Make layers as a python module (#6564)

* Make cast op support bool

Also add `elemwise_sub/mul/abs/clip` layers

* Make fuild.layers as a module

* Move layers as a module

* Split layers.py into layers module

* Fix CI

* Fix CI

python/paddle/v2/fluid/layers/__init__.py

+import ops
+from ops import *
+import nn
+from nn import *
+import io
+from io import *
+import tensor
+from tensor import *
+import control_flow
+from control_flow import *
+
+__all__ = []
+__all__ += nn.__all__
+__all__ += io.__all__
+__all__ += tensor.__all__
+__all__ += control_flow.__all__
+__all__ += ops.__all__

python/paddle/v2/fluid/layers.py → python/paddle/v2/fluid/layers/control_flow.py

+from ..layer_helper import LayerHelper, unique_name
+from ..framework import Program, Variable, Operator
+from .. import core
+from tensor import assign, fill_constant
 import contextlib
 
-import proto.framework_pb2 as framework_pb2
-import core
-from framework import OpProtoHolder, Variable, Program, Operator
-from initializer import Constant, Normal, Xavier, Initializer
-from paddle.v2.fluid.layer_helper import LayerHelper, unique_name
-from registry import register_layer
-from param_attr import ParamAttr
-
 __all__ = [
-    'fc', 'data', 'cross_entropy', 'conv2d', 'pool2d', 'embedding', 'concat',
-    'StaticRNN', 'cast', 'sequence_conv', 'sequence_pool', 'sums', 'cos_sim',
-    'batch_norm', 'accuracy', 'split_lod_tensor', 'While'
-]
-
-_REGISTER_LAYER_FROM_OPS = [
-    'mean', 'mul', 'dropout', 'reshape', 'sigmoid', 'scale', 'transpose',
-    'sigmoid_cross_entropy_with_logits', 'elementwise_add', 'elementwise_div',
-    'elementwise_sub', 'elementwise_mul', 'clip', 'abs'
+    'split_lod_tensor', 'merge_lod_tensor', 'BlockGuard', 'StaticRNNGuard',
+    'StaticRNNMemoryLink', 'WhileGuard', 'While', 'lod_rank_table',
+    'max_sequence_len', 'topk', 'lod_tensor_to_array', 'array_to_lod_tensor',
+    'increment', 'array_write', 'create_array', 'less_than', 'array_read',
+    'shrink_memory', 'array_length', 'IfElse', 'DynamicRNN', 'ConditionalBlock',
+    'StaticRNN'
 ]
 
