Hidden-hidden weight = {:math:`W_{ch}, W_{ih}, \
                                                W_{fh}, W_{oh}`}.
548 549
                               - The shape of hidden-hidden weight is (P x 4D),
                                 where P is the projection size and D the hidden
Y
Yibing Liu 已提交
550 551
                                 size.
                               - Projection weight = {:math:`W_{rh}`}.
552
                               - The shape of projection weight is (D x P).
C
chengduo 已提交
553 554 555 556 557

                               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.
558
        bias_attr(ParamAttr|None): The bias attribute for the learnable bias
Y
Yibing Liu 已提交
559 560 561 562 563 564
                              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`}.
565
                                - The shape is (1 x 4D).
Y
Yibing Liu 已提交
566 567 568
                              2. `use_peepholes = True`
                                - Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic}, \
                                                 W_{fc}, W_{oc}`}.
569
                                - The shape is (1 x 7D).
C
chengduo 已提交
570 571 572 573 574

                              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.
Y
Yibing Liu 已提交
575 576 577 578 579 580 581 582 583
        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.
584
                              Choices = ["sigmoid", "tanh", "relu", "identity"],
Y
Yibing Liu 已提交
585 586
                              default "tanh".
        proj_activation(str): The activation for projection output.
587
                              Choices = ["sigmoid", "tanh", "relu", "identity"],
Y
Yibing Liu 已提交
588 589
                              default "tanh".
        dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
590 591
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.
Y
Yibing Liu 已提交
592 593

    Returns:
594 595 596 597
        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`.
Y
Yibing Liu 已提交
598 599

    Examples:
600

Y
Yibing Liu 已提交
601 602
        .. code-block:: python

603 604 605 606
            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])
Y
Yibing Liu 已提交
607
            hidden_dim, proj_dim = 512, 256
608
            fc_out = fluid.layers.fc(input=emb, size=hidden_dim * 4,
Y
Yibing Liu 已提交
609
                                     act=None, bias_attr=None)
610 611 612
            proj_out, _ = fluid.layers.dynamic_lstmp(input=fc_out,
                                                     size=hidden_dim * 4,
                                                     proj_size=proj_dim,
Y
Yibing Liu 已提交
613 614 615 616
                                                     use_peepholes=False,
                                                     is_reverse=True,
                                                     cell_activation="tanh",
                                                     proj_activation="tanh")
Y
Yibing Liu 已提交
617
    """
618

C
chengduo 已提交
619
    assert bias_attr is not False, "bias_attr should not be False in dynamic_lstmp."
Y
Yibing Liu 已提交
620
    helper = LayerHelper('lstmp', **locals())
M
minqiyang 已提交
621
    size = size // 4
Y
Yibing Liu 已提交
622 623 624 625 626 627 628 629 630 631
    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)

X
Xin Pan 已提交
632 633 634 635 636 637
    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)
Y
Yibing Liu 已提交
638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665

    helper.append_op(
        type='lstmp',
        inputs={
            'Input': input,
            'Weight': weight,
            'ProjWeight': proj_weight,
            'Bias': bias
        },
        outputs={
            'Projection': projection,
            'Cell': cell,
            'OrderedP0': ordered_proj0,
            'BatchHidden': batch_hidden,
            '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,
            'proj_activation': proj_activation
        })
    return projection, cell


G
guosheng 已提交
666 667 668 669 670 671 672 673 674
def dynamic_gru(input,
                size,
                param_attr=None,
                bias_attr=None,
                is_reverse=False,
                gate_activation='sigmoid',
                candidate_activation='tanh',
                h_0=None):
    """
675
    **Gated Recurrent Unit (GRU) Layer**
G
guosheng 已提交
676

677
    Refer to `Empirical Evaluation of Gated Recurrent Neural Networks on
678
    Sequence Modeling <https://arxiv.org/abs/1412.3555>`_ .
679

G
guosheng 已提交
680 681 682 683 684 685 686 687 688
    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)
689

G
guosheng 已提交
690
        h_t & = (1-u_t) \odot h_{t-1} + u_t \odot \\tilde{h_t}
691

G
guosheng 已提交
692
    The :math:`\odot` is the element-wise product of the vectors. :math:`act_g`
693 694
    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
G
guosheng 已提交
695 696 697 698
    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
699
    to use fully-connect layer before GRU layer.
G
guosheng 已提交
700 701

    Args:
702 703
        input(Variable): The input of dynamic_gru layer, which supports
            variable-time length input sequence. The underlying tensor in this
G
guosheng 已提交
704
            Variable is a matrix with shape :math:`(T \\times 3D)`, where
705
            :math:`T` is the total time steps in this mini-batch, :math:`D`
G
guosheng 已提交
706 707
            is the hidden size.
        size(int): The dimension of the gru cell.
708
        param_attr(ParamAttr|None): The parameter attribute for the learnable
G
guosheng 已提交
709 710
            hidden-hidden weight matrix. Note:

711
            - The shape of the weight matrix is :math:`(T \\times 3D)`, where
G
guosheng 已提交
712
              :math:`D` is the hidden size.
713
            - All elements in the weight matrix can be divided into two parts.
G
guosheng 已提交
714
              The first part are weights of the update gate and reset gate with
715
              shape :math:`(D \\times 2D)`, and the second part are weights for
G
guosheng 已提交
716
              candidate hidden state with shape :math:`(D \\times D)`.
717 718 719 720 721 722 723 724 725 726 727 728

            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
            of GRU. Note that the bias with :math:`(1 \\times 3D)` concatenates 
            the bias in the update gate, reset gate and candidate calculations.
            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 
            bias_attr. If the Initializer of the bias_attr is not set, the bias
            is initialized zero. Default: None.
729
        is_reverse(bool): Whether to compute reversed GRU, default
G
guosheng 已提交
730 731 732
            :attr:`False`.
        gate_activation(str): The activation for update gate and reset gate.
            Choices = ["sigmoid", "tanh", "relu", "identity"], default "sigmoid".
733
        candidate_activation(str): The activation for candidate hidden state.
G
guosheng 已提交
734
            Choices = ["sigmoid", "tanh", "relu", "identity"], default "tanh".
735 736 737 738
        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.
G
guosheng 已提交
739 740

    Returns:
G
guosheng 已提交
741
        Variable: The hidden state of GRU. The shape is :math:`(T \\times D)`, \
742
            and sequence length is the same with the input.
743

G
guosheng 已提交
744
    Examples:
745

G
guosheng 已提交
746 747
        .. code-block:: python

748 749 750 751
            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])
G
guosheng 已提交
752
            hidden_dim = 512
753
            x = fluid.layers.fc(input=emb, size=hidden_dim * 3)
G
guosheng 已提交
754 755 756 757 758 759 760 761 762 763
            hidden = fluid.layers.dynamic_gru(input=x, dim=hidden_dim)
    """

    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)
Y
Yancey 已提交
764
    batch_size = input.shape[0]
G
guosheng 已提交
765
    inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
S
sneaxiy 已提交
766
    if h_0:
G
guosheng 已提交
767
        assert h_0.shape == (
Y
Yancey 已提交
768 769 770
            batch_size, size
        ), 'The shape of h0 should be(batch_size, %d)' % size
        inputs['H0'] = h_0
G
guosheng 已提交
771

X
Xin Pan 已提交
772 773 774 775
    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)
G
guosheng 已提交
776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793

    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,
            'activation': candidate_activation
        })
    return hidden


Y
Yu Yang 已提交
794 795 796
def gru_unit(input,
             hidden,
             size,
797 798
             param_attr=None,
             bias_attr=None,
Y
Yu Yang 已提交
799
             activation='tanh',
800
             gate_activation='sigmoid'):
Y
Yu Yang 已提交
801
    """
802
    GRU unit layer. The equation of a gru step is:
Y
Yu Yang 已提交
803

804 805
        .. math::
            u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u)
Y
Yu Yang 已提交
806

807
            r_t & = actGate(xr_{t} + W_r h_{t-1} + b_r)
Y
Yu Yang 已提交
808

809
            m_t & = actNode(xm_t + W_c dot(r_t, h_{t-1}) + b_m)
810

811
            h_t & = dot((1-u_t), m_t) + dot(u_t, h_{t-1})
812 813

    The inputs of gru unit includes :math:`z_t`, :math:`h_{t-1}`. In terms
814 815 816
    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
817 818
    connected layer has to be applied, such that :math:`z_t = W_{fc}x_t`.

819 820
    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
821 822 823
    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`.
824 825 826

    Args:
        input (Variable): The fc transformed input value of current step.
827
        hidden (Variable): The hidden value of gru unit from previous step.
828
        size (integer): The input dimension value.
829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849
        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
            of GRU. Note that the bias with :math:`(1 \\times 3D)` concatenates 
            the bias in the update gate, reset gate and candidate calculations.
            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 
            bias_attr. If the Initializer of the bias_attr is not set, the bias
            is initialized zero. Default: None.
850 851 852 853
        activation (string): The activation type for cell (actNode).
                             Default: 'tanh'
        gate_activation (string): The activation type for gates (actGate).
                                  Default: 'sigmoid'
Y
Yu Yang 已提交
854

855 856 857 858 859 860
    Returns:
        tuple: The hidden value, reset-hidden value and gate values.

    Examples:

        .. code-block:: python
Y
Yu Yang 已提交
861

862
             # assuming we have x_t_data and prev_hidden of size=10
863
             x_t = fluid.layers.fc(input=x_t_data, size=30)
864 865
             hidden_val, r_h_val, gate_val = fluid.layers.gru_unit(input=x_t,
                                                    hidden = prev_hidden)
Y
Yu Yang 已提交
866 867 868 869 870 871 872 873 874 875 876 877

    """
    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()
M
minqiyang 已提交
878
    size = size // 3
Y
Yu Yang 已提交
879 880

    # create weight
881 882
    weight = helper.create_parameter(
        attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
Y
Yu Yang 已提交
883

X
Xin Pan 已提交
884 885 886
    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)
887
    inputs = {'Input': input, 'HiddenPrev': hidden, 'Weight': weight}
Y
Yu Yang 已提交
888
    # create bias
889
    if helper.bias_attr:
Y
Yu Yang 已提交
890 891 892
        bias_size = [1, 3 * size]
        bias = helper.create_parameter(
            attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
893
        inputs['Bias'] = bias
Y
Yu Yang 已提交
894 895 896

    helper.append_op(
        type='gru_unit',
897
        inputs=inputs,
Y
Yu Yang 已提交
898 899 900 901 902 903
        outputs={
            'Gate': gate,
            'ResetHiddenPrev': reset_hidden_pre,
            'Hidden': updated_hidden,
        },
        attrs={
904 905
            'activation': 2,  # tanh
            'gate_activation': 1,  # sigmoid
Y
Yu Yang 已提交
906 907 908 909 910
        })

    return updated_hidden, reset_hidden_pre, gate


Y
yuyang18 已提交
911
@templatedoc()
912
def linear_chain_crf(input, label, param_attr=None):
Y
yuyang18 已提交
913 914 915 916 917 918 919
    """
    Linear Chain CRF.

    ${comment}

    Args:
        input(${emission_type}): ${emission_comment}
D
dzhwinter 已提交
920
        input(${transition_type}): ${transition_comment}
Y
yuyang18 已提交
921 922 923 924
        label(${label_type}): ${label_comment}
        param_attr(ParamAttr): The attribute of the learnable parameter.

    Returns:
D
dzhwinter 已提交
925 926 927
        output(${emission_exps_type}): ${emission_exps_comment} \n
        output(${transition_exps_type}): ${transition_exps_comment} \n
        output(${log_likelihood_type}): ${log_likelihood_comment}
Y
yuyang18 已提交
928 929

    """
Y
Yu Yang 已提交
930 931 932 933 934 935
    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())
X
Xin Pan 已提交
936 937 938 939 940 941 942 943
    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())
Y
Yu Yang 已提交
944 945 946 947 948 949 950 951 952 953 954 955 956 957 958
    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


Y
yuyang18 已提交
959
@templatedoc()
960
def crf_decoding(input, param_attr, label=None):
Y
yuyang18 已提交
961 962 963 964 965
    """
    ${comment}

    Args:
        input(${emission_type}): ${emission_comment}
Y
yi.wu 已提交
966

Y
yuyang18 已提交
967
        param_attr(ParamAttr): The parameter attribute for training.
Y
yi.wu 已提交
968

Y
yuyang18 已提交
969 970 971
        label(${label_type}): ${label_comment}

    Returns:
Y
update  
yi.wu 已提交
972
        Variable: ${viterbi_path_comment}
973

Y
yi.wu 已提交
974 975 976 977 978
    Examples:
        .. code-block:: python

           crf_decode = layers.crf_decoding(
                input=hidden, param_attr=ParamAttr(name="crfw"))
Y
yuyang18 已提交
979
    """
Y
Yu Yang 已提交
980 981
    helper = LayerHelper('crf_decoding', **locals())
    transition = helper.get_parameter(param_attr.name)
X
Xin Pan 已提交
982 983
    viterbi_path = helper.create_variable_for_type_inference(
        dtype=helper.input_dtype())
Y
Yu Yang 已提交
984 985 986 987 988 989 990 991 992 993
    helper.append_op(
        type='crf_decoding',
        inputs={"Emission": [input],
                "Transition": transition,
                "Label": label},
        outputs={"ViterbiPath": [viterbi_path]})

    return viterbi_path


Y
yi.wu 已提交
994
@templatedoc()
F
fengjiayi 已提交
995
def cos_sim(X, Y):
Y
Yu Yang 已提交
996
    """
Y
yi.wu 已提交
997 998 999
    ${comment}

    Args:
1000 1001
        X (Variable): ${x_comment}.
        Y (Variable): ${y_comment}.
F
fengjiayi 已提交
1002

Y
yi.wu 已提交
1003
    Returns:
1004
        Variable: the output of cosine(X, Y).
Y
Yu Yang 已提交
1005
    """
F
fengjiayi 已提交
1006
    helper = LayerHelper('cos_sim', **locals())
X
Xin Pan 已提交
1007 1008 1009
    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)
Y
Yu Yang 已提交
1010 1011 1012 1013 1014 1015 1016 1017 1018 1019
    helper.append_op(
        type='cos_sim',
        inputs={'X': [X],
                'Y': [Y]},
        outputs={'Out': [out],
                 'XNorm': [xnorm],
                 'YNorm': [ynorm]})
    return out


P
phlrain 已提交
1020 1021 1022 1023 1024
def dropout(x,
            dropout_prob,
            is_test=False,
            seed=None,
            name=None,
P
phlrain 已提交
1025
            dropout_implementation="downgrade_in_infer"):
1026 1027 1028 1029 1030
    """
    Computes dropout.

    Drop or keep each element of `x` independently. Dropout is a regularization
    technique for reducing overfitting by preventing neuron co-adaption during
1031
    training. The dropout operator randomly sets (according to the given dropout
1032 1033 1034 1035
    probability) the outputs of some units to zero, while others are remain
    unchanged.

    Args:
1036 1037
        x (Variable): The input tensor variable.
        dropout_prob (float): Probability of setting units to zero.
1038 1039 1040 1041 1042 1043 1044
        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.
P
phlrain 已提交
1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058
        dropout_implementation(string): ['downgrade_in_infer'(defauld)|'upscale_in_train']
                                        1. downgrade_in_infer(default), downgrade the outcome at inference
                                           train: out = input * mask
                                           inference: out = input * dropout_prob
                                           (make is a tensor same shape with input, value is 0 or 1
                                            ratio of 0 is dropout_prob)
                                        2. upscale_in_train, upscale the outcome at training time
                                           train: out = input * mask / ( 1.0 - dropout_prob )
                                           inference: out = input
                                           (make is a tensor same shape with input, value is 0 or 1
                                            ratio of 0 is dropout_prob)
                                           dropout op can be removed from the program. 
                                           the program will be efficient
                                        
P
phlrain 已提交
1059

1060 1061

    Returns:
1062
        Variable: A tensor variable is the shape with `x`.
1063 1064

    Examples:
1065

1066 1067
        .. code-block:: python

1068 1069
            x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
            droped = fluid.layers.dropout(x, dropout_prob=0.5)
1070 1071
    """

F
fengjiayi 已提交
1072
    helper = LayerHelper('dropout', **locals())
X
Xin Pan 已提交
1073 1074 1075
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
    mask = helper.create_variable_for_type_inference(
        dtype=x.dtype, stop_gradient=True)
C
chengduo 已提交
1076 1077 1078 1079

    if (seed is None or seed == 0) and helper.main_program.random_seed != 0:
        seed = helper.main_program.random_seed

1080 1081 1082 1083 1084
    helper.append_op(
        type='dropout',
        inputs={'X': [x]},
        outputs={'Out': [out],
                 'Mask': [mask]},
1085 1086 1087 1088
        attrs={
            'dropout_prob': dropout_prob,
            'is_test': is_test,
            'fix_seed': seed is not None,
P
phlrain 已提交
1089 1090
            'seed': seed if seed is not None else 0,
            'dropout_implementation': dropout_implementation,
1091
        })
1092 1093 1094
    return out


1095
def cross_entropy(input, label, soft_label=False, ignore_index=-100):
Y
Yu Yang 已提交
1096
    """
Y
Yibing Liu 已提交
1097 1098
    **Cross Entropy Layer**

1099 1100 1101
    This layer computes the cross entropy between `input` and `label`. It
    supports both standard cross-entropy and soft-label cross-entropy loss
    computation.
Y
Yibing Liu 已提交
1102 1103

    1) One-hot cross-entropy:
F
fengjiayi 已提交
1104
        `soft_label = False`, `Label[i, 0]` indicates the class index for sample i:
Y
yangyaming 已提交
1105

Y
Yibing Liu 已提交
1106
        .. math::
Y
yangyaming 已提交
1107

Y
Yibing Liu 已提交
1108 1109 1110
            Y[i] = -\log(X[i, Label[i]])

    2) Soft-label cross-entropy:
F
fengjiayi 已提交
1111 1112
        `soft_label = True`, `Label[i, j]` indicates the soft label of class j
        for sample i:
Y
Yibing Liu 已提交
1113 1114 1115 1116 1117

        .. math::

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

Y
Yibing Liu 已提交
1118
       Please make sure that in this case the summation of each row of `label`
Y
Yibing Liu 已提交
1119 1120 1121
       equals one.

    3) One-hot cross-entropy with vecterized `label`:
F
fengjiayi 已提交
1122 1123
         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
Y
Yibing Liu 已提交
1124
         to a one-hot cross-entropy with one-hot label representation.
Y
yangyaming 已提交
1125

Y
Yibing Liu 已提交
1126
    Args:
Y
yangyaming 已提交
1127
        input (Variable|list):  a 2-D tensor with shape [N x D], where N is the
1128 1129 1130 1131
                                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.
Y
yangyaming 已提交
1132
        label (Variable|list): the ground truth which is a 2-D tensor. When
1133 1134 1135 1136
                               `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].
F
fengjiayi 已提交
1137
        soft_label (bool): a flag indicating whether to
1138
                                           interpretate the given labels as soft
1139
                                           labels. Default: `False`.
M
minqiyang 已提交
1140 1141
        ignore_index (int): Specifies a target value that is ignored and does
                            not contribute to the input gradient. Only valid
1142
                            if soft_label is set to False. Default: -100
Y
Yibing Liu 已提交
1143 1144 1145 1146 1147

    Returns:
         A 2-D tensor with shape [N x 1], the cross entropy loss.

    Raises:
1148 1149 1150 1151 1152
        `ValueError`: 1) the 1st dimension of `input` and `label` are not equal.
                      2) when `soft_label == True`, and the 2nd dimension of
                         `input` and `label` are not equal.
                      3) when `soft_label == False`, and the 2nd dimension of
                         `label` is not 1.
Y
Yibing Liu 已提交
1153 1154 1155 1156 1157 1158

    Examples:
        .. code-block:: python

          predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
          cost = fluid.layers.cross_entropy(input=predict, label=label)
Y
Yu Yang 已提交
1159
    """
F
fengjiayi 已提交
1160
    helper = LayerHelper('cross_entropy', **locals())
X
Xin Pan 已提交
1161
    out = helper.create_variable_for_type_inference(dtype=input.dtype)
Y
Yu Yang 已提交
1162 1163 1164 1165 1166
    helper.append_op(
        type='cross_entropy',
        inputs={'X': [input],
                'Label': [label]},
        outputs={'Y': [out]},
1167 1168
        attrs={"soft_label": soft_label,
               "ignore_index": ignore_index})
Y
Yu Yang 已提交
1169 1170 1171
    return out


F
fengjiayi 已提交
1172
def square_error_cost(input, label):
Y
Yu Yang 已提交
1173
    """
1174 1175
    **Square error cost layer**

1176 1177
    This layer accepts input predictions and target label and returns the
    squared error cost.
Y
ying 已提交
1178

1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191
    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:
1192 1193
        input (Variable): Input tensor, has predictions.
        label (Variable): Label tensor, has target labels.
1194 1195

    Returns:
G
guosheng 已提交
1196
        Variable: The tensor variable storing the element-wise squared error \
1197
                  difference of input and label.
1198 1199 1200 1201 1202 1203 1204 1205

    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)

Y
Yu Yang 已提交
1206
    """
F
fengjiayi 已提交
1207
    helper = LayerHelper('square_error_cost', **locals())
X
Xin Pan 已提交
1208
    minus_out = helper.create_variable_for_type_inference(dtype=input.dtype)
Y
Yu Yang 已提交
1209 1210 1211 1212 1213 1214
    helper.append_op(
        type='elementwise_sub',
        inputs={'X': [input],
                'Y': [label]},
        outputs={'Out': [minus_out]})

X
Xin Pan 已提交
1215
    square_out = helper.create_variable_for_type_inference(dtype=input.dtype)
Y
Yu Yang 已提交
1216
    helper.append_op(
F
fengjiayi 已提交
1217 1218
        type='square', inputs={'X': [minus_out]},
        outputs={'Out': [square_out]})
Y
Yu Yang 已提交
1219 1220 1221
    return square_out


Y
yi.wu 已提交
1222
@templatedoc()
Y
Yu Yang 已提交
1223 1224 1225 1226
def chunk_eval(input,
               label,
               chunk_scheme,
               num_chunk_types,
F
fengjiayi 已提交
1227
               excluded_chunk_types=None):
Y
Yu Yang 已提交
1228
    """
Y
yi.wu 已提交
1229
    **Chunk Evaluator**
Y
yi.wu 已提交
1230

Y
yangyaming 已提交
1231
    This function computes and outputs the precision, recall and
1232
    F1-score of chunk detection.
Y
yi.wu 已提交
1233

Y
yi.wu 已提交
1234 1235 1236 1237 1238 1239 1240 1241
    For some basics of chunking, please refer to
    'Chunking with Support Vector Machines <https://aclanthology.info/pdf/N/N01/N01-1025.pdf>'.

    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
1242

Y
yi.wu 已提交
1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267
       ====== ====== ======  =====  ==  ============   =====  ===== =====  ==  =========
              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
1268

Y
yi.wu 已提交
1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292
       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.

Y
yi.wu 已提交
1293
    Args:
1294 1295 1296 1297 1298
        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}
F
fengjiayi 已提交
1299

Y
yi.wu 已提交
1300
    Returns:
Y
update  
yi.wu 已提交
1301 1302 1303
        tuple: tuple containing: precision, recall, f1_score,
        num_infer_chunks, num_label_chunks,
        num_correct_chunks
1304

Y
yi.wu 已提交
1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316
    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)
Y
Yu Yang 已提交
1317
    """
F
fengjiayi 已提交
1318
    helper = LayerHelper("chunk_eval", **locals())
Y
Yu Yang 已提交
1319 1320

    # prepare output
X
Xin Pan 已提交
1321 1322 1323 1324 1325 1326 1327
    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")
Y
Yu Yang 已提交
1328 1329 1330 1331 1332 1333 1334 1335

    helper.append_op(
        type="chunk_eval",
        inputs={"Inference": [input],
                "Label": [label]},
        outputs={
            "Precision": [precision],
            "Recall": [recall],
1336 1337 1338 1339
            "F1-Score": [f1_score],
            "NumInferChunks": [num_infer_chunks],
            "NumLabelChunks": [num_label_chunks],
            "NumCorrectChunks": [num_correct_chunks]
Y
Yu Yang 已提交
1340 1341 1342
        },
        attrs={
            "num_chunk_types": num_chunk_types,
G
guosheng 已提交
1343 1344
            "chunk_scheme": chunk_scheme,
            "excluded_chunk_types": excluded_chunk_types or []
Y
Yu Yang 已提交
1345
        })
1346 1347
    return (precision, recall, f1_score, num_infer_chunks, num_label_chunks,
            num_correct_chunks)
Y
Yu Yang 已提交
1348 1349


1350
@templatedoc()
Y
Yu Yang 已提交
1351 1352 1353 1354 1355 1356 1357
def sequence_conv(input,
                  num_filters,
                  filter_size=3,
                  filter_stride=1,
                  padding=None,
                  bias_attr=None,
                  param_attr=None,
C
chengduo 已提交
1358 1359
                  act=None,
                  name=None):
Y
Yu Yang 已提交
1360 1361 1362 1363
    """
    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.
1364 1365 1366 1367 1368 1369 1370

    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.
C
chengduo 已提交
1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383
        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.
F
fengjiayi 已提交
1384

1385 1386
    Returns:
        Variable: output of sequence_conv
Y
Yu Yang 已提交
1387 1388 1389 1390 1391 1392 1393
    """

    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)
X
Xin Pan 已提交
1394
    pre_bias = helper.create_variable_for_type_inference(dtype)
Y
Yu Yang 已提交
1395 1396 1397 1398 1399 1400 1401 1402 1403 1404

    helper.append_op(
        type='sequence_conv',
        inputs={
            'X': [input],
            'Filter': [filter_param],
        },
        outputs={"Out": pre_bias},
        attrs={
            'contextStride': filter_stride,
M
minqiyang 已提交
1405
            'contextStart': -int(filter_size // 2),
Y
Yu Yang 已提交
1406 1407 1408 1409 1410 1411
            'contextLength': filter_size
        })
    pre_act = helper.append_bias_op(pre_bias)
    return helper.append_activation(pre_act)


C
chengduo 已提交
1412
def sequence_softmax(input, use_cudnn=False, name=None):
1413 1414 1415
    """
    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
1416
    input Tensor can be either :math:`[N, 1]` or :math:`[N]`, where :math:`N`
1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432
    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 \
C
chengduo 已提交
1433 1434 1435
            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.
1436

1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447
    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)
    """
1448 1449
    helper = LayerHelper('sequence_softmax', **locals())
    dtype = helper.input_dtype()
X
Xin Pan 已提交
1450
    softmax_out = helper.create_variable_for_type_inference(dtype)
1451 1452 1453 1454 1455 1456 1457 1458
    helper.append_op(
        type="sequence_softmax",
        inputs={"X": input},
        outputs={"Out": softmax_out},
        attrs={"use_cudnn": use_cudnn})
    return softmax_out


C
chengduo 已提交
1459
def softmax(input, use_cudnn=True, name=None):
Q
qiaolongfei 已提交
1460
    """
1461
    The input of the softmax operator is a tensor of any rank. The output tensor
F
fengjiayi 已提交
1462
    has the same shape as the input.
Q
qiaolongfei 已提交
1463

1464 1465 1466 1467 1468 1469
    The input tensor will first be logically flattened to a 2-D matrix. The matrix's
    second dimension(row length) is as same as the last dimension of the input
    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
    of the input tensor's last dimension) vector of arbitrary real values to a
F
fengjiayi 已提交
1470
    K-dimensional vector of real values in the range [0, 1] that add up to 1.
Q
qiaolongfei 已提交
1471 1472 1473 1474 1475 1476 1477

    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.