-for _OP in set(_REGISTER_LAYER_FROM_OPS):
-    globals()[_OP] = register_layer(_OP)
-    __all__.append(_OP)
-
-
-def fc(input,
-       size,
-       num_flatten_dims=1,
-       param_attr=None,
-       bias_attr=None,
-       act=None,
-       name=None,
-       main_program=None,
-       startup_program=None):
-    """
-    Fully Connected Layer.
-
-    Args:
-       input: The input tensor to the function
-       size: The size of the layer
-       num_flatten_dims: Number of columns in input
-       param_attr: The parameters/weights to the FC Layer
-       param_initializer: Initializer used for the weight/parameter. If None, XavierInitializer() is used
-       bias_attr: The bias parameter for the FC layer
-       bias_initializer: Initializer used for the bias. If None, then ConstantInitializer() is used
-       act: Activation to be applied to the output of FC layer
-       name: Name/alias of the function
-       main_program: Name of the main program that calls this
-       startup_program: Name of the startup program
-
-    This function can take in multiple inputs and performs the Fully Connected
-    function (linear transformation) on top of each of them.
-    So for input x, the output will be : Wx + b. Where W is the parameter,
-    b the bias and x is the input.
-
-    The function also applies an activation (non-linearity) on top of the
-    output, if activation is passed in the input.
-
-    All the input variables of this function are passed in as local variables
-    to the LayerHelper constructor.
-
-    """
-    helper = LayerHelper('fc', **locals())
-
-    dtype = helper.input_dtype()
-
-    mul_results = []
-    for input_var, param_attr in helper.iter_inputs_and_params():
-        input_shape = input_var.shape
-        param_shape = [
-            reduce(lambda a, b: a * b, input_shape[num_flatten_dims:], 1)
-        ] + [size]
-        w = helper.create_parameter(
-            attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False)
-        tmp = helper.create_tmp_variable(dtype)
-        helper.append_op(
-            type="mul",
-            inputs={
-                "X": input_var,
-                "Y": w,
-            },
-            outputs={"Out": tmp},
-            attrs={'x_num_col_dims': num_flatten_dims,
-                   'y_num_col_dims': 1})
-        mul_results.append(tmp)
-
-    # sum
-    if len(mul_results) == 1:
-        pre_bias = mul_results[0]
-    else:
-        pre_bias = helper.create_tmp_variable(dtype)
-        helper.append_op(
-            type="sum", inputs={"X": mul_results}, outputs={"Out": pre_bias})
-    # add bias
-    pre_activation = helper.append_bias_op(pre_bias)
-    # add activation
-    return helper.append_activation(pre_activation)
-
-
-def embedding(input,
-              size,
-              is_sparse=False,
-              param_attr=None,
-              dtype='float32',
-              main_program=None,
-              startup_program=None):
-    """
-    Embedding Layer.
-
-    Args:
-       param_initializer:
-       input: The input to the function
-       size: The size of the layer
-       is_sparse: A flag that decleares whether the input is sparse
-       param_attr: Parameters for this layer
-       dtype: The type of data : float32, float_16, int etc
-       main_program: Name of the main program that calls this
-       startup_program: Name of the startup program
-
-    This function can take in the input (which is a vector of IDs) and
-    performs a lookup in the lookup_table using these IDs, to result into
-    the embedding of each ID in the input.
-
-    All the input variables of this function are passed in as local variables
-    to the LayerHelper constructor.
-
-    """
-
-    helper = LayerHelper('embedding', **locals())
-    w = helper.create_parameter(
-        attr=helper.param_attr, shape=size, dtype=dtype, is_bias=False)
-    tmp = helper.create_tmp_variable(dtype)
-    helper.append_op(
-        type='lookup_table',
-        inputs={'Ids': input,
-                'W': w},
-        outputs={'Out': tmp},
-        attrs={'is_sparse': is_sparse})
-    return tmp
-
-
-# TODO(qijun): expose H0 and C0
-def dynamic_lstm(input,
-                 size,
-                 param_attr=None,
-                 bias_attr=None,
-                 use_peepholes=True,
-                 is_reverse=False,
-                 gate_activation='sigmoid',
-                 cell_activation='tanh',
-                 candidate_activation='tanh',
-                 dtype='float32',
-                 main_program=None,
-                 startup_program=None):
-    helper = LayerHelper('lstm', **locals())
-    size = size / 4
-    weight = helper.create_parameter(
-        attr=helper.param_attr, shape=[size, 4 * size], dtype=dtype)
-    bias_size = [1, 7 * size]
-    if not use_peepholes:
-        bias_size[1] = 4 * size
-    bias = helper.create_parameter(
-        attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
-
-    hidden = helper.create_tmp_variable(dtype)
-    cell = helper.create_tmp_variable(dtype)
-    batch_gate = helper.create_tmp_variable(dtype)
-    batch_cell_pre_act = helper.create_tmp_variable(dtype)
-
-    helper.append_op(
-        type='lstm',
-        inputs={'Input': input,
-                'Weight': weight,
-                'Bias': bias},
-        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
-
-
-def gru_unit(input,
-             hidden,
-             size,
-             weight=None,
-             bias=None,
-             activation='tanh',
-             gate_activation='sigmoid',
-             main_program=None,
-             startup_program=None):
-    """
-    GRUUnit Operator implements partial calculations of the GRU unit as following:
-
-    $$
-    update \ gate: u_t = actGate(xu_t + W_u * h_{t-1} + b_u) \\
-    reset \ gate: r_t = actGate(xr_t + W_r * h_{t-1} + b_r)  \\
-    output \ candidate: {h}_t = actNode(xc_t + W_c * dot(r_t, h_{t-1}) + b_c) \\
-    output: h_t = dot((1 - u_t), h_{t-1}) + dot(u_t, {h}_t)
-    $$
-
-    which is same as one time step of GRU Operator.
-
-    @note To implement the complete GRU unit, fully-connected operator must be
-    used before to feed xu, xr and xc as the Input of GRUUnit operator.
-
-    TODO(ChunweiYan) add more document here
-    """
-    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()
-    size = size / 3
-
-    # create weight
-    if weight is None:
-        weight = helper.create_parameter(
-            attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
-
-    # create bias
-    if bias is None:
-        bias_size = [1, 3 * size]
-        bias = helper.create_parameter(
-            attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
-
-    gate = helper.create_tmp_variable(dtype)
-    reset_hidden_pre = helper.create_tmp_variable(dtype)
-    updated_hidden = helper.create_tmp_variable(dtype)
-
-    helper.append_op(
-        type='gru_unit',
-        inputs={'Input': input,
-                'HiddenPrev': hidden,
-                'Weight': weight},
-        outputs={
-            'Gate': gate,
-            'ResetHiddenPrev': reset_hidden_pre,
-            'Hidden': updated_hidden,
-        },
-        attrs={
-            'activation': 0,
-            'gate_activation': 1,
-        })
-
-    return updated_hidden, reset_hidden_pre, gate
-
-
-def data(name,
-         shape,
-         append_batch_size=True,
-         dtype='float32',
-         lod_level=0,
-         type=core.VarDesc.VarType.LOD_TENSOR,
-         main_program=None,
-         startup_program=None,
-         stop_gradient=True):
-    """
-    Data Layer.
-
-    Args:
-       name: The name/alias of the function
-       shape: Tuple declaring the shape.
-       append_batch_size: Whether or not to append the data as a batch.
-       dtype: The type of data : float32, float_16, int etc
-       type: The output type. By default it is LOD_TENSOR.
-       lod_level(int): The LoD Level. 0 means the input data is not a sequence.
-       main_program: Name of the main program that calls this
-       startup_program: Name of the startup program
-       stop_gradient: A boolean that mentions whether gradient should flow.
-
-    This function takes in input and based on whether data has
-    to be returned back as a minibatch, it creates the global variable using
-    the helper functions. The global variables can be accessed by all the
-    following operations and layers in the graph.
-
-    All the input variables of this function are passed in as local variables
-    to the LayerHelper constructor.
-
-    """
-    helper = LayerHelper('data', **locals())
-    shape = list(shape)
-    for i in xrange(len(shape)):
-        if shape[i] is None:
-            shape[i] = -1
-            append_batch_size = False
-        elif shape[i] < 0:
-            append_batch_size = False
-
-    if append_batch_size:
-        shape = [-1] + shape  # append batch size as -1
-
-    return helper.create_global_variable(
-        name=name,
-        shape=shape,
-        dtype=dtype,
-        type=type,
-        stop_gradient=stop_gradient,
-        lod_level=lod_level)
-
-
-def create_tensor(dtype, name=None, main_program=None, startup_program=None):
-    helper = LayerHelper("create_tensor", **locals())
-    return helper.create_variable(name=helper.name, dtype=dtype)
-
-
-def cast(x, dtype, main_program=None):
-    """
-    This function takes in the input with input_dtype
-    and casts it to the output_dtype as the output.
-    """
-    helper = LayerHelper('cast', **locals())
-    out = helper.create_tmp_variable(dtype=dtype)
-    helper.append_op(
-        type='cast',
-        inputs={'X': [x]},
-        outputs={'Out': [out]},
-        attrs={'in_dtype': x.dtype,
-               'out_dtype': out.dtype})
-    return out
-
-
-def concat(input, axis, main_program=None, startup_program=None):
-    """
-    This function concats the input along the axis mentioned
-    and returns that as the output.
-    """
-    helper = LayerHelper('concat', **locals())
-    out = helper.create_tmp_variable(dtype=helper.input_dtype())
-    helper.append_op(
-        type='concat',
-        inputs={'X': input},
-        outputs={'Out': [out]},
-        attrs={'axis': axis})
-    return out
-
-
-def sums(input, out=None, main_program=None, startup_program=None):
-    """
-    This function takes in the input and performs the sum operation on it
-    and returns that as the output.
-    """
-    helper = LayerHelper('sum', **locals())
-    if out is None:
-        out = helper.create_tmp_variable(dtype=helper.input_dtype())
-    helper.append_op(type='sum', inputs={'X': input}, outputs={'Out': out})
-    return out
-
-
-def linear_chain_crf(input,
-                     label,
-                     param_attr=None,
-                     main_program=None,
-                     startup_program=None):
-    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())
-    alpha = helper.create_tmp_variable(dtype=helper.input_dtype())
-    emission_exps = helper.create_tmp_variable(dtype=helper.input_dtype())
-    transition_exps = helper.create_tmp_variable(dtype=helper.input_dtype())
-    log_likelihood = helper.create_tmp_variable(dtype=helper.input_dtype())
-    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
-
-
-def crf_decoding(input,
-                 param_attr,
-                 label=None,
-                 main_program=None,
-                 startup_program=None):
-    helper = LayerHelper('crf_decoding', **locals())
-    transition = helper.get_parameter(param_attr.name)
-    viterbi_path = helper.create_tmp_variable(dtype=helper.input_dtype())
-    helper.append_op(
-        type='crf_decoding',
-        inputs={"Emission": [input],
-                "Transition": transition,
-                "Label": label},
-        outputs={"ViterbiPath": [viterbi_path]})
-
-    return viterbi_path
-
-
-def assign(input, output, main_program=None, startup_program=None):
-    helper = LayerHelper('assign', **locals())
-    helper.append_op(
-        type='scale',
-        inputs={'X': [input]},
-        outputs={'Out': [output]},
-        attrs={'scale': 1.0})
-    return output
-
 
 def split_lod_tensor(input,
                      mask,
@@ -460,404 +54,6 @@ def merge_lod_tensor(in_true,
     return out
 