F
fengjiayi 已提交
1478
    For each row :math:`i` and each column :math:`j` in the matrix, we have:
Q
qiaolongfei 已提交
1479 1480 1481 1482 1483 1484 1485 1486

    .. 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 \
C
chengduo 已提交
1487 1488 1489
            library is installed.
        name (str|None): A name for this layer(optional). If set None, the layer
            will be named automatically. Default: None.
Q
qiaolongfei 已提交
1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501

    Returns:
        Variable: output of softmax

    Examples:

        .. code-block:: python

             fc = fluid.layers.fc(input=x, size=10)
             softmax = fluid.layers.softmax(input=fc)

    """
1502 1503
    helper = LayerHelper('softmax', **locals())
    dtype = helper.input_dtype()
X
Xin Pan 已提交
1504
    softmax_out = helper.create_variable_for_type_inference(dtype)
1505 1506 1507 1508 1509 1510 1511 1512
    helper.append_op(
        type="softmax",
        inputs={"X": input},
        outputs={"Out": softmax_out},
        attrs={"use_cudnn": use_cudnn})
    return softmax_out


Y
Yu Yang 已提交
1513 1514 1515
def conv2d(input,
           num_filters,
           filter_size,
C
chengduoZH 已提交
1516 1517
           stride=1,
           padding=0,
1518
           dilation=1,
Y
Yu Yang 已提交
1519 1520 1521
           groups=None,
           param_attr=None,
           bias_attr=None,
C
chengduoZH 已提交
1522
           use_cudnn=True,
1523 1524
           act=None,
           name=None):
Y
Yu Yang 已提交
1525
    """
C
chengduoZH 已提交
1526
    The convolution2D layer calculates the output based on the input, filter
T
tensor-tang 已提交
1527 1528
    and strides, paddings, dilations, groups parameters. Input and
    Output are in NCHW format, where N is batch size, C is the number of
1529
    channels, H is the height of the feature, and W is the width of the feature.
T
tensor-tang 已提交
1530 1531 1532 1533 1534 1535 1536
    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.
1537 1538 1539
    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.
C
chengduoZH 已提交
1540

1541
    For each input :math:`X`, the equation is:
C
refine  
chengduoZH 已提交
1542

C
chengduoZH 已提交
1543 1544
    .. math::

C
refine  
chengduoZH 已提交
1545
        Out = \sigma (W \\ast X + b)
C
chengduoZH 已提交
1546

T
tensor-tang 已提交
1547
    Where:
C
chengduoZH 已提交
1548

1549 1550 1551 1552 1553
    * :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.
T
tensor-tang 已提交
1554
    * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
C
chengduoZH 已提交
1555 1556 1557

    Example:

1558 1559
        - Input:

W
weixing02 已提交
1560
          Input shape: :math:`(N, C_{in}, H_{in}, W_{in})`
C
refine  
chengduoZH 已提交
1561

W
weixing02 已提交
1562
          Filter shape: :math:`(C_{out}, C_{in}, H_f, W_f)`
C
refine  
chengduoZH 已提交
1563

1564
        - Output:
T
tensor-tang 已提交
1565

W
weixing02 已提交
1566
          Output shape: :math:`(N, C_{out}, H_{out}, W_{out})`
C
refine  
chengduoZH 已提交
1567

C
chengduoZH 已提交
1568
        Where
1569 1570

        .. math::
C
chengduoZH 已提交
1571

W
weixing02 已提交
1572 1573
            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
C
chengduoZH 已提交
1574 1575

    Args:
1576
        input (Variable): The input image with [N, C, H, W] format.
T
tensor-tang 已提交
1577
        num_filters(int): The number of filter. It is as same as the output
1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594
            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
C
chengduo 已提交
1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605
            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)`,
             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 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.
1606 1607
        use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
            library is installed. Default: True
C
chengduo 已提交
1608 1609
        act (str): Activation type, if it is set to None, activation is not appended.
            Default: None
1610
        name (str|None): A name for this layer(optional). If set None, the layer
C
chengduo 已提交
1611
            will be named automatically. Default: None
C
chengduoZH 已提交
1612 1613

    Returns:
G
guosheng 已提交
1614
        Variable: The tensor variable storing the convolution and \
C
chengduoZH 已提交
1615 1616
                  non-linearity activation result.

C
refine  
chengduoZH 已提交
1617
    Raises:
1618 1619
        ValueError: If the shapes of input, filter_size, stride, padding and
                    groups mismatch.
C
refine  
chengduoZH 已提交
1620

C
chengduoZH 已提交
1621 1622 1623
    Examples:
        .. code-block:: python

1624 1625
          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")
Y
Yu Yang 已提交
1626 1627 1628
    """

    num_channels = input.shape[1]
C
chengduo 已提交
1629
    assert param_attr is not False, "param_attr should not be False here."
1630
    l_type = 'conv2d'
X
xzl 已提交
1631 1632
    if (num_channels == groups and num_filters % num_channels == 0 and
            not use_cudnn):
1633
        l_type = 'depthwise_conv2d'
1634 1635 1636 1637

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

Y
Yu Yang 已提交
1638 1639 1640 1641 1642
    if groups is None:
        num_filter_channels = num_channels
    else:
        if num_channels % groups != 0:
            raise ValueError("num_channels must be divisible by groups.")
M
minqiyang 已提交
1643
        num_filter_channels = num_channels // groups
Y
Yu Yang 已提交
1644

C
chengduoZH 已提交
1645 1646 1647
    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')
1648
    dilation = utils.convert_to_list(dilation, 2, 'dilation')
C
chengduoZH 已提交
1649

C
chengduoZH 已提交
1650 1651
    if not isinstance(use_cudnn, bool):
        raise ValueError("use_cudnn should be True or False")
Y
Yu Yang 已提交
1652 1653

    input_shape = input.shape
M
minqiyang 已提交
1654
    filter_shape = [num_filters, int(num_filter_channels)] + filter_size
Y
Yu Yang 已提交
1655 1656

    def _get_default_param_initializer():
C
chengduo 已提交
1657 1658
        filter_elem_num = filter_size[0] * filter_size[1] * num_channels
        std = (2.0 / filter_elem_num)**0.5
Y
Yu Yang 已提交
1659 1660 1661 1662 1663 1664 1665 1666
        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())

X
Xin Pan 已提交
1667
    pre_bias = helper.create_variable_for_type_inference(dtype)
Y
Yu Yang 已提交
1668 1669

    helper.append_op(
1670
        type=l_type,
Y
Yu Yang 已提交
1671 1672 1673 1674 1675
        inputs={
            'Input': input,
            'Filter': filter_param,
        },
        outputs={"Output": pre_bias},
C
chengduoZH 已提交
1676 1677 1678
        attrs={
            'strides': stride,
            'paddings': padding,
1679
            'dilations': dilation,
C
chengduoZH 已提交
1680
            'groups': groups,
1681
            'use_cudnn': use_cudnn,
1682
            'use_mkldnn': False
C
chengduoZH 已提交
1683
        })
Y
Yu Yang 已提交
1684 1685 1686 1687 1688 1689

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

    return helper.append_activation(pre_act)


C
chengduoZH 已提交
1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706
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
1707 1708 1709 1710 1711 1712
    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.
C
chengduoZH 已提交
1713 1714 1715 1716 1717 1718 1719 1720 1721

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

    .. math::

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

    In the above equation:

1722 1723
    * :math:`X`: Input value, a tensor with NCDHW format.
    * :math:`W`: Filter value, a tensor with MCDHW format.
C
chengduoZH 已提交
1724 1725 1726
    * :math:`\\ast`: Convolution operation.
    * :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
    * :math:`\\sigma`: Activation function.
1727
    * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
C
chengduoZH 已提交
1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752

    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,
1753
            it must contain three integers, (filter_size_D, filter_size_H, filter_size_W).
C
chengduoZH 已提交
1754 1755
            Otherwise, the filter will be a square.
        stride (int|tuple): The stride size. If stride is a tuple, it must
1756
            contain three integers, (stride_D, stride_H, stride_W). Otherwise, the
C
chengduoZH 已提交
1757 1758
            stride_D = stride_H = stride_W = stride. Default: stride = 1.
        padding (int|tuple): The padding size. If padding is a tuple, it must
1759
            contain three integers, (padding_D, padding_H, padding_W). Otherwise, the
C
chengduoZH 已提交
1760 1761
            padding_D = padding_H = padding_W = padding. Default: padding = 0.
        dilation (int|tuple): The dilation size. If dilation is a tuple, it must
1762
            contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the
C
chengduoZH 已提交
1763 1764 1765 1766 1767 1768
            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
C
chengduo 已提交
1769 1770 1771 1772 1773 1774 1775 1776 1777 1778
        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.
C
chengduoZH 已提交
1779 1780
        use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
            library is installed. Default: True
C
chengduo 已提交
1781 1782
        act (str): Activation type, if it is set to None, activation is not appended.
            Default: None.
C
chengduoZH 已提交
1783
        name (str|None): A name for this layer(optional). If set None, the layer
C
chengduo 已提交
1784
            will be named automatically. Default: None.
C
chengduoZH 已提交
1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796

    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

1797 1798
          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")
C
chengduoZH 已提交
1799 1800 1801
    """

    l_type = 'conv3d'
C
chengduo 已提交
1802
    assert param_attr is not False, "param_attr should not be False here."
C
chengduoZH 已提交
1803 1804 1805 1806 1807 1808 1809 1810 1811 1812
    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.")
M
minqiyang 已提交
1813
        num_filter_channels = num_channels // groups
C
chengduoZH 已提交
1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826

    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():
C
chengduo 已提交
1827 1828 1829
        filter_elem_num = filter_size[0] * filter_size[1] * filter_size[
            2] * num_channels
        std = (2.0 / filter_elem_num)**0.5
C
chengduoZH 已提交
1830 1831 1832 1833 1834 1835 1836 1837
        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())

X
Xin Pan 已提交
1838
    pre_bias = helper.create_variable_for_type_inference(dtype)
C
chengduoZH 已提交
1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852

    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,
X
Xin Pan 已提交
1853
            'use_mkldnn': False
C
chengduoZH 已提交
1854 1855
        })

1856
    pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
C
chengduoZH 已提交
1857 1858 1859 1860

    return helper.append_activation(pre_act)


J
Jacek Czaja 已提交
1861
def sequence_pool(input, pool_type, is_test=False):
Y
Yu Yang 已提交
1862
    """
Y
yangyaming 已提交
1863 1864 1865
    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.
L
Luo Tao 已提交
1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876

    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:
1877
         x.lod = [[2, 3, 2]]
L
Luo Tao 已提交
1878 1879 1880 1881 1882
         x.data = [1, 3, 2, 4, 6, 5, 1]
         x.dims = [7, 1]

       then output is a Tensor:
         out.dim = [3, 1]
1883
         with condition len(x.lod[-1]) == out.dims[0]
L
Luo Tao 已提交
1884 1885 1886 1887 1888 1889 1890

       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)
1891 1892
         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)
F
fengjiayi 已提交
1893

L
Luo Tao 已提交
1894 1895
    Args:
        input(variable): The input variable which is a LoDTensor.
Y
yangyaming 已提交
1896
        pool_type (string): The pooling type of sequence_pool.
L
Luo Tao 已提交
1897
            It supports average, sum, sqrt and max.
J
Jacek Czaja 已提交
1898
        is_test(bool, Default False): Used distinguish training from scoring mode.
L
Luo Tao 已提交
1899 1900 1901 1902 1903 1904 1905

    Returns:
        The sequence pooling variable which is a Tensor.

    Examples:

        .. code-block:: python
F
fengjiayi 已提交
1906

Y
yangyaming 已提交
1907
             x = fluid.layers.data(name='x', shape=[7, 1],
L
Luo Tao 已提交
1908 1909 1910 1911 1912
                              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')
1913 1914
             last_x = fluid.layers.sequence_pool(input=x, pool_type='last')
             first_x = fluid.layers.sequence_pool(input=x, pool_type='first')
Y
Yu Yang 已提交
1915
    """
F
fengjiayi 已提交
1916
    helper = LayerHelper('sequence_pool', **locals())
Y
Yu Yang 已提交
1917
    dtype = helper.input_dtype()
X
Xin Pan 已提交
1918 1919
    pool_out = helper.create_variable_for_type_inference(dtype)
    max_index = helper.create_variable_for_type_inference(dtype)
Y
Yu Yang 已提交
1920 1921 1922 1923 1924 1925

    helper.append_op(
        type="sequence_pool",
        inputs={"X": input},
        outputs={"Out": pool_out,
                 "MaxIndex": max_index},
J
Jacek Czaja 已提交
1926 1927
        attrs={"pooltype": pool_type.upper(),
               "is_test": is_test})
Y
Yu Yang 已提交
1928

Y
yangyaming 已提交
1929 1930 1931 1932 1933
    # 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

Y
Yu Yang 已提交
1934 1935 1936
    return pool_out


C
add doc  
chengduoZH 已提交
1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955
@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])
    """
    helper = LayerHelper('sequence_concat', **locals())
X
Xin Pan 已提交
1956
    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
C
add doc  
chengduoZH 已提交
1957 1958 1959 1960 1961
    helper.append_op(
        type='sequence_concat', inputs={'X': input}, outputs={'Out': [out]})
    return out


F
fengjiayi 已提交
1962
def sequence_first_step(input):
L
Luo Tao 已提交
1963
    """
L
Luo Tao 已提交
1964
    This function gets the first step of sequence.
L
Luo Tao 已提交
1965 1966 1967 1968

    .. code-block:: text

       x is a 1-level LoDTensor:
1969
         x.lod = [[2, 3, 2]]
L
Luo Tao 已提交
1970 1971 1972 1973 1974
         x.data = [1, 3, 2, 4, 6, 5, 1]
         x.dims = [7, 1]

       then output is a Tensor:
         out.dim = [3, 1]
1975
         with condition len(x.lod[-1]) == out.dims[0]
L
Luo Tao 已提交
1976
         out.data = [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
F
fengjiayi 已提交
1977

L
Luo Tao 已提交
1978 1979 1980 1981 1982 1983 1984 1985 1986
    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
F
fengjiayi 已提交
1987

Y
yangyaming 已提交
1988
             x = fluid.layers.data(name='x', shape=[7, 1],
L
Luo Tao 已提交
1989 1990 1991
                              dtype='float32', lod_level=1)
             x_first_step = fluid.layers.sequence_first_step(input=x)
    """
1992 1993 1994
    return sequence_pool(input=input, pool_type="first")


F
fengjiayi 已提交
1995
def sequence_last_step(input):
L
Luo Tao 已提交
1996
    """
L
Luo Tao 已提交
1997
    This function gets the last step of sequence.
L
Luo Tao 已提交
1998 1999 2000 2001

    .. code-block:: text

       x is a 1-level LoDTensor:
2002
         x.lod = [[2, 3, 2]]
L
Luo Tao 已提交
2003 2004 2005 2006 2007
         x.data = [1, 3, 2, 4, 6, 5, 1]
         x.dims = [7, 1]

       then output is a Tensor:
         out.dim = [3, 1]
2008
         with condition len(x.lod[-1]) == out.dims[0]
L
Luo Tao 已提交
2009
         out.data = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
F
fengjiayi 已提交
2010

L
Luo Tao 已提交
2011 2012 2013 2014 2015 2016 2017 2018 2019
    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
F
fengjiayi 已提交
2020

Y
yangyaming 已提交
2021
             x = fluid.layers.data(name='x', shape=[7, 1],
L
Luo Tao 已提交
2022 2023 2024
                              dtype='float32', lod_level=1)
             x_last_step = fluid.layers.sequence_last_step(input=x)
    """
2025 2026 2027
    return sequence_pool(input=input, pool_type="last")


Y
Yibing Liu 已提交
2028 2029 2030 2031
def sequence_slice(input, offset, length, name=None):
    """
    **Sequence Slice Layer**

2032
    The layer crops a subsequence from given sequence with given start
Y
Yibing Liu 已提交
2033 2034 2035 2036 2037
    offset and subsequence length.

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

    .. code-block:: text
2038

Y
Yibing Liu 已提交
2039 2040
	- Case:

2041
            Given the input Variable **input**:
2042

2043 2044 2045
                input.data = [[a1, a2], [b1, b2], [c1, c2], [d1, d2], [e1, e2]],
                input.lod = [[3, 2]],
                input.dims = (5, 2),
Y
Yibing Liu 已提交
2046

2047
            with offset.data = [[0], [1]] and length.data = [[2], [1]],
Y
Yibing Liu 已提交
2048

2049
            the output Variable will be
2050

2051 2052 2053
                out.data = [[a1, a2], [b1, b2], [e1, e2]],
                out.lod = [[2, 1]],
                out.dims = (3, 2).
2054 2055

    NOTE: The first dimension size of **input**, **offset** and **length**
2056
          should be equal. The **offset** should start from 0.
2057

Y
Yibing Liu 已提交
2058
    Args:
2059
        input(Variable): The input Variable which consists of the complete
Y
Yibing Liu 已提交
2060
                         sequences.
Y
Yibing Liu 已提交
2061 2062 2063 2064 2065 2066
        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:
Y
Yibing Liu 已提交
2067
        Variable: The output subsequences.
Y
Yibing Liu 已提交
2068 2069 2070 2071 2072 2073 2074 2075 2076 2077

    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"))
2078
             subseqs = fluid.layers.sequence_slice(input=seqs, offset=offset,
Y
Yibing Liu 已提交
2079 2080 2081 2082
                                                   length=length)
    """
    helper = LayerHelper("sequence_slice", **locals())
    dtype = helper.input_dtype()
X
Xin Pan 已提交
2083
    out = helper.create_variable_for_type_inference(dtype)
Y
Yibing Liu 已提交
2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097

    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


F
fengjiayi 已提交
2098
@templatedoc()
Y
Yu Yang 已提交
2099
def pool2d(input,
C
chengduoZH 已提交
2100 2101
           pool_size=-1,
           pool_type="max",
C
chengduoZH 已提交
2102 2103
           pool_stride=1,
           pool_padding=0,
C
caoying03 已提交
2104
           global_pooling=False,
C
chengduoZH 已提交
2105
           use_cudnn=True,
2106
           ceil_mode=False,
2107 2108
           name=None,
           exclusive=True):
Y
Yu Yang 已提交
2109
    """
F
fengjiayi 已提交
2110
    ${comment}
2111 2112

    Args:
2113 2114 2115
        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
F
fengjiayi 已提交
2116
                          feature, and W is the width of the feature.
2117
        pool_size (int): The side length of pooling windows. All pooling
F
fengjiayi 已提交
2118
                         windows are squares with pool_size on a side.
F
fengjiayi 已提交
2119
        pool_type: ${pooling_type_comment}
2120 2121
        pool_stride (int): stride of the pooling layer.
        pool_padding (int): padding size.
2122 2123 2124
        global_pooling (bool): ${global_pooling_comment}
        use_cudnn (bool): ${use_cudnn_comment}
        ceil_mode (bool): ${ceil_mode_comment}
2125
        name (str|None): A name for this layer(optional). If set None, the
F
fengjiayi 已提交
2126
                        layer will be named automatically.
2127 2128
        exclusive (bool): Whether to exclude padding points in average pooling 
                          mode, default is true
F
fengjiayi 已提交
2129

2130
    Returns:
F
fengjiayi 已提交
2131
        Variable: The pooling result.
F
fengjiayi 已提交
2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144

    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')
          conv2d = fluid.layers.pool2d(
2145 2146 2147 2148
                            input=data,
                            pool_size=2,
                            pool_type='max',
                            pool_stride=1,
F
fengjiayi 已提交
2149
                            global_pooling=False)
Y
Yu Yang 已提交
2150 2151 2152 2153 2154
    """
    if pool_type not in ["max", "avg"]:
        raise ValueError(
            "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.",
            str(pool_type))
C
chengduoZH 已提交
2155

C
chengduoZH 已提交
2156 2157 2158 2159 2160
    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))

C
chengduoZH 已提交
2161 2162 2163 2164
    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')

C
chengduoZH 已提交
2165 2166
    if not isinstance(use_cudnn, bool):
        raise ValueError("use_cudnn should be True or False")
Y
Yu Yang 已提交
2167

C
Add doc  
chengduoZH 已提交
2168
    l_type = 'pool2d'
2169 2170

    helper = LayerHelper(l_type, **locals())
Y
Yu Yang 已提交
2171
    dtype = helper.input_dtype()
X
Xin Pan 已提交
2172
    pool_out = helper.create_variable_for_type_inference(dtype)
Y
Yu Yang 已提交
2173 2174

    helper.append_op(
2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185
        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,
2186 2187
            "use_mkldnn": False,
            "exclusive": exclusive,
2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200
        })

    return pool_out


def pool3d(input,
           pool_size=-1,
           pool_type="max",
           pool_stride=1,
           pool_padding=0,
           global_pooling=False,
           use_cudnn=True,
           ceil_mode=False,
2201 2202
           name=None,
           exclusive=True):
2203 2204
    """
    This function adds the operator for pooling in 3-dimensions, using the
Y
Yu Yang 已提交
2205
    pooling configurations mentioned in input parameters.
2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217

    Args:
        input (Variable): ${input_comment}
        pool_size (int): ${ksize_comment}
        pool_type (str): ${pooling_type_comment}
        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.
2218 2219
        exclusive (bool): Whether to exclude padding points in average pooling 
                          mode, default is true
2220

2221
    Returns:
2222
        Variable: output of pool3d layer.
Y
Yu Yang 已提交
2223 2224 2225 2226 2227
    """
    if pool_type not in ["max", "avg"]:
        raise ValueError(
            "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.",
            str(pool_type))
C
chengduoZH 已提交
2228

C
chengduoZH 已提交
2229 2230 2231 2232 2233
    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))

2234 2235 2236
    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')
C
chengduoZH 已提交
2237

C
chengduoZH 已提交
2238 2239
    if not isinstance(use_cudnn, bool):
        raise ValueError("use_cudnn should be True or False")
Y
Yu Yang 已提交
2240

2241 2242
    l_type = "pool3d"
    helper = LayerHelper(l_type, **locals())
Y
Yu Yang 已提交
2243
    dtype = helper.input_dtype()
X
Xin Pan 已提交
2244
    pool_out = helper.create_variable_for_type_inference(dtype)
Y
Yu Yang 已提交
2245 2246

    helper.append_op(
2247
        type=l_type,
Y
Yu Yang 已提交
2248 2249 2250 2251 2252 2253 2254
        inputs={"X": input},
        outputs={"Out": pool_out},
        attrs={
            "pooling_type": pool_type,
            "ksize": pool_size,
            "global_pooling": global_pooling,
            "strides": pool_stride,
C
chengduoZH 已提交
2255
            "paddings": pool_padding,
2256
            "use_cudnn": use_cudnn,
2257
            "ceil_mode": ceil_mode,
2258 2259
            "use_mkldnn": False,
            "exclusive": exclusive,
Y
Yu Yang 已提交
2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271
        })

    return pool_out


def batch_norm(input,
               act=None,
               is_test=False,
               momentum=0.9,
               epsilon=1e-05,
               param_attr=None,
               bias_attr=None,
C
caoying03 已提交
2272
               data_layout='NCHW',
Y
Yang Yang 已提交
2273
               in_place=False,
2274 2275
               name=None,
               moving_mean_name=None,
W
wanghaoshuang 已提交
2276
               moving_variance_name=None,
2277 2278
               do_model_average_for_mean_and_var=False,
               fuse_with_relu=False):
Y
Yu Yang 已提交
2279
    """
Q
qiaolongfei 已提交
2280 2281 2282 2283
    **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:
Q
qiaolongfei 已提交
2284

Q
qiaolongfei 已提交
2285
    1. NHWC `[batch, in_height, in_width, in_channels]`
Q
qiaolongfei 已提交
2286

Q
qiaolongfei 已提交
2287 2288
    2. NCHW `[batch, in_channels, in_height, in_width]`

Q
qiaolongfei 已提交
2289 2290 2291
    Refer to `Batch Normalization: Accelerating Deep Network Training by Reducing
    Internal Covariate Shift <https://arxiv.org/pdf/1502.03167.pdf>`_
    for more details.
Q
qiaolongfei 已提交
2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303

    :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
2304 2305

    Args:
Q
qiaolongfei 已提交
2306
        input(variable): The input variable which is a LoDTensor.
Q
qiaolongfei 已提交
2307 2308 2309 2310
        act(string, Default None): Activation type, linear|relu|prelu|...
        is_test(bool, Default False): Used for training or training.
        momentum(float, Default 0.9):
        epsilon(float, Default 1e-05):
C
chengduo 已提交
2311 2312 2313 2314 2315 2316 2317 2318
        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.
Q
qiaolongfei 已提交
2319
        data_layout(string, default NCHW): NCHW|NHWC
2320
        in_place(bool, Default False): Make the input and output of batch norm reuse memory.
Q
qiaolongfei 已提交
2321 2322 2323 2324
        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.
Q
qiaolongfei 已提交
2325
        do_model_average_for_mean_and_var(bool, Default False): Do model average for mean and variance or not.
2326
        fuse_with_relu (bool): if True, this OP performs relu after batch norm.
2327 2328

    Returns:
Q
qiaolongfei 已提交
2329
        Variable: A tensor variable which is the result after applying batch normalization on the input.
Q
qiaolongfei 已提交
2330 2331 2332 2333 2334 2335 2336

    Examples:

        .. code-block:: python

            hidden1 = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
            hidden2 = fluid.layers.batch_norm(input=hidden1)
Y
Yu Yang 已提交
2337
    """
C
chengduo 已提交
2338
    assert bias_attr is not False, "bias_attr should not be False in batch_norm."
Y
Yu Yang 已提交
2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360
    helper = LayerHelper('batch_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]

    # create parameter
    scale = helper.create_parameter(
        attr=helper.param_attr,
        shape=param_shape,
        dtype=dtype,
        default_initializer=Constant(1.0))

    bias = helper.create_parameter(
2361
        attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True)
Y
Yu Yang 已提交
2362

2363 2364
    mean = helper.create_parameter(
        attr=ParamAttr(
W
wanghaoshuang 已提交
2365 2366 2367
            name=moving_mean_name,
            initializer=Constant(0.0),
            trainable=False,
W
wanghaoshuang 已提交
2368
            do_model_average=do_model_average_for_mean_and_var),
Q
QI JUN 已提交
2369
        shape=param_shape,
2370 2371 2372 2373 2374 2375 2376
        dtype=input.dtype)
    mean.stop_gradient = True

    variance = helper.create_parameter(
        attr=ParamAttr(
            name=moving_variance_name,
            initializer=Constant(1.0),
W
wanghaoshuang 已提交
2377
            trainable=False,
W
wanghaoshuang 已提交
2378
            do_model_average=do_model_average_for_mean_and_var),
Q
QI JUN 已提交
2379
        shape=param_shape,
2380 2381
        dtype=input.dtype)
    variance.stop_gradient = True
Y
Yu Yang 已提交
2382 2383 2384 2385 2386 2387