 
-def cos_sim(X, Y, **kwargs):
-    """
-    This function performs the cosine similarity between two tensors
-    X and Y and returns that as the output.
-    """
-    helper = LayerHelper('cos_sim', **kwargs)
-    out = helper.create_tmp_variable(dtype=X.dtype)
-    xnorm = helper.create_tmp_variable(dtype=X.dtype)
-    ynorm = helper.create_tmp_variable(dtype=X.dtype)
-    helper.append_op(
-        type='cos_sim',
-        inputs={'X': [X],
-                'Y': [Y]},
-        outputs={'Out': [out],
-                 'XNorm': [xnorm],
-                 'YNorm': [ynorm]})
-    return out
-
-
-def cross_entropy(input, label, **kwargs):
-    """
-    This function computes cross_entropy using the input and label.
-    """
-    helper = LayerHelper('cross_entropy', **kwargs)
-    out = helper.create_tmp_variable(dtype=input.dtype)
-    helper.append_op(
-        type='cross_entropy',
-        inputs={'X': [input],
-                'Label': [label]},
-        outputs={'Y': [out]},
-        attrs=kwargs)
-    return out
-
-
-def square_error_cost(input, label, **kwargs):
-    """
-    This functions returns the squared error cost using the input and label.
-    The output is appending the op to do the above.
-    """
-    helper = LayerHelper('square_error_cost', **kwargs)
-    minus_out = helper.create_tmp_variable(dtype=input.dtype)
-    helper.append_op(
-        type='elementwise_sub',
-        inputs={'X': [input],
-                'Y': [label]},
-        outputs={'Out': [minus_out]})
-
-    square_out = helper.create_tmp_variable(dtype=input.dtype)
-    helper.append_op(
-        type='square', inputs={'X': [minus_out]}, outputs={'Y': [square_out]})
-    return square_out
-
-
-def accuracy(input, label, k=1, correct=None, total=None, **kwargs):
-    """
-    This function computes the accuracy using the input and label.
-    The output is the top_k inputs and their indices.
-    """
-    helper = LayerHelper("accuracy", **kwargs)
-    topk_out = helper.create_tmp_variable(dtype=input.dtype)
-    topk_indices = helper.create_tmp_variable(dtype="int64")
-    helper.append_op(
-        type="top_k",
-        inputs={"X": [input]},
-        outputs={"Out": [topk_out],
-                 "Indices": [topk_indices]},
-        attrs={"k": k})
-    acc_out = helper.create_tmp_variable(dtype="float32")
-    if correct is None:
-        correct = helper.create_tmp_variable(dtype="int64")
-    if total is None:
-        total = helper.create_tmp_variable(dtype="int64")
-    helper.append_op(
-        type="accuracy",
-        inputs={
-            "Out": [topk_out],
-            "Indices": [topk_indices],
-            "Label": [label]
-        },
-        outputs={
-            "Accuracy": [acc_out],
-            "Correct": [correct],
-            "Total": [total],
-        })
-    return acc_out
-
-
-def chunk_eval(input,
-               label,
-               chunk_scheme,
-               num_chunk_types,
-               excluded_chunk_types=None,
-               **kwargs):
-    """
-    This function computes the accuracy using the input and label.
-    The output is the top_k inputs and their indices.
-    """
-    helper = LayerHelper("chunk_eval", **kwargs)
-
-    # prepare output
-    precision = helper.create_tmp_variable(dtype="float32")
-    recall = helper.create_tmp_variable(dtype="float32")
-    f1_score = helper.create_tmp_variable(dtype="float32")
-
-    helper.append_op(
-        type="chunk_eval",
-        inputs={"Inference": [input],
-                "Label": [label]},
-        outputs={
-            "Precision": [precision],
-            "Recall": [recall],
-            "F1-Score": [f1_score]
-        },
-        attrs={
-            "num_chunk_types": num_chunk_types,
-            'chunk_scheme': chunk_scheme,
-            'excluded_chunk_types': excluded_chunk_types or []
-        })
-    return precision, recall, f1_score
-
-
-def sequence_conv(input,
-                  num_filters,
-                  filter_size=3,
-                  filter_stride=1,
-                  padding=None,
-                  bias_attr=None,
-                  param_attr=None,
-                  act=None,
-                  main_program=None,
-                  startup_program=None):
-    """
-    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.
-    """
-
-    # FIXME(dzh) : want to unify the argument of python layer
-    # function. So we ignore some unecessary attributes.
-    # such as, padding_trainable, context_start.
-
-    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)
-    pre_bias = helper.create_tmp_variable(dtype)
-
-    helper.append_op(
-        type='sequence_conv',
-        inputs={
-            'X': [input],
-            'Filter': [filter_param],
-        },
-        outputs={"Out": pre_bias},
-        attrs={
-            'contextStride': filter_stride,
-            'contextStart': -int(filter_size / 2),
-            'contextLength': filter_size
-        })
-    pre_act = helper.append_bias_op(pre_bias)
-    return helper.append_activation(pre_act)
-
-
-def conv2d(input,
-           num_filters,
-           filter_size,
-           stride=None,
-           padding=None,
-           groups=None,
-           param_attr=None,
-           bias_attr=None,
-           act=None,
-           name=None,
-           main_program=None,
-           startup_program=None):
-    """
-    This function creates the op for a 2-dimensional Convolution.
-    This is performed using the parameters of filters(size, dimensionality etc)
-    , stride and other configurations for a Convolution operation.
-    This funciton can also append an activation on top of the
-    conv-2d output, if mentioned in the input parameters.
-    """
-
-    if stride is None:
-        stride = [1, 1]
-    helper = LayerHelper('conv2d', **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.")
-        num_filter_channels = num_channels / groups
-
-    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]
-
-    input_shape = input.shape
-    filter_shape = [num_filters, num_filter_channels] + filter_size
-
-    def _get_default_param_initializer():
-        std = (2.0 / (filter_size[0]**2 * num_channels))**0.5
-        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())
-
-    pre_bias = helper.create_tmp_variable(dtype)
-
-    helper.append_op(
-        type='conv2d_cudnn',
-        inputs={
-            'Input': input,
-            'Filter': filter_param,
-        },
-        outputs={"Output": pre_bias},
-        attrs={'strides': stride,
-               'paddings': padding,
-               'groups': groups})
-
-    pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
-
-    return helper.append_activation(pre_act)
-
-
-def sequence_pool(input, pool_type, **kwargs):
-    """
-    This function add the operator for sequence pooling.
-    This is applied on top of the input using pool_type mentioned
-    in the parameters.
-    """
-    helper = LayerHelper('sequence_pool', input=input, **kwargs)
-    dtype = helper.input_dtype()
-    pool_out = helper.create_tmp_variable(dtype)
-    max_index = helper.create_tmp_variable(dtype)
-
-    helper.append_op(
-        type="sequence_pool",
-        inputs={"X": input},
-        outputs={"Out": pool_out,
-                 "MaxIndex": max_index},
-        attrs={"pooltype": pool_type.upper()})
-
-    return pool_out
-
-
-def pool2d(input,
-           pool_size,
-           pool_type,
-           pool_stride=None,
-           pool_padding=None,
-           global_pooling=False,
-           main_program=None,
-           startup_program=None):
-    """
-    This function adds the operator for pooling in 2 dimensions, using the
-    pooling configurations mentioned in input parameters.
-    """
-    if pool_padding is None:
-        pool_padding = [0, 0]
-    if pool_stride is None:
-        pool_stride = [1, 1]
-    if pool_type not in ["max", "avg"]:
-        raise ValueError(
-            "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.",
-            str(pool_type))
-    if isinstance(pool_size, int):
-        pool_size = [pool_size, pool_size]
-    if isinstance(pool_stride, int):
-        pool_stride = [pool_stride, pool_stride]
-    if isinstance(pool_padding, int):
-        pool_padding = [pool_padding, pool_padding]
-
-    helper = LayerHelper('pool2d', **locals())
-    dtype = helper.input_dtype()
-    pool_out = helper.create_tmp_variable(dtype)
-
-    helper.append_op(
-        type="pool2d",
-        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
-        })
-
-    return pool_out
-
-
-def batch_norm(input,
-               act=None,
-               is_test=False,
-               momentum=0.9,
-               epsilon=1e-05,
-               param_attr=None,
-               bias_attr=None,
-               data_layout='NCHW',
-               main_program=None,
-               startup_program=None):
-    """
-    This function helps create an operator to implement
-    the BatchNorm layer using the configurations from the input parameters.
-    """
-    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(
-        attr=helper.param_attr, shape=param_shape, dtype=dtype, is_bias=True)
-
-    mean = helper.create_global_variable(
-        dtype=input.dtype, shape=param_shape, persistable=True)
-    helper.set_variable_initializer(var=mean, initializer=Constant(0.0))
-
-    variance = helper.create_global_variable(
-        dtype=input.dtype, shape=param_shape, persistable=True)
-    helper.set_variable_initializer(var=variance, initializer=Constant(1.0))
-
-    # create output
-    # mean and mean_out share the same memory
-    mean_out = mean
-    # variance and variance out share the same memory
-    variance_out = variance
-    saved_mean = helper.create_tmp_variable(dtype)
-    saved_variance = helper.create_tmp_variable(dtype)
-
-    batch_norm_out = helper.create_tmp_variable(dtype)
-
-    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
-        },
-        attrs={"momentum": momentum,
-               "epsilon": epsilon,
-               "is_test": is_test})
-
-    return helper.append_activation(batch_norm_out)
-
-
-def beam_search_decode(ids, scores, main_program=None, startup_program=None):
-    helper = LayerHelper('beam_search_decode', **locals())
-    sentence_ids = helper.create_tmp_variable(dtype=ids.dtype)
-    sentence_scores = helper.create_tmp_variable(dtype=ids.dtype)
-
-    helper.append_op(
-        type="beam_search_decode",
-        inputs={"Ids": ids,
-                "Scores": scores},
-        outputs={
-            "SentenceIds": sentence_ids,
-            "SentenceScores": sentence_scores
-        })
-
-    return sentence_ids, sentence_scores
-
-
 class BlockGuard(object):
     """
     BlockGuard class.
@@ -1210,50 +406,6 @@ class While(object):
             attrs={'sub_block': while_block})
 
 
-def lstm(x,
-         c_pre_init,
-         hidden_dim,
-         forget_bias=None,
-         main_program=None,
-         startup_program=None):
-    """
-    This function helps create an operator for the LSTM (Long Short Term
-    Memory) cell that can be used inside an RNN.
-    """
-    helper = LayerHelper('lstm_unit', **locals())
-    rnn = StaticRNN()
-    with rnn.step():
-        c_pre = rnn.memory(init=c_pre_init)
-        x_t = rnn.step_input(x)
-
-        before_fc = concat(
-            input=[x_t, c_pre],
-            axis=1,
-            main_program=main_program,
-            startup_program=startup_program)
-        after_fc = fc(input=before_fc,
-                      size=hidden_dim * 4,
-                      main_program=main_program,
-                      startup_program=startup_program)
-
-        dtype = x.dtype
-        c = helper.create_tmp_variable(dtype)
-        h = helper.create_tmp_variable(dtype)
-
-        helper.append_op(
-            type='lstm_unit',
-            inputs={"X": after_fc,
-                    "C_prev": c_pre},
-            outputs={"C": c,
-                     "H": h},
-            attrs={"forget_bias": forget_bias})
-
-        rnn.update_memory(c_pre, c)
-        rnn.output(h)
-
-    return rnn()
-
-
 def lod_rank_table(x, level=0, main_program=None):
     """
     This function creates an operator for creating a LOD_RANK_TABLE
@@ -1331,72 +483,6 @@ def array_to_lod_tensor(x, table, main_program=None, startup_program=None):
     return tmp
 
 
-def fill_constant(shape,
-                  dtype,
-                  value,
-                  out=None,
-                  main_program=None,
-                  startup_program=None):
-    """
-    This function creates a tensor , with shape as mentioned in the input and
-    specified dtype and fills this up with a constant value that
-    comes in the input. It also sets the stop_gradient to be True.
-    """
-    helper = LayerHelper("fill_constant", **locals())
-    if out is None:
-        out = helper.create_tmp_variable(dtype=dtype)
-    helper.append_op(
-        type='fill_constant',
-        inputs={},
-        outputs={'Out': [out]},
-        attrs={'shape': shape,
-               'dtype': out.dtype,
-               'value': float(value)})
-    out.stop_gradient = True
-    return out
-
-
-def fill_constant_batch_size_like(input,
-                                  shape,
-                                  dtype,
-                                  value,
-                                  input_dim_idx=0,
-                                  output_dim_idx=0,
-                                  main_program=None,
-                                  startup_program=None):
-    helper = LayerHelper("fill_constant_batch_size_like", **locals())
-    out = helper.create_tmp_variable(dtype=dtype)
-    helper.append_op(
-        type='fill_constant_batch_size_like',
-        inputs={'Input': input},
-        outputs={'Out': [out]},
-        attrs={
-            'shape': shape,
-            'dtype': out.dtype,
-            'value': float(value),
-            'input_dim_idx': input_dim_idx,
-            'output_dim_idx': output_dim_idx
-        })
-    out.stop_gradient = True
-    return out
-
-
-def ones(shape, dtype, main_program=None):
-    """
-    This function performs the same function as fill_constant() declared above
-    with the constant value being 1.0.
-    """
-    return fill_constant(value=1.0, **locals())
-
-
-def zeros(shape, dtype, main_program=None):
-    """
-    This function performs the same function as fill_constant() declared above
-    with the constant value being 0.0.
-    """
-    return fill_constant(value=0.0, **locals())
-
-
 def increment(x,
               value=1.0,
               in_place=True,
@@ -1508,95 +594,6 @@ def array_length(array, main_program=None):
     return tmp
 
 
-def conv2d_transpose(input,
-                     num_filters,
-                     output_size=None,
-                     filter_size=None,
-                     padding=None,
-                     stride=None,
-                     param_attr=None,
-                     main_program=None,
-                     startup_program=None):
-    """
-    The transpose of conv2d layer.
-
-    This layer is also known as deconvolution layer.
-
-    Args:
-        input(Variable): The input image with [N, C, H, W] format.
-        num_filters(int): The number of filter. It is as same as the output
-            image channel.
-        output_size(int|tuple|None): The output image size. If output size is a
-            tuple, it must contain two integers, (image_H, image_W). This
-            parameter only works when filter_size is None.
-        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.
-        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.
-        param_attr: Parameter Attribute.
-        main_program(Program): the main program
-        startup_program(Program): the startup program
-
-    Returns:
-        Variable: Output image.
-    """
-    helper = LayerHelper("conv2d_transpose", **locals())
-    if not isinstance(input, Variable):
-        raise TypeError("Input of conv2d_transpose must be Variable")
-    input_channel = input.shape[1]
-
-    op_attr = dict()
-
-    if isinstance(padding, int):
-        op_attr['paddings'] = [padding, padding]
-    elif padding is not None:
-        op_attr['paddings'] = padding
-
-    if isinstance(stride, int):
-        op_attr['strides'] = stride
-    elif stride is not None:
-        op_attr['strides'] = stride
-
-    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]
-
-        padding = op_attr.get('paddings', [0, 0])
-        stride = op_attr.get('strides', [1, 1])
-
-        h_in = input.shape[2]
-        w_in = input.shape[3]
-        filter_size_h = output_size[0] - \
-            (h_in - 1) * stride[0] + 2 * padding[0]
-        filter_size_w = output_size[1] - \
-            (w_in - 1) * stride[1] + 2 * padding[1]
-        filter_size = [filter_size_h, filter_size_w]
-    elif isinstance(filter_size, int):
-        filter_size = [filter_size, filter_size]
-
-    filter_shape = [input_channel, num_filters] + filter_size
-    img_filter = helper.create_parameter(
-        dtype=input.dtype, shape=filter_shape, attr=helper.param_attr)
-
-    out = helper.create_tmp_variable(dtype=input.dtype)
-    helper.append_op(
-        type='conv2d_transpose',
-        inputs={'Input': [input],
-                'Filter': [img_filter]},
-        outputs={'Output': out},
-        attrs=op_attr)
-
-    return out
-
-
 class ConditionalBlockGuard(BlockGuard):
     def __init__(self, block):
         if not isinstance(block, ConditionalBlock):