    # create output
    # mean and mean_out share the same memory
    mean_out = mean
    # variance and variance out share the same memory
    variance_out = variance
X
Xin Pan 已提交
2388 2389 2390 2391
    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)
Y
Yu Yang 已提交
2392

X
Xin Pan 已提交
2393 2394
    batch_norm_out = input if in_place else helper.create_variable_for_type_inference(
        dtype)
Y
Yu Yang 已提交
2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411

    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
        },
2412 2413 2414 2415
        attrs={
            "momentum": momentum,
            "epsilon": epsilon,
            "is_test": is_test,
X
Xin Pan 已提交
2416
            "use_mkldnn": False,
2417
            "fuse_with_relu": fuse_with_relu
2418
        })
Y
Yu Yang 已提交
2419 2420 2421 2422

    return helper.append_activation(batch_norm_out)


Y
yuyang18 已提交
2423
@templatedoc()
G
guosheng 已提交
2424 2425 2426 2427 2428 2429 2430 2431 2432 2433
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):
    """
Y
yuyang18 已提交
2434
    ${comment}
G
guosheng 已提交
2435 2436 2437

    The formula is as follows:

Y
yuyang18 已提交
2438
    ..  math::
G
guosheng 已提交
2439 2440 2441 2442 2443 2444 2445

        \\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)

Y
yuyang18 已提交
2446 2447 2448 2449 2450 2451 2452 2453
    * :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.
Y
yuyang18 已提交
2454

G
guosheng 已提交
2455 2456
    Args:
        input(Variable): The input tensor variable.
2457
        scale(bool): Whether to learn the adaptive gain :math:`g` after
S
sneaxiy 已提交
2458
            normalization. Default True.
2459
        shift(bool): Whether to learn the adaptive bias :math:`b` after
S
sneaxiy 已提交
2460 2461
            normalization. Default True.
        begin_norm_axis(int): The normalization will be performed along
G
guosheng 已提交
2462
            dimensions from :attr:`begin_norm_axis` to :attr:`rank(input)`.
S
sneaxiy 已提交
2463
            Default 1.
2464
        epsilon(float): The small value added to the variance to prevent
S
sneaxiy 已提交
2465
            division by zero. Default 1e-05.
G
guosheng 已提交
2466
        param_attr(ParamAttr|None): The parameter attribute for the learnable
S
sneaxiy 已提交
2467 2468
            gain :math:`g`. If :attr:`scale` is False, :attr:`param_attr` is
            omitted. If :attr:`scale` is True and :attr:`param_attr` is None,
2469 2470
            a default :code:`ParamAttr` would be added as scale. The
            :attr:`param_attr` is initialized as 1 if it is added. Default None.
G
guosheng 已提交
2471
        bias_attr(ParamAttr|None): The parameter attribute for the learnable
S
sneaxiy 已提交
2472 2473
            bias :math:`b`. If :attr:`shift` is False, :attr:`bias_attr` is
            omitted. If :attr:`shift` is True and :attr:`param_attr` is None,
2474
            a default :code:`ParamAttr` would be added as bias. The
S
sneaxiy 已提交
2475
            :attr:`bias_attr` is initialized as 0 if it is added. Default None.
G
guosheng 已提交
2476
        act(str): Activation to be applied to the output of layer normalizaiton.
S
sneaxiy 已提交
2477 2478 2479
                  Default None.
        name(str): The name of this layer. It is optional. Default None, and a
                   unique name would be generated automatically.
G
guosheng 已提交
2480 2481

    Returns:
Y
yuyang18 已提交
2482
        ${y_comment}
G
guosheng 已提交
2483 2484 2485

    Examples:

Y
yuyang18 已提交
2486 2487 2488
        >>> data = fluid.layers.data(name='data', shape=[3, 32, 32],
        >>>                          dtype='float32')
        >>> x = fluid.layers.layer_norm(input=data, begin_norm_axis=1)
G
guosheng 已提交
2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503
    """
    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
G
guosheng 已提交
2504
    if shift:
G
guosheng 已提交
2505 2506 2507 2508 2509 2510
        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
X
Xin Pan 已提交
2511 2512 2513 2514 2515
    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)
G
guosheng 已提交
2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530

    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)


Y
Yu Yang 已提交
2531 2532 2533 2534
def conv2d_transpose(input,
                     num_filters,
                     output_size=None,
                     filter_size=None,
C
chengduoZH 已提交
2535 2536 2537
                     padding=0,
                     stride=1,
                     dilation=1,
2538
                     groups=None,
C
caoying03 已提交
2539
                     param_attr=None,
2540
                     bias_attr=None,
C
chengduoZH 已提交
2541
                     use_cudnn=True,
2542
                     act=None,
C
caoying03 已提交
2543
                     name=None):
Y
Yu Yang 已提交
2544
    """
2545 2546 2547 2548 2549 2550 2551 2552
    **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
2553 2554
    layer, please refer to the following explanation and references
    `therein <http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf>`_.
2555 2556 2557
    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.
2558 2559 2560 2561 2562

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

    .. math::

2563
        Out = \sigma (W \\ast X + b)
2564

2565
    Where:
2566 2567 2568

    * :math:`X`: Input value, a tensor with NCHW format.
    * :math:`W`: Filter value, a tensor with MCHW format.
2569 2570 2571 2572
    * :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.
Y
Yu Yang 已提交
2573

2574 2575 2576 2577
    Example:

        - Input:

2578
          Input shape: :math:`(N, C_{in}, H_{in}, W_{in})`
2579

2580
          Filter shape: :math:`(C_{in}, C_{out}, H_f, W_f)`
2581 2582 2583

        - Output:

2584
          Output shape: :math:`(N, C_{out}, H_{out}, W_{out})`
2585 2586

        Where
Y
Yu Yang 已提交
2587

2588 2589
        .. math::

2590 2591 2592 2593
           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 \\\\
           H_{out} \in [ H^\prime_{out}, H^\prime_{out} + strides[0] ) \\\\
           W_{out} \in [ W^\prime_{out}, W^\prime_{out} + strides[1] )
Y
Yu Yang 已提交
2594 2595

    Args:
2596 2597 2598 2599
        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
2600 2601 2602 2603
            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.
2604 2605 2606 2607 2608 2609 2610 2611 2612 2613 2614 2615 2616 2617 2618 2619 2620 2621
        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.
C
chengduo 已提交
2622 2623 2624 2625 2626 2627 2628 2629 2630 2631
            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.
2632
        use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn
C
chengduo 已提交
2633 2634 2635
            library is installed. Default: True.
        act (str): Activation type, if it is set to None, activation is not appended.
            Default: None.
2636
        name(str|None): A name for this layer(optional). If set None, the layer
C
chengduo 已提交
2637
            will be named automatically. Default: True.
Y
Yu Yang 已提交
2638 2639

    Returns:
2640
        Variable: The tensor variable storing the convolution transpose result.
2641 2642

    Raises:
2643 2644
        ValueError: If the shapes of input, filter_size, stride, padding and
                    groups mismatch.
2645 2646 2647 2648

    Examples:
       .. code-block:: python

2649 2650
          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)
Y
Yu Yang 已提交
2651
    """
C
chengduo 已提交
2652
    assert param_attr is not False, "param_attr should not be False in conv2d_transpose."
2653 2654 2655 2656 2657 2658 2659 2660
    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())
Y
Yu Yang 已提交
2661 2662 2663
    if not isinstance(input, Variable):
        raise TypeError("Input of conv2d_transpose must be Variable")

C
chengduoZH 已提交
2664 2665 2666
    padding = utils.convert_to_list(padding, 2, 'padding')
    stride = utils.convert_to_list(stride, 2, 'stride')
    dilation = utils.convert_to_list(dilation, 2, 'dilation')
G
guosheng 已提交
2667

C
chengduoZH 已提交
2668 2669
    if not isinstance(use_cudnn, bool):
        raise ValueError("use_cudnn should be True or False")
G
guosheng 已提交
2670

Y
Yu Yang 已提交
2671 2672 2673 2674 2675
    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]
G
guosheng 已提交
2676

Y
Yu Yang 已提交
2677 2678
        h_in = input.shape[2]
        w_in = input.shape[3]
G
guosheng 已提交
2679

C
chengduoZH 已提交
2680
        filter_size_h = (output_size[0] - (h_in - 1) * stride[0] + 2 *
M
minqiyang 已提交
2681
                         padding[0] - 1) // dilation[0] + 1
C
chengduoZH 已提交
2682
        filter_size_w = (output_size[1] - (w_in - 1) * stride[1] + 2 *
M
minqiyang 已提交
2683
                         padding[1] - 1) // dilation[1] + 1
Y
Yu Yang 已提交
2684
        filter_size = [filter_size_h, filter_size_w]
C
chengduoZH 已提交
2685 2686 2687
    else:
        filter_size = utils.convert_to_list(filter_size, 2,
                                            'conv2d_transpose.filter_size')
C
chengduo 已提交
2688

2689 2690 2691 2692 2693 2694 2695
    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')
2696
    groups = 1 if groups is None else groups
M
minqiyang 已提交
2697
    filter_shape = [input_channel, num_filters // groups] + filter_size
C
chengduo 已提交
2698

Y
Yu Yang 已提交
2699 2700 2701
    img_filter = helper.create_parameter(
        dtype=input.dtype, shape=filter_shape, attr=helper.param_attr)

X
Xin Pan 已提交
2702
    pre_bias = helper.create_variable_for_type_inference(dtype=input.dtype)
Y
Yu Yang 已提交
2703
    helper.append_op(
2704
        type=op_type,
Y
Yu Yang 已提交
2705 2706
        inputs={'Input': [input],
                'Filter': [img_filter]},
2707
        outputs={'Output': pre_bias},
C
chengduoZH 已提交
2708
        attrs={
2709
            'output_size': output_size,
2710 2711 2712 2713 2714
            'strides': stride,
            'paddings': padding,
            'dilations': dilation,
            'groups': groups,
            'use_cudnn': use_cudnn
Y
Yu Yang 已提交
2715 2716
        })

2717 2718 2719
    pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
    out = helper.append_activation(pre_act)
    return out
Y
Yu Yang 已提交
2720 2721


2722
def conv3d_transpose(input,
Y
Yu Yang 已提交
2723 2724 2725
                     num_filters,
                     output_size=None,
                     filter_size=None,
C
chengduoZH 已提交
2726 2727 2728
                     padding=0,
                     stride=1,
                     dilation=1,
2729
                     groups=None,
C
caoying03 已提交
2730
                     param_attr=None,
2731
                     bias_attr=None,
C
chengduoZH 已提交
2732
                     use_cudnn=True,
2733
                     act=None,
C
caoying03 已提交
2734
                     name=None):
Y
Yu Yang 已提交
2735
    """
2736
    **Convlution3D transpose layer**
2737

2738
    The convolution3D transpose layer calculates the output based on the input,
2739
    filter, and dilations, strides, paddings. Input(Input) and output(Output)
2740 2741 2742 2743 2744 2745
    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>`_.
2746 2747 2748
    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.
2749 2750 2751 2752 2753

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

    .. math::

2754
        Out = \sigma (W \\ast X + b)
2755 2756 2757

    In the above equation:

2758 2759
    * :math:`X`: Input value, a tensor with NCDHW format.
    * :math:`W`: Filter value, a tensor with MCDHW format.
2760 2761 2762 2763
    * :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.
Y
Yu Yang 已提交
2764

2765 2766 2767 2768
    Example:

        - Input:

2769
          Input shape: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`
2770

2771
          Filter shape: :math:`(C_{in}, C_{out}, D_f, H_f, W_f)`
2772 2773 2774

        - Output:

2775
          Output shape: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`
2776 2777

        Where
Y
Yu Yang 已提交
2778

2779 2780
        .. math::

2781 2782 2783
           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
Y
Yu Yang 已提交
2784 2785

    Args:
2786
        input(Variable): The input image with [N, C, D, H, W] format.
2787 2788 2789
        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
2790
            tuple, it must contain three integers, (image_D, image_H, image_W). This
2791 2792
            parameter only works when filter_size is None.
        filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
2793
            it must contain three integers, (filter_size_D, filter_size_H, filter_size_W).
2794 2795 2796
            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
2797 2798
            contain three integers, (padding_D, padding_H, padding_W). Otherwise, the
            padding_D = padding_H = padding_W = padding. Default: padding = 0.
2799
        stride(int|tuple): The stride size. If stride is a tuple, it must
2800 2801
            contain three integers, (stride_D, stride_H, stride_W). Otherwise, the
            stride_D = stride_H = stride_W = stride. Default: stride = 1.
2802
        dilation(int|tuple): The dilation size. If dilation is a tuple, it must
2803 2804 2805
            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
2806 2807 2808 2809 2810
            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
C
chengduo 已提交
2811 2812 2813 2814 2815 2816 2817 2818 2819
        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.
2820 2821
        use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn
            library is installed. Default: True
C
chengduo 已提交
2822 2823
        act (str): Activation type, if it is set to None, activation is not appended.
            Default: None.
2824 2825
        name(str|None): A name for this layer(optional). If set None, the layer
            will be named automatically.
Y
Yu Yang 已提交
2826 2827

    Returns:
2828
        Variable: The tensor variable storing the convolution transpose result.
2829 2830

    Raises:
2831 2832
        ValueError: If the shapes of input, filter_size, stride, padding and
                    groups mismatch.
2833 2834 2835 2836

    Examples:
       .. code-block:: python

2837 2838
          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)
Y
Yu Yang 已提交
2839
    """
C
chengduo 已提交
2840
    assert param_attr is not False, "param_attr should not be False in conv3d_transpose."
2841 2842
    l_type = "conv3d_transpose"
    helper = LayerHelper(l_type, **locals())
Y
Yu Yang 已提交
2843
    if not isinstance(input, Variable):
2844
        raise TypeError("Input of conv3d_transpose must be Variable")
Y
Yu Yang 已提交
2845 2846
    input_channel = input.shape[1]

2847 2848 2849
    padding = utils.convert_to_list(padding, 3, 'padding')
    stride = utils.convert_to_list(stride, 3, 'stride')
    dilation = utils.convert_to_list(dilation, 3, 'dilation')
C
chengduoZH 已提交
2850

C
chengduoZH 已提交
2851 2852 2853
    if not isinstance(use_cudnn, bool):
        raise ValueError("use_cudnn should be True or False")

Y
Yu Yang 已提交
2854 2855 2856 2857 2858 2859
    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]

2860 2861 2862
        d_in = input.shape[2]
        h_in = input.shape[3]
        w_in = input.shape[4]
C
chengduoZH 已提交
2863

2864
        filter_size_d = (output_size[0] - (d_in - 1) * stride[0] + 2 *
M
minqiyang 已提交
2865
                         padding[0] - 1) // dilation[0] + 1
2866
        filter_size_h = (output_size[1] - (h_in - 1) * stride[1] + 2 *
M
minqiyang 已提交
2867
                         padding[1] - 1) // dilation[1] + 1
2868
        filter_size_w = (output_size[2] - (w_in - 1) * stride[2] + 2 *
M
minqiyang 已提交
2869
                         padding[2] - 1) // dilation[2] + 1
2870
        filter_size = [filter_size_d, filter_size_h, filter_size_w]
C
chengduoZH 已提交
2871
    else:
2872 2873
        filter_size = utils.convert_to_list(filter_size, 3,
                                            'conv3d_transpose.filter_size')
Y
Yu Yang 已提交
2874

2875
    groups = 1 if groups is None else groups
M
minqiyang 已提交
2876
    filter_shape = [input_channel, num_filters // groups] + filter_size
Y
Yu Yang 已提交
2877 2878 2879
    img_filter = helper.create_parameter(
        dtype=input.dtype, shape=filter_shape, attr=helper.param_attr)

X
Xin Pan 已提交
2880
    pre_bias = helper.create_variable_for_type_inference(dtype=input.dtype)
Y
Yu Yang 已提交
2881
    helper.append_op(
2882
        type=l_type,
Y
Yu Yang 已提交
2883 2884
        inputs={'Input': [input],
                'Filter': [img_filter]},
2885
        outputs={'Output': pre_bias},
C
chengduoZH 已提交
2886 2887 2888 2889
        attrs={
            'strides': stride,
            'paddings': padding,
            'dilations': dilation,
2890
            'groups': groups,
C
chengduoZH 已提交
2891 2892
            'use_cudnn': use_cudnn
        })
Y
Yu Yang 已提交
2893

2894 2895
    pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
    out = helper.append_activation(pre_act)
Y
Yu Yang 已提交
2896
    return out
Y
yangyaming 已提交
2897 2898


Y
yangyaming 已提交
2899
def sequence_expand(x, y, ref_level=-1, name=None):
2900
    """Sequence Expand Layer. This layer will expand the input variable **x**
Y
yangyaming 已提交
2901 2902 2903 2904
    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:
2905 2906 2907 2908 2909

    .. code-block:: text

        * Case 1
            x is a LoDTensor:
2910
                x.lod  = [[2,        2]]
Y
yangyaming 已提交
2911
                x.data = [[a], [b], [c], [d]]
2912 2913 2914
                x.dims = [4, 1]

            y is a LoDTensor:
2915 2916
                y.lod = [[2,    2],
                         [3, 3, 1, 1]]
2917

Y
yangyaming 已提交
2918
            ref_level: 0
2919

Y
yangyaming 已提交
2920
            then output is a 1-level LoDTensor:
2921
                out.lod =  [[2,        2,        2,        2]]
Y
yangyaming 已提交
2922
                out.data = [[a], [b], [a], [b], [c], [d], [c], [d]]
2923 2924 2925 2926
                out.dims = [8, 1]

        * Case 2
            x is a Tensor:
Y
yangyaming 已提交
2927
                x.data = [[a], [b], [c]]
2928 2929 2930
                x.dims = [3, 1]

            y is a LoDTensor:
2931
                y.lod = [[2, 0, 3]]
2932

Y
yangyaming 已提交
2933
            ref_level: -1
2934

Y
yangyaming 已提交
2935 2936 2937
            then output is a Tensor:
                out.data = [[a], [a], [c], [c], [c]]
                out.dims = [5, 1]
2938 2939 2940
    Args:
        x (Variable): The input variable which is a Tensor or LoDTensor.
        y (Variable): The input variable which is a LoDTensor.
Y
yangyaming 已提交
2941 2942
        ref_level (int): Lod level of `y` to be referred by `x`. If set to -1,
                         refer the last level of lod.
C
caoying03 已提交
2943
        name(str|None): A name for this layer(optional). If set None, the layer
Y
yangyaming 已提交
2944
                        will be named automatically.
2945 2946 2947 2948 2949 2950 2951 2952 2953 2954

    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)
Y
yangyaming 已提交
2955
            out = layers.sequence_expand(x=x, y=y, ref_level=0)
2956
    """
Y
yangyaming 已提交
2957
    helper = LayerHelper('sequence_expand', input=x, **locals())
2958
    dtype = helper.input_dtype()
X
Xin Pan 已提交
2959
    tmp = helper.create_variable_for_type_inference(dtype)
2960
    helper.append_op(
Y
yangyaming 已提交
2961 2962 2963 2964 2965
        type='sequence_expand',
        inputs={'X': x,
                'Y': y},
        outputs={'Out': tmp},
        attrs={'ref_level': ref_level})
2966
    return tmp
2967 2968


C
chengduo 已提交
2969 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024
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)
    """
    helper = LayerHelper('sequence_expand_as', input=x, **locals())
    dtype = helper.input_dtype()
X
Xin Pan 已提交
3025
    tmp = helper.create_variable_for_type_inference(dtype)
C
chengduo 已提交
3026 3027 3028 3029 3030 3031 3032 3033
    helper.append_op(
        type='sequence_expand_as',
        inputs={'X': x,
                'Y': y},
        outputs={'Out': tmp})
    return tmp


F
fengjiayi 已提交
3034
@templatedoc()
3035
def sequence_pad(x, pad_value, maxlen=None, name=None):
F
fengjiayi 已提交
3036 3037 3038 3039 3040
    """
    ${comment}

    Args:
        x(Variable): Input variable which should contain lod information.
M
minqiyang 已提交
3041 3042 3043
        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
F
fengjiayi 已提交
3044
            automatically broadcasted to the shape of time step.
M
minqiyang 已提交
3045 3046 3047 3048
        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
3049 3050 3051
            longest original sequence.
        name(str|None): A name for this layer(optional). If set None, the layer
            will be named automatically.
M
minqiyang 已提交
3052

F
fengjiayi 已提交
3053
    Returns:
M
minqiyang 已提交
3054
        Variable: The padded sequence batch and the original lengths before
3055
                  padding. All sequences has the same length.
M
minqiyang 已提交
3056

F
fengjiayi 已提交
3057 3058 3059 3060 3061 3062 3063
    Examples:
        .. code-block:: python

            import numpy

            x = fluid.layers.data(name='y', shape=[10, 5],
                             dtype='float32', lod_level=1)
G
gmcather 已提交
3064
            pad_value = fluid.layers.assign(
D
dongzhihong 已提交
3065
                input=numpy.array([0.0], dtype=numpy.float32))
F
fengjiayi 已提交
3066 3067 3068 3069 3070
            out = fluid.layers.sequence_pad(x=x, pad_value=pad_value)
    """

    helper = LayerHelper('sequence_pad', input=x, **locals())
    dtype = helper.input_dtype()
X
Xin Pan 已提交
3071 3072
    out = helper.create_variable_for_type_inference(dtype)
    length = helper.create_variable_for_type_inference(dtype)
3073 3074 3075 3076

    pad_value.stop_gradient = True
    length.stop_gradient = True

F
fengjiayi 已提交
3077 3078 3079 3080 3081 3082
    if maxlen is None:
        maxlen = -1
    helper.append_op(
        type='sequence_pad',
        inputs={'X': x,
                'PadValue': pad_value},
3083 3084
        outputs={'Out': out,
                 'Length': length},
F
fengjiayi 已提交
3085
        attrs={'padded_length': maxlen})
3086
    return out, length
F
fengjiayi 已提交
3087 3088


3089
def sequence_unpad(x, length, name=None):
Y
Yibing Liu 已提交
3090
    """
3091
    **Sequence Unpad Layer**
Y
Yibing Liu 已提交
3092

3093 3094
    This layer removes the padding data in the input sequences and convert
    them into sequences with actual length as output, identitied by lod
Y
Yibing Liu 已提交
3095 3096 3097 3098 3099 3100 3101 3102 3103
    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],
3104 3105 3106
		      [11.0, 12.0, 13.0, 14.0, 15.0]],

	in which there are 3 sequences padded to length 5, and the acutal length
3107
	specified by input Variable **length**:
Y
Yibing Liu 已提交
3108 3109 3110 3111 3112 3113

	    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]]
3114
	    out.lod = [[2, 3, 4]]
Y
Yibing Liu 已提交
3115 3116 3117 3118 3119 3120

    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.
3121 3122
        name(str|None): A name for this layer(optional). If set None, the layer
            will be named automatically.
Y
Yibing Liu 已提交
3123 3124 3125 3126 3127 3128 3129 3130 3131 3132 3133 3134 3135 3136

    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)
    """

    helper = LayerHelper('sequence_unpad', input=x, **locals())
    dtype = helper.input_dtype()
X
Xin Pan 已提交
3137
    out = helper.create_variable_for_type_inference(dtype)
Y
Yibing Liu 已提交
3138 3139 3140 3141 3142 3143 3144 3145 3146 3147 3148

    length.stop_gradient = True

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


3149 3150 3151 3152 3153 3154 3155 3156 3157
def beam_search(pre_ids,
                pre_scores,
                ids,
                scores,
                beam_size,
                end_id,
                level=0,
                name=None):
    """
3158 3159
    Beam search is a classical algorithm for selecting candidate words in a
    machine translation task.
Y
Yan Chunwei 已提交
3160 3161 3162

    Refer to `Beam search <https://en.wikipedia.org/wiki/Beam_search>`_
    for more details.
M
minqiyang 已提交
3163 3164

    This layer does the search in beams for one time step. Specifically, it
3165 3166 3167 3168 3169 3170
    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
    computation cell. 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.
M
minqiyang 已提交
3171

3172 3173 3174 3175 3176 3177 3178 3179
    Note that the :attr:`scores` passed in should be accumulated scores, and
    length penalty should be done with extra operators before calculating the
    accumulated scores if needed, also suggest finding top-K before it and
    using the top-K candidates following.

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

        fluid/tests/book/test_machine_translation.py
Y
Yan Chunwei 已提交
3180

3181
    Args:
3182 3183 3184 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 3196 3197 3198 3199 3200 3201 3202 3203 3204 3205 3206
        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.
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.
F
fengjiayi 已提交
3207

3208
    Returns:
3209 3210
        Variable: The LodTensor pair containing the selected ids and the \
            corresponding scores.
Y
Yan Chunwei 已提交
3211 3212 3213 3214

    Examples:
        .. code-block:: python

3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 3228 3229 3230 3231
            # 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)
    """
Q
Qiao Longfei 已提交
3232 3233 3234 3235
    helper = LayerHelper('beam_search', **locals())
    score_type = scores.dtype
    id_type = ids.dtype

X
Xin Pan 已提交
3236 3237 3238
    selected_scores = helper.create_variable_for_type_inference(
        dtype=score_type)
    selected_ids = helper.create_variable_for_type_inference(dtype=id_type)
Q
Qiao Longfei 已提交
3239 3240 3241 3242 3243

    helper.append_op(
        type='beam_search',
        inputs={
            'pre_ids': pre_ids,
3244
            'pre_scores': pre_scores,
Q
Qiao Longfei 已提交
3245 3246 3247 3248 3249 3250 3251 3252 3253 3254 3255 3256 3257 3258 3259 3260 3261
            'ids': ids,
            'scores': scores,
        },
        outputs={
            'selected_ids': selected_ids,
            'selected_scores': selected_scores,
        },
        attrs={
            # TODO(ChunweiYan) to assure other value support
            'level': level,
            'beam_size': beam_size,
            'end_id': end_id,
        })

    return selected_ids, selected_scores


3262 3263 3264 3265 3266 3267 3268
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
G
guosheng 已提交
3269

3270 3271 3272 3273 3274 3275 3276 3277 3278
    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.
G
guosheng 已提交
3279

3280 3281 3282 3283 3284 3285
    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.
G
guosheng 已提交
3286

3287 3288 3289 3290 3291 3292 3293 3294
    Examples:
        .. code-block:: python
            # 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())
X
Xin Pan 已提交
3295 3296
    sentence_ids = helper.create_variable_for_type_inference(dtype=ids.dtype)
    sentence_scores = helper.create_variable_for_type_inference(dtype=ids.dtype)
3297 3298 3299 3300 3301 3302 3303 3304 3305 3306 3307 3308 3309 3310 3311

    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


Y
yangyaming 已提交
3312 3313 3314 3315
def lstm_unit(x_t,
              hidden_t_prev,
              cell_t_prev,
              forget_bias=0.0,
Y
yangyaming 已提交
3316
              param_attr=None,
C
caoying03 已提交
3317 3318
              bias_attr=None,
              name=None):
Y
yangyaming 已提交
3319 3320 3321 3322
    """Lstm unit layer. The equation of a lstm step is:

        .. math::

3323
            i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i)
Y
yangyaming 已提交
3324

3325
            f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + b_f)
Y
yangyaming 已提交
3326

3327
            c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t + W_{h_c}h_{t-1} + b_c)
Y
yangyaming 已提交
3328

3329
            o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + b_o)
Y
yangyaming 已提交
3330 3331 3332

            h_t & = o_t tanh(c_t)

3333 3334 3335 3336 3337 3338
    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:
Y
yangyaming 已提交
3339 3340 3341

        .. math::

3342
            L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i
Y
yangyaming 已提交
3343 3344 3345 3346 3347 3348 3349 3350

    The non-linear transformation is applied by calling `lstm_unit_op` and the
    equation is:

        .. math::

            i_t = \sigma(L_{i_t})

Y
yangyaming 已提交
3351
    This layer has two outputs including :math:`h_t` and :math:`o_t`.
Y
yangyaming 已提交
3352 3353

    Args:
Y
yangyaming 已提交
3354 3355 3356 3357 3358 3359
        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.
Y
yangyaming 已提交
3360
        forget_bias (float): The forget bias of lstm unit.
C
chengduo 已提交
3361 3362 3363 3364 3365 3366 3367 3368 3369 3370 3371 3372
        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.
C
caoying03 已提交
3373 3374
        name(str|None): A name for this layer(optional). If set None, the layer
                       will be named automatically.
Y
yangyaming 已提交
3375 3376

    Returns:
Y
yangyaming 已提交
3377
        tuple: The hidden value and cell value of lstm unit.
Y
yangyaming 已提交
3378 3379

    Raises:
3380 3381 3382 3383
        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.
Y
yangyaming 已提交
3384 3385 3386 3387 3388 3389

    Examples:

        .. code-block:: python

             x_t = fluid.layers.fc(input=x_t_data, size=10)
3390
             prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=30)
Y
yangyaming 已提交
3391
             prev_cell = fluid.layers.fc(input=prev_cell_data, size=30)
Y
yangyaming 已提交
3392
             hidden_value, cell_value = fluid.layers.lstm_unit(x_t=x_t,
Y
yangyaming 已提交
3393 3394 3395 3396 3397 3398 3399 3400 3401 3402 3403 3404 3405 3406 3407 3408
                                                    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]:
Y
yangyaming 已提交
3409
        raise ValueError("The 1st dimensions of x_t, hidden_t_prev and "
3410 3411 3412 3413
                         "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 "
Y
yangyaming 已提交
3414 3415
                         "cell_t_prev must be the same.")