python/paddle/v2/fluid/layers/io.py

+from .. import core
+from ..layer_helper import LayerHelper
+
+__all__ = ['data']
+
+
+def data(name,
+         shape,
+         append_batch_size=True,
+         dtype='float32',
+         lod_level=0,
+         type=core.VarDesc.VarType.LOD_TENSOR,
+         main_program=None,
+         startup_program=None,
+         stop_gradient=True):
+    """
+    Data Layer.
+
+    Args:
+       name: The name/alias of the function
+       shape: Tuple declaring the shape.
+       append_batch_size: Whether or not to append the data as a batch.
+       dtype: The type of data : float32, float_16, int etc
+       type: The output type. By default it is LOD_TENSOR.
+       lod_level(int): The LoD Level. 0 means the input data is not a sequence.
+       main_program: Name of the main program that calls this
+       startup_program: Name of the startup program
+       stop_gradient: A boolean that mentions whether gradient should flow.
+
+    This function takes in input and based on whether data has
+    to be returned back as a minibatch, it creates the global variable using
+    the helper functions. The global variables can be accessed by all the
+    following operations and layers in the graph.
+
+    All the input variables of this function are passed in as local variables
+    to the LayerHelper constructor.
+
+    """
+    helper = LayerHelper('data', **locals())
+    shape = list(shape)
+    for i in xrange(len(shape)):
+        if shape[i] is None:
+            shape[i] = -1
+            append_batch_size = False
+        elif shape[i] < 0:
+            append_batch_size = False
+
+    if append_batch_size:
+        shape = [-1] + shape  # append batch size as -1
+
+    return helper.create_global_variable(
+        name=name,
+        shape=shape,
+        dtype=dtype,
+        type=type,
+        stop_gradient=stop_gradient,
+        lod_level=lod_level)