Y
yangyaming 已提交
3416 3417 3418
    if bias_attr is None:
        bias_attr = ParamAttr()

Y
yangyaming 已提交
3419
    size = cell_t_prev.shape[1]
3420
    concat_out = concat(input=[x_t, hidden_t_prev], axis=1)
Y
yangyaming 已提交
3421 3422
    fc_out = fc(input=concat_out,
                size=4 * size,
Y
yangyaming 已提交
3423
                param_attr=param_attr,
3424
                bias_attr=bias_attr)
Y
yangyaming 已提交
3425
    dtype = x_t.dtype
X
Xin Pan 已提交
3426 3427
    c = helper.create_variable_for_type_inference(dtype)
    h = helper.create_variable_for_type_inference(dtype)
Y
yangyaming 已提交
3428 3429 3430 3431 3432 3433 3434 3435 3436

    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})

Y
yangyaming 已提交
3437
    return h, c
G
guosheng 已提交
3438 3439


C
caoying03 已提交
3440
def reduce_sum(input, dim=None, keep_dim=False, name=None):
G
guosheng 已提交
3441
    """
Y
yangyaming 已提交
3442
    Computes the sum of tensor elements over the given dimension.
G
guosheng 已提交
3443 3444 3445

    Args:
        input (Variable): The input variable which is a Tensor or LoDTensor.
W
whs 已提交
3446
        dim (list|int|None): The dimensions along which the sum is performed. If
Y
yangyaming 已提交
3447 3448
            :attr:`None`, sum all elements of :attr:`input` and return a
            Tensor variable with a single element, otherwise must be in the
W
whs 已提交
3449 3450
            range :math:`[-rank(input), rank(input))`. If :math:`dim[i] < 0`,
            the dimension to reduce is :math:`rank + dim[i]`.
3451
        keep_dim (bool|False): Whether to reserve the reduced dimension in the
Y
yangyaming 已提交
3452
            output Tensor. The result tensor will have one fewer dimension
G
guosheng 已提交
3453
            than the :attr:`input` unless :attr:`keep_dim` is true.
C
caoying03 已提交
3454 3455
        name(str|None): A name for this layer(optional). If set None, the layer
                       will be named automatically.
G
guosheng 已提交
3456 3457 3458

    Returns:
        Variable: The reduced Tensor variable.
F
fengjiayi 已提交
3459

G
guosheng 已提交
3460 3461 3462 3463 3464 3465
    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]]
Q
qiaolongfei 已提交
3466
            # Each example is followed by the corresponding output tensor.
G
guosheng 已提交
3467 3468 3469 3470
            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]]
W
whs 已提交
3471 3472 3473 3474

            # x is a Tensor variable with shape [2, 2, 2] and elements as below:
            #      [[[1, 2], [3, 4]],
            #      [[5, 6], [7, 8]]]
Q
qiaolongfei 已提交
3475
            # Each example is followed by the corresponding output tensor.
W
whs 已提交
3476 3477 3478
            fluid.layers.reduce_sum(x, dim=[1, 2]) # [10, 26]
            fluid.layers.reduce_sum(x, dim=[0, 1]) # [16, 20]

G
guosheng 已提交
3479 3480
    """
    helper = LayerHelper('reduce_sum', **locals())
X
Xin Pan 已提交
3481
    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
W
whs 已提交
3482 3483
    if dim is not None and not isinstance(dim, list):
        dim = [dim]
G
guosheng 已提交
3484 3485 3486 3487 3488
    helper.append_op(
        type='reduce_sum',
        inputs={'X': input},
        outputs={'Out': out},
        attrs={
W
whs 已提交
3489
            'dim': dim if dim != None else [0],
G
guosheng 已提交
3490 3491 3492 3493
            'keep_dim': keep_dim,
            'reduce_all': True if dim == None else False
        })
    return out
G
guosheng 已提交
3494 3495


C
caoying03 已提交
3496
def reduce_mean(input, dim=None, keep_dim=False, name=None):
G
guosheng 已提交
3497
    """
Y
Yibing Liu 已提交
3498
    Computes the mean of the input tensor's elements along the given dimension.
G
guosheng 已提交
3499 3500 3501

    Args:
        input (Variable): The input variable which is a Tensor or LoDTensor.
Y
Yibing Liu 已提交
3502 3503 3504
        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
Y
yangyaming 已提交
3505
            must be in the range :math:`[-rank(input), rank(input))`. If
3506
            :math:`dim[i] < 0`, the dimension to reduce is
Y
Yibing Liu 已提交
3507
            :math:`rank(input) + dim[i]`.
Y
yangyaming 已提交
3508 3509
        keep_dim (bool): Whether to reserve the reduced dimension in the
            output Tensor. The result tensor will have one fewer dimension
G
guosheng 已提交
3510
            than the :attr:`input` unless :attr:`keep_dim` is true.
Y
Yibing Liu 已提交
3511
        name(str|None): A name for this layer(optional). If set `None`, the layer
C
caoying03 已提交
3512
                       will be named automatically.
G
guosheng 已提交
3513 3514

    Returns:
Y
Yibing Liu 已提交
3515
        Variable: The reduced mean Variable.
F
fengjiayi 已提交
3516

G
guosheng 已提交
3517 3518 3519 3520 3521 3522 3523 3524 3525 3526
    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]
F
stash  
fengjiayi 已提交
3527 3528
            fluid.layers.reduce_mean(
                x, dim=1, keep_dim=True)  # [[0.475], [0.4]]
W
whs 已提交
3529 3530 3531 3532 3533 3534 3535

            # 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]
G
guosheng 已提交
3536 3537
    """
    helper = LayerHelper('reduce_mean', **locals())
X
Xin Pan 已提交
3538
    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
W
whs 已提交
3539 3540
    if dim is not None and not isinstance(dim, list):
        dim = [dim]
G
guosheng 已提交
3541 3542 3543 3544 3545
    helper.append_op(
        type='reduce_mean',
        inputs={'X': input},
        outputs={'Out': out},
        attrs={
W
whs 已提交
3546
            'dim': dim if dim != None else [0],
G
guosheng 已提交
3547 3548 3549 3550
            'keep_dim': keep_dim,
            'reduce_all': True if dim == None else False
        })
    return out
3551 3552


C
caoying03 已提交
3553
def reduce_max(input, dim=None, keep_dim=False, name=None):
3554
    """
Y
yangyaming 已提交
3555
    Computes the maximum of tensor elements over the given dimension.
3556 3557 3558

    Args:
        input (Variable): The input variable which is a Tensor or LoDTensor.
W
whs 已提交
3559
        dim (list|int|None): The dimension along which the maximum is computed.
Y
yangyaming 已提交
3560 3561 3562
            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))`.
W
whs 已提交
3563
            If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
Y
yangyaming 已提交
3564 3565
        keep_dim (bool): Whether to reserve the reduced dimension in the
            output Tensor. The result tensor will have one fewer dimension
3566
            than the :attr:`input` unless :attr:`keep_dim` is true.
C
caoying03 已提交
3567 3568
        name(str|None): A name for this layer(optional). If set None, the layer
                       will be named automatically.
3569 3570 3571

    Returns:
        Variable: The reduced Tensor variable.
Y
yangyaming 已提交
3572

3573 3574 3575 3576 3577 3578 3579 3580 3581 3582 3583
    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]]
W
whs 已提交
3584 3585 3586 3587 3588 3589 3590

            # 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]
3591 3592
    """
    helper = LayerHelper('reduce_max', **locals())
X
Xin Pan 已提交
3593
    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
W
whs 已提交
3594 3595
    if dim is not None and not isinstance(dim, list):
        dim = [dim]
3596 3597 3598 3599 3600
    helper.append_op(
        type='reduce_max',
        inputs={'X': input},
        outputs={'Out': out},
        attrs={
W
whs 已提交
3601
            'dim': dim if dim != None else [0],
3602 3603 3604 3605 3606 3607
            'keep_dim': keep_dim,
            'reduce_all': True if dim == None else False
        })
    return out


C
caoying03 已提交
3608
def reduce_min(input, dim=None, keep_dim=False, name=None):
3609
    """
Y
yangyaming 已提交
3610
    Computes the minimum of tensor elements over the given dimension.
3611 3612 3613

    Args:
        input (Variable): The input variable which is a Tensor or LoDTensor.
W
whs 已提交
3614
        dim (list|int|None): The dimensions along which the minimum is computed.
Y
yangyaming 已提交
3615 3616 3617
            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))`.
W
whs 已提交
3618
            If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
Y
yangyaming 已提交
3619 3620
        keep_dim (bool): Whether to reserve the reduced dimension in the
            output Tensor. The result tensor will have one fewer dimension
3621
            than the :attr:`input` unless :attr:`keep_dim` is true.
C
caoying03 已提交
3622 3623
        name(str|None): A name for this layer(optional). If set None, the layer
                       will be named automatically.
3624 3625 3626

    Returns:
        Variable: The reduced Tensor variable.
Y
yangyaming 已提交
3627

3628 3629 3630 3631 3632 3633 3634 3635 3636 3637 3638
    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]]
W
whs 已提交
3639 3640 3641 3642 3643 3644 3645

            # 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]
3646 3647
    """
    helper = LayerHelper('reduce_min', **locals())
X
Xin Pan 已提交
3648
    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
W
whs 已提交
3649 3650
    if dim is not None and not isinstance(dim, list):
        dim = [dim]
3651 3652 3653 3654 3655
    helper.append_op(
        type='reduce_min',
        inputs={'X': input},
        outputs={'Out': out},
        attrs={
W
whs 已提交
3656
            'dim': dim if dim != None else [0],
3657 3658 3659 3660
            'keep_dim': keep_dim,
            'reduce_all': True if dim == None else False
        })
    return out
G
guosheng 已提交
3661 3662


3663 3664 3665 3666 3667 3668
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.
W
whs 已提交
3669
        dim (list|int|None): The dimensions along which the product is performed. If
3670 3671
            :attr:`None`, multipy all elements of :attr:`input` and return a
            Tensor variable with a single element, otherwise must be in the
W
whs 已提交
3672 3673
            range :math:`[-rank(input), rank(input))`. If :math:`dim[i] < 0`,
            the dimension to reduce is :math:`rank + dim[i]`.
3674 3675 3676
        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.
Y
yangyaming 已提交
3677
        name(str|None): A name for this layer(optional). If set None, the
Z
zhouhanqing 已提交
3678
            layer will be named automatically.
3679 3680 3681 3682 3683 3684 3685 3686 3687 3688 3689 3690 3691 3692

    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]
Y
yangyaming 已提交
3693
            fluid.layers.reduce_prod(x, dim=1,
Z
zhouhanqing 已提交
3694
                                     keep_dim=True)  # [[0.027], [0.0084]]
W
whs 已提交
3695 3696 3697 3698 3699 3700 3701

            # 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]
3702 3703
    """
    helper = LayerHelper('reduce_prod', **locals())
X
Xin Pan 已提交
3704
    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
W
whs 已提交
3705 3706
    if dim is not None and not isinstance(dim, list):
        dim = [dim]
3707 3708 3709 3710 3711
    helper.append_op(
        type='reduce_prod',
        inputs={'X': input},
        outputs={'Out': out},
        attrs={
W
whs 已提交
3712
            'dim': dim if dim != None else [0],
3713 3714 3715 3716 3717 3718
            'keep_dim': keep_dim,
            'reduce_all': True if dim == None else False
        })
    return out


C
caoying03 已提交
3719
def split(input, num_or_sections, dim=-1, name=None):
G
guosheng 已提交
3720
    """
C
caoying03 已提交
3721
    Split the input tensor into multiple sub-tensors.
G
guosheng 已提交
3722 3723 3724

    Args:
        input (Variable): The input variable which is a Tensor or LoDTensor.
C
caoying03 已提交
3725 3726 3727 3728 3729
        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'
G
guosheng 已提交
3730
            :attr:`dim` dimension orderly.
C
caoying03 已提交
3731
        dim (int): The dimension along which to split. If :math:`dim < 0`, the
G
guosheng 已提交
3732
            dimension to split along is :math:`rank(input) + dim`.
C
caoying03 已提交
3733 3734
        name(str|None): A name for this layer(optional). If set None, the layer
                       will be named automatically.
G
guosheng 已提交
3735 3736

    Returns:
D
dzhwinter 已提交
3737
        list(Variable): The list of segmented tensor variables.
G
guosheng 已提交
3738 3739 3740 3741 3742 3743 3744 3745 3746

    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]
F
stash  
fengjiayi 已提交
3747 3748
            x0, x1, x2 = fluid.layers.split(
                x, num_or_sections=[2, 3, 4], dim=1)
G
guosheng 已提交
3749 3750 3751 3752 3753 3754 3755 3756 3757 3758 3759 3760 3761 3762 3763
            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:
        assert len(num_or_sections) < input_shape[
            dim], 'len(num_or_sections) must not be more than input.shape[dim].'
        num = len(num_or_sections)
    outs = [
X
Xin Pan 已提交
3764
        helper.create_variable_for_type_inference(dtype=helper.input_dtype())
G
guosheng 已提交
3765 3766 3767 3768 3769 3770 3771 3772 3773 3774 3775 3776 3777
        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
C
caoying03 已提交
3778 3779 3780 3781 3782 3783 3784 3785 3786


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

3787
    .. math::
3788 3789

        y = \\frac{x}{ \sqrt{\sum {x^2} + epsion }}
C
caoying03 已提交
3790 3791 3792 3793 3794

    For `x` with more dimensions, this layer independently normalizes each 1-D
    slice along dimension `axis`.

    Args:
3795
        x(Variable|list): The input tensor to l2_normalize layer.
3796
        axis(int): The axis on which to apply normalization. If `axis < 0`, \
3797 3798
            the dimension to normalization is rank(X) + axis. -1 is the
            last dimension.
3799
        epsilon(float): The epsilon value is used to avoid division by zero, \
3800
            the defalut value is 1e-10.
3801
        name(str|None): A name for this layer(optional). If set None, the layer \
3802
            will be named automatically.
C
caoying03 已提交
3803 3804

    Returns:
3805
        Variable: The output tensor variable is the same shape with `x`.
C
caoying03 已提交
3806 3807

    Examples:
3808

C
caoying03 已提交
3809 3810
        .. code-block:: python

3811 3812 3813 3814
            data = fluid.layers.data(name="data",
                                     shape=(3, 17, 13),
                                     dtype="float32")
            normed = fluid.layers.l2_normalize(x=data, axis=1)
C
caoying03 已提交
3815 3816
    """

F
fengjiayi 已提交
3817 3818
    if len(x.shape) == 1:
        axis = 0
C
caoying03 已提交
3819 3820
    helper = LayerHelper("l2_normalize", **locals())

X
Xin Pan 已提交
3821 3822
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
    norm = helper.create_variable_for_type_inference(dtype=x.dtype)
C
caoying03 已提交
3823
    helper.append_op(
3824 3825 3826 3827
        type="norm",
        inputs={"X": x},
        outputs={"Out": out,
                 "Norm": norm},
C
caoying03 已提交
3828
        attrs={
3829 3830
            "axis": 1 if axis is None else axis,
            "epsilon": epsilon,
C
caoying03 已提交
3831 3832
        })
    return out
3833 3834


S
sneaxiy 已提交
3835
def matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None):
G
guosheng 已提交
3836
    """
Y
ying 已提交
3837 3838 3839 3840
    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.
G
guosheng 已提交
3841

C
chengduoZH 已提交
3842
    The actual behavior depends on the shapes of :math:`x`, :math:`y` and the
3843
    flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:
G
guosheng 已提交
3844

3845 3846 3847 3848 3849
    - 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
3850
      :math:`[1, D]` in transposed form.
G
guosheng 已提交
3851

C
chengduoZH 已提交
3852
    - After transpose, the two tensors are 2-D or n-D and matrix multiplication
3853
      performs in the following way.
G
guosheng 已提交
3854

3855
      - If both are 2-D, they are multiplied like conventional matrices.
C
chengduoZH 已提交
3856
      - If either is n-D, it is treated as a stack of matrices residing in the
Y
ying 已提交
3857
        last two dimensions and a batched matrix multiply supporting broadcast
3858
        applies on the two tensors.
G
guosheng 已提交
3859

Y
ying 已提交
3860 3861
    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
C
chengduoZH 已提交
3862
    removed after matrix multiplication.
G
guosheng 已提交
3863 3864 3865

    Args:
        x (Variable): The input variable which is a Tensor or LoDTensor.
3866 3867 3868
        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.
S
sneaxiy 已提交
3869
        alpha (float): The scale of output. Default 1.0.
3870
        name(str|None): A name for this layer(optional). If set None, the layer
3871
            will be named automatically.
G
guosheng 已提交
3872 3873

    Returns:
3874
        Variable: The product Tensor variable.
G
guosheng 已提交
3875

G
guosheng 已提交
3876 3877 3878
    Examples:
        .. code-block:: python

3879
            # Examples to clarify shapes of the inputs and output
C
chengduoZH 已提交
3880 3881
            # x: [B, ..., M, K], y: [B, ..., K, N]
            fluid.layers.matmul(x, y)  # out: [B, ..., M, N]
Y
ying 已提交
3882

3883 3884
            # x: [B, M, K], y: [B, K, N]
            fluid.layers.matmul(x, y)  # out: [B, M, N]
Y
ying 已提交
3885

3886 3887
            # x: [B, M, K], y: [K, N]
            fluid.layers.matmul(x, y)  # out: [B, M, N]
Y
ying 已提交
3888

3889 3890
            # x: [M, K], y: [K, N]
            fluid.layers.matmul(x, y)  # out: [M, N]
Y
ying 已提交
3891 3892 3893 3894

            # x: [B, M, K], y: [K]
            fluid.layers.matmul(x, y)  # out: [B, M]

3895 3896
            # x: [K], y: [K]
            fluid.layers.matmul(x, y)  # out: [1]
3897

Y
ying 已提交
3898
            # x: [M], y: [N]
3899
            fluid.layers.matmul(x, y, True, True)  # out: [M, N]
G
guosheng 已提交
3900
    """
Y
ying 已提交
3901 3902 3903 3904 3905 3906 3907 3908 3909 3910 3911 3912

    def __check_input(x, y):
        if len(y.shape) > len(x.shape):
            raise ValueError(
                "Invalid inputs for matmul. "
                "x's rank should be always greater than or equal to y'rank.")

        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:
Y
ying 已提交
3913
            y_shape = y_shape + [1]
Y
ying 已提交
3914 3915 3916 3917 3918 3919 3920 3921 3922 3923 3924 3925 3926 3927 3928 3929

        # 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]:
            raise ValueError("Invalid inputs for matmul.")

        if len(y_shape) > 2:
            for i, dim_x in enumerate(x_shape[:-2]):
                if dim_x != y_shape[i]:
                    raise ValueError("Invalid inputs for matmul.")

    __check_input(x, y)

3930
    helper = LayerHelper('matmul', **locals())
X
Xin Pan 已提交
3931
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
G
guosheng 已提交
3932
    helper.append_op(
3933 3934 3935 3936
        type='matmul',
        inputs={'X': x,
                'Y': y},
        outputs={'Out': out},
S
sneaxiy 已提交
3937 3938 3939
        attrs={
            'transpose_X': transpose_x,
            'transpose_Y': transpose_y,
S
sneaxiy 已提交
3940
            'alpha': float(alpha),
S
sneaxiy 已提交
3941
        })
3942
    return out
3943 3944


3945
def topk(input, k, name=None):
Q
qingqing01 已提交
3946 3947 3948 3949
    """
    This operator is used to find values and indices of the k largest entries
    for the last dimension.

F
fengjiayi 已提交
3950
    If the input is a vector (1-D Tensor), finds the k largest entries in the vector
Q
qingqing01 已提交
3951 3952 3953 3954 3955 3956
    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.

F
fengjiayi 已提交
3957 3958 3959 3960 3961 3962 3963 3964 3965 3966 3967 3968 3969 3970 3971 3972 3973 3974 3975 3976 3977
    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]]

Q
qingqing01 已提交
3978 3979 3980
    Args:
        input(Variable): The input variable which can be a vector or Tensor with
            higher rank.
3981
        k(int):  The number of top elements to look for along the last dimension
F
fengjiayi 已提交
3982
                 of input.
3983
        name(str|None): A name for this layer(optional). If set None, the layer
3984
                       will be named automatically.
F
fengjiayi 已提交
3985
                       Default: None
Q
qingqing01 已提交
3986 3987

    Returns:
3988 3989 3990
        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
F
fengjiayi 已提交
3991
        within the last dimension of input.
Q
qingqing01 已提交
3992

F
fengjiayi 已提交
3993 3994
    Raises:
        ValueError: If k < 1 or k is not less than the last dimension of input
Q
qingqing01 已提交
3995 3996 3997 3998 3999 4000 4001

    Examples:
        .. code-block:: python

            top5_values, top5_indices = layers.topk(input, k=5)
    """
    helper = LayerHelper("top_k", **locals())
X
Xin Pan 已提交
4002 4003
    values = helper.create_variable_for_type_inference(dtype=input.dtype)
    indices = helper.create_variable_for_type_inference(dtype="int64")
Q
qingqing01 已提交
4004 4005 4006 4007 4008 4009 4010 4011 4012 4013 4014
    helper.append_op(
        type="top_k",
        inputs={"X": [input]},
        outputs={"Out": [values],
                 "Indices": [indices]},
        attrs={"k": k})
    values.stop_gradient = True
    indices.stop_gradient = True
    return values, indices


4015
def edit_distance(input, label, normalized=True, ignored_tokens=None):
4016
    """
Y
ying 已提交
4017 4018 4019 4020 4021 4022 4023 4024 4025
    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:
W
wanghaoshuang 已提交
4026

Y
ying 已提交
4027
    "kitten" -> "sitten" -> "sittin" -> "sitting"
W
wanghaoshuang 已提交
4028

4029
    The input is a LoDTensor consisting of all the hypothesis strings with
Y
ying 已提交
4030 4031
    the total number denoted by `batch_size`, and the separation is specified
    by the LoD information. And the `batch_size` reference strings are arranged
4032
    in order in the same way in the input LoDTensor.
W
wanghaoshuang 已提交
4033

4034
    The output contains the `batch_size` results and each stands for the edit
Y
ying 已提交
4035 4036
    distance for a pair of strings respectively. If Attr(normalized) is true,
    the edit distance will be divided by the length of reference string.
W
wanghaoshuang 已提交
4037

4038 4039 4040
    Args:
        input(Variable): The indices for hypothesis strings.
        label(Variable): The indices for reference strings.
4041
        normalized(bool, default True): Indicated whether to normalize the edit distance by
Y
ying 已提交
4042
                          the length of reference string.
4043
        ignored_tokens(list<int>, default None): Tokens that should be removed before
Y
ying 已提交
4044
                                     calculating edit distance.
4045
        name (str): The name of this layer. It is optional.
4046

W
wanghaoshuang 已提交
4047
    Returns:
W
wanghaoshuang 已提交
4048
        Variable: sequence-to-sequence edit distance in shape [batch_size, 1].
W
wanghaoshuang 已提交
4049 4050 4051 4052 4053

    Examples:
        .. code-block:: python

            x = fluid.layers.data(name='x', shape=[8], dtype='float32')
4054
            y = fluid.layers.data(name='y', shape=[7], dtype='float32')
4055
            cost = fluid.layers.edit_distance(input=x,label=y)
4056
    """
4057
    helper = LayerHelper("edit_distance", **locals())
4058

4059
    # remove some tokens from input and labels
W
wanghaoshuang 已提交
4060
    if ignored_tokens is not None and len(ignored_tokens) > 0:
X
Xin Pan 已提交
4061 4062
        erased_input = helper.create_variable_for_type_inference(dtype="int64")
        erased_label = helper.create_variable_for_type_inference(dtype="int64")
4063 4064 4065 4066 4067

        helper.append_op(
            type="sequence_erase",
            inputs={"X": [input]},
            outputs={"Out": [erased_input]},
W
wanghaoshuang 已提交
4068
            attrs={"tokens": ignored_tokens})
4069 4070 4071 4072 4073
        input = erased_input

        helper.append_op(
            type="sequence_erase",
            inputs={"X": [label]},
W
whs 已提交
4074
            outputs={"Out": [erased_label]},
W
wanghaoshuang 已提交
4075
            attrs={"tokens": ignored_tokens})
4076 4077
        label = erased_label

4078
    # edit distance op
X
Xin Pan 已提交
4079 4080
    edit_distance_out = helper.create_variable_for_type_inference(dtype="int64")
    sequence_num = helper.create_variable_for_type_inference(dtype="int64")
4081 4082 4083 4084
    helper.append_op(
        type="edit_distance",
        inputs={"Hyps": [input],
                "Refs": [label]},
4085 4086
        outputs={"Out": [edit_distance_out],
                 "SequenceNum": [sequence_num]},
4087 4088
        attrs={"normalized": normalized})

4089
    return edit_distance_out, sequence_num
4090 4091 4092 4093 4094


def ctc_greedy_decoder(input, blank, name=None):
    """
    This op is used to decode sequences by greedy policy by below steps:
Y
yi.wu 已提交
4095

Y
ying 已提交
4096 4097 4098 4099
    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.
4100 4101 4102 4103 4104 4105 4106 4107 4108 4109 4110 4111 4112 4113 4114 4115 4116

    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]]

4117
        input.lod = [[4, 4]]
4118 4119 4120 4121 4122 4123 4124

        Then:

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

4125
        output.lod = [[2, 1]]
4126 4127 4128

    Args:

Y
ying 已提交
4129 4130 4131 4132 4133 4134 4135 4136 4137
        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).
4138
        name (str): The name of this layer. It is optional.
4139 4140

    Returns:
4141
        Variable: CTC greedy decode result. If all the sequences in result were
4142
        empty, the result LoDTensor will be [-1] with LoD [[]] and dims [1, 1].
4143 4144 4145 4146 4147

    Examples:
        .. code-block:: python

            x = fluid.layers.data(name='x', shape=[8], dtype='float32')
W
wanghaoshuang 已提交
4148

4149
            cost = fluid.layers.ctc_greedy_decoder(input=x, blank=0)
W
wanghaoshuang 已提交
4150
    """
4151
    helper = LayerHelper("ctc_greedy_decoder", **locals())
Q
qingqing01 已提交
4152
    _, topk_indices = topk(input, k=1)
4153 4154

    # ctc align op
X
Xin Pan 已提交
4155
    ctc_out = helper.create_variable_for_type_inference(dtype="int64")
4156 4157 4158
    helper.append_op(
        type="ctc_align",
        inputs={"Input": [topk_indices]},
W
wanghaoshuang 已提交
4159
        outputs={"Output": [ctc_out]},
4160 4161
        attrs={"merge_repeated": True,
               "blank": blank})
4162
    return ctc_out
4163 4164


F
fengjiayi 已提交
4165
def warpctc(input, label, blank=0, norm_by_times=False):
W
wanghaoshuang 已提交
4166
    """
4167 4168
    An operator integrating the open source Warp-CTC library
    (https://github.com/baidu-research/warp-ctc)
W
wanghaoshuang 已提交
4169
    to compute Connectionist Temporal Classification (CTC) loss.
4170 4171
    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
W
wanghaoshuang 已提交
4172 4173 4174
    input tensor.

    Args:
4175
       input (Variable): The unscaled probabilities of variable-length sequences,
W
wanghaoshuang 已提交
4176 4177 4178 4179
         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).
4180
       label (Variable): The ground truth of variable-length sequence,
4181 4182 4183
         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
W
wanghaoshuang 已提交
4184 4185
         Temporal Classification (CTC) loss, which is in the
         half-opened interval [0, num_classes + 1).
4186 4187 4188
       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
4189
         follewed by a mean_op.
W
wanghaoshuang 已提交
4190 4191

    Returns:
4192 4193
        Variable: The Connectionist Temporal Classification (CTC) loss,
        which is a 2-D Tensor of the shape [batch_size, 1].
W
wanghaoshuang 已提交
4194 4195

    Examples:
4196

W
wanghaoshuang 已提交
4197
        .. code-block:: python
4198

4199 4200 4201
            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)
W
wanghaoshuang 已提交
4202 4203

    """
F
fengjiayi 已提交
4204
    helper = LayerHelper('warpctc', **locals())
X
Xin Pan 已提交
4205 4206
    loss_out = helper.create_variable_for_type_inference(dtype=input.dtype)
    grad_out = helper.create_variable_for_type_inference(dtype=input.dtype)
W
wanghaoshuang 已提交
4207 4208 4209 4210 4211 4212 4213 4214 4215
    helper.append_op(
        type='warpctc',
        inputs={'Logits': [input],
                'Label': [label]},
        outputs={'WarpCTCGrad': [grad_out],
                 'Loss': [loss_out]},
        attrs={'blank': blank,
               'norm_by_times': norm_by_times})
    return loss_out
4216 4217 4218 4219 4220 4221 4222 4223 4224 4225 4226 4227 4228 4229 4230


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]]
4231 4232 4233
            x.data = [[1,  2], [3,  4],
                      [5,  6], [7,  8],
                      [9, 10], [11, 12]]
4234 4235 4236 4237 4238
            x.dims = [6, 2]

        set new_dim = 4

        then out is a LoDTensor:
4239

4240
            out.lod  = [[0, 1, 3]]
4241 4242 4243 4244

            out.data = [[1,  2,  3,  4],
                        [5,  6,  7,  8],
                        [9, 10, 11, 12]]
4245 4246 4247 4248 4249 4250 4251
            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:
4252 4253 4254

       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.
4255 4256

    Returns:
4257

4258 4259 4260 4261 4262
        Variable: Reshaped LoDTensor according to new dimension.

    Examples:
        .. code-block:: python

4263
            x = fluid.layers.data(shape=[5, 20], dtype='float32', lod_level=1)
4264
            x_reshaped = fluid.layers.sequence_reshape(input=x, new_dim=10)
4265 4266
    """
    helper = LayerHelper('sequence_reshape', **locals())
X
Xin Pan 已提交
4267
    out = helper.create_variable_for_type_inference(helper.input_dtype())
4268 4269 4270 4271 4272 4273
    helper.append_op(
        type='sequence_reshape',
        inputs={'X': [input]},
        outputs={'Out': [out]},
        attrs={'new_dim': new_dim})
    return out
Y
ying 已提交
4274 4275


4276 4277 4278 4279
# 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")
Y
Yang Yu 已提交
4280 4281 4282 4283 4284 4285
def nce(input,
        label,
        num_total_classes,
        sample_weight=None,
        param_attr=None,
        bias_attr=None,
C
chengduo 已提交
4286 4287
        num_neg_samples=None,
        name=None):
4288 4289 4290 4291 4292 4293 4294
    """
    ${comment}

    Args:
        input (Variable): input variable.
        label (Variable): label.
        num_total_classes (int):${num_total_classes_comment}
4295 4296
        sample_weight (Variable|None): A Variable of shape [batch_size, 1]
            storing a weight for each sample. The default weight for each
4297
            sample is 1.0.
C
chengduo 已提交
4298 4299 4300 4301 4302 4303 4304 4305 4306
        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.
4307
        num_neg_samples (int): ${num_neg_samples_comment}
C
chengduo 已提交
4308 4309
        name (str|None): A name for this layer(optional). If set None, the layer
             will be named automatically. Default: None.
F
fengjiayi 已提交
4310

4311
    Returns:
Y
Yibing Liu 已提交
4312 4313 4314 4315 4316 4317 4318 4319 4320 4321 4322 4323 4324 4325 4326 4327 4328 4329 4330 4331 4332 4333 4334 4335 4336 4337 4338
        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')
4339
    """
Y
Yang Yu 已提交
4340 4341 4342
    helper = LayerHelper('nce', **locals())
    assert isinstance(input, Variable)
    assert isinstance(label, Variable)
C
chengduo 已提交
4343 4344

    dim = input.shape[1]
Y
Yang Yu 已提交
4345 4346 4347 4348 4349 4350
    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)
C
chengduo 已提交
4351 4352 4353 4354 4355 4356 4357 4358 4359 4360 4361 4362 4363
    inputs = {
        'Input': input,
        'Label': label,
        'Weight': w,
        'SampleWeight': sample_weight if sample_weight is not None else []
    }
    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
X
Xin Pan 已提交
4364 4365 4366
    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)
Y
Yang Yu 已提交
4367

Y
Yang Yu 已提交
4368 4369 4370 4371 4372 4373 4374 4375 4376
    if num_neg_samples is None:
        num_neg_samples = 10
    else:
        num_neg_samples = int(num_neg_samples)

    attrs = {
        'num_total_classes': int(num_total_classes),
        'num_neg_samples': num_neg_samples
    }
Y
Yang Yu 已提交
4377 4378 4379

    helper.append_op(
        type='nce',
C
chengduo 已提交
4380
        inputs=inputs,
Y
Yang Yu 已提交
4381 4382 4383 4384 4385 4386
        outputs={
            'Cost': cost,
            'SampleLogits': sample_logits,
            'SampleLabels': sample_labels
        },
        attrs=attrs)
Y
Yang Yu 已提交
4387
    return cost / (num_neg_samples + 1)
4388 4389


C
chengduo 已提交
4390 4391 4392 4393 4394 4395
def hsigmoid(input,
             label,
             num_classes,
             param_attr=None,
             bias_attr=None,
             name=None):
W
weixing02 已提交
4396 4397
    """
    The hierarchical sigmoid operator is used to accelerate the training
M
minqiyang 已提交
4398
    process of language model. This operator organizes the classes into a
G
guosheng 已提交
4399 4400 4401 4402 4403 4404 4405 4406 4407
    complete binary tree, each leaf node represents a class(a word) and each
    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.

    Refer to `Hierarchical Probabilistic Neural Network Language Model
    <http://www.iro.umontreal.ca/~lisa/pointeurs/hierarchical-nnlm-aistats05.pdf>`_
M
minqiyang 已提交
4408

W
weixing02 已提交
4409
    Args:
M
minqiyang 已提交
4410
        input (Variable): The input tensor variable with shape
G
guosheng 已提交
4411 4412 4413 4414 4415
            :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]`.
        num_classes: (int), The number of classes, must not be less than 2.
C
chengduo 已提交
4416 4417 4418 4419 4420 4421 4422 4423 4424 4425 4426
        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.
W
weixing02 已提交
4427 4428 4429 4430 4431 4432 4433 4434

    Returns:
        Out: (Tensor) The cost of hierarchical sigmoid operator. the shape is [N, 1]

    Examples:

        .. code-block:: python

G
guosheng 已提交
4435 4436 4437
            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)
W
weixing02 已提交
4438 4439 4440 4441
    """

    helper = LayerHelper('hierarchical_sigmoid', **locals())
    dtype = helper.input_dtype()
X
Xin Pan 已提交
4442 4443
    out = helper.create_variable_for_type_inference(dtype)
    pre_out = helper.create_variable_for_type_inference(dtype)
W
weixing02 已提交
4444 4445
    dim = input.shape[1]
    if num_classes < 2:
G
guosheng 已提交
4446
        raise ValueError("num_classes must not be less than 2.")
W
weixing02 已提交
4447 4448 4449 4450 4451
    weights = helper.create_parameter(
        attr=helper.param_attr,
        shape=[num_classes - 1, dim],
        is_bias=False,
        dtype=input.dtype)
W
weixing02 已提交
4452 4453 4454 4455 4456 4457 4458 4459
    inputs = {"X": input, "W": weights, "Label": label}
    if helper.bias_attr:
        bias = helper.create_parameter(
            attr=helper.bias_attr,
            shape=[1, num_classes - 1],
            is_bias=True,
            dtype=input.dtype)
        inputs['Bias'] = bias
W
weixing02 已提交
4460 4461
    helper.append_op(
        type="hierarchical_sigmoid",
W
weixing02 已提交
4462
        inputs=inputs,
W
weixing02 已提交
4463 4464 4465 4466 4467 4468
        outputs={"Out": out,
                 "PreOut": pre_out},
        attrs={"num_classes": num_classes})
    return out


Y
fix ci.  
ying 已提交
4469
def transpose(x, perm, name=None):
Y
ying 已提交
4470 4471 4472 4473 4474 4475 4476
    """
    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:
4477 4478 4479
        x (Variable): The input Tensor.
        perm (list): A permutation of the dimensions of `input`.
        name (str): The name of this layer. It is optional.
Y
ying 已提交
4480 4481 4482 4483 4484 4485 4486

    Returns:
        Variable: A transposed Tensor.

    Examples:
        .. code-block:: python

4487 4488 4489 4490
            # use append_batch_size=False to avoid prepending extra 
            # batch size in shape
            x = fluid.layers.data(name='x', shape=[5, 10, 15], 
                            dtype='float32', append_batch_size=False)
Y
fix ci.  
ying 已提交
4491
            x_transposed = layers.transpose(x, perm=[1, 0, 2])
Y
ying 已提交
4492 4493
    """

Y
fix ci.  
ying 已提交
4494
    if len(perm) != len(x.shape):
Y
ying 已提交
4495 4496 4497
        raise ValueError(
            "Input(perm) is the permutation of dimensions of Input(input). "
            "It's length shoud be equal to Input(input)'s rank.")
Y
ying 已提交
4498 4499 4500 4501 4502 4503
    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)))
Y
ying 已提交
4504 4505

    helper = LayerHelper('transpose', **locals())
X
Xin Pan 已提交
4506 4507
    out = helper.create_variable_for_type_inference(x.dtype)
    x_shape = helper.create_variable_for_type_inference(x.dtype)
Y
ying 已提交
4508
    helper.append_op(
4509
        type='transpose2',
Y
fix ci.  
ying 已提交
4510
        inputs={'X': [x]},
4511 4512
        outputs={'Out': [out],
                 'XShape': [x_shape]},
Y
ying 已提交
4513 4514
        attrs={'axis': perm})
    return out
4515 4516


4517 4518 4519 4520 4521 4522 4523
def im2sequence(input,
                filter_size=1,
                stride=1,
                padding=0,
                input_image_size=None,
                out_stride=1,
                name=None):
4524
    """
4525 4526 4527 4528 4529 4530 4531
    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:
4532 4533 4534 4535 4536 4537 4538 4539 4540 4541

    .. 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.
W
wanghaoshuang 已提交
4542 4543 4544 4545 4546 4547 4548 4549 4550 4551 4552 4553 4554 4555 4556 4557 4558 4559

        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.

4560 4561 4562 4563 4564 4565 4566 4567 4568
        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.

4569 4570 4571
        name (int): The name of this layer. It is optional.

    Returns:
W
wanghaoshuang 已提交
4572 4573 4574 4575 4576
        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.
4577 4578 4579 4580 4581 4582 4583 4584 4585 4586 4587 4588 4589 4590 4591 4592 4593 4594 4595 4596 4597 4598 4599 4600 4601 4602 4603

    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:

W
wanghaoshuang 已提交
4604 4605 4606
            filter = [2, 2]
            stride = [1, 1]
            padding = [0, 0]
4607 4608 4609 4610 4611 4612 4613 4614 4615 4616 4617 4618

            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.]]

4619
            output.dims = {8, 8}
4620

4621
            output.lod = [[4, 4]]
4622

D
dzhwinter 已提交
4623
     Examples:
4624 4625 4626

        .. code-block:: python

4627 4628
            output = fluid.layers.im2sequence(
                input=layer, stride=[1, 1], filter_size=[2, 2])
4629 4630

    """
W
wanghaoshuang 已提交
4631 4632 4633 4634 4635 4636 4637 4638 4639 4640

    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])
4641 4642 4643 4644 4645 4646 4647
    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
4648
    helper = LayerHelper('im2sequence', **locals())
X
Xin Pan 已提交
4649
    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
4650
    helper.append_op(
4651
        type='im2sequence', inputs=inputs, outputs={'Out': out}, attrs=attrs)
4652
    return out
4653 4654


Y
yuyang18 已提交
4655
@templatedoc()
4656
def row_conv(input, future_context_size, param_attr=None, act=None):
Y
yuyang18 已提交
4657 4658
    """
    ${comment}
4659 4660

    Args:
Y
yuyang18 已提交
4661
        input (${x_type}): ${x_comment}.
Y
yangyaming 已提交
4662 4663
        future_context_size (int): Future context size. Please note, the shape
            of convolution kernel is [future_context_size + 1, D].
4664 4665 4666 4667 4668
        param_attr (ParamAttr): Attributes of parameters, including
            name, initializer etc.
        act (str): Non-linear activation to be applied to output variable.

    Returns:
Y
yuyang18 已提交
4669
        ${out_comment}.
4670 4671

    Examples:
Y
yuyang18 已提交
4672 4673 4674 4675
        >>> 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)
4676 4677 4678 4679 4680 4681
    """
    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)
X
Xin Pan 已提交
4682
    out = helper.create_variable_for_type_inference(dtype)
4683 4684 4685 4686 4687
    helper.append_op(
        type='row_conv',
        inputs={'X': [input],
                'Filter': [filter_param]},
        outputs={'Out': [out]})
Y
yangyaming 已提交
4688
    return helper.append_activation(out)
4689 4690


Y
yuyang18 已提交
4691
@templatedoc()
4692 4693
def multiplex(inputs, index):
    """
Y
yuyang18 已提交
4694 4695 4696 4697 4698 4699 4700
    ${comment}

    >>> 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)
4701 4702

    Args:
Y
yuyang18 已提交
4703 4704
       inputs (list): ${x_comment}.
       index (${ids_type}): ${ids_comment}.
4705 4706

    Returns:
Y
yuyang18 已提交
4707
        ${out_comment}.
4708 4709
    """
    helper = LayerHelper('multiplex', **locals())
Y
yangyaming 已提交
4710 4711 4712 4713 4714

    if not isinstance(inputs, list) and len(inputs) < 2:
        raise ValueError("inputs should be a list object and contains at least "
                         "2 elements.")

X
Xin Pan 已提交
4715
    out = helper.create_variable_for_type_inference(inputs[0].dtype)
4716 4717 4718 4719 4720 4721
    helper.append_op(
        type='multiplex',
        inputs={'X': inputs,
                'Ids': index},
        outputs={'Out': [out]})
    return out
4722 4723


4724 4725 4726
def softmax_with_cross_entropy(logits,
                               label,
                               soft_label=False,
S
sneaxiy 已提交
4727 4728
                               ignore_index=-100,
                               numeric_stable_mode=False):
4729 4730
    """
    **Softmax With Cross Entropy Operator.**
4731

4732 4733 4734 4735
    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.
4736

4737 4738 4739
    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.
4740

4741 4742 4743
    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.
4744

4745
    The equation is as follows:
4746

4747
    1) Hard label (one-hot label, so every sample has exactly one class)
4748

4749 4750 4751 4752
    .. math::

        loss_j =  -\\text{logit}_{label_j} +
        \\log\\left(\\sum_{i=0}^{K}\\exp(\\text{logit}_i)\\right), j = 1,..., K
4753

4754 4755 4756
    2) Soft label (each sample can have a distribution over all classes)

    .. math::
4757

4758 4759 4760 4761
        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

S
sneaxiy 已提交
4762 4763 4764 4765 4766 4767 4768 4769 4770 4771 4772 4773
    3) If numeric_stable_mode is True, softmax is calculated first by:

    .. math::
        
        max_j = \\max_{i=0}^{K}{\\text{logit}_i}

        log\\_max\\_sum_j = \\log\\sum_{i=0}^{K}\\exp(logit_i - max_j)

        softmax_j = \\exp(logit_j - max_j - {log\\_max\\_sum}_j)

    and then cross entropy loss is calculated by softmax and label.

4774 4775 4776 4777 4778 4779 4780 4781
    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.
M
minqiyang 已提交
4782 4783
        ignore_index (int): Specifies a target value that is ignored and does
                            not contribute to the input gradient. Only valid
4784
                            if soft_label is set to False. Default: -100
S
sneaxiy 已提交
4785 4786 4787 4788 4789 4790 4791
        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.
                                    When soft_label is True or CPU is used, 
                                    the algorithm is always numerically stable. 
                                    Note that the speed may be slower when use 
                                    stable algorithm. Default: False
4792

4793 4794 4795 4796 4797 4798 4799 4800 4801
    Returns:
        Variable: The cross entropy loss is a 2-D tensor with shape [N x 1].

    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)
F
stash  
fengjiayi 已提交
4802 4803
            out = fluid.layers.softmax_with_cross_entropy(
                logits=fc, label=label)
4804 4805
    """
    helper = LayerHelper('softmax_with_cross_entropy', **locals())
X
Xin Pan 已提交
4806 4807
    softmax = helper.create_variable_for_type_inference(dtype=logits.dtype)
    loss = helper.create_variable_for_type_inference(dtype=logits.dtype)
4808 4809 4810 4811 4812 4813
    helper.append_op(
        type='softmax_with_cross_entropy',
        inputs={'Logits': logits,
                'Label': label},
        outputs={'Softmax': softmax,
                 'Loss': loss},
S
sneaxiy 已提交
4814 4815 4816 4817 4818
        attrs={
            'soft_label': soft_label,
            'ignore_index': ignore_index,
            'numeric_stable_mode': numeric_stable_mode
        })
4819 4820 4821 4822 4823
    return loss


def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
    """
Y
Yibing Liu 已提交
4824 4825
    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.
Q
qingqing01 已提交
4826
    For each instance, it computes the smooth L1 loss element by element first
4827
    and then sums all the losses. So the shape of ouput Variable is
4828
    [batch_size, 1].
4829

4830 4831
    Args:
        x (Variable): A tensor with rank at least 2. The input value of smooth
Q
qingqing01 已提交
4832
            L1 loss op with shape [batch_size, dim1, ..., dimN].
4833
        y (Variable): A tensor with rank at least 2. The target value of smooth
Y
Yibing Liu 已提交
4834
            L1 loss op with same shape as :attr:`x`.
4835
        inside_weight (Variable|None):  A tensor with rank at least 2. This
4836 4837
            input is optional and should have same shape with :attr:`x`. If
            provided, the result of (:attr:`x` - :attr:`y`) will be multiplied
Y
Yibing Liu 已提交
4838
            by this tensor element by element.
4839
        outside_weight (Variable|None): A tensor with rank at least 2. This
4840 4841
            input is optional and should have same shape with :attr:`x`. If
            provided, the out smooth L1 loss will be multiplied by this tensor
Y
Yibing Liu 已提交
4842
            element by element.
4843
        sigma (float|None): Hyper parameter of smooth L1 loss layer. A float
4844 4845
           scalar with default value 1.0.

4846
    Returns:
4847
        Variable: The output smooth L1 loss with shape [batch_size, 1].
4848 4849 4850 4851 4852

    Examples:
        .. code-block:: python

            data = fluid.layers.data(name='data', shape=[128], dtype='float32')
F
stash  
fengjiayi 已提交
4853 4854
            label = fluid.layers.data(
                name='label', shape=[100], dtype='float32')
4855
            fc = fluid.layers.fc(input=data, size=100)
F
fengjiayi 已提交
4856
            out = fluid.layers.smooth_l1(x=fc, y=label)
4857
    """
4858

4859
    helper = LayerHelper('smooth_l1_loss', **locals())
X
Xin Pan 已提交
4860 4861
    diff = helper.create_variable_for_type_inference(dtype=x.dtype)
    loss = helper.create_variable_for_type_inference(dtype=x.dtype)
4862 4863 4864 4865 4866 4867 4868 4869 4870 4871 4872 4873
    helper.append_op(
        type='smooth_l1_loss',
        inputs={
            'X': x,
            'Y': y,
            'InsideWeight': inside_weight,
            'OutsideWeight': outside_weight
        },
        outputs={'Diff': diff,
                 'Out': loss},
        attrs={'sigma': sigma})
    return loss
4874 4875 4876 4877


def one_hot(input, depth):
    """
Y
Yibing Liu 已提交
4878
    This layer creates the one-hot representations for input indices.
4879 4880

    Args:
Y
Yibing Liu 已提交
4881 4882
        input(Variable): Input indices, last dimension must be 1.
        depth(scalar): An interger defining the depth of the one-hot dimension.
4883 4884

    Returns:
Y
Yibing Liu 已提交
4885
        Variable: The one-hot representations of input.
4886 4887

    Examples:
C
caoying03 已提交
4888
        .. code-block:: python
4889

Y
Yibing Liu 已提交
4890 4891
            label = layers.data(name="label", shape=[1], dtype="float32")
            one_hot_label = layers.one_hot(input=label, depth=10)
4892 4893
    """
    helper = LayerHelper("one_hot", **locals())
X
Xin Pan 已提交
4894
    one_hot_out = helper.create_variable_for_type_inference(dtype='float32')
4895 4896 4897 4898 4899 4900
    helper.append_op(
        type="one_hot",
        inputs={'X': input},
        attrs={'depth': depth},
        outputs={'Out': one_hot_out})
    return one_hot_out
Y
Yu Yang 已提交
4901 4902


Y
Yu Yang 已提交
4903
def autoincreased_step_counter(counter_name=None, begin=1, step=1):
Y
Yu Yang 已提交
4904
    """
Y
yi.wu 已提交
4905 4906 4907
    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.
Y
Yu Yang 已提交
4908 4909 4910 4911 4912 4913

    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.

4914 4915
    Returns:
        Variable: The global run counter.
Y
yi.wu 已提交
4916 4917 4918 4919 4920 4921

    Examples:
        .. code-block:: python

           global_step = fluid.layers.autoincreased_step_counter(
               counter_name='@LR_DECAY_COUNTER@', begin=begin, step=1)
Y
Yu Yang 已提交
4922 4923
    """
    helper = LayerHelper('global_step_counter')
Y
Yu Yang 已提交
4924 4925
    if counter_name is None:
        counter_name = '@STEP_COUNTER@'
Y
Yu Yang 已提交
4926 4927 4928 4929 4930
    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(
Y
Yu Yang 已提交
4931
                value=begin - 1, force_cpu=True))
W
Wu Yi 已提交
4932
        helper.main_program.global_block()._prepend_op(
Y
Yu Yang 已提交
4933 4934
            type='increment',
            inputs={'X': [counter]},
Y
Yu Yang 已提交
4935 4936
            outputs={'Out': [counter]},
            attrs={'step': float(step)})
Y
Yu Yang 已提交
4937 4938 4939
        counter.stop_gradient = True

    return counter
Y
yangyaming 已提交
4940 4941


4942
def reshape(x, shape, actual_shape=None, act=None, inplace=False, name=None):
C
caoying03 已提交
4943
    """
C
caoying03 已提交
4944 4945
    Gives a new shape to the input Tensor without changing its data.

4946 4947 4948 4949 4950
    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.
C
caoying03 已提交
4951

4952
    Some tricks exist when specifying the target shape.
C
caoying03 已提交
4953

4954 4955 4956 4957
    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.

4958
    2. 0 means the actual dimension value is going to be copied from the
4959 4960 4961 4962
    corresponding dimension of x. The indice of 0s in shape can not exceed
    Rank(X).

    Here are some examples to explain it.
C
caoying03 已提交
4963 4964

    1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
W
wanghaoshuang 已提交
4965
    is [6, 8], the reshape operator will transform x into a 2-D tensor with
4966
    shape [6, 8] and leaving x's data unchanged.
C
caoying03 已提交
4967

4968
    2. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
4969 4970
    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
W
wanghaoshuang 已提交
4971 4972
    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
4973
    dimensions.
C
caoying03 已提交
4974

4975
    3. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
4976 4977 4978 4979
    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.
C
caoying03 已提交
4980 4981

    Args:
4982
        x(variable): The input tensor.
C
caoying03 已提交
4983 4984
        shape(list): The new shape. At most one dimension of the new shape can
                     be -1.
4985 4986 4987 4988 4989
        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`.
4990 4991
        act (str): The non-linear activation to be applied to the reshaped tensor
                   variable.
4992 4993 4994 4995 4996 4997 4998
        inplace(bool): Must use :attr:`False` if :attr:`x` is used in multiple
                       operators. If this flag is set :attr:`True`, reuse input
                       :attr:`x` to reshape, which will change the shape of
                       tensor variable :attr:`x` and might cause errors when
                       :attr:`x` is used in multiple operators. If :attr:`False`,
                       preserve the shape :attr:`x` and create a new output tensor
                       variable whose data is copied from input x but reshaped.
4999
        name (str): The name of this layer. It is optional.
C
caoying03 已提交
5000

5001
    Returns:
G
guosheng 已提交
5002 5003 5004 5005
        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.
C
caoying03 已提交
5006

X
Xin Pan 已提交
5007 5008 5009
    Raises:
        TypeError: if actual_shape is neither Variable nor None.