python/paddle/v2/fluid/layers/nn.py

+"""
+All layers just related to the neural network.
+"""
+
+from ..layer_helper import LayerHelper
+from ..initializer import Normal, Constant
+from ..framework import Variable
+
+__all__ = [
+    'fc', 'embedding', 'dynamic_lstm', 'gru_unit', 'linear_chain_crf',
+    'crf_decoding', 'cos_sim', 'cross_entropy', 'square_error_cost', 'accuracy',
+    'chunk_eval', 'sequence_conv', 'conv2d', 'sequence_pool', 'pool2d',
+    'batch_norm', 'beam_search_decode', 'conv2d_transpose'
+]
+
+
+def fc(input,
+       size,
+       num_flatten_dims=1,
+       param_attr=None,
+       bias_attr=None,
+       act=None,
+       name=None,
+       main_program=None,
+       startup_program=None):
+    """
+    Fully Connected Layer.
+
+    Args:
+       input: The input tensor to the function
+       size: The size of the layer
+       num_flatten_dims: Number of columns in input
+       param_attr: The parameters/weights to the FC Layer
+       param_initializer: Initializer used for the weight/parameter. If None, XavierInitializer() is used
+       bias_attr: The bias parameter for the FC layer
+       bias_initializer: Initializer used for the bias. If None, then ConstantInitializer() is used
+       act: Activation to be applied to the output of FC layer
+       name: Name/alias of the function
+       main_program: Name of the main program that calls this
+       startup_program: Name of the startup program
+
+    This function can take in multiple inputs and performs the Fully Connected
+    function (linear transformation) on top of each of them.
+    So for input x, the output will be : Wx + b. Where W is the parameter,
+    b the bias and x is the input.
+
+    The function also applies an activation (non-linearity) on top of the
+    output, if activation is passed in the input.
+
+    All the input variables of this function are passed in as local variables
+    to the LayerHelper constructor.
+
+    """
+    helper = LayerHelper('fc', **locals())
+
+    dtype = helper.input_dtype()
+
+    mul_results = []
+    for input_var, param_attr in helper.iter_inputs_and_params():
+        input_shape = input_var.shape
+        param_shape = [
+            reduce(lambda a, b: a * b, input_shape[num_flatten_dims:], 1)
+        ] + [size]
+        w = helper.create_parameter(
+            attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False)
+        tmp = helper.create_tmp_variable(dtype)
+        helper.append_op(
+            type="mul",
+            inputs={
+                "X": input_var,
+                "Y": w,
+            },
+            outputs={"Out": tmp},
+            attrs={'x_num_col_dims': num_flatten_dims,
+                   'y_num_col_dims': 1})
+        mul_results.append(tmp)
+
+    # sum
+    if len(mul_results) == 1:
+        pre_bias = mul_results[0]
+    else:
+        pre_bias = helper.create_tmp_variable(dtype)
+        helper.append_op(
+            type="sum", inputs={"X": mul_results}, outputs={"Out": pre_bias})
+    # add bias
+    pre_activation = helper.append_bias_op(pre_bias)
+    # add activation
+    return helper.append_activation(pre_activation)
+
+
+def embedding(input,
+              size,
+              is_sparse=False,
+              param_attr=None,
+              dtype='float32',
+              main_program=None,
+              startup_program=None):
+    """
+    Embedding Layer.
+
+    Args:
+       param_initializer:
+       input: The input to the function
+       size: The size of the layer
+       is_sparse: A flag that decleares whether the input is sparse
+       param_attr: Parameters for this layer
+       dtype: The type of data : float32, float_16, int etc
+       main_program: Name of the main program that calls this
+       startup_program: Name of the startup program
+
+    This function can take in the input (which is a vector of IDs) and
+    performs a lookup in the lookup_table using these IDs, to result into
+    the embedding of each ID in the input.
+
+    All the input variables of this function are passed in as local variables
+    to the LayerHelper constructor.
+
+    """
+
+    helper = LayerHelper('embedding', **locals())
+    w = helper.create_parameter(
+        attr=helper.param_attr, shape=size, dtype=dtype, is_bias=False)
+    tmp = helper.create_tmp_variable(dtype)
+    helper.append_op(
+        type='lookup_table',
+        inputs={'Ids': input,
+                'W': w},
+        outputs={'Out': tmp},
+        attrs={'is_sparse': is_sparse})
+    return tmp
+
+
+# TODO(qijun): expose H0 and C0
+def dynamic_lstm(input,
+                 size,
+                 param_attr=None,
+                 bias_attr=None,
+                 use_peepholes=True,
+                 is_reverse=False,
+                 gate_activation='sigmoid',
+                 cell_activation='tanh',
+                 candidate_activation='tanh',
+                 dtype='float32',
+                 main_program=None,
+                 startup_program=None):
+    helper = LayerHelper('lstm', **locals())
+    size = size / 4
+    weight = helper.create_parameter(
+        attr=helper.param_attr, shape=[size, 4 * size], dtype=dtype)
+    bias_size = [1, 7 * size]
+    if not use_peepholes:
+        bias_size[1] = 4 * size
+    bias = helper.create_parameter(
+        attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
+
+    hidden = helper.create_tmp_variable(dtype)
+    cell = helper.create_tmp_variable(dtype)
+    batch_gate = helper.create_tmp_variable(dtype)
+    batch_cell_pre_act = helper.create_tmp_variable(dtype)
+
+    helper.append_op(
+        type='lstm',
+        inputs={'Input': input,
+                'Weight': weight,
+                'Bias': bias},
+        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
+
+
+def gru_unit(input,
+             hidden,
+             size,
+             weight=None,
+             bias=None,
+             activation='tanh',
+             gate_activation='sigmoid',
+             main_program=None,
+             startup_program=None):
+    """
+    GRUUnit Operator implements partial calculations of the GRU unit as following:
+
+    $$
+    update \ gate: u_t = actGate(xu_t + W_u * h_{t-1} + b_u) \\
+    reset \ gate: r_t = actGate(xr_t + W_r * h_{t-1} + b_r)  \\
+    output \ candidate: {h}_t = actNode(xc_t + W_c * dot(r_t, h_{t-1}) + b_c) \\
+    output: h_t = dot((1 - u_t), h_{t-1}) + dot(u_t, {h}_t)
+    $$
+
+    which is same as one time step of GRU Operator.
+
+    @note To implement the complete GRU unit, fully-connected operator must be
+    used before to feed xu, xr and xc as the Input of GRUUnit operator.
+
+    TODO(ChunweiYan) add more document here
+    """
+    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()
+    size = size / 3
+
+    # create weight
+    if weight is None:
+        weight = helper.create_parameter(
+            attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
+
+    # create bias
+    if bias is None:
+        bias_size = [1, 3 * size]
+        bias = helper.create_parameter(
+            attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
+
+    gate = helper.create_tmp_variable(dtype)
+    reset_hidden_pre = helper.create_tmp_variable(dtype)
+    updated_hidden = helper.create_tmp_variable(dtype)
+
+    helper.append_op(
+        type='gru_unit',
+        inputs={'Input': input,
+                'HiddenPrev': hidden,
+                'Weight': weight},
+        outputs={
+            'Gate': gate,
+            'ResetHiddenPrev': reset_hidden_pre,
+            'Hidden': updated_hidden,
+        },
+        attrs={
+            'activation': 0,
+            'gate_activation': 1,
+        })
+
+    return updated_hidden, reset_hidden_pre, gate
+
+
+def linear_chain_crf(input,
+                     label,
+                     param_attr=None,
+                     main_program=None,
+                     startup_program=None):
+    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())
+    alpha = helper.create_tmp_variable(dtype=helper.input_dtype())
+    emission_exps = helper.create_tmp_variable(dtype=helper.input_dtype())
+    transition_exps = helper.create_tmp_variable(dtype=helper.input_dtype())
+    log_likelihood = helper.create_tmp_variable(dtype=helper.input_dtype())
+    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
+
+
+def crf_decoding(input,
+                 param_attr,
+                 label=None,
+                 main_program=None,
+                 startup_program=None):
+    helper = LayerHelper('crf_decoding', **locals())
+    transition = helper.get_parameter(param_attr.name)
+    viterbi_path = helper.create_tmp_variable(dtype=helper.input_dtype())
+    helper.append_op(
+        type='crf_decoding',
+        inputs={"Emission": [input],
+                "Transition": transition,
+                "Label": label},
+        outputs={"ViterbiPath": [viterbi_path]})
+
+    return viterbi_path
+
+
+def cos_sim(X, Y, **kwargs):
+    """
+    This function performs the cosine similarity between two tensors
+    X and Y and returns that as the output.
+    """
+    helper = LayerHelper('cos_sim', **kwargs)
+    out = helper.create_tmp_variable(dtype=X.dtype)
+    xnorm = helper.create_tmp_variable(dtype=X.dtype)
+    ynorm = helper.create_tmp_variable(dtype=X.dtype)
+    helper.append_op(
+        type='cos_sim',
+        inputs={'X': [X],
+                'Y': [Y]},
+        outputs={'Out': [out],
+                 'XNorm': [xnorm],
+                 'YNorm': [ynorm]})
+    return out
+
+
+def cross_entropy(input, label, **kwargs):
+    """
+    This function computes cross_entropy using the input and label.
+    """
+    helper = LayerHelper('cross_entropy', **kwargs)
+    out = helper.create_tmp_variable(dtype=input.dtype)
+    helper.append_op(
+        type='cross_entropy',
+        inputs={'X': [input],
+                'Label': [label]},
+        outputs={'Y': [out]},
+        attrs=kwargs)
+    return out
+
+
+def square_error_cost(input, label, **kwargs):
+    """
+    This functions returns the squared error cost using the input and label.
+    The output is appending the op to do the above.
+    """
+    helper = LayerHelper('square_error_cost', **kwargs)
+    minus_out = helper.create_tmp_variable(dtype=input.dtype)
+    helper.append_op(
+        type='elementwise_sub',
+        inputs={'X': [input],
+                'Y': [label]},
+        outputs={'Out': [minus_out]})
+
+    square_out = helper.create_tmp_variable(dtype=input.dtype)
+    helper.append_op(
+        type='square', inputs={'X': [minus_out]}, outputs={'Y': [square_out]})
+    return square_out
+
+
+def accuracy(input, label, k=1, correct=None, total=None, **kwargs):
+    """
+    This function computes the accuracy using the input and label.
+    The output is the top_k inputs and their indices.
+    """
+    helper = LayerHelper("accuracy", **kwargs)
+    topk_out = helper.create_tmp_variable(dtype=input.dtype)
+    topk_indices = helper.create_tmp_variable(dtype="int64")
+    helper.append_op(
+        type="top_k",
+        inputs={"X": [input]},
+        outputs={"Out": [topk_out],
+                 "Indices": [topk_indices]},