C
caoying03 已提交
5010 5011
    Examples:
        .. code-block:: python
G
guosheng 已提交
5012

5013
            data = fluid.layers.data(
5014
                name='data', shape=[2, 4, 6], dtype='float32')
C
caoying03 已提交
5015
            reshaped = fluid.layers.reshape(
G
guosheng 已提交
5016
                x=data, shape=[-1, 0, 3, 2], inplace=True)
C
caoying03 已提交
5017 5018 5019
    """

    if not (isinstance(shape, list) or isinstance(shape, tuple)):
L
luotao1 已提交
5020
        raise ValueError("Input shape must be a python list or tuple.")
X
Xin Pan 已提交
5021 5022 5023 5024 5025
    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")
C
caoying03 已提交
5026

5027 5028 5029 5030 5031 5032 5033 5034 5035 5036 5037 5038 5039 5040 5041
    # 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.")

5042
    helper = LayerHelper("reshape2", **locals())
5043 5044
    out = x if inplace else helper.create_variable_for_type_inference(
        dtype=x.dtype)
X
Xin Pan 已提交
5045
    x_shape = helper.create_variable_for_type_inference(dtype=x.dtype)
C
caoying03 已提交
5046
    helper.append_op(
5047
        type="reshape2",
X
Xin Pan 已提交
5048
        inputs=inputs,
D
dzhwinter 已提交
5049
        attrs={"shape": shape},
5050 5051
        outputs={"Out": out,
                 "XShape": x_shape})
C
caoying03 已提交
5052

D
dzhwinter 已提交
5053
    return helper.append_activation(out)
5054

5055

5056
def squeeze(input, axes, name=None):
Y
Yibing Liu 已提交
5057
    """
M
minqiyang 已提交
5058 5059 5060
    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
Y
Yibing Liu 已提交
5061
    selected with shape entry not equal to one, an error is raised.
M
minqiyang 已提交
5062

Y
Yibing Liu 已提交
5063 5064
    Examples:
    Case 1:
M
minqiyang 已提交
5065
      Given
Y
Yibing Liu 已提交
5066 5067 5068 5069 5070 5071 5072 5073
        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)
M
minqiyang 已提交
5074
        and
Y
Yibing Liu 已提交
5075 5076 5077
          axes = []
        we get:
          Out.shape = (3, 5)
M
minqiyang 已提交
5078

Y
Yibing Liu 已提交
5079
    Args:
5080
        input (Variable): The input variable to be squeezed.
Y
Yibing Liu 已提交
5081
        axes (list): List of integers, indicating the dimensions to be squeezed.
5082
        name (str|None): Name for this layer.
Y
Yibing Liu 已提交
5083 5084 5085 5086 5087 5088 5089 5090

    Returns:
        Variable: Output squeezed variable.

    Examples:
        .. code-block:: python

            x = layers.data(name='x', shape=[5, 1, 10])
5091
            y = layers.sequeeze(input=x, axes=[1])
Y
Yibing Liu 已提交
5092 5093
    """
    helper = LayerHelper("squeeze", **locals())
X
Xin Pan 已提交
5094 5095
    out = helper.create_variable_for_type_inference(dtype=input.dtype)
    x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)
Y
Yibing Liu 已提交
5096
    helper.append_op(
5097
        type="squeeze2",
5098
        inputs={"X": input},
Y
Yibing Liu 已提交
5099
        attrs={"axes": axes},
5100 5101
        outputs={"Out": out,
                 "XShape": x_shape})
Y
Yibing Liu 已提交
5102

5103 5104 5105
    return out


5106
def unsqueeze(input, axes, name=None):
Y
Yibing Liu 已提交
5107
    """
M
minqiyang 已提交
5108 5109 5110
    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.
Y
Yibing Liu 已提交
5111

M
minqiyang 已提交
5112 5113
    For example:
      Given a tensor such that tensor with shape [3, 4, 5],
Y
Yibing Liu 已提交
5114
      then Unsqueezed tensor with axes=[0, 4] has shape [1, 3, 4, 5, 1].
M
minqiyang 已提交
5115

Y
Yibing Liu 已提交
5116
    Args:
5117
        input (Variable): The input variable to be unsqueezed.
Y
Yibing Liu 已提交
5118
        axes (list): List of integers, indicating the dimensions to be inserted.
5119
        name (str|None): Name for this layer.
Y
Yibing Liu 已提交
5120 5121 5122 5123 5124 5125 5126 5127

    Returns:
        Variable: Output unsqueezed variable.

    Examples:
        .. code-block:: python

            x = layers.data(name='x', shape=[5, 10])
5128
            y = layers.unsequeeze(input=x, axes=[1])
Y
Yibing Liu 已提交
5129 5130
    """
    helper = LayerHelper("unsqueeze", **locals())
X
Xin Pan 已提交
5131 5132
    out = helper.create_variable_for_type_inference(dtype=input.dtype)
    x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)
Y
Yibing Liu 已提交
5133
    helper.append_op(
5134
        type="unsqueeze2",
5135
        inputs={"X": input},
Y
Yibing Liu 已提交
5136
        attrs={"axes": axes},
5137 5138
        outputs={"Out": out,
                 "XShape": x_shape})
Y
Yibing Liu 已提交
5139

5140 5141
    return out

5142

Y
yangyaming 已提交
5143
def lod_reset(x, y=None, target_lod=None):
Y
yangyaming 已提交
5144
    """
Y
Yibing Liu 已提交
5145
    Set LoD of :attr:`x` to a new one specified by :attr:`y` or
5146 5147 5148 5149
    :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
Y
Yibing Liu 已提交
5150
    :attr:`Y.data` or :attr:`target_lod`, only one level LoD is supported.
Y
yangyaming 已提交
5151 5152 5153 5154 5155 5156

    .. code-block:: text

        * Example 1:

            Given a 1-level LoDTensor x:
5157
                x.lod =  [[ 2,           3,                   1 ]]
Y
yangyaming 已提交
5158 5159 5160
                x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
                x.dims = [6, 1]

5161
            target_lod: [4, 2]
Y
yangyaming 已提交
5162 5163

            then we get a 1-level LoDTensor:
5164
                out.lod =  [[4,                          2]]
Y
yangyaming 已提交
5165 5166 5167 5168 5169 5170
                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:
5171
                x.lod =  [[2,            3,                   1]]
Y
yangyaming 已提交
5172 5173 5174 5175
                x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
                x.dims = [6, 1]

            y is a Tensor:
5176
                y.data = [[2, 4]]
Y
yangyaming 已提交
5177 5178 5179
                y.dims = [1, 3]

            then we get a 1-level LoDTensor:
5180
                out.lod =  [[2,            4]]
Y
yangyaming 已提交
5181 5182 5183 5184 5185 5186
                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:
5187
                x.lod =  [[2,            3,                   1]]
Y
yangyaming 已提交
5188 5189 5190 5191
                x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
                x.dims = [6, 1]

            y is a 2-level LoDTensor:
5192
                y.lod =  [[2, 2], [2, 2, 1, 1]]
Y
yangyaming 已提交
5193 5194 5195 5196
                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:
5197
                out.lod =  [[2, 2], [2, 2, 1, 1]]
Y
yangyaming 已提交
5198 5199 5200 5201 5202
                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.
5203
        y (Variable|None): If provided, output's LoD would be derived
Y
Yibing Liu 已提交
5204
                           from :attr:`y`.
Y
yangyaming 已提交
5205
        target_lod (list|tuple|None): One level LoD which should be considered
Y
Yibing Liu 已提交
5206
                                      as target LoD when :attr:`y` not provided.
Y
yangyaming 已提交
5207 5208

    Returns:
Y
Yibing Liu 已提交
5209
        Variable: Output variable with LoD specified by this layer.
Y
yangyaming 已提交
5210 5211

    Raises:
Y
Yibing Liu 已提交
5212
        ValueError: If :attr:`y` and :attr:`target_lod` are both None.
Y
yangyaming 已提交
5213 5214 5215 5216 5217 5218 5219 5220 5221

    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())
X
Xin Pan 已提交
5222
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
Y
yangyaming 已提交
5223 5224 5225 5226 5227 5228 5229 5230 5231 5232 5233 5234 5235 5236
    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
D
dragonwarrior 已提交
5237 5238 5239 5240 5241 5242 5243 5244 5245 5246 5247


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::

D
dzhwinter 已提交
5248
      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}
D
dragonwarrior 已提交
5249 5250 5251 5252 5253 5254 5255 5256 5257 5258 5259 5260 5261 5262 5263 5264 5265 5266 5267 5268 5269 5270 5271 5272 5273 5274 5275 5276

    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

F
stash  
fengjiayi 已提交
5277 5278
          data = fluid.layers.data(
              name="data", shape=[3, 112, 112], dtype="float32")
D
dragonwarrior 已提交
5279 5280 5281 5282 5283 5284 5285 5286 5287 5288 5289 5290
          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))

X
Xin Pan 已提交
5291 5292 5293
    mid_out = helper.create_variable_for_type_inference(
        dtype=dtype, stop_gradient=True)
    lrn_out = helper.create_variable_for_type_inference(dtype)
D
dragonwarrior 已提交
5294 5295 5296 5297 5298 5299 5300 5301 5302 5303 5304 5305 5306
    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
G
guosheng 已提交
5307 5308 5309 5310


def pad(x, paddings, pad_value=0., name=None):
    """
G
guosheng 已提交
5311
    Pads a tensor with a constant value given by :attr:`pad_value`, and the
W
wanghaoshuang 已提交
5312
    padded width is specified by :attr:`paddings`.
G
guosheng 已提交
5313

G
guosheng 已提交
5314 5315 5316 5317
    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]`.
G
guosheng 已提交
5318 5319 5320 5321 5322 5323 5324 5325 5326 5327 5328 5329 5330 5331 5332 5333 5334 5335 5336 5337 5338 5339

    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.
W
wanghaoshuang 已提交
5340
                         The length of :attr:paddings must be
G
guosheng 已提交
5341 5342 5343 5344 5345 5346 5347 5348 5349 5350
                         :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
G
guosheng 已提交
5351

G
guosheng 已提交
5352 5353 5354 5355 5356 5357
            # 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()
X
Xin Pan 已提交
5358
    out = helper.create_variable_for_type_inference(dtype)
G
guosheng 已提交
5359 5360 5361 5362 5363 5364 5365
    helper.append_op(
        type='pad',
        inputs={'X': x},
        outputs={'Out': out},
        attrs={'paddings': paddings,
               'pad_value': float(pad_value)})
    return out
5366 5367


C
chengduo 已提交
5368 5369 5370 5371 5372 5373 5374 5375 5376 5377 5378 5379 5380 5381 5382 5383 5384 5385 5386 5387 5388 5389 5390 5391 5392 5393 5394 5395 5396 5397 5398 5399 5400 5401 5402 5403 5404 5405 5406 5407 5408 5409 5410 5411 5412 5413 5414 5415 5416 5417 5418 5419 5420 5421 5422 5423 5424 5425 5426 5427 5428 5429 5430 5431 5432 5433 5434 5435 5436 5437
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)

    And
        pad_value = -1,

    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)

    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()
X
Xin Pan 已提交
5438
    out = helper.create_variable_for_type_inference(dtype)
C
chengduo 已提交
5439 5440 5441 5442 5443 5444 5445 5446 5447
    helper.append_op(
        type='pad_constant_like',
        inputs={'X': x,
                'Y': y},
        outputs={'Out': out},
        attrs={'pad_value': float(pad_value)})
    return out


5448 5449 5450 5451 5452 5453 5454
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
5455 5456
    called label-smoothing regularization (LSR).

5457 5458 5459 5460 5461 5462 5463 5464 5465 5466 5467 5468 5469 5470 5471 5472 5473 5474 5475 5476 5477 5478 5479
    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
5480
                              be :math:`(1, class\_num)`.
5481 5482
        epsilon(float): The weight used to mix up the original ground-truth
                        distribution and the fixed distribution.
5483
        dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32,
5484 5485 5486 5487 5488 5489 5490 5491 5492 5493 5494 5495 5496 5497 5498 5499 5500 5501 5502
                                                  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
X
Xin Pan 已提交
5503
    smooth_label = helper.create_variable_for_type_inference(dtype)
5504 5505 5506 5507 5508 5509 5510
    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
5511 5512


Y
yi.wu 已提交
5513
@templatedoc()
5514 5515
def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
    """
Y
yi.wu 已提交
5516
    ${comment}
5517 5518

    Args:
Y
yi.wu 已提交
5519 5520
        input (Variable): ${x_comment}
        rois (Variable): ROIs (Regions of Interest) to pool over.
Y
yi.wu 已提交
5521 5522 5523
        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
5524 5525

    Returns:
Y
update  
yi.wu 已提交
5526
        Variable: ${out_comment}.
5527 5528

    Examples:
5529 5530
        .. code-block:: python

5531
            pool_out = fluid.layers.roi_pool(input=x, rois=rois, 7, 7, 1.0)
5532 5533 5534
    """
    helper = LayerHelper('roi_pool', **locals())
    dtype = helper.input_dtype()
X
Xin Pan 已提交
5535 5536
    pool_out = helper.create_variable_for_type_inference(dtype)
    argmaxes = helper.create_variable_for_type_inference(dtype='int32')
5537 5538 5539 5540 5541 5542 5543 5544 5545 5546 5547 5548
    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
W
whs 已提交
5549 5550


J
jerrywgz 已提交
5551 5552 5553 5554 5555 5556
@templatedoc()
def roi_align(input,
              rois,
              pooled_height=1,
              pooled_width=1,
              spatial_scale=1.0,
J
jerrywgz 已提交
5557 5558
              sampling_ratio=-1,
              name=None):
J
jerrywgz 已提交
5559 5560 5561 5562 5563 5564 5565 5566 5567 5568 5569 5570 5571 5572 5573 5574
    """
    ${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

5575 5576 5577
            align_out = fluid.layers.roi_align(input=x,
                                               rois=rois,
                                               pooled_height=7,
J
jerrywgz 已提交
5578 5579 5580 5581 5582 5583
                                               pooled_width=7,
                                               spatial_scale=0.5,
                                               sampling_ratio=-1)
    """
    helper = LayerHelper('roi_align', **locals())
    dtype = helper.input_dtype()
X
Xin Pan 已提交
5584
    align_out = helper.create_variable_for_type_inference(dtype)
J
jerrywgz 已提交
5585 5586 5587 5588 5589 5590 5591 5592 5593 5594 5595 5596 5597 5598
    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


W
whs 已提交
5599 5600 5601 5602 5603 5604 5605 5606 5607 5608 5609 5610 5611 5612 5613 5614 5615 5616 5617 5618 5619 5620 5621 5622 5623 5624
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:
5625 5626
        .. code-block:: python

W
whs 已提交
5627 5628 5629 5630
            predictions = fluid.layers.softmax(x)
            loss = fluid.layers.dice_loss(input=predictions, label=label, 2)
    """
    label = one_hot(label, depth=input.shape[-1])
5631
    reduce_dim = list(range(1, len(input.shape)))
W
whs 已提交
5632 5633 5634 5635 5636 5637
    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)
5638 5639


5640 5641 5642 5643 5644
def image_resize(input,
                 out_shape=None,
                 scale=None,
                 name=None,
                 resample='BILINEAR'):
5645
    """
Q
qiaolongfei 已提交
5646
    **Resize a Batch of Images**
F
stash  
fengjiayi 已提交
5647

5648
    The input must be a tensor of the shape (num_batches, channels, in_h, in_w),
5649 5650 5651
    and the resizing only applies on the last two dimensions(hight and width).

    Supporting resample methods:
Q
update  
qiaolongfei 已提交
5652

5653
        'BILINEAR' : Bilinear interpolation
F
stash  
fengjiayi 已提交
5654

5655
    Args:
5656
        input (Variable): The input tensor of image resize layer,
5657 5658
                          This is a 4-D tensor of the shape
                          (num_batches, channels, in_h, in_w).
5659
        out_shape(list|tuple|Variable|None): Output shape of image resize
5660 5661
                                    layer, the shape is (out_h, out_w).
                                    Default: None
B
baiyf 已提交
5662
        scale(float|None): The multiplier for the input height or width.
5663 5664 5665
                         At least one of out_shape or scale must be set.
                         And out_shape has a higher priority than scale.
                         Default: None
5666 5667
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.
5668 5669
        resample(str): The resample method. It can only be 'BILINEAR' currently.
                       Default: 'BILINEAR'
5670 5671

    Returns:
Q
update  
qiaolongfei 已提交
5672 5673
        Variable: The output is a 4-D tensor of the shape
        (num_batches, channls, out_h, out_w).
F
stash  
fengjiayi 已提交
5674

5675 5676 5677
    Examples:
        .. code-block:: python

5678
            out = fluid.layers.image_resize(input, out_shape=[12, 12])
5679
    """
5680 5681 5682 5683
    resample_methods = {'BILINEAR': 'bilinear_interp'}
    if resample not in resample_methods:
        raise ValueError(
            "The 'resample' of image_resize can only be 'BILINEAR' currently.")
5684 5685
    if out_shape is None and scale is None:
        raise ValueError("One of out_shape and scale must not be None")
5686 5687
    helper = LayerHelper('bilinear_interp', **locals())
    dtype = helper.input_dtype()
5688 5689 5690 5691

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

5692 5693 5694
    out_h = 0
    out_w = 0
    inputs = {"X": input}
5695
    if out_shape is not None:
B
baiyf 已提交
5696 5697 5698
        if not (_is_list_or_turple_(out_shape) and
                len(out_shape) == 2) and not isinstance(out_shape, Variable):
            raise ValueError('out_shape should be a list or tuple or variable')
5699 5700 5701 5702 5703 5704
        if _is_list_or_turple_(out_shape):
            out_shape = list(map(int, out_shape))
            out_h = out_shape[0]
            out_w = out_shape[1]
        else:
            inputs['OutSize'] = out_shape
5705 5706 5707 5708
    else:
        out_h = int(input.shape[2] * scale)
        out_w = int(input.shape[3] * scale)

X
Xin Pan 已提交
5709
    out = helper.create_variable_for_type_inference(dtype)
5710
    helper.append_op(
5711
        type=resample_methods[resample],
5712
        inputs=inputs,
5713 5714 5715 5716
        outputs={"Out": out},
        attrs={"out_h": out_h,
               "out_w": out_w})
    return out
F
stash  
fengjiayi 已提交
5717 5718


Y
yuyang18 已提交
5719
@templatedoc(op_type="bilinear_interp")
5720 5721
def resize_bilinear(input, out_shape=None, scale=None, name=None):
    """
Y
yuyang18 已提交
5722 5723 5724 5725 5726 5727
    ${comment}

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

        out_shape(${out_size_type}): ${out_size_comment}.
5728

Y
yuyang18 已提交
5729 5730 5731 5732 5733 5734 5735 5736
        scale(float|None): The multiplier for the input height or width. At
             least one of out_shape or scale must be set. And out_shape has
             a higher priority than scale. Default: None.

        name(str|None): The output variable name.

    Returns:
        ${out_comment}.
5737 5738 5739 5740 5741 5742 5743
    """

    return image_resize(input, out_shape, scale, name, 'BILINEAR')


def image_resize_short(input, out_short_len, resample='BILINEAR'):
    """
5744 5745 5746
    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
5747 5748 5749 5750 5751 5752 5753
    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.
5754
        resample (str): resample method, default: BILINEAR.
F
fengjiayi 已提交
5755

5756
    Returns:
Q
update  
qiaolongfei 已提交
5757
        Variable: The output is a 4-D tensor of the shape
5758
        (num_batches, channls, out_h, out_w).
5759 5760 5761 5762 5763 5764 5765 5766 5767 5768
    """
    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
F
fengjiayi 已提交
5769 5770 5771
    out_shape[long_idx] = int(
        float(out_shape[long_idx]) * (float(out_short_len) / float(hw[
            short_idx])) + 0.5)
5772 5773 5774
    return image_resize(input=input, out_shape=out_shape, resample=resample)


W
whs 已提交
5775 5776
def gather(input, index):
    """
Q
qiaolongfei 已提交
5777 5778
    **Gather Layer**

5779
    Output is obtained by gathering entries of the outer-most dimension
W
whs 已提交
5780 5781 5782 5783
    of X indexed by `index` and concatenate them together.

    .. math::

5784
        Out = X[Index]
W
whs 已提交
5785 5786 5787 5788 5789 5790 5791


    .. code-block:: text


                Given:

5792 5793
                X = [[1, 2],
                     [3, 4],
W
whs 已提交
5794 5795 5796 5797 5798 5799 5800 5801 5802 5803
                     [5, 6]]

                Index = [1, 2]

                Then:

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

    Args:
5804
        input (Variable): The source input with rank>=1.
W
whs 已提交
5805 5806 5807 5808 5809 5810
        index (Variable): The index input with rank=1.

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

    Examples:
W
whs 已提交
5811

W
whs 已提交
5812 5813 5814 5815 5816 5817
        .. code-block:: python

            output = fluid.layers.gather(x, index)
    """
    helper = LayerHelper('gather', **locals())
    dtype = helper.input_dtype()
X
Xin Pan 已提交
5818
    out = helper.create_variable_for_type_inference(dtype)
W
whs 已提交
5819 5820 5821 5822 5823 5824 5825 5826
    helper.append_op(
        type="gather",
        inputs={"X": input,
                "Index": index},
        outputs={"Out": out})
    return out


5827 5828 5829 5830 5831 5832 5833 5834 5835 5836 5837 5838 5839 5840 5841 5842 5843 5844 5845 5846 5847 5848 5849 5850 5851 5852 5853 5854 5855 5856 5857
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()
X
Xin Pan 已提交
5858
    out = helper.create_variable_for_type_inference(dtype)
5859 5860 5861 5862 5863 5864 5865 5866 5867
    helper.append_op(
        type="scatter",
        inputs={"X": input,
                "Ids": index,
                "Updates": updates},
        outputs={"Out": out})
    return out


Q
Qingsheng Li 已提交
5868 5869 5870 5871 5872 5873 5874 5875 5876 5877 5878 5879 5880 5881 5882 5883 5884 5885 5886 5887 5888 5889 5890 5891 5892 5893 5894 5895 5896 5897 5898 5899 5900 5901 5902 5903 5904 5905 5906 5907 5908 5909 5910 5911 5912 5913 5914 5915 5916 5917
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:
    Given the following input:
    .. code-block:: text
        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:
    .. code-block:: text
        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:
        output (Variable): The output is a tensor with the same shape as input.

    Examples:

        .. code-block:: python

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

    """
    helper = LayerHelper('sequence_scatter', **locals())
    dtype = helper.input_dtype()
X
Xin Pan 已提交
5918
    out = helper.create_variable_for_type_inference(dtype)
Q
Qingsheng Li 已提交
5919 5920 5921 5922 5923 5924 5925 5926 5927
    helper.append_op(
        type="sequence_scatter",
        inputs={"X": input,
                "Ids": index,
                "Updates": updates},
        outputs={"Out": out})
    return out


Y
yuyang18 已提交
5928 5929 5930 5931 5932 5933 5934 5935 5936 5937 5938 5939 5940
@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}
5941

5942 5943 5944
    Examples:
        >>> img = fluid.layers.data("img", [3, 256, 256])
        >>> cropped_img = fluid.layers.random_crop(img, shape=[3, 224, 224])
Y
yuyang18 已提交
5945
    """
F
stash  
fengjiayi 已提交
5946
    helper = LayerHelper("random_crop", **locals())
F
fengjiayi 已提交
5947
    dtype = x.dtype
X
Xin Pan 已提交
5948
    out = helper.create_variable_for_type_inference(dtype)
Y
yuyang18 已提交
5949
    if seed is None:
5950
        seed = np.random.randint(-65536, 65536)
F
fengjiayi 已提交
5951
    op_attrs = {"shape": shape}
F
stash  
fengjiayi 已提交
5952
    if isinstance(seed, int):
F
fengjiayi 已提交
5953 5954 5955 5956 5957
        op_attrs["startup_seed"] = seed
        seed = helper.create_variable(
            name=unique_name.generate("random_crop_seed"),
            dtype="int64",
            persistable=True)
F
stash  
fengjiayi 已提交
5958 5959 5960 5961
    elif not isinstance(seed, Variable):
        raise ValueError("'seed' must be a Variable or an int.")
    helper.append_op(
        type="random_crop",
F
fix  
fengjiayi 已提交
5962
        inputs={"X": x,
F
stash  
fengjiayi 已提交
5963 5964
                "Seed": seed},
        outputs={"Out": out,
F
fengjiayi 已提交
5965 5966
                 "SeedOut": seed},
        attrs=op_attrs)
F
stash  
fengjiayi 已提交
5967
    return out
W
whs 已提交
5968 5969


5970
def log(x, name=None):
W
wanghaoshuang 已提交
5971 5972 5973 5974 5975
    """
    Calculates the natural log of the given input tensor, element-wise.

    .. math::

5976
        Out = \\ln(x)
W
wanghaoshuang 已提交
5977 5978

    Args:
5979
        x (Variable): Input tensor.
5980 5981
        name (str|None, default None): A name for this layer If set None,
            the layer will be named automatically.
W
wanghaoshuang 已提交
5982 5983 5984 5985 5986 5987 5988 5989

    Returns:
        Variable: The natural log of the input tensor computed element-wise.

    Examples:

        .. code-block:: python

5990
            output = fluid.layers.log(x)
W
wanghaoshuang 已提交
5991 5992
    """
    helper = LayerHelper('log', **locals())
W
wanghaoshuang 已提交
5993
    dtype = helper.input_dtype(input_param_name='x')
X
Xin Pan 已提交
5994
    out = helper.create_variable_for_type_inference(dtype)
W
wanghaoshuang 已提交
5995
    helper.append_op(type="log", inputs={"X": x}, outputs={"Out": out})
W
wanghaoshuang 已提交
5996 5997 5998
    return out


5999
def relu(x, name=None):
W
wanghaoshuang 已提交
6000 6001
    """
    Relu takes one input data (Tensor) and produces one output data (Tensor)
6002
    where the rectified linear function, y = max(0, x), is applied to
W
wanghaoshuang 已提交
6003 6004 6005 6006
    the tensor elementwise.

    .. math::

6007
        Out = \\max(0, x)
W
wanghaoshuang 已提交
6008 6009

    Args:
6010
        x (Variable): The input tensor.
6011 6012
        name (str|None, default None): A name for this layer If set None,
            the layer will be named automatically.
W
wanghaoshuang 已提交
6013 6014 6015 6016 6017 6018 6019 6020

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

    Examples:

        .. code-block:: python

6021
            output = fluid.layers.relu(x)
W
wanghaoshuang 已提交
6022 6023
    """
    helper = LayerHelper('relu', **locals())
W
wanghaoshuang 已提交
6024
    dtype = helper.input_dtype(input_param_name='x')
X
Xin Pan 已提交
6025
    out = helper.create_variable_for_type_inference(dtype)
W
wanghaoshuang 已提交
6026
    helper.append_op(type="relu", inputs={"X": x}, outputs={"Out": out})
W
wanghaoshuang 已提交
6027
    return out
6028 6029


W
whs 已提交
6030 6031 6032
def mean_iou(input, label, num_classes):
    """
    Mean Intersection-Over-Union is a common evaluation metric for
6033 6034 6035 6036
    semantic image segmentation, which first computes the IOU for each
    semantic class and then computes the average over classes.
    IOU is defined as follows:

W
whs 已提交
6037
    .. math::
6038 6039

        IOU = \\frac{true\_positiv}{(true\_positive + false\_positive + false\_negative)}.
W
whs 已提交
6040

6041
    The predictions are accumulated in a confusion matrix and mean-IOU
W
whs 已提交
6042 6043 6044 6045 6046
    is then calculated from it.