+        attrs={"k": k})
+    acc_out = helper.create_tmp_variable(dtype="float32")
+    if correct is None:
+        correct = helper.create_tmp_variable(dtype="int64")
+    if total is None:
+        total = helper.create_tmp_variable(dtype="int64")
+    helper.append_op(
+        type="accuracy",
+        inputs={
+            "Out": [topk_out],
+            "Indices": [topk_indices],
+            "Label": [label]
+        },
+        outputs={
+            "Accuracy": [acc_out],
+            "Correct": [correct],
+            "Total": [total],
+        })
+    return acc_out
+
+
+def chunk_eval(input,
+               label,
+               chunk_scheme,
+               num_chunk_types,
+               excluded_chunk_types=None,
+               **kwargs):
+    """
+    This function computes the accuracy using the input and label.
+    The output is the top_k inputs and their indices.
+    """
+    helper = LayerHelper("chunk_eval", **kwargs)
+
+    # prepare output
+    precision = helper.create_tmp_variable(dtype="float32")
+    recall = helper.create_tmp_variable(dtype="float32")
+    f1_score = helper.create_tmp_variable(dtype="float32")
+
+    helper.append_op(
+        type="chunk_eval",
+        inputs={"Inference": [input],
+                "Label": [label]},
+        outputs={
+            "Precision": [precision],
+            "Recall": [recall],
+            "F1-Score": [f1_score]
+        },
+        attrs={
+            "num_chunk_types": num_chunk_types,
+            'chunk_scheme': chunk_scheme,
+            'excluded_chunk_types': excluded_chunk_types or []
+        })
+    return precision, recall, f1_score
+
+
+def sequence_conv(input,
+                  num_filters,
+                  filter_size=3,
+                  filter_stride=1,
+                  padding=None,
+                  bias_attr=None,
+                  param_attr=None,
+                  act=None,
+                  main_program=None,
+                  startup_program=None):
+    """
+    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.
+    """
+
+    # FIXME(dzh) : want to unify the argument of python layer
+    # function. So we ignore some unecessary attributes.
+    # such as, padding_trainable, context_start.
+
+    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)
+    pre_bias = helper.create_tmp_variable(dtype)
+
+    helper.append_op(
+        type='sequence_conv',
+        inputs={
+            'X': [input],
+            'Filter': [filter_param],
+        },
+        outputs={"Out": pre_bias},
+        attrs={
+            'contextStride': filter_stride,
+            'contextStart': -int(filter_size / 2),
+            'contextLength': filter_size
+        })
+    pre_act = helper.append_bias_op(pre_bias)
+    return helper.append_activation(pre_act)
+
+
+def conv2d(input,
+           num_filters,
+           filter_size,
+           stride=None,
+           padding=None,
+           groups=None,
+           param_attr=None,
+           bias_attr=None,
+           act=None,
+           name=None,
+           main_program=None,
+           startup_program=None):
+    """
+    This function creates the op for a 2-dimensional Convolution.
+    This is performed using the parameters of filters(size, dimensionality etc)
+    , stride and other configurations for a Convolution operation.
+    This funciton can also append an activation on top of the
+    conv-2d output, if mentioned in the input parameters.
+    """
+
+    if stride is None:
+        stride = [1, 1]
+    helper = LayerHelper('conv2d', **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.")
+        num_filter_channels = num_channels / groups
+
+    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]
+
+    input_shape = input.shape
+    filter_shape = [num_filters, num_filter_channels] + filter_size
+
+    def _get_default_param_initializer():
+        std = (2.0 / (filter_size[0]**2 * num_channels))**0.5
+        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())
+
+    pre_bias = helper.create_tmp_variable(dtype)
+
+    helper.append_op(
+        type='conv2d_cudnn',
+        inputs={
+            'Input': input,
+            'Filter': filter_param,
+        },
+        outputs={"Output": pre_bias},
+        attrs={'strides': stride,
+               'paddings': padding,
+               'groups': groups})
+
+    pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
+
+    return helper.append_activation(pre_act)
+
+
+def sequence_pool(input, pool_type, **kwargs):
+    """
+    This function add the operator for sequence pooling.
+    This is applied on top of the input using pool_type mentioned
+    in the parameters.
+    """
+    helper = LayerHelper('sequence_pool', input=input, **kwargs)
+    dtype = helper.input_dtype()
+    pool_out = helper.create_tmp_variable(dtype)
+    max_index = helper.create_tmp_variable(dtype)
+
+    helper.append_op(
+        type="sequence_pool",
+        inputs={"X": input},
+        outputs={"Out": pool_out,
+                 "MaxIndex": max_index},
+        attrs={"pooltype": pool_type.upper()})
+
+    return pool_out
+
+
+def pool2d(input,
+           pool_size,
+           pool_type,
+           pool_stride=None,
+           pool_padding=None,
+           global_pooling=False,
+           main_program=None,
+           startup_program=None):
+    """
+    This function adds the operator for pooling in 2 dimensions, using the
+    pooling configurations mentioned in input parameters.
+    """
+    if pool_padding is None:
+        pool_padding = [0, 0]
+    if pool_stride is None:
+        pool_stride = [1, 1]
+    if pool_type not in ["max", "avg"]:
+        raise ValueError(
+            "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.",
+            str(pool_type))
+    if isinstance(pool_size, int):
+        pool_size = [pool_size, pool_size]
+    if isinstance(pool_stride, int):
+        pool_stride = [pool_stride, pool_stride]
+    if isinstance(pool_padding, int):
+        pool_padding = [pool_padding, pool_padding]
+
+    helper = LayerHelper('pool2d', **locals())
+    dtype = helper.input_dtype()
+    pool_out = helper.create_tmp_variable(dtype)
+
+    helper.append_op(
+        type="pool2d",
+        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
+        })
+
+    return pool_out
+
+
+def batch_norm(input,
+               act=None,
+               is_test=False,
+               momentum=0.9,
+               epsilon=1e-05,
+               param_attr=None,
+               bias_attr=None,
+               data_layout='NCHW',
+               main_program=None,
+               startup_program=None):
+    """
+    This function helps create an operator to implement
+    the BatchNorm layer using the configurations from the input parameters.
+    """
+    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(
+        attr=helper.param_attr, shape=param_shape, dtype=dtype, is_bias=True)
+
+    mean = helper.create_global_variable(
+        dtype=input.dtype, shape=param_shape, persistable=True)
+    helper.set_variable_initializer(var=mean, initializer=Constant(0.0))
+
+    variance = helper.create_global_variable(
+        dtype=input.dtype, shape=param_shape, persistable=True)
+    helper.set_variable_initializer(var=variance, initializer=Constant(1.0))
+
+    # create output
+    # mean and mean_out share the same memory
+    mean_out = mean
+    # variance and variance out share the same memory
+    variance_out = variance
+    saved_mean = helper.create_tmp_variable(dtype)
+    saved_variance = helper.create_tmp_variable(dtype)
+
+    batch_norm_out = helper.create_tmp_variable(dtype)
+
+    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
+        },
+        attrs={"momentum": momentum,
+               "epsilon": epsilon,
+               "is_test": is_test})
+
+    return helper.append_activation(batch_norm_out)
+
+
+def beam_search_decode(ids, scores, main_program=None, startup_program=None):
+    helper = LayerHelper('beam_search_decode', **locals())
+    sentence_ids = helper.create_tmp_variable(dtype=ids.dtype)
+    sentence_scores = helper.create_tmp_variable(dtype=ids.dtype)
+
+    helper.append_op(
+        type="beam_search_decode",
+        inputs={"Ids": ids,
+                "Scores": scores},
+        outputs={
+            "SentenceIds": sentence_ids,
+            "SentenceScores": sentence_scores
+        })
+
+    return sentence_ids, sentence_scores
+
+
+def conv2d_transpose(input,
+                     num_filters,
+                     output_size=None,
+                     filter_size=None,
+                     padding=None,
+                     stride=None,
+                     param_attr=None,
+                     main_program=None,
+                     startup_program=None):
+    """
+    The transpose of conv2d layer.
+
+    This layer is also known as deconvolution layer.
+
+    Args:
+        input(Variable): The input image with [N, C, H, W] format.
+        num_filters(int): The number of filter. It is as same as the output
+            image channel.
+        output_size(int|tuple|None): The output image size. If output size is a
+            tuple, it must contain two integers, (image_H, image_W). This
+            parameter only works when filter_size is None.
+        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.
+        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.
+        param_attr: Parameter Attribute.
+        main_program(Program): the main program
+        startup_program(Program): the startup program
+
+    Returns:
+        Variable: Output image.
+    """
+    helper = LayerHelper("conv2d_transpose", **locals())
+    if not isinstance(input, Variable):
+        raise TypeError("Input of conv2d_transpose must be Variable")
+    input_channel = input.shape[1]
+
+    op_attr = dict()
+
+    if isinstance(padding, int):
+        op_attr['paddings'] = [padding, padding]
+    elif padding is not None:
+        op_attr['paddings'] = padding
+
+    if isinstance(stride, int):
+        op_attr['strides'] = stride
+    elif stride is not None:
+        op_attr['strides'] = stride
+
+    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]
+
+        padding = op_attr.get('paddings', [0, 0])
+        stride = op_attr.get('strides', [1, 1])
+
+        h_in = input.shape[2]
+        w_in = input.shape[3]
+        filter_size_h = output_size[0] - \
+                        (h_in - 1) * stride[0] + 2 * padding[0]
+        filter_size_w = output_size[1] - \
+                        (w_in - 1) * stride[1] + 2 * padding[1]
+        filter_size = [filter_size_h, filter_size_w]
+    elif isinstance(filter_size, int):
+        filter_size = [filter_size, filter_size]
+
+    filter_shape = [input_channel, num_filters] + filter_size
+    img_filter = helper.create_parameter(
+        dtype=input.dtype, shape=filter_shape, attr=helper.param_attr)
+
+    out = helper.create_tmp_variable(dtype=input.dtype)
+    helper.append_op(
+        type='conv2d_transpose',
+        inputs={'Input': [input],
+                'Filter': [img_filter]},
+        outputs={'Output': out},
+        attrs=op_attr)
+
+    return out