    Args:
        input (Variable): A Tensor of prediction results for semantic labels with type int32 or int64.
6047
        label (Variable): A Tensor of ground truth labels with type int32 or int64.
W
whs 已提交
6048
                           Its shape should be the same as input.
6049
        num_classes (int): The possible number of labels.
W
whs 已提交
6050 6051 6052 6053

    Returns:
        mean_iou (Variable): A Tensor representing the mean intersection-over-union with shape [1].
        out_wrong(Variable): A Tensor with shape [num_classes]. The wrong numbers of each class.
6054
        out_correct(Variable): A Tensor with shape [num_classes]. The correct numbers of each class.
W
whs 已提交
6055 6056 6057 6058

    Examples:

        .. code-block:: python
6059

W
whs 已提交
6060 6061 6062 6063
            iou, wrongs, corrects = fluid.layers.mean_iou(predict, label, num_classes)
    """
    helper = LayerHelper('mean_iou', **locals())
    dtype = helper.input_dtype()
X
Xin Pan 已提交
6064 6065 6066
    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')
W
whs 已提交
6067 6068
    helper.append_op(
        type="mean_iou",
W
whs 已提交
6069 6070
        inputs={"Predictions": input,
                "Labels": label},
W
whs 已提交
6071
        outputs={
W
whs 已提交
6072 6073 6074
            "OutMeanIou": out_mean_iou,
            "OutWrong": out_wrong,
            "OutCorrect": out_correct
W
whs 已提交
6075 6076 6077
        },
        attrs={"num_classes": num_classes})
    return out_mean_iou, out_wrong, out_correct
6078 6079 6080 6081 6082 6083 6084 6085 6086 6087 6088 6089 6090 6091 6092 6093 6094 6095 6096 6097 6098 6099 6100 6101 6102 6103 6104 6105 6106 6107 6108 6109 6110 6111 6112 6113 6114 6115 6116 6117 6118 6119 6120 6121 6122 6123 6124 6125 6126 6127 6128 6129 6130 6131 6132 6133 6134 6135 6136 6137 6138 6139 6140 6141 6142 6143 6144 6145 6146 6147 6148 6149 6150 6151


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")
            crop = fluid.layers.crop(z, shape=[2, 3])

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

    if not (isinstance(shape, list) or isinstance(shape, tuple) or \
C
chengduo 已提交
6152
                    isinstance(shape, Variable)):
6153 6154 6155 6156 6157
        raise ValueError("The shape should be a list, tuple or Variable.")

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

X
Xin Pan 已提交
6158
    out = helper.create_variable_for_type_inference(x.dtype)
6159 6160 6161 6162 6163 6164 6165 6166 6167 6168 6169 6170 6171 6172 6173 6174 6175
    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
6176 6177


W
whs 已提交
6178 6179 6180 6181 6182 6183 6184 6185 6186 6187 6188 6189 6190 6191 6192 6193 6194 6195 6196 6197 6198 6199 6200 6201 6202 6203 6204 6205 6206 6207 6208 6209 6210 6211 6212 6213 6214 6215 6216 6217 6218 6219 6220 6221 6222 6223 6224 6225 6226 6227 6228 6229 6230 6231 6232 6233 6234 6235 6236 6237 6238 6239 6240 6241 6242 6243 6244 6245 6246 6247 6248 6249 6250 6251 6252 6253 6254 6255 6256 6257 6258 6259 6260 6261 6262 6263 6264 6265 6266 6267 6268 6269 6270 6271 6272 6273 6274 6275 6276 6277 6278 6279 6280 6281 6282 6283 6284 6285 6286 6287 6288 6289 6290 6291 6292 6293 6294 6295
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]]]
      
              out_shape = [2, 3, 5, 5]
      
          Step 1:
      
              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:
      
              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].
        out_shape (Variable | list | tuple): The shape of target output with format [N, C, H, W].
        out_shape can be a Variable or a list or tuple.
        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
            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 \
        isinstance(out_shape, Variable)):
        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


6296 6297 6298 6299 6300 6301 6302 6303
def rank_loss(label, left, right, name=None):
    """
    **Rank loss layer for RankNet**

    RankNet(http://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf)
    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:
M
minqiyang 已提交
6304

6305 6306
    P = {0, 1} or {0, 0.5, 1}, where 0.5 means that there is no information
    about the rank of the input pair.
M
minqiyang 已提交
6307

6308 6309 6310 6311
    Rank loss layer takes three inputs: left (o_i), right (o_j) and
    label (P_{i,j}). The inputs respectively represent RankNet's output scores
    for documents A and B and the value of label P. The following equation
    computes rank loss C_{i,j} from the inputs:
M
minqiyang 已提交
6312

6313 6314 6315 6316 6317
    $$
      C_{i,j} = -\tilde{P_{ij}} * o_{i,j} + \log(1 + e^{o_{i,j}}) \\
      o_{i,j} =  o_i - o_j  \\
      \tilde{P_{i,j}} = \left \{0, 0.5, 1 \right \} \ or \ \left \{0, 1 \right \}
    $$
M
minqiyang 已提交
6318 6319 6320

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

6321 6322 6323 6324 6325 6326 6327 6328 6329 6330 6331 6332 6333 6334 6335 6336 6337 6338 6339 6340 6341 6342 6343 6344 6345 6346 6347 6348 6349 6350 6351 6352 6353 6354 6355
    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")

X
Xin Pan 已提交
6356
    out = helper.create_variable_for_type_inference("float32")
6357 6358 6359 6360 6361 6362 6363 6364

    helper.append_op(
        type='rank_loss',
        inputs={"Label": label,
                "Left": left,
                "Right": right},
        outputs={'Out': out})
    return out
6365 6366


M
minqiyang 已提交
6367 6368
def margin_rank_loss(label, left, right, margin=0.1, name=None):
    """
M
minqiyang 已提交
6369
    Margin Ranking Loss Layer for ranking problem,
M
minqiyang 已提交
6370
    which compares left score and right score passed in.
M
minqiyang 已提交
6371
    The ranking loss can be defined as following equation:
M
minqiyang 已提交
6372 6373 6374 6375 6376 6377

    .. math::

        rank\_loss &= max(0, -label * (left - right) + margin)

    Args:
M
minqiyang 已提交
6378
       label (Variable): Indicates whether the left is ranked higher than the right or not.
M
minqiyang 已提交
6379 6380
       left (Variable): Ranking score for left.
       right (Variable): Ranking score for right.
M
minqiyang 已提交
6381
       margin (float): Indicates the given margin.
M
minqiyang 已提交
6382 6383 6384
       name (str|None): A name for this layer (optional). If set None, the layer
                       will be named automatically.
    Returns:
M
minqiyang 已提交
6385
       Variable: The ranking loss.
M
minqiyang 已提交
6386
    Raises:
M
minqiyang 已提交
6387
       ValueError: Any of label, left, and right is not a Variable.
M
minqiyang 已提交
6388 6389 6390 6391 6392 6393 6394
    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.margin_rank_loss(label, left, right)
    """
M
minqiyang 已提交
6395
    helper = LayerHelper('margin_rank_loss', **locals())
M
minqiyang 已提交
6396 6397 6398 6399 6400 6401
    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.")
X
Xin Pan 已提交
6402 6403
    out = helper.create_variable_for_type_inference(left.dtype)
    act = helper.create_variable_for_type_inference(left.dtype)
M
minqiyang 已提交
6404 6405 6406 6407 6408 6409 6410 6411 6412 6413 6414
    helper.append_op(
        type='margin_rank_loss',
        inputs={"Label": label,
                "X1": left,
                "X2": right},
        outputs={'Out': out,
                 'Activated': act},
        attrs={'margin': margin})
    return out


W
whs 已提交
6415 6416 6417 6418 6419 6420 6421 6422 6423 6424 6425 6426 6427 6428
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:

      Given that X is a channel of image from input:
M
minqiyang 已提交
6429

W
whs 已提交
6430 6431
      X = [[1, 2, 3],
           [4, 5, 6]]
M
minqiyang 已提交
6432

W
whs 已提交
6433
      Case 0:
M
minqiyang 已提交
6434

W
whs 已提交
6435 6436 6437
        paddings = [0, 1, 2, 3],
        mode = 'constant'
        pad_value = 0
M
minqiyang 已提交
6438

W
whs 已提交
6439 6440 6441
        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]]
M
minqiyang 已提交
6442

W
whs 已提交
6443
      Case 1:
M
minqiyang 已提交
6444

W
whs 已提交
6445 6446
        paddings = [0, 1, 2, 1],
        mode = 'reflect'
M
minqiyang 已提交
6447

W
whs 已提交
6448 6449 6450
        Out = [[3, 2, 1, 2, 3, 2]
               [6, 5, 4, 5, 6, 5]
               [3, 2, 1, 2, 3, 2]]
M
minqiyang 已提交
6451

W
whs 已提交
6452
      Case 2:
M
minqiyang 已提交
6453

W
whs 已提交
6454 6455
        paddings = [0, 1, 2, 1],
        mode = 'edge'
M
minqiyang 已提交
6456

W
whs 已提交
6457 6458 6459
        Out = [[1, 1, 1, 2, 3, 3]
               [4, 4, 4, 5, 6, 6]
               [4, 4, 4, 5, 6, 6]]
M
minqiyang 已提交
6460 6461


W
whs 已提交
6462 6463 6464 6465 6466 6467 6468 6469 6470 6471 6472 6473 6474 6475 6476 6477 6478 6479 6480 6481 6482 6483 6484 6485 6486 6487
    Args:
        input (Variable): The input image with [N, C, H, W] format or [N, H, W, C] format.
        paddings (tuple|list): The padding size. If padding is a tuple, it must
            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')
X
Xin Pan 已提交
6488
    out = helper.create_variable_for_type_inference(dtype)
W
whs 已提交
6489 6490 6491 6492 6493 6494 6495 6496 6497 6498 6499 6500 6501 6502
    helper.append_op(
        type='pad2d',
        inputs={'X': input},
        outputs={"Out": out},
        attrs={
            'paddings': paddings,
            'mode': mode,
            'pad_value': pad_value,
            'data_frmat': data_format
        })

    return out


6503 6504 6505 6506 6507 6508 6509 6510 6511 6512 6513 6514 6515 6516
@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}
    """
    helper = LayerHelper('elu', **locals())
X
Xin Pan 已提交
6517
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
6518 6519 6520 6521 6522 6523 6524 6525 6526 6527 6528 6529 6530 6531 6532 6533 6534 6535 6536 6537 6538 6539
    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}
    """
    helper = LayerHelper('relu6', **locals())
X
Xin Pan 已提交
6540
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
6541 6542 6543 6544 6545 6546 6547 6548 6549 6550 6551 6552 6553 6554 6555 6556 6557 6558 6559 6560 6561 6562
    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}
    """
    helper = LayerHelper('pow', **locals())
X
Xin Pan 已提交
6563
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
6564 6565 6566 6567 6568 6569 6570 6571 6572 6573 6574 6575 6576 6577 6578 6579 6580 6581 6582 6583 6584 6585 6586
    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}
    """
    helper = LayerHelper('stanh', **locals())
X
Xin Pan 已提交
6587
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
6588 6589 6590 6591 6592 6593 6594 6595 6596 6597 6598 6599 6600 6601 6602 6603 6604 6605 6606 6607 6608 6609 6610 6611
    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}
    """
    helper = LayerHelper('hard_sigmoid', **locals())
X
Xin Pan 已提交
6612
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
6613 6614 6615 6616 6617 6618 6619 6620 6621 6622 6623 6624 6625 6626 6627 6628 6629 6630 6631 6632 6633 6634 6635
    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}
    """
    helper = LayerHelper('swish', **locals())
X
Xin Pan 已提交
6636
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
6637 6638 6639 6640 6641 6642 6643 6644
    helper.append_op(
        type='swish',
        inputs={'X': x},
        outputs={'Out': out},
        attrs={'slope': beta})
    return out


J
jerrywgz 已提交
6645 6646 6647 6648 6649 6650 6651 6652 6653 6654 6655 6656 6657 6658
def prelu(x, mode, param_attr=None, name=None):
    """
    Equation:

        y = \max(0, x) + alpha \min(0, x)

    Args:
        x (Variable): The input tensor.
	  param_attr(ParamAttr|None): The parameter attribute for the learnable
                                    weight (alpha).
        mode (string): The mode for weight sharing
		       all: all elements share same weight
 		       channel:elements in a channel share same weight
 		       element:each element has a weight
W
whs 已提交
6659
	name(str|None): A name for this layer(optional). If set None, the layer
M
minqiyang 已提交
6660
                        will be named automatically.
J
jerrywgz 已提交
6661 6662 6663 6664 6665 6666 6667 6668 6669 6670 6671 6672 6673 6674 6675 6676 6677 6678 6679 6680 6681 6682 6683 6684 6685 6686 6687

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

    Examples:

        .. code-block:: python

         x = fluid.layers.data(name="x", shape=[10,10], dtype="float32")
            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(
        attr=param_attr,
        shape=alpha_shape,
        dtype='float32',
        is_bias=False,
        default_initializer=Constant(1.0))
X
Xin Pan 已提交
6688
    out = helper.create_variable_for_type_inference(dtype)
J
jerrywgz 已提交
6689 6690 6691 6692 6693 6694 6695 6696 6697
    helper.append_op(
        type="prelu",
        inputs={"X": x,
                'Alpha': alpha},
        attrs={"mode": mode},
        outputs={"Out": out})
    return out


6698 6699 6700 6701 6702 6703 6704 6705 6706 6707 6708 6709 6710 6711
@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): ${t_max_comment}
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.
     Returns:
        output(${out_type}): ${out_comment}
    """
    helper = LayerHelper('brelu', **locals())
X
Xin Pan 已提交
6712
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
6713 6714 6715 6716 6717 6718 6719 6720 6721 6722 6723 6724 6725 6726 6727 6728 6729 6730 6731 6732 6733 6734
    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): ${alpha_comment}
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.
     Returns:
        output(${out_type}): ${out_comment}
    """
    helper = LayerHelper('leaky_relu', **locals())
X
Xin Pan 已提交
6735
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
6736 6737 6738 6739 6740 6741 6742 6743 6744 6745 6746 6747 6748 6749 6750 6751 6752 6753 6754 6755 6756
    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): ${threshold_comment}
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.
     Returns:
        output(${out_type}): ${out_comment}
    """
    helper = LayerHelper('soft_relu', **locals())
X
Xin Pan 已提交
6757
    out = helper.create_variable_for_type_inference(dtype=x.dtype)
6758 6759 6760 6761 6762 6763 6764 6765
    helper.append_op(
        type='soft_relu',
        inputs={'X': x},
        outputs={'Out': out},
        attrs={'threshold': threshold})
    return out


6766 6767 6768 6769 6770 6771 6772 6773 6774 6775 6776 6777 6778
def flatten(x, axis=1, name=None):
    """
    **Flatten layer**
    Flattens the input tensor into a 2D matrix.

    Examples:
    Case 1:
      Given
        X.shape = (3, 100, 100, 4)
      and
        axis = 2
      We get:
        Out.shape = (3 * 100, 4 * 100)
6779

6780 6781 6782 6783 6784 6785 6786 6787 6788 6789
    Case 2:
      Given
        X.shape = (3, 100, 100, 4)
      and
        axis = 0
      We get:
        Out.shape = (1, 3 * 100 * 100 * 4)

    Args:
        x (Variable): A tensor of rank >= axis.
6790 6791
        axis (int): Indicate up to which input dimensions (exclusive) should
                    be flattened to the outer dimension of the output.
6792 6793 6794 6795 6796 6797 6798 6799 6800 6801 6802 6803 6804 6805 6806
                    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:
        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
                  inner dimension of the output.

    Raises:
        ValueError: If x is not a variable.
6807
        ValueError: If axis is not in range [0, rank(x)].
6808 6809 6810 6811 6812 6813 6814 6815 6816 6817 6818 6819 6820 6821 6822 6823

    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)]")

X
Xin Pan 已提交
6824 6825
    out = helper.create_variable_for_type_inference(x.dtype)
    x_shape = helper.create_variable_for_type_inference(x.dtype)
6826
    helper.append_op(
6827
        type='flatten2',
6828
        inputs={"X": x},
6829 6830
        outputs={'Out': out,
                 'XShape': x_shape},
6831 6832
        attrs={"axis": axis})
    return out
X
Xin Pan 已提交
6833 6834


C
chenweihang 已提交
6835
def sequence_enumerate(input, win_size, pad_value=0, name=None):
C
chenweihang 已提交
6836
    """
C
chenweihang 已提交
6837
    Generate a new sequence for the input index sequence, which enumerates all the
M
minqiyang 已提交
6838
    sub-sequences with length `win_size` of the input.
C
chenweihang 已提交
6839 6840
    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.
M
minqiyang 已提交
6841

C
chenweihang 已提交
6842 6843 6844 6845
    Examples:
    Case 1:
      Input:
        X.lod = [[0, 3, 5]]
6846
        X.data = [[1], [2], [3], [4], [5]]
C
chenweihang 已提交
6847 6848 6849 6850 6851 6852
        X.dims = [5, 1]
      Attrs:
        win_size = 2
        pad_value = 0
      Output:
        Out.lod = [[0, 3, 5]]
6853
        Out.data = [[1, 2], [2, 3], [3, 0], [4, 5], [5, 0]]
C
chenweihang 已提交
6854 6855 6856
        Out.dims = [5, 2]

    Args:
C
chenweihang 已提交
6857 6858 6859
        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.
C
chenweihang 已提交
6860 6861 6862 6863 6864 6865 6866 6867 6868 6869 6870

    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)
    """
    helper = LayerHelper('sequence_enumerate', **locals())
X
Xin Pan 已提交
6871 6872
    out = helper.create_variable_for_type_inference(
        helper.input_dtype(), stop_gradient=True)
C
chenweihang 已提交
6873 6874 6875 6876 6877 6878
    helper.append_op(
        type='sequence_enumerate',
        inputs={'X': input},
        outputs={'Out': out},
        attrs={'win_size': win_size,
               'pad_value': pad_value})
M
minqiyang 已提交
6879
    return out
6880

6881

S
sneaxiy 已提交
6882 6883 6884 6885 6886 6887 6888 6889 6890
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:
6891

S
sneaxiy 已提交
6892
    .. math::
6893

S
sneaxiy 已提交
6894 6895 6896
        y(i_1, i_2,..., i_n, j) = (j < x(i_1, i_2,..., i_n))

    Args:
6897
        x (Variable): Input tensor of sequence_mask layer,
S
sneaxiy 已提交
6898 6899 6900 6901
                      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.
6902 6903 6904
        name (str|None): A name for this layer(optional). If set None, the
                         layer will be named automatically.

S
sneaxiy 已提交
6905 6906
    Returns:
        Variable: The output sequence mask.
6907

S
sneaxiy 已提交
6908 6909
    """

Q
qingqing01 已提交
6910
    helper = LayerHelper('sequence_mask', **locals())
S
sneaxiy 已提交
6911
    if name is None:
X
Xin Pan 已提交
6912
        out = helper.create_variable_for_type_inference(dtype=dtype)
S
sneaxiy 已提交
6913
    else:
X
Xin Pan 已提交
6914
        out = helper.create_variable_for_type_inference(dtype=dtype, name=name)
S
sneaxiy 已提交
6915

Q
qingqing01 已提交
6916 6917 6918
    helper.append_op(
        type='sequence_mask',
        inputs={'X': [x]},
S
sneaxiy 已提交
6919 6920
        outputs={'Y': out},
        attrs={
6921
            'maxlen': maxlen if maxlen is not None else -1,
S
sneaxiy 已提交
6922 6923 6924
            'out_dtype': out.dtype
        })
    return out
S
sneaxiy 已提交
6925 6926


X
Xin Pan 已提交
6927
def stack(x, axis=0):
S
sneaxiy 已提交
6928 6929 6930 6931
    """
    **Stack Layer**

    This layer stacks all of the input :code:`x` along axis.
6932 6933 6934 6935 6936 6937 6938

    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}]`.
S
sneaxiy 已提交
6939
    If :code:`axis` < 0, it would be replaced with :code:`axis+rank(x[0])+1`.
6940
    If :code:`axis` is None, it would be replaced with 0.
S
sneaxiy 已提交
6941 6942

    Args:
6943
        x (Variable|list(Variable)|tuple(Variable)): Input variables.
S
sneaxiy 已提交
6944
        axis (int|None): The axis along which all inputs are stacked.
6945

S
sneaxiy 已提交
6946 6947
    Returns:
        Variable: The stacked variable.
6948

S
sneaxiy 已提交
6949 6950
    """

X
Xin Pan 已提交
6951 6952 6953 6954 6955 6956
    helper = LayerHelper('stack', **locals())
    axis = 0 if axis is None else axis

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

X
Xin Pan 已提交
6957
    out = helper.create_variable_for_type_inference(x[0].dtype)
X
Xin Pan 已提交
6958
    helper.append_op(
S
sneaxiy 已提交
6959 6960
        type='stack', inputs={'X': x}, outputs={'Y': out},
        attrs={'axis': axis})
6961

X
Xin Pan 已提交
6962
    return out
D
dzhwinter 已提交
6963 6964 6965 6966 6967 6968 6969


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

    This layer unstacks input :code:`x` into several tensors along axis.
M
minqiyang 已提交
6970

D
dzhwinter 已提交
6971 6972 6973
    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
M
minqiyang 已提交
6974
    raised.
D
dzhwinter 已提交
6975 6976

    Args:
M
minqiyang 已提交
6977
        x (Variable): Input variable.
D
dzhwinter 已提交
6978 6979
        axis (int): The axis along which the input is unstacked.
        num (int|None): The number of output variables.
M
minqiyang 已提交
6980

D
dzhwinter 已提交
6981 6982
    Returns:
        list(Variable): The unstacked variables.
M
minqiyang 已提交
6983

D
dzhwinter 已提交
6984 6985 6986 6987 6988 6989 6990 6991 6992 6993 6994
    """

    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 = []
    for _ in num:
X
Xin Pan 已提交
6995
        outs.append(helper.create_variable_for_type_inference(x.dtype))
D
dzhwinter 已提交
6996 6997 6998 6999 7000 7001 7002 7003

    helper.append_op(
        type='unstack',
        inputs={'X': [x]},
        outputs={'Y': outs},
        attrs={'axis': axis,
               'num': num})
    return outs
W
whs 已提交
7004 7005 7006 7007 7008 7009 7010 7011 7012 7013 7014 7015


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]:
M
minqiyang 已提交
7016

W
whs 已提交
7017 7018 7019 7020
                [
                   [[1], [2], [3]],
                   [[4], [5], [6]]
                ]
M
minqiyang 已提交
7021

W
whs 已提交
7022
        Attr(expand_times):  [1, 2, 2]
M
minqiyang 已提交
7023

W
whs 已提交
7024
        Output(Out) is a 3-D tensor with shape [2, 6, 2]:
M
minqiyang 已提交
7025

W
whs 已提交
7026 7027 7028 7029
                [
                    [[1, 1], [2, 2], [3, 3], [1, 1], [2, 2], [3, 3]],
                    [[4, 4], [5, 5], [6, 6], [4, 4], [5, 5], [6, 6]]
                ]
M
minqiyang 已提交
7030

W
whs 已提交
7031 7032 7033 7034 7035 7036 7037 7038 7039 7040 7041 7042 7043 7044 7045 7046
    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')
X
Xin Pan 已提交
7047
    out = helper.create_variable_for_type_inference(dtype)
W
whs 已提交
7048 7049 7050 7051 7052 7053
    helper.append_op(
        type='expand',
        inputs={'X': x},
        outputs={'Out': out},
        attrs={'expand_times': expand_times})
    return out
S
sneaxiy 已提交
7054 7055


G
fix  
gongweibao 已提交
7056 7057 7058
from paddle.fluid.framework import convert_np_dtype_to_dtype_


G
gongweibao 已提交
7059
@templatedoc()
G
fix  
gongweibao 已提交
7060 7061 7062 7063 7064 7065 7066 7067 7068
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):
    """
G
gongweibao 已提交
7069
    ${comment}
G
fix  
gongweibao 已提交
7070 7071

    Args:
G
gongweibao 已提交
7072 7073 7074
        input (Variable): ${input_comment}
        shape (tuple|list): ${shape_comment}
        input_dim_idx (Int): ${input_dim_idx_comment}
G
gongweibao 已提交
7075
        output_dim_idx (Int): ${output_dim_idx_comment}
G
gongweibao 已提交
7076 7077 7078
        min (Float): ${min_comment}
        max (Float): ${max_comment}
        seed (Int): ${seed_comment}
G
fix  
gongweibao 已提交
7079 7080
        dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc
    Returns:
G
gongweibao 已提交
7081
        out (Variable): ${out_comment}
G
fix  
gongweibao 已提交
7082 7083 7084 7085

    """

    helper = LayerHelper('uniform_random_batch_size_like', **locals())
X
Xin Pan 已提交
7086
    out = helper.create_variable_for_type_inference(dtype)
G
fix  
gongweibao 已提交
7087 7088 7089 7090 7091 7092 7093 7094 7095 7096 7097 7098 7099 7100 7101 7102
    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
G
fix  
gongweibao 已提交
7103 7104


G
gongweibao 已提交
7105
@templatedoc()
X
Xin Pan 已提交
7106
def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
G
fix  
gongweibao 已提交
7107
    """
G
gongweibao 已提交
7108
    ${comment}
G
fix  
gongweibao 已提交
7109 7110

    Args:
G
gongweibao 已提交
7111 7112 7113 7114
        shape (tuple|list): ${shape_comment}
        mean (Float): ${mean_comment}
        std (Float): ${std_comment}
        seed (Int): ${seed_comment}
G
fix  
gongweibao 已提交
7115 7116 7117
        dtype(np.dtype|core.VarDesc.VarType|str): Output data type.

    Returns:
G
gongweibao 已提交
7118
        out (Variable): ${out_comment}
G
fix  
gongweibao 已提交
7119 7120 7121 7122

    """

    helper = LayerHelper('gaussian_random', **locals())
X
Xin Pan 已提交
7123
    out = helper.create_variable_for_type_inference(dtype)
G
fix  
gongweibao 已提交
7124 7125 7126 7127 7128 7129 7130 7131 7132 7133
    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,
X
Xin Pan 已提交
7134
            'use_mkldnn': False
G
fix  
gongweibao 已提交
7135 7136 7137 7138 7139
        })

    return out


G
gongweibao 已提交
7140
@templatedoc()
G
fix  
gongweibao 已提交
7141
def sampling_id(x, min=0.0, max=1.0, seed=0, dtype='float32'):
G
fix  
gongweibao 已提交
7142
    """
G
gongweibao 已提交
7143
    ${comment}
G
fix  
gongweibao 已提交
7144 7145

    Args:
G
gongweibao 已提交
7146 7147 7148 7149
        x (Variable): ${x_comment}
        min (Float): ${min_comment}
        max (Float): ${max_comment}
        seed (Float): ${seed_comment}
G
fix  
gongweibao 已提交
7150
        dtype(np.dtype|core.VarDesc.VarType|str): The type of output data : float32, float_16, int etc
G
fix  
gongweibao 已提交
7151 7152

    Returns:
G
gongweibao 已提交
7153
        out (Variable): ${out_comment}
G
fix  
gongweibao 已提交
7154 7155 7156 7157

    """

    helper = LayerHelper('sampling_id', **locals())
X
Xin Pan 已提交
7158
    out = helper.create_variable_for_type_inference(dtype)
G
fix  
gongweibao 已提交
7159 7160 7161 7162 7163 7164 7165 7166 7167 7168 7169
    helper.append_op(
        type='sampling_id',
        inputs={'X': x},
        outputs={'Out': out},
        attrs={'min': min,
               'max': max,
               'seed': seed})

    return out


G
gongweibao 已提交
7170
@templatedoc()
G
fix  
gongweibao 已提交
7171 7172 7173 7174 7175 7176 7177 7178 7179
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'):
    """
G
gongweibao 已提交
7180
    ${comment}
G
fix  
gongweibao 已提交
7181 7182

    Args:
G
gongweibao 已提交
7183 7184
        input (Variable): ${input_comment}
        shape (tuple|list): ${shape_comment}
G
gongweibao 已提交
7185
        input_dim_idx (Int): ${input_dim_idx_comment}
G
gongweibao 已提交
7186 7187 7188 7189
        output_dim_idx (Int): ${output_dim_idx_comment}
        mean (Float): ${mean_comment}
        std (Float): ${std_comment}
        seed (Int): ${seed_comment}
G
fix  
gongweibao 已提交
7190
        dtype(np.dtype|core.VarDesc.VarType|str): The type of output data : float32, float_16, int etc
G
fix  
gongweibao 已提交
7191 7192

    Returns:
G
gongweibao 已提交
7193
        out (Variable): ${out_comment}
G
fix  
gongweibao 已提交
7194 7195 7196
    """

    helper = LayerHelper('gaussian_random_batch_size_like', **locals())
X
Xin Pan 已提交
7197
    out = helper.create_variable_for_type_inference(dtype)
G
fix  
gongweibao 已提交
7198 7199 7200 7201 7202 7203 7204 7205 7206 7207 7208 7209 7210 7211 7212 7213 7214 7215
    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


G
gongweibao 已提交
7216
@templatedoc()
X
Xin Pan 已提交
7217
def sum(x):
G
fix  
gongweibao 已提交
7218
    """
G
gongweibao 已提交
7219
    ${comment}
G
fix  
gongweibao 已提交
7220 7221

    Args:
G
gongweibao 已提交
7222
        x (Variable): ${x_comment}
G
fix  
gongweibao 已提交
7223 7224

    Returns:
G
gongweibao 已提交
7225
        out (Variable): ${out_comment}
G
fix  
gongweibao 已提交
7226 7227 7228
    """

    helper = LayerHelper('sum', **locals())
X
Xin Pan 已提交
7229 7230
    out = helper.create_variable_for_type_inference(
        dtype=helper.input_dtype('x'))
G
fix  
gongweibao 已提交
7231 7232 7233 7234
    helper.append_op(
        type='sum',
        inputs={'X': x},
        outputs={'Out': out},
X
Xin Pan 已提交
7235
        attrs={'use_mkldnn': False})
G
fix  
gongweibao 已提交
7236 7237 7238 7239

    return out


G
gongweibao 已提交
7240
@templatedoc()
G
fix  
gongweibao 已提交
7241 7242
def slice(input, axes, starts, ends):
    """
G
gongweibao 已提交
7243
    ${comment}
G
fix  
gongweibao 已提交
7244 7245

    Args:
G
gongweibao 已提交
7246 7247 7248 7249
        input (Variable): ${input_comment}.
        axes (List): ${axes_comment}
        starts (List): ${starts_comment}
        ends (List): ${ends_comment}
G
fix  
gongweibao 已提交
7250 7251

    Returns:
G
gongweibao 已提交
7252
        out (Variable): ${out_comment}
G
fix  
gongweibao 已提交
7253 7254 7255 7256

    """

    helper = LayerHelper('slice', **locals())
X
Xin Pan 已提交
7257 7258
    out = helper.create_variable_for_type_inference(
        dtype=helper.input_dtype('input'))
G
fix  
gongweibao 已提交
7259 7260 7261 7262 7263 7264 7265 7266 7267 7268 7269
    helper.append_op(
        type='slice',
        inputs={'Input': input},
        outputs={'Out': out},
        attrs={'axes': axes,
               'starts': starts,
               'ends': ends})

    return out


G
gongweibao 已提交
7270
@templatedoc()
G
fix  
gongweibao 已提交
7271 7272
def shape(input):
    """
G
gongweibao 已提交
7273
    ${comment}
G
fix  
gongweibao 已提交
7274 7275

    Args:
G
gongweibao 已提交
7276
        input (Variable): ${input_comment}
G
fix  
gongweibao 已提交
7277 7278

    Returns:
G
gongweibao 已提交
7279
        out (Variable): ${out_comment}
G
fix  
gongweibao 已提交
7280 7281 7282 7283

    """

    helper = LayerHelper('shape', **locals())
X
Xin Pan 已提交
7284 7285
    out = helper.create_variable_for_type_inference(
        dtype=helper.input_dtype('input'))
G
fix  
gongweibao 已提交
7286
    helper.append_op(
G
fix  
gongweibao 已提交
7287
        type='shape', inputs={'Input': input}, outputs={'Out': out})
G
fix  
gongweibao 已提交
7288 7289

    return out
G
merge  
gongweibao 已提交
7290 7291


S
sneaxiy 已提交
7292 7293 7294 7295 7296 7297 7298 7299
def _elementwise_op(helper):
    op_type = helper.layer_type
    x = helper.kwargs.get('x', None)
    y = helper.kwargs.get('y', None)
    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)
S
sneaxiy 已提交
7300 7301
    name = helper.kwargs.get('name', None)
    if name is None:
X
Xin Pan 已提交
7302
        out = helper.create_variable_for_type_inference(dtype=x.dtype)
S
sneaxiy 已提交
7303 7304 7305
    else:
        out = helper.create_variable(
            name=name, dtype=x.dtype, persistable=False)
S
sneaxiy 已提交
7306

S
sneaxiy 已提交
7307 7308 7309 7310 7311 7312 7313 7314 7315 7316 7317
    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()
S
sneaxiy 已提交
7318
def scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None):
S
sneaxiy 已提交
7319 7320 7321 7322 7323 7324 7325 7326
    """
    ${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}
S
sneaxiy 已提交
7327
        act(basestring|None): Activation applied to the output.
M
minqiyang 已提交
7328
        name(basestring|None): Name of the output.
S
sneaxiy 已提交
7329 7330 7331 7332 7333 7334

    Returns:
        out(${out_type}): ${out_comment}
    """

    helper = LayerHelper('scale', **locals())
S
sneaxiy 已提交
7335
    if name is None:
X
Xin Pan 已提交
7336
        out = helper.create_variable_for_type_inference(dtype=x.dtype)
S
sneaxiy 已提交
7337 7338 7339
    else:
        out = helper.create_variable(
            name=name, dtype=x.dtype, persistable=False)
S
sneaxiy 已提交
7340 7341 7342 7343 7344 7345 7346 7347 7348 7349

    helper.append_op(
        type='scale',
        inputs={'X': x},
        outputs={'Out': out},
        attrs={
            'scale': float(scale),
            'bias': float(bias),
            'bias_after_scale': bias_after_scale
        })
S
sneaxiy 已提交
7350
    return helper.append_activation(out)
S
sneaxiy 已提交
7351 7352


X
Xin Pan 已提交
7353
def elementwise_add(x, y, axis=-1, act=None, name=None):
S
sneaxiy 已提交
7354 7355 7356
    return _elementwise_op(LayerHelper('elementwise_add', **locals()))


X
Xin Pan 已提交
7357
def elementwise_div(x, y, axis=-1, act=None, name=None):
S
sneaxiy 已提交
7358 7359 7360
    return _elementwise_op(LayerHelper('elementwise_div', **locals()))


X
Xin Pan 已提交
7361
def elementwise_sub(x, y, axis=-1, act=None, name=None):
S
sneaxiy 已提交
7362 7363 7364
    return _elementwise_op(LayerHelper('elementwise_sub', **locals()))


X
Xin Pan 已提交
7365
def elementwise_mul(x, y, axis=-1, act=None, name=None):
S
sneaxiy 已提交
7366 7367 7368
    return _elementwise_op(LayerHelper('elementwise_mul', **locals()))


X
Xin Pan 已提交
7369
def elementwise_max(x, y, axis=-1, act=None, name=None):
S
sneaxiy 已提交
7370 7371 7372
    return _elementwise_op(LayerHelper('elementwise_max', **locals()))


X
Xin Pan 已提交
7373
def elementwise_min(x, y, axis=-1, act=None, name=None):
S
sneaxiy 已提交
7374 7375 7376
    return _elementwise_op(LayerHelper('elementwise_min', **locals()))


X
Xin Pan 已提交
7377
def elementwise_pow(x, y, axis=-1, act=None, name=None):
S
sneaxiy 已提交
7378 7379 7380 7381 7382 7383 7384 7385 7386 7387 7388
    return _elementwise_op(LayerHelper('elementwise_pow', **locals()))


for func in [
        elementwise_add, elementwise_div, elementwise_sub, elementwise_mul,
        elementwise_max, elementwise_min, elementwise_pow
]:
    op_proto = OpProtoHolder.instance().get_op_proto(func.__name__)
    func.__doc__ = _generate_doc_string_(
        op_proto,
        additional_args_lines=[
S
sneaxiy 已提交
7389 7390
            "act (basestring|None): Activation applied to the output.",
            "name (basestring|None): Name of the output."
S
sneaxiy 已提交
7391
        ])
M
minqiyang 已提交
7392 7393


7394
def _logical_op(op_name, x, y, out=None, name=None, binary_op=True):
M
minqiyang 已提交
7395 7396
    helper = LayerHelper(op_name, **locals())

M
minqiyang 已提交
7397 7398
    if binary_op:
        assert x.dtype == y.dtype
M
minqiyang 已提交
7399 7400 7401

    if out is None:
        if name is None:
X
Xin Pan 已提交
7402
            out = helper.create_variable_for_type_inference(dtype=x.dtype)
M
minqiyang 已提交
7403 7404 7405 7406 7407 7408 7409 7410 7411 7412 7413 7414 7415 7416 7417
        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()
7418
def logical_and(x, y, out=None, name=None):
M
minqiyang 已提交
7419 7420 7421 7422 7423 7424 7425 7426 7427 7428 7429 7430 7431 7432 7433 7434 7435 7436
    """
    ${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}
    """

    return _logical_op(
        op_name="logical_and", x=x, y=y, name=name, out=out, binary_op=True)


@templatedoc()
7437
def logical_or(x, y, out=None, name=None):
M
minqiyang 已提交
7438 7439 7440 7441 7442 7443 7444 7445 7446 7447 7448 7449 7450 7451 7452 7453 7454 7455
    """
    ${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}
    """

    return _logical_op(
        op_name="logical_or", x=x, y=y, name=name, out=out, binary_op=True)


@templatedoc()
7456
def logical_xor(x, y, out=None, name=None):
M
minqiyang 已提交
7457 7458 7459 7460 7461 7462 7463 7464 7465 7466 7467 7468 7469 7470 7471 7472 7473 7474
    """
    ${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}
    """

    return _logical_op(
        op_name="logical_xor", x=x, y=y, name=name, out=out, binary_op=True)


@templatedoc()
7475
def logical_not(x, out=None, name=None):
M
minqiyang 已提交
7476 7477 7478 7479 7480 7481 7482 7483 7484 7485 7486 7487 7488 7489
    """
    ${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}
    """

    return _logical_op(
        op_name="logical_not", x=x, y=None, name=name, out=out, binary_op=False)
7490 7491 7492 7493 7494 7495 7496 7497 7498 7499 7500 7501 7502 7503 7504 7505 7506 7507 7508 7509


@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}
    """

    helper = LayerHelper("clip", **locals())

    if name is None:
S
sneaxiy 已提交
7510 7511 7512 7513
        name = unique_name.generate(".".join([helper.name, 'tmp']))

    out = helper.create_variable(
        type=x.type, name=name, dtype=x.dtype, persistable=False)
7514 7515 7516 7517 7518 7519 7520 7521 7522 7523 7524 7525 7526 7527 7528 7529 7530 7531 7532 7533 7534 7535 7536 7537 7538 7539 7540 7541

    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}
    """

    helper = LayerHelper("clip_by_norm", **locals())

    if name is None:
S
sneaxiy 已提交
7542 7543 7544 7545
        name = unique_name.generate(".".join([helper.name, 'tmp']))

    out = helper.create_variable(
        type=x.type, name=name, dtype=x.dtype, persistable=False)
7546 7547 7548 7549 7550 7551 7552 7553

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

    return out
X
Xin Pan 已提交
7554 7555 7556 7557 7558 7559 7560 7561 7562 7563 7564 7565 7566 7567 7568 7569 7570 7571


@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:
X
Xin Pan 已提交
7572
        out = helper.create_variable_for_type_inference(dtype=x.dtype)
X
Xin Pan 已提交
7573 7574 7575 7576 7577 7578 7579 7580 7581 7582 7583 7584 7585 7586 7587 7588 7589 7590 7591 7592 7593 7594 7595 7596 7597 7598 7599 7600 7601
    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


@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:
X
Xin Pan 已提交
7602
        out = helper.create_variable_for_type_inference(dtype=x.dtype)
X
Xin Pan 已提交
7603 7604 7605 7606 7607 7608 7609 7610 7611
    else:
        out = helper.create_variable(
            name=name, dtype=x.dtype, persistable=False)

    helper.append_op(
        type="mul",
        inputs={"X": x,
                "Y": y},
        attrs={
X
fix  
Xin Pan 已提交
7612 7613
            "x_num_col_dims": x_num_col_dims,
            "y_num_col_dims": y_num_col_dims
X
Xin Pan 已提交
7614 7615 7616 7617 7618 7619 7620 7621 7622 7623 7624 7625 7626 7627 7628 7629 7630 7631 7632 7633 7634 7635
        },
        outputs={"Out": out})
    return out


@templatedoc()
def sigmoid_cross_entropy_with_logits(x, label, name=None):
    """
    ${comment}

    Args:
        x(${x_type}): ${x_comment}
        label(${label_type}): ${label_comment}
        name(basestring|None): Name of the output.

    Returns:
        out(${out_type}): ${out_comment}
    """

    helper = LayerHelper("sigmoid_cross_entropy_with_logits", **locals())

    if name is None:
X
Xin Pan 已提交
7636
        out = helper.create_variable_for_type_inference(dtype=x.dtype)
X
Xin Pan 已提交
7637 7638 7639 7640 7641 7642 7643 7644 7645 7646 7647 7648 7649 7650 7651 7652 7653 7654 7655 7656 7657 7658 7659 7660 7661 7662 7663 7664 7665
    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},
        attrs={},
        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:
X
Xin Pan 已提交
7666
        out = helper.create_variable_for_type_inference(dtype=x.dtype)
X
Xin Pan 已提交
7667 7668 7669 7670 7671 7672 7673 7674 7675 7676
    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
M
minqiyang 已提交
7677 7678


J
JiabinYang 已提交
7679
def space_to_depth(x, blocksize, name=None):
J
JiabinYang 已提交
7680
    """
J
JiabinYang 已提交
7681
    Gives a blocksize to space_to_depth the input LoDtensor with Layout: [batch, channel, height, width]
J
JiabinYang 已提交
7682
    
J
JiabinYang 已提交
7683
    This op rearranges blocks of spatial data, into depth. More specifically, this op outputs a copy of the 
J
JiabinYang 已提交
7684
    input LoDtensor where values from the height and width dimensions are moved to the channel dimension. 
J
JiabinYang 已提交
7685
    The attr blocksize indicates the input block size.
J
JiabinYang 已提交
7686 7687
    
    space_to_depth will reorgnize the elements of input with shape[batch, channel, height, width] according 
J
JiabinYang 已提交
7688
    to blocksize to construct output with shape [batch, channel * blocksize * blocksize, height/blocksize, width/blocksize]:
J
JiabinYang 已提交
7689 7690 7691
    
    space_to_depth is used to This operation is useful for resizing the activations between convolutions 
    (but keeping all data)
J
JiabinYang 已提交
7692

J
JiabinYang 已提交
7693 7694 7695 7696 7697 7698 7699
    - Non-overlapping blocks of size block_size x block size are rearranged into depth at each location.
    - The depth of the output tensor is block_size * block_size * input channel 
    - 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


J
JiabinYang 已提交
7700
    Args:
J
JiabinYang 已提交
7701
        x(variable): The input LoDtensor.
J
JiabinYang 已提交
7702
        blocksize(variable): The blocksize to select the element on each feature map should be > 2
J
JiabinYang 已提交
7703 7704

    Returns:
J
JiabinYang 已提交
7705
        Variable: The output LoDtensor.
J
JiabinYang 已提交
7706 7707

    Raises:
J
JiabinYang 已提交
7708
        TypeError: blocksize type must be a long.
J
JiabinYang 已提交
7709 7710 7711 7712 7713 7714

    Examples:
        .. code-block:: python

            data = fluid.layers.data(
                name='data', shape=[1, 4, 2, 2], dtype='float32')
J
JiabinYang 已提交
7715
            space_to_depthed = fluid.layers.space_to_depth(
J
JiabinYang 已提交
7716
                x=data, blocksize=2)
J
JiabinYang 已提交
7717 7718
    """

J
JiabinYang 已提交
7719
    helper = LayerHelper("space_to_depth", **locals())
J
JiabinYang 已提交
7720

J
JiabinYang 已提交
7721 7722
    if not (isinstance(blocksize, int)):
        raise ValueError("blocksize must be a python Int")
J
JiabinYang 已提交
7723 7724

    if name is None:
J
JiabinYang 已提交
7725 7726
        out = helper.create_variable_for_type_inference(
            dtype=x.dtype)  #fix create
J
JiabinYang 已提交
7727 7728 7729 7730 7731
    else:
        out = helper.create_variable(
            name=name, dtype=x.dtype, persistable=False)

    helper.append_op(
J
JiabinYang 已提交
7732
        type="space_to_depth",
J
JiabinYang 已提交
7733
        inputs={"X": x},
J
JiabinYang 已提交
7734
        attrs={"blocksize": blocksize},
J
JiabinYang 已提交
7735
        outputs={"Out": out})
J
JiabinYang 已提交
7736 7737
    return out

J
JiabinYang 已提交
7738

S
sneaxiy 已提交
7739 7740 7741 7742 7743 7744 7745 7746 7747 7748 7749 7750 7751 7752
@templatedoc()
def sequence_reverse(x, name=None):
    """ 
    ${comment}

    Args:
        x(${x_type}): ${x_comment}
        name(basestring|None): Name of the output.

    Returns:
        out(${y_type}): ${y_comment}
    """
    helper = LayerHelper("sequence_reverse", **locals())
    if name is None:
S
sneaxiy 已提交
7753
        out = helper.create_variable_for_type_inference(dtype=x.dtype)
S
sneaxiy 已提交
7754 7755 7756 7757 7758 7759 7760 7761 7762 7763
    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
S
sneaxiy 已提交
7764 7765


7766 7767 7768 7769 7770 7771
def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None):
    """
    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.
7772

7773 7774 7775 7776 7777 7778 7779 7780 7781 7782 7783 7784 7785 7786 7787 7788 7789 7790 7791
    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.

    Returns:
        out (Variable): A tensor of the same shape and data layout with x.
    """
    helper = LayerHelper("affine_channel", **locals())

    if name is None:
X
Xin Pan 已提交
7792
        out = helper.create_variable_for_type_inference(dtype=x.dtype)
7793 7794 7795 7796 7797 7798 7799 7800 7801 7802 7803 7804
    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})
    return out
7805 7806


M
minqiyang 已提交
7807 7808
def hash(input, hash_size, num_hash=1, name=None):
    """
M
minqiyang 已提交
7809 7810
    Hash the input to an integer whose value is less than the given hash size.

M
minqiyang 已提交
7811 7812
    The hash algorithm we used was xxHash - Extremely fast hash algorithm
    (https://github.com/Cyan4973/xxHash/tree/v0.6.5)
M
minqiyang 已提交
7813 7814 7815 7816 7817 7818 7819 7820 7821 7822 7823 7824 7825 7826 7827 7828 7829 7830 7831 7832 7833 7834 7835 7836 7837 7838 7839 7840 7841 7842 7843 7844 7845 7846 7847 7848 7849 7850

    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]`.
M
minqiyang 已提交
7851
        num_hash (int): The times of hash, default 1.
M
minqiyang 已提交
7852
        name (str, default None): The name of this layer.
M
minqiyang 已提交
7853 7854 7855 7856 7857 7858 7859 7860 7861

    Returns:
       Variable: The hash result variable which is a LoDTensor.

    Examples:
       .. code-block:: python
           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)
M
minqiyang 已提交
7862 7863
    """
    helper = LayerHelper('hash', **locals())
M
minqiyang 已提交
7864 7865
    out = helper.create_variable_for_type_inference(
        helper.input_dtype(), stop_gradient=True)
M
minqiyang 已提交
7866 7867 7868 7869 7870 7871 7872
    helper.append_op(
        type='hash',
        inputs={'X': input},
        outputs={'Out': out},
        attrs={'num_hash': num_hash,
               'mod_by': hash_size})
    return out
G
gmcather 已提交
7873 7874


D
dengkaipeng 已提交
7875
@templatedoc()
7876 7877
def grid_sampler(x, grid, name=None):
    """
7878 7879 7880 7881 7882 7883 7884
    This operation samples input X by using bilinear interpolation based on 
    flow field grid, which is usually gennerated by affine_grid. The grid of
    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 
    interpolation value of 4 nearest corner points.
7885 7886 7887 7888 7889 7890 7891 7892 7893 7894 7895 7896 7897 7898 7899 7900 7901 7902 7903 7904 7905 7906 7907 7908 7909 7910 7911 7912 7913 7914 7915 7916 7917 7918 7919 7920 7921 7922

    Step 1:
    Get (x, y) grid coordinates and scale to [0, H-1/W-1].

    grid_x = 0.5 * (grid[:, :, :, 0] + 1) * (W - 1)
    grid_y = 0.5 * (grid[:, :, :, 1] + 1) * (H - 1)

    Step 2:
    Indices input data X with grid (x, y) in each [H, W] area, and bilinear 
    interpolate point value by 4 nearest points.

      wn ------- y_n ------- en
      |           |           |
      |          d_n          |
      |           |           |
     x_w --d_w-- grid--d_e-- x_e
      |           |           |
      |          d_s          |
      |           |           |
      ws ------- y_s ------- wn

    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

    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

    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

    output = wn * d_e * d_s + en * d_w * d_s
           + ws * d_e * d_n + es * d_w * d_n
D
dengkaipeng 已提交
7923 7924

    Args:
7925 7926 7927
        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.
D
dengkaipeng 已提交
7928 7929

    Returns:
7930 7931 7932 7933 7934 7935 7936 7937 7938 7939
        out(Variable): Output of shape [N, C, H, W] data samples input X 
        using bilnear interpolation based on input grid.

    Exmples:
    .. 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)
D
dengkaipeng 已提交
7940 7941 7942 7943 7944 7945 7946 7947 7948
    """
    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")

7949
    out = helper.create_variable_for_type_inference(x.dtype)
D
dengkaipeng 已提交
7950 7951
    ipts = {'X': x, 'Grid': grid}

7952
    helper.append_op(type='grid_sampler', inputs=ipts, outputs={'Output': out})
7953 7954 7955
    return out


G
gmcather 已提交
7956 7957 7958 7959 7960 7961 7962 7963 7964 7965 7966 7967 7968 7969 7970 7971 7972 7973 7974 7975 7976 7977 7978 7979 7980 7981 7982 7983 7984 7985 7986 7987 7988 7989 7990 7991 7992 7993 7994 7995 7996 7997 7998 7999 8000 8001 8002 8003 8004 8005 8006 8007 8008 8009 8010 8011 8012 8013 8014 8015 8016 8017 8018 8019 8020 8021 8022 8023 8024 8025 8026 8027 8028 8029 8030 8031 8032 8033 8034 8035 8036 8037 8038 8039 8040 8041 8042 8043 8044 8045 8046 8047 8048 8049
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


def add_position_encoding(input, alpha, beta, name=None):
    """
    **Add Position Encoding Layer**

    This layer accepts an input 3D-Tensor of shape [N x M x P], and return an
    output Tensor of shape [N x M x P] with positional encoding value.

    Refer to `Attention Is All You Need<http://arxiv.org/pdf/1706.03762.pdf>`_ .

    .. math::
        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)

    Where:
    * PE(pos, 2i): the increment for the number at even position
    * PE(pos, 2i + 1): the increment for the number at odd position

    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)
    """
    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
Q
Qiao Longfei 已提交
8050 8051 8052 8053 8054 8055 8056 8057 8058 8059


def bilinear_tensor_product(x,
                            y,
                            size,
                            act=None,
                            name=None,
                            param_attr=None,
                            bias_attr=None):
    """
Q
Qiao Longfei 已提交
8060
    **Add Bilinear Tensor Product Layer**
Q
Qiao Longfei 已提交
8061

Q
Qiao Longfei 已提交
8062
    This layer performs bilinear tensor product on two inputs.
Q
Qiao Longfei 已提交
8063 8064 8065
    For example:

    .. math::
8066
       out{i} = x * W_{i} * {y^\mathrm{T}}, i=0,1,...,size-1
Q
Qiao Longfei 已提交
8067 8068

    In this formular:
8069 8070
      - :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].
Q
Qiao Longfei 已提交
8071
      - :math:`W_{i}`: the i-th learned weight, shape is [M, N]
8072
      - :math:`out{i}`: the i-th element of out, shape is [batch_size, size].
Q
Qiao Longfei 已提交
8073 8074 8075 8076 8077 8078 8079 8080 8081
      - :math:`y^\mathrm{T}`: the transpose of :math:`y_{2}`.

    The simple usage is:

    .. code-block:: python

       tensor = bilinear_tensor_product(x=layer1, y=layer2, size=1000)

    Args:
8082 8083
        x (Variable): 2-D input tensor with shape [batch_size, M]
        y (Variable): 2-D input tensor with shape [batch_size, N]
Q
Qiao Longfei 已提交
8084 8085 8086 8087 8088 8089 8090 8091 8092 8093 8094 8095 8096 8097 8098 8099 8100 8101
        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.
        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
            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:
        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)
    """
    helper = LayerHelper('bilinear_tensor_product', **locals())
Q
Qiao Longfei 已提交
8102
    dtype = helper.input_dtype('x')
Q
Qiao Longfei 已提交
8103 8104 8105 8106

    param_shape = [size, x.shape[1], y.shape[1]]

    w = helper.create_parameter(
Q
Qiao Longfei 已提交
8107
        attr=helper.param_attr, shape=param_shape, dtype=dtype, is_bias=False)
Q
Qiao Longfei 已提交
8108 8109 8110 8111 8112 8113 8114 8115 8116 8117 8118 8119 8120 8121 8122 8123 8124

    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)