python/paddle/v2/fluid/layers/ops.py

+from ..registry import register_layer
+__all__ = [
+    'mean', 'mul', 'dropout', 'reshape', 'sigmoid', 'scale', 'transpose',
+    'sigmoid_cross_entropy_with_logits', 'elementwise_add', 'elementwise_div',
+    'elementwise_sub', 'elementwise_mul', 'clip', 'abs'
+]
+
+for _OP in set(__all__):
+    globals()[_OP] = register_layer(_OP)

python/paddle/v2/fluid/layers/tensor.py

+from ..layer_helper import LayerHelper
+
+__all__ = [
+    'create_tensor', 'cast', 'concat', 'sums', 'assign',
+    'fill_constant_batch_size_like', 'fill_constant', 'ones', 'zeros'
+]
+
+
+def create_tensor(dtype, name=None, main_program=None, startup_program=None):
+    helper = LayerHelper("create_tensor", **locals())
+    return helper.create_variable(name=helper.name, dtype=dtype)
+
+
+def cast(x, dtype, main_program=None):
+    """
+    This function takes in the input with input_dtype
+    and casts it to the output_dtype as the output.
+    """
+    helper = LayerHelper('cast', **locals())
+    out = helper.create_tmp_variable(dtype=dtype)
+    helper.append_op(
+        type='cast',
+        inputs={'X': [x]},
+        outputs={'Out': [out]},
+        attrs={'in_dtype': x.dtype,
+               'out_dtype': out.dtype})
+    return out
+
+
+def concat(input, axis, main_program=None, startup_program=None):
+    """
+    This function concats the input along the axis mentioned
+    and returns that as the output.
+    """
+    helper = LayerHelper('concat', **locals())
+    out = helper.create_tmp_variable(dtype=helper.input_dtype())
+    helper.append_op(
+        type='concat',
+        inputs={'X': input},
+        outputs={'Out': [out]},
+        attrs={'axis': axis})
+    return out
+
+
+def sums(input, out=None, main_program=None, startup_program=None):
+    """
+    This function takes in the input and performs the sum operation on it
+    and returns that as the output.
+    """
+    helper = LayerHelper('sum', **locals())
+    if out is None:
+        out = helper.create_tmp_variable(dtype=helper.input_dtype())
+    helper.append_op(type='sum', inputs={'X': input}, outputs={'Out': out})
+    return out
+
+
+def assign(input, output, main_program=None, startup_program=None):
+    helper = LayerHelper('assign', **locals())
+    helper.append_op(
+        type='scale',
+        inputs={'X': [input]},
+        outputs={'Out': [output]},
+        attrs={'scale': 1.0})
+    return output
+
+
+def fill_constant(shape,
+                  dtype,
+                  value,
+                  out=None,
+                  main_program=None,
+                  startup_program=None):
+    """
+    This function creates a tensor , with shape as mentioned in the input and
+    specified dtype and fills this up with a constant value that
+    comes in the input. It also sets the stop_gradient to be True.
+    """
+    helper = LayerHelper("fill_constant", **locals())
+    if out is None:
+        out = helper.create_tmp_variable(dtype=dtype)
+    helper.append_op(
+        type='fill_constant',
+        inputs={},
+        outputs={'Out': [out]},
+        attrs={'shape': shape,
+               'dtype': out.dtype,
+               'value': float(value)})
+    out.stop_gradient = True
+    return out
+
+
+def fill_constant_batch_size_like(input,
+                                  shape,
+                                  dtype,
+                                  value,
+                                  input_dim_idx=0,
+                                  output_dim_idx=0,
+                                  main_program=None,
+                                  startup_program=None):
+    helper = LayerHelper("fill_constant_batch_size_like", **locals())
+    out = helper.create_tmp_variable(dtype=dtype)
+    helper.append_op(
+        type='fill_constant_batch_size_like',
+        inputs={'Input': input},
+        outputs={'Out': [out]},
+        attrs={
+            'shape': shape,
+            'dtype': out.dtype,
+            'value': float(value),
+            'input_dim_idx': input_dim_idx,
+            'output_dim_idx': output_dim_idx
+        })
+    out.stop_gradient = True
+    return out
+
+
+def ones(shape, dtype, main_program=None):
+    """
+    This function performs the same function as fill_constant() declared above
+    with the constant value being 1.0.
+    """
+    return fill_constant(value=1.0, **locals())
+
+
+def zeros(shape, dtype, main_program=None):
+    """
+    This function performs the same function as fill_constant() declared above
+    with the constant value being 0.0.
+    """
+    return fill_constant(value=0.0, **locals())

python/paddle/v2/fluid/tests/book/test_image_classification_train.py

 from __future__ import print_function
 
-import numpy as np
+import sys
+
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
-import sys
 
 
 def resnet_cifar10(input, depth=32):

python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py

 import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
+from paddle.v2.fluid.layer_helper import LayerHelper
+
+
+def lstm(x,
+         c_pre_init,
+         hidden_dim,
+         forget_bias=None,
+         main_program=None,
+         startup_program=None):
+    """
+    This function helps create an operator for the LSTM (Long Short Term
+    Memory) cell that can be used inside an RNN.
+    """
+    helper = LayerHelper('lstm_unit', **locals())
+    rnn = fluid.layers.StaticRNN()
+    with rnn.step():
+        c_pre = rnn.memory(init=c_pre_init)
+        x_t = rnn.step_input(x)
+
+        before_fc = fluid.layers.concat(
+            input=[x_t, c_pre],
+            axis=1,
+            main_program=main_program,
+            startup_program=startup_program)
+        after_fc = fluid.layers.fc(input=before_fc,
+                                   size=hidden_dim * 4,
+                                   main_program=main_program,
+                                   startup_program=startup_program)
+
+        dtype = x.dtype
+        c = helper.create_tmp_variable(dtype)
+        h = helper.create_tmp_variable(dtype)
+
+        helper.append_op(
+            type='lstm_unit',
+            inputs={"X": after_fc,
+                    "C_prev": c_pre},
+            outputs={"C": c,
+                     "H": h},
+            attrs={"forget_bias": forget_bias})
+
+        rnn.update_memory(c_pre, c)
+        rnn.output(h)
+
+    return rnn()
 
 
 def lstm_net(dict_dim, class_dim=2, emb_dim=32, seq_len=80, batch_size=50):
@@ -23,8 +68,7 @@ def lstm_net(dict_dim, class_dim=2, emb_dim=32, seq_len=80, batch_size=50):
     c_pre_init = fluid.layers.fill_constant(
         dtype=emb.dtype, shape=[batch_size, emb_dim], value=0.0)
     c_pre_init.stop_gradient = False
-    layer_1_out = fluid.layers.lstm(
-        emb, c_pre_init=c_pre_init, hidden_dim=emb_dim)
+    layer_1_out = lstm(emb, c_pre_init=c_pre_init, hidden_dim=emb_dim)
     layer_1_out = fluid.layers.transpose(x=layer_1_out, axis=[1, 0, 2])
 
     prediction = fluid.layers.fc(input=layer_1_out,

python/setup.py.in

@@ -68,6 +68,7 @@ packages=['paddle',
           'paddle.v2.plot',
           'paddle.v2.fluid',
           'paddle.v2.fluid.proto',
+          'paddle.v2.fluid.layers',
           'py_paddle']
 
 with open('@PADDLE_SOURCE_DIR@/python/requirements.txt') as f: