diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
index b59f2f657d7cd0232be87ee2699e00f08ac3dc5b..5de1c18950a3236faa91edabf0119b590b22c6d9 100644
--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -888,7 +888,7 @@ def mixed_layer(size=0,
     :type size: int
     :param input: The input of this layer. It is an optional parameter. If set,
                   then this function will just return layer's name.
-    :param act: Activation Type. LinearActivation is the default.
+    :param act: Activation Type. LinearActivation is the default activation.
     :type act: BaseActivation
     :param bias_attr: The bias attribute. If the parameter is set to False or an object
                       whose type is not ParameterAttribute, no bias is defined. If the
@@ -1030,7 +1030,7 @@ def fc_layer(input,
     :type input: LayerOutput | list | tuple
     :param size: The layer dimension.
     :type size: int
-    :param act: Activation Type. TanhActivation is the default.
+    :param act: Activation Type. TanhActivation is the default activation.
     :type act: BaseActivation
     :param param_attr: The Parameter Attribute|list.
     :type param_attr: ParameterAttribute
@@ -1527,7 +1527,7 @@ def lstmemory(input,
     :type input: LayerOutput
     :param reverse: is sequence process reversed or not.
     :type reverse: bool
-    :param act: Activation type. TanhActivation is the default. :math:`h_t`
+    :param act: Activation type. TanhActivation is the default activation.
     :type act: BaseActivation
     :param gate_act: gate activation type, SigmoidActivation by default.
     :type gate_act: BaseActivation
@@ -1920,7 +1920,7 @@ def repeat_layer(input,
                           False for treating input as column vector and repeating
                           in the row direction.
     :type as_row_vector: bool
-    :param act: Activation type. IdentityActivation is the default.
+    :param act: Activation type. IdentityActivation is the default activation.
     :type act: BaseActivation
     :type name: basestring
     :param layer_attr: extra layer attributes.
@@ -1974,7 +1974,7 @@ def seq_reshape_layer(input,
     :type reshape_size: int
     :param name: The name of this layer. It is optional.
     :type name: basestring
-    :param act: Activation type. IdentityActivation is the default.
+    :param act: Activation type. IdentityActivation is the default activation.
     :type act: BaseActivation
     :param layer_attr: extra layer attributes.
     :type layer_attr: ExtraLayerAttribute.
@@ -2487,7 +2487,7 @@ def img_conv_layer(input,
                         shape will be (filter_size, filter_size_y).
     :type filter_size_y: int | None
     :param num_filters: Each filter group's number of filter
-    :param act: Activation type. ReluActivation is the default.
+    :param act: Activation type. ReluActivation is the default activation.
     :type act: BaseActivation
     :param groups: Group size of filters.
     :type groups: int
@@ -3255,7 +3255,7 @@ def addto_layer(input, act=None, name=None, bias_attr=None, layer_attr=None):
     :param input: Input layers. It could be a LayerOutput or list/tuple of
                  LayerOutput.
     :type input: LayerOutput | list | tuple
-    :param act: Activation Type. LinearActivation is the default.
+    :param act: Activation Type. LinearActivation is the default activation.
     :type act: BaseActivation
     :param bias_attr: The bias attribute. If the parameter is set to False or an object
                       whose type is not ParameterAttribute, no bias is defined. If the
@@ -3313,7 +3313,7 @@ def concat_layer(input, act=None, name=None, layer_attr=None, bias_attr=None):
     :type name: basestring
     :param input: input layers or projections
     :type input: list | tuple | collections.Sequence
-    :param act: Activation type. IdentityActivation is the default.
+    :param act: Activation type. IdentityActivation is the default activation.
     :type act: BaseActivation
     :param layer_attr: Extra Layer Attribute.
     :type layer_attr: ExtraLayerAttribute
@@ -3408,7 +3408,7 @@ def seq_concat_layer(a, b, act=None, name=None, layer_attr=None,
     :type a: LayerOutput
     :param b: input sequence layer
     :type b: LayerOutput
-    :param act: Activation type. IdentityActivation is the default.
+    :param act: Activation type. IdentityActivation is the default activation.
     :type act: BaseActivation
     :param layer_attr: Extra Layer Attribute.
     :type layer_attr: ExtraLayerAttribute
@@ -3574,30 +3574,32 @@ def lstm_step_layer(input,
         ...
 
 
-    This layer has two outputs. Default output is :math:`h_t`. The other
-    output is :math:`o_t`, whose name is 'state' and can use
+    This layer has two outputs. The default output is :math:`h_t`. The other
+    output is :math:`o_t`, whose name is 'state' and users can use
     :code:`get_output_layer` to extract this output.
 
     :param name: The name of this layer. It is optional.
     :type name: basestring
-    :param size: Layer's size. NOTE: lstm layer's size, should be equal to
-                 :code:`input.size/4`, and should be equal to
-                 :code:`state.size`.
+    :param size: The dimension of this layer's output, which must be
+                 equal to the dimension of the state.
     :type size: int
-    :param input: input layer. :math:`Wx_t + Wh_{t-1}`
+    :param input: The input of this layer.
     :type input: LayerOutput
-    :param state: State Layer. :math:`c_{t-1}`
+    :param state: The state of the LSTM unit.
     :type state: LayerOutput
-    :param act: Activation type. TanhActivation is the default.
+    :param act: Activation type. TanhActivation is the default activation.
     :type act: BaseActivation
-    :param gate_act: Gate Activation Type. SigmoidActivation is the default.
+    :param gate_act: Activation type of the gate. SigmoidActivation is the
+                     default activation.
     :type gate_act: BaseActivation
-    :param state_act: State Activation Type. TanhActivation is the default.
+    :param state_act: Activation type of the state. TanhActivation is the
+                      default activation.
     :type state_act: BaseActivation
-    :param bias_attr: The parameter attribute for bias. If this parameter is
-                     set to True or None, the bias is initialized to zero.
-    :type bias_attr: ParameterAttribute | None | True
-    :param layer_attr: layer's extra attribute.
+    :param bias_attr: The bias attribute. If the parameter is set to False or an object
+                      whose type is not ParameterAttribute, no bias is defined. If the
+                      parameter is set to True, the bias is initialized to zero.
+    :type bias_attr: ParameterAttribute | None | bool | Any
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for details.
     :type layer_attr: ExtraLayerAttribute
     :return: LayerOutput object.
     :rtype: LayerOutput
@@ -3642,23 +3644,31 @@ def gru_step_layer(input,
                    layer_attr=None):
     """
 
-    :param input:
+    :param input: The input of this layer, whose dimension can be divided by 3.
     :type input: LayerOutput
-    :param output_mem:
-    :param size:
-    :param act:
+    :param output_mem: A memory which memorizes the output of this layer at previous
+                       time step.
+    :type output_mem: LayerOutput
+    :param size: The dimension of this layer's output. If it is not set or set to None,
+                 it will be set to one-third of the dimension of the input automatically.
+    :type size: int
+    :param act: Activation type of this layer's output. TanhActivation
+                is the default activation.
     :type act: BaseActivation
     :param name: The name of this layer. It is optional.
-    :param gate_act: Activation type of this layer's two gates. Default is Sigmoid.
+    :type name: basestring
+    :param gate_act: Activation type of this layer's two gates. SigmoidActivation is
+                     the default activation.
     :type gate_act: BaseActivation
     :param bias_attr: The parameter attribute for bias. If this parameter is set to
                       False or an object whose type is not ParameterAttribute, no bias
                       is defined. If this parameter is set to True,
                       the bias is initialized to zero.
     :type bias_attr: ParameterAttribute | None | bool | Any
-    :param param_attr: the parameter_attribute for transforming the output_mem
-                       from previous step.
-    :param layer_attr:
+    :param param_attr: The parameter attribute. See ParameterAttribute for details.
+    :type param_attr: ParameterAttribute
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for details.
+    :type layer_attr: ExtraLayerAttribute
     :return: LayerOutput object.
     :rtype: LayerOutput
     """
@@ -3703,25 +3713,34 @@ def gru_step_naive_layer(input,
                          param_attr=None,
                          layer_attr=None):
     """
-    GRU Step Layer, but using MixedLayer to generate. It support ERROR_CLIPPING
+    GRU Step Layer, which is realized using PaddlePaddle API. It supports ERROR_CLIPPING
     and DROPOUT.
 
-    :param input:
-    :param output_mem:
-    :param size:
+    :param input: The input of this layer, whose dimensionality can be divided by 3.
+    :param output_mem: A memory which memorizes the output of this layer at previous
+                       time step.
+    :type output_mem: LayerOutput
+    :param size: The dimension of this layer's output. If it is not set or set to None,
+                 it will be set to one-third of the dimension of the input automatically.
+    :type size: int
     :param name: The name of this layer. It is optional.
-    :param act:
+    :type name: basestring
+    :param act: Activation type of this layer's output. TanhActivation
+                is the default activation.
     :type act: BaseActivation
-    :param gate_act: Activation type of this layer's two gates. Default is Sigmoid.
+    :param gate_act: Activation type of this layer's two gates. SigmoidActivation
+                     is the default activation.
     :type gate_act: BaseActivation
     :param bias_attr: The parameter attribute for bias. If this parameter is set to
                       False or an object whose type is not ParameterAttribute, no bias
                       is defined. If this parameter is set to True,
                       the bias is initialized to zero.
     :type bias_attr: ParameterAttribute | None | bool | Any
-    :param param_attr:
-    :param layer_attr:
-    :return:
+    :param param_attr: The parameter attribute. See ParameterAttribute for details.
+    :type param_attr: ParameterAttribute
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for details.
+    :type layer_attr: ExtraLayerAttribute
+    :return: LayerOutput object.
     :rtype: LayerOutput
     """
     if input.size % 3 != 0:
@@ -3783,12 +3802,13 @@ def get_output_layer(input, arg_name, name=None, layer_attr=None):
 
     :param name: The name of this layer. It is optional.
     :type name: basestring
-    :param input: get output layer's input. And this layer should contains
+    :param input: The input layer. And this layer should contain
                    multiple outputs.
     :type input: LayerOutput
-    :param arg_name: Output name from input.
+    :param arg_name: The name of the output to be extracted from the input layer.
     :type arg_name: basestring
-    :param layer_attr: Layer's extra attribute.
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
     :return: LayerOutput object.
     :rtype: LayerOutput
     """
@@ -3845,18 +3865,20 @@ def recurrent_layer(input,
 
     :param input: The input of this layer.
     :type input: LayerOutput
-    :param act: Activation type. TanhActivation is the default.
+    :param act: Activation type. TanhActivation is the default activation.
     :type act: BaseActivation
     :param bias_attr: The parameter attribute for bias. If this parameter is set to 
                       False or an object whose type is not ParameterAttribute,
                       no bias is defined. If the parameter is set to True,
                       the bias is initialized to zero.
     :type bias_attr: ParameterAttribute | None | bool | Any
-    :param param_attr: parameter attribute.
+    :param param_attr: The parameter attribute. See ParameterAttribute for
+                       details.
     :type param_attr: ParameterAttribute
     :param name: The name of this layer. It is optional.
     :type name: basestring
-    :param layer_attr: Layer Attribute.
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
     :type layer_attr: ExtraLayerAttribute
     :return: LayerOutput object.
     :rtype: LayerOutput
@@ -3881,7 +3903,7 @@ def recurrent_layer(input,
 class StaticInput(object):
     """
     StaticInput is only used in recurrent_group which defines a read-only memory
-    that can be a sequence or non-sequence.
+    and can be a sequence or non-sequence.
     :param size: DEPRECATED
     :param is_seq: DEPRECATED
     """
@@ -3914,8 +3936,8 @@ def recurrent_group(step, input, reverse=False, name=None, targetInlink=None):
     Recurrent layer group is an extremely flexible recurrent unit in
     PaddlePaddle. As long as the user defines the calculation done within a
     time step, PaddlePaddle will iterate such a recurrent calculation over
-    sequence input. This is extremely usefull for attention based model, or
-    Neural Turning Machine like models.
+    sequence input. This is useful for attention-based models, or Neural
+    Turning Machine like models.
 
     The basic usage (time steps) is:
 
@@ -3937,18 +3959,17 @@ def recurrent_group(step, input, reverse=False, name=None, targetInlink=None):
                   demo/seqToseq/seqToseq_net.py
     - sequence steps: paddle/gserver/tests/sequence_nest_layer_group.conf
 
-    :param step: recurrent one time step function.The input of this function is
-                 input of the group. The return of this function will be
-                 recurrent group's return value.
+    :param step: A step function which takes the input of recurrent_group as its own
+                 input and returns values as recurrent_group's output every time step.
 
-                 The recurrent group scatter a sequence into time steps. And
-                 for each time step, will invoke step function, and return
-                 a time step result. Then gather each time step of output into
+                 The recurrent group scatters a sequence into time steps. And
+                 for each time step, it will invoke step function, and return
+                 a time step result. Then gather outputs of each time step into
                  layer group's output.
 
     :type step: callable
 
-    :param name: recurrent_group's name.
+    :param name: The recurrent_group's name. It is optional.
     :type name: basestring
 
     :param input: Input links array.
@@ -3956,11 +3977,11 @@ def recurrent_group(step, input, reverse=False, name=None, targetInlink=None):
                   LayerOutput will be scattered into time steps.
                   SubsequenceInput will be scattered into sequence steps.
                   StaticInput will be imported to each time step, and doesn't change
-                  through time. It's a mechanism to access layer outside step function.
+                  over time. It's a mechanism to access layer outside step function.
 
     :type input: LayerOutput | StaticInput | SubsequenceInput | list | tuple
 
-    :param reverse: If reverse is set true, the recurrent unit will process the
+    :param reverse: If reverse is set to True, the recurrent unit will process the
                     input sequence in a reverse order.
     :type reverse: bool
 
@@ -4095,7 +4116,8 @@ def maxid_layer(input, name=None, layer_attr=None):
     :type input: LayerOutput
     :param name: The name of this layer. It is optional.
     :type name: basestring
-    :param layer_attr: extra layer attributes.
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
     :type layer_attr: ExtraLayerAttribute.
     :return: LayerOutput object.
     :rtype: LayerOutput
@@ -4128,11 +4150,12 @@ def out_prod_layer(input1, input2, name=None, layer_attr=None):
 
     :param name: The name of this layer. It is optional.
     :type name: basestring
-    :param input1: The first input layer name.
+    :param input1: The first input layer.
     :type input: LayerOutput
-    :param input2: The second input layer name.
+    :param input2: The second input layer.
     :type input2: LayerOutput
-    :param layer_attr: extra layer attributes.
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
     :type layer_attr: ExtraLayerAttribute.
     :return: LayerOutput object.
     :rtype: LayerOutput
@@ -4171,9 +4194,10 @@ def eos_layer(input, eos_id, name=None, layer_attr=None):
     :type name: basestring
     :param input: The input of this layer.
     :type input: LayerOutput
-    :param eos_id: end id of sequence
+    :param eos_id: End id of sequence
     :type eos_id: int
-    :param layer_attr: extra layer attributes.
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
     :type layer_attr: ExtraLayerAttribute.
     :return: LayerOutput object.
     :rtype: LayerOutput
@@ -4234,8 +4258,9 @@ def beam_search(step,
     - machine translation : demo/seqToseq/translation/gen.conf \
                             demo/seqToseq/seqToseq_net.py
 
-    :param name: Name of the recurrent unit that generates sequences.
-    :type name: base string
+    :param name: The name of the recurrent unit that is responsible for
+                 generating sequences. It is optional.
+    :type name: basestring
     :param step: A callable function that defines the calculation in a time
                  step, and it is applied to sequences with arbitrary length by
                  sharing a same set of weights.
@@ -4360,16 +4385,18 @@ def square_error_cost(input,
 
     :param name: The name of this layer. It is optional.
     :type name: basestring
-    :param input: Network prediction.
+    :param input: The first input layer.
     :type input: LayerOutput
-    :param label: Data label.
+    :param label: The input label.
     :type label: LayerOutput
-    :param weight: The weight affects the cost, namely the scale of cost.
-                   It is an optional argument.
+    :param weight: The weight layer defines a weight for each sample in the
+                   mini-batch. It is optional.
     :type weight: LayerOutput
-    :param coeff: The coefficient affects the gradient in the backward.
+    :param coeff: The weight of the gradient in the back propagation.
+                  1.0 is the default value.
     :type coeff: float
-    :param layer_attr: layer's extra attribute.
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
     :type layer_attr: ExtraLayerAttribute
     :return: LayerOutput object.
     :rtype: LayerOutput
@@ -4402,17 +4429,20 @@ def classification_cost(input,
 
     :param name: The name of this layer. It is optional.
     :type name: basestring
-    :param input: input layer name. network output.
+    :param input: The first input layer.
     :type input: LayerOutput
-    :param label: label layer name. data_layer often.
+    :param label: The input label.
     :type label: LayerOutput
-    :param weight: The weight affects the cost, namely the scale of cost.
-                   It is an optional argument.
+    :param weight: The weight layer defines a weight for each sample in the
+                   mini-batch. It is optional.
     :type weight: LayerOutput
-    :param evaluator: Evaluator method.
-    :param layer_attr: layer's extra attribute.
+    :param evaluator: Evaluator method. classification_error_evaluator is the default.
+    :type evaluator: Evaluator method
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
     :type layer_attr: ExtraLayerAttribute
-    :param coeff: The coefficient affects the gradient in the backward.
+    :param coeff: The weight of the gradient in the back propagation.
+                  1.0 is the default value.
     :type coeff: float
     :return: LayerOutput object.
     :rtype: LayerOutput
@@ -4465,7 +4495,7 @@ def conv_operator(img,
     Different from img_conv_layer, conv_op is an Operator, which can be used
     in mixed_layer. And conv_op takes two inputs to perform convolution.
     The first input is the image and the second is filter kernel. It only
-    support GPU mode.
+    supports GPU mode.
 
     The example usage is:
 
@@ -4477,27 +4507,31 @@ def conv_operator(img,
                           num_filters=64,
                           num_channels=64)
 
-    :param img: input image
+    :param img: The input image.
     :type img: LayerOutput
-    :param filter: input filter
+    :param filter: The input filter.
     :type filter: LayerOutput
-    :param filter_size: The x dimension of a filter kernel.
+    :param filter_size: The dimension of the filter kernel on the x axis.
     :type filter_size: int
-    :param filter_size_y: The y dimension of a filter kernel. Since
-                        PaddlePaddle now supports rectangular filters,
-                        the filter's shape can be (filter_size, filter_size_y).
+    :param filter_size_y: The dimension of the filter kernel on the y axis.
+                          If the parameter is not set or set to None, it will
+                          set to 'filter_size' automatically.
     :type filter_size_y: int
-    :param num_filters: channel of output data.
+    :param num_filters: The number of the output channels.
     :type num_filters: int
-    :param num_channels: channel of input data.
+    :param num_channels: The number of the input channels. If the parameter is not set
+                         or set to None, it will be automatically set to the channel
+                         number of the 'img'.
     :type num_channels: int
-    :param stride: The x dimension of the stride.
+    :param stride: The stride on the x axis.
     :type stride: int
-    :param stride_y: The y dimension of the stride.
+    :param stride_y: The stride on the y axis. If the parameter is not set or
+                     set to None, it will be set to 'stride' automatically.
     :type stride_y: int
-    :param padding: The x dimension of padding.
+    :param padding: The padding size on the x axis.
     :type padding: int
-    :param padding_y: The y dimension of padding.
+    :param padding_y: The padding size on the y axis. If the parameter is not set
+                      or set to None, it will be set to 'padding' automatically.
     :type padding_y: int
     :return: A ConvOperator Object.
     :rtype: ConvOperator
@@ -4548,9 +4582,9 @@ def conv_projection(input,
                     param_attr=None,
                     trans=False):
     """
-    Different from img_conv_layer and conv_op, conv_projection is an Projection,
-    which can be used in mixed_layer and conat_layer. It use cudnn to implement
-    conv and only support GPU mode.
+    Different from img_conv_layer and conv_op, conv_projection is a Projection,
+    which can be used in mixed_layer and concat_layer. It uses cudnn to implement
+    convolution and only supports GPU mode.
 
     The example usage is:
 
@@ -4563,32 +4597,45 @@ def conv_projection(input,
 
     :param input: The input of this layer.
     :type input: LayerOutput
-    :param filter_size: The x dimension of a filter kernel.
-    :type filter_size: int
-    :param filter_size_y: The y dimension of a filter kernel. Since
-                          PaddlePaddle now supports rectangular filters,
-                          the filter's shape can be (filter_size, filter_size_y).
+    :param filter_size: The dimensions of the filter kernel. If the parameter is
+                        set to one integer, the two dimensions on x and y axises
+                        will be same when filter_size_y is not set. If it is set
+                        to a list, the first element indicates the dimension on
+                        the x axis, and the second is used to specify the dimension
+                        on the y axis when filter_size is not provided.
+    :type filter_size: int | tuple | list
+    :param filter_size_y: The dimension of the filter kernel on the y axis. If the parameter
+                          is not set, it will be set automatically according to filter_size.
     :type filter_size_y: int
-    :param num_filters: channel of output data.
+    :param num_filters: The number of filters.
     :type num_filters: int
-    :param num_channels: channel of input data.
+    :param num_channels: The number of the input channels.
     :type num_channels: int
-    :param stride: The x dimension of the stride.
-    :type stride: int
-    :param stride_y: The y dimension of the stride.
+    :param stride: The strides. If the parameter is set to one integer, the strides
+                   on x and y axises will be same when stride_y is not set. If it is
+                   set to a list, the first element indicates the stride on the x axis,
+                   and the second is used to specify the stride on the y axis when
+                   stride_y is not provided.
+    :type stride: int | tuple | list
+    :param stride_y: The stride on the y axis.
     :type stride_y: int
-    :param padding: The x dimension of padding.
-    :type padding: int
-    :param padding_y: The y dimension of padding.
+    :param padding: The padding sizes. If the parameter is set to one integer, the padding
+                    sizes on x and y axises will be same when padding_y is not set. If it
+                    is set to a list, the first element indicates the padding size on the
+                    x axis, and the second is used to specify the padding size on the y axis
+                    when padding_y is not provided.
+    :type padding: int | tuple | list
+    :param padding_y: The padding size on the y axis.
     :type padding_y: int
     :param groups: The group number.
     :type groups: int
-    :param param_attr: Convolution param attribute. None means default attribute
+    :param param_attr: The parameter attribute of the convolution. See ParameterAttribute for
+                       details.
     :type param_attr: ParameterAttribute
-    :param trans: whether it is convTrans or conv
+    :param trans: Whether it is ConvTransProjection or ConvProjection
     :type trans: bool
-    :return: A DotMulProjection Object.
-    :rtype: DotMulProjection
+    :return: A Projection Object.
+    :rtype: ConvTransProjection | ConvProjection
     """
     if num_channels is None:
         assert input.num_filters is not None
@@ -4653,13 +4700,13 @@ def pad_layer(input,
               layer_attr=None):
     """
     This operation pads zeros to the input data according to pad_c,pad_h
-    and pad_w. pad_c, pad_h, pad_w specifies the which dimension and size
-    of padding. And the input data shape is NCHW.
+    and pad_w. pad_c, pad_h, pad_w specify the size in the corresponding
+    dimension. And the input data shape is NCHW.
 
-    For example, pad_c=[2,3] means padding 2 zeros before the
-    input data and 3 zeros after the input data in channel dimension.
-    pad_h means padding zeros in height dimension. pad_w means padding zeros
-    in width dimension.
+    For example, pad_c=[2,3] means padding 2 zeros before the input data
+    and 3 zeros after the input data in the channel dimension. pad_h means
+    padding zeros in the height dimension. pad_w means padding zeros in the
+    width dimension.
 
     For example,
 
@@ -4696,13 +4743,14 @@ def pad_layer(input,
 
     :param input: The input of this layer.
     :type input: LayerOutput
-    :param pad_c: padding size in channel dimension.
+    :param pad_c: The padding size in the channel dimension.
     :type pad_c: list | None
-    :param pad_h: padding size in height dimension.
+    :param pad_h: The padding size in the height dimension.
     :type pad_h: list | None
-    :param pad_w: padding size in width dimension.
+    :param pad_w: The padding size in the width dimension.
     :type pad_w: list | None
-    :param layer_attr: Extra Layer Attribute.
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
     :type layer_attr: ExtraLayerAttribute
     :param name: The name of this layer. It is optional.
     :type name: basestring
@@ -4751,7 +4799,7 @@ def pad_layer(input,
 @layer_support()
 def conv_shift_layer(a, b, name=None, layer_attr=None):
     """
-    This layer performs cyclic convolution for two input. For example:
+    This layer performs cyclic convolution on two inputs. For example:
       - a[in]: contains M elements.
       - b[in]: contains N elements (N should be odd).
       - c[out]: contains M elements.
@@ -4760,7 +4808,7 @@ def conv_shift_layer(a, b, name=None, layer_attr=None):
 
         c[i] = \sum_{j=-(N-1)/2}^{(N-1)/2}a_{i+j} * b_{j}
 
-    In this formular:
+    In this formula:
      - a's index is computed modulo M. When it is negative, then get item from
        the right side (which is the end of array) to the left.
      - b's index is computed modulo N. When it is negative, then get item from
@@ -4774,11 +4822,12 @@ def conv_shift_layer(a, b, name=None, layer_attr=None):
 
     :param name: The name of this layer. It is optional.
     :type name: basestring
-    :param a: Input layer a.
+    :param a: The first input of this layer.
     :type a: LayerOutput
-    :param b: input layer b.
+    :param b: The second input of this layer.
     :type b: LayerOutput
-    :param layer_attr: layer's extra attribute.
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
     :type layer_attr: ExtraLayerAttribute
     :return: LayerOutput object.
     :rtype: LayerOutput
@@ -4809,8 +4858,8 @@ def tensor_layer(a,
                  bias_attr=None,
                  layer_attr=None):
     """
-    This layer performs tensor operation for two input.
-    For example, each sample:
+    This layer performs tensor operation on two inputs.
+    For example:
 
     .. math::
        y_{i} = a * W_{i} * {b^\mathrm{T}}, i=0,1,...,K-1
@@ -4830,22 +4879,24 @@ def tensor_layer(a,
 
     :param name: The name of this layer. It is optional.
     :type name: basestring
-    :param a: Input layer a.
+    :param a: The first input of this layer.
     :type a: LayerOutput
-    :param b: input layer b.
+    :param b: The second input of this layer.
     :type b: LayerOutput
-    :param size: the layer dimension.
-    :type size: int.
-    :param act: Activation type. LinearActivation is the default.
+    :param size: The dimension of this layer.
+    :type size: int
+    :param act: Activation type. LinearActivation is the default activation.
     :type act: BaseActivation
-    :param param_attr: The Parameter Attribute.
+    :param param_attr: The parameter attribute. See ParameterAttribute for
+                       details.
     :type param_attr: ParameterAttribute
     :param bias_attr: The parameter attribute for bias. If this parameter is set to
                       False or an object whose type is not ParameterAttribute,
                       no bias is defined. If this parameter is set to True,
                       the bias is initialized to zero.
     :type bias_attr: ParameterAttribute | None | bool | Any
-    :param layer_attr: Extra Layer config.
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
     :type layer_attr: ExtraLayerAttribute | None
     :return: LayerOutput object.
     :rtype: LayerOutput
@@ -4881,7 +4932,7 @@ def selective_fc_layer(input,
                        layer_attr=None):
     """
     Selectived fully connected layer. Different from fc_layer, the output
-    of this layer maybe sparse. It requires an additional input to indicate
+    of this layer can be sparse. It requires an additional input to indicate
     several selected columns for output. If the selected columns is not
     specified, selective_fc_layer acts exactly like fc_layer.
 
@@ -4895,22 +4946,34 @@ def selective_fc_layer(input,
     :type name: basestring
     :param input: The input of this layer.
     :type input: LayerOutput | list | tuple
-    :param select: The select layer. The output of select layer should be a
-                   sparse binary matrix, and treat as the mask of selective fc.
-                   If is None, acts exactly like fc_layer.
+    :param select: The layer to select columns to output. It should be a sparse
+                   binary matrix, and is treated as the mask of selective fc. If
+                   it is not set or set to None, selective_fc_layer acts exactly
+                   like fc_layer.
     :type select: LayerOutput
-    :param size: The layer dimension.
+    :param size: The dimension of this layer, which should be equal to that of
+                 the layer 'select'.
     :type size: int
-    :param act: Activation type. TanhActivation is the default.
+    :param act: Activation type. TanhActivation is the default activation.
     :type act: BaseActivation
-    :param param_attr: The Parameter Attribute.
+    :param pass_generation: The flag which indicates whether it is during generation.
+    :type pass_generation: bool
+    :param has_selected_colums: The flag which indicates whether the parameter 'select'
+                                has been set. True is the default.
+    :type has_selected_colums: bool
+    :param mul_ratio: A ratio helps to judge how sparse the output is and determine
+                      the computation method for speed consideration.
+    :type mul_ratio: float
+    :param param_attr: The parameter attribute. See ParameterAttribute for
+                       details.
     :type param_attr: ParameterAttribute
     :param bias_attr: The parameter attribute for bias. If this parameter is set to
                       False or an object whose type is not ParameterAttribute,
                       no bias is defined. If this parameter is set to True,
                       the bias is initialized to zero.
     :type bias_attr: ParameterAttribute | None | bool | Any
-    :param layer_attr: Extra Layer config.
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
     :type layer_attr: ExtraLayerAttribute | None
     :return: LayerOutput object.
     :rtype: LayerOutput
@@ -4961,7 +5024,7 @@ def selective_fc_layer(input,
 @layer_support()
 def sampling_id_layer(input, name=None, layer_attr=None):
     """
-    A layer for sampling id from multinomial distribution from the input layer.
+    A layer for sampling id from a multinomial distribution from the input layer.
     Sampling one id for one sample.
 
     The simple usage is:
@@ -4974,8 +5037,9 @@ def sampling_id_layer(input, name=None, layer_attr=None):
     :type input: LayerOutput
     :param name: The name of this layer. It is optional.
     :type name: basestring
-    :param layer_attr: Extra Layer config.
-    :type layer_attr: ExtraLayerAttribute | None
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
+    :type layer_attr: ExtraLayerAttribute
     :return: LayerOutput object.
     :rtype: LayerOutput
     """
@@ -4996,8 +5060,7 @@ def slope_intercept_layer(input,
                           intercept=0.0,
                           layer_attr=None):
     """
-    This layer for applying a slope and an intercept to the input
-    element-wise. There is no activation and weight.
+    This layer for applying a slope and an intercept to the input.
 
     ..  math::
         y = slope * x + intercept
@@ -5012,12 +5075,13 @@ def slope_intercept_layer(input,
     :type input: LayerOutput
     :param name: The name of this layer. It is optional.
     :type name: basestring
-    :param slope: the scale factor.
-    :type slope: float.
-    :param intercept: the offset.
-    :type intercept: float.
-    :param layer_attr: Extra Layer config.
-    :type layer_attr: ExtraLayerAttribute | None
+    :param slope: The scale factor.
+    :type slope: float
+    :param intercept: The offset.
+    :type intercept: float
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
+    :type layer_attr: ExtraLayerAttribute
     :return: LayerOutput object.
     :rtype: LayerOutput
     """
@@ -5072,12 +5136,13 @@ def linear_comb_layer(weights, vectors, size=None, name=None, layer_attr=None):
     :type weights: LayerOutput
     :param vectors: The vector layer.
     :type vectors: LayerOutput
-    :param size: the dimension of this layer.
+    :param size: The dimension of this layer.
     :type size: int
     :param name: The name of this layer. It is optional.
     :type name: basestring
-    :param layer_attr: Extra Layer config.
-    :type layer_attr: ExtraLayerAttribute | None
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
+    :type layer_attr: ExtraLayerAttribute
     :return: LayerOutput object.
     :rtype: LayerOutput
     """
@@ -5124,11 +5189,11 @@ def block_expand_layer(input,
 
        outputW = 1 + (2 * padding_x + imgSizeW - block_x + stride_x - 1) / stride_x
 
-    The expand method is the same with ExpandConvLayer, but saved the transposed
+    The expanding method is the same with ExpandConvLayer, but saved the transposed
     value. After expanding, output.sequenceStartPositions will store timeline.
-    The number of time steps are outputH * outputW and the dimension of each
+    The number of time steps is outputH * outputW and the dimension of each
     time step is block_y * block_x * num_channels. This layer can be used after
-    convolution neural network, and before recurrent neural network.
+    convolutional neural network, and before recurrent neural network.
 
     The simple usage is:
 
@@ -5143,8 +5208,10 @@ def block_expand_layer(input,
 
     :param input: The input of this layer.
     :type input: LayerOutput
-    :param num_channels: The channel number of input layer.
-    :type num_channels: int | None
+    :param num_channels: The number of input channels. If the parameter is not set or
+                         set to None, its actual value will be automatically set to
+                         the channels number of the input.
+    :type num_channels: int
     :param block_x: The width of sub block.
     :type block_x: int
     :param block_y: The width of sub block.
@@ -5158,9 +5225,10 @@ def block_expand_layer(input,
     :param padding_y: The padding size in vertical direction.
     :type padding_y: int
     :param name: The name of this layer. It is optional.
-    :type name: None | basestring.
-    :param layer_attr: Extra Layer config.
-    :type layer_attr: ExtraLayerAttribute | None
+    :type name: basestring.
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
+    :type layer_attr: ExtraLayerAttribute
     :return: LayerOutput object.
     :rtype: LayerOutput
     """
@@ -5190,12 +5258,19 @@ def block_expand_layer(input,
 @layer_support()
 def maxout_layer(input, groups, num_channels=None, name=None, layer_attr=None):
     """
-    A layer to do max out on conv layer output.
-      - Input: output of a conv layer.
-      - Output: feature map size same as input. Channel is (input channel) / groups.
+    A layer to do max out on convolutional layer output.
+      - Input: the output of a convolutional layer.
+      - Output: feature map size same as the input's, and its channel number is
+        (input channel) / groups.
 
     So groups should be larger than 1, and the num of channels should be able
-    to devided by groups.
+    to be devided by groups.
+
+    Reference:
+        Maxout Networks
+        http://www.jmlr.org/proceedings/papers/v28/goodfellow13.pdf
+        Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
+        https://arxiv.org/pdf/1312.6082v4.pdf
 
     .. math::
        y_{si+j} = \max_k x_{gsi + sk + j}
@@ -5205,12 +5280,6 @@ def maxout_layer(input, groups, num_channels=None, name=None, layer_attr=None):
        0 \le j < s
        0 \le k < groups
 
-    Please refer to Paper:
-      - Maxout Networks: http://www.jmlr.org/proceedings/papers/v28/goodfellow13.pdf
-      - Multi-digit Number Recognition from Street View \
-        Imagery using Deep Convolutional Neural Networks: \
-        https://arxiv.org/pdf/1312.6082v4.pdf
-
     The simple usage is:
 
     .. code-block:: python
@@ -5221,14 +5290,16 @@ def maxout_layer(input, groups, num_channels=None, name=None, layer_attr=None):
 
     :param input: The input of this layer.
     :type input: LayerOutput
-    :param num_channels: The channel number of input layer. If None will be set
-                     automatically from previous output.
-    :type num_channels: int | None
+    :param num_channels: The number of input channels. If the parameter is not set or
+                         set to None, its actual value will be automatically set to
+                         the channels number of the input.
+    :type num_channels: int
     :param groups: The group number of input layer.
     :type groups: int
     :param name: The name of this layer. It is optional.
-    :type name: None | basestring.
-    :param layer_attr: Extra Layer attribute.
+    :type name: basestring
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
     :type layer_attr: ExtraLayerAttribute
     :return: LayerOutput object.
     :rtype: LayerOutput
@@ -5260,20 +5331,20 @@ def ctc_layer(input,
               layer_attr=None):
     """
     Connectionist Temporal Classification (CTC) is designed for temporal
-    classication task. That is, for sequence labeling problems where the
+    classication task. e.g. sequence labeling problems where the
     alignment between the inputs and the target labels is unknown.
 
-    More details can be found by referring to `Connectionist Temporal
-    Classification: Labelling Unsegmented Sequence Data with Recurrent
-    Neural Networks <http://machinelearning.wustl.edu/mlpapers/paper_files/
-    icml2006_GravesFGS06.pdf>`_
+    Reference:
+        Connectionist Temporal Classification: Labelling Unsegmented Sequence Data
+        with Recurrent Neural Networks
+        http://machinelearning.wustl.edu/mlpapers/paper_files/icml2006_GravesFGS06.pdf
 
     Note:
-        Considering the 'blank' label needed by CTC, you need to use
-        (num_classes + 1) as the input size. num_classes is the category number.
-        And the 'blank' is the last category index. So the size of 'input' layer, such as
-        fc_layer with softmax activation, should be num_classes + 1. The size of ctc_layer
-        should also be num_classes + 1.
+        Considering the 'blank' label needed by CTC, you need to use (num_classes + 1)
+        as the size of the input, where num_classes is the category number.
+        And the 'blank' is the last category index. So the size of 'input' layer (e.g.
+        fc_layer with softmax activation) should be (num_classes + 1). The size of
+        ctc_layer should also be (num_classes + 1).
 
     The example usage is:
 
@@ -5286,16 +5357,17 @@ def ctc_layer(input,
 
     :param input: The input of this layer.
     :type input: LayerOutput
-    :param label: The data layer of label with variable length.
+    :param label: The input label.
     :type label: LayerOutput
-    :param size: category numbers + 1.
+    :param size: The dimension of this layer, which must be equal to (category number + 1).
     :type size: int
     :param name: The name of this layer. It is optional.
-    :type name: basestring | None
-    :param norm_by_times: Whether to normalization by times. False by default.
+    :type name: basestring
+    :param norm_by_times: Whether to do normalization by times. False is the default.
     :type norm_by_times: bool
-    :param layer_attr: Extra Layer config.
-    :type layer_attr: ExtraLayerAttribute | None
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
+    :type layer_attr: ExtraLayerAttribute
     :return: LayerOutput object.
     :rtype: LayerOutput
     """
@@ -5336,20 +5408,19 @@ def warp_ctc_layer(input,
     building process, PaddlePaddle will clone the source codes, build and
     install it to :code:`third_party/install/warpctc` directory.
 
-    More details of CTC can be found by referring to `Connectionist Temporal
-    Classification: Labelling Unsegmented Sequence Data with Recurrent
-    Neural Networks <http://machinelearning.wustl.edu/mlpapers/paper_files/
-    icml2006_GravesFGS06.pdf>`_.
+    Reference:
+        Connectionist Temporal Classification: Labelling Unsegmented Sequence Data
+        with Recurrent Neural Networks
+        http://machinelearning.wustl.edu/mlpapers/paper_files/icml2006_GravesFGS06.pdf
 
     Note:
-        - Let num_classes represent the category number. Considering the 'blank'
-          label needed by CTC, you need to use (num_classes + 1) as the input size.
-          Thus, the size of both warp_ctc layer and 'input' layer should be set to
-          num_classes + 1.
+        - Let num_classes represents the category number. Considering the 'blank'
+          label needed by CTC, you need to use (num_classes + 1) as the size of
+          warp_ctc layer.
         - You can set 'blank' to any value ranged in [0, num_classes], which
-          should be consistent as that used in your labels.
+          should be consistent with those used in your labels.
         - As a native 'softmax' activation is interated to the warp-ctc library,
-          'linear' activation is expected instead in the 'input' layer.
+          'linear' activation is expected to be used instead in the 'input' layer.
 
     The example usage is:
 
@@ -5363,18 +5434,19 @@ def warp_ctc_layer(input,
 
     :param input: The input of this layer.
     :type input: LayerOutput
-    :param label: The data layer of label with variable length.
+    :param label: The input label.
     :type label: LayerOutput
-    :param size: category numbers + 1.
+    :param size: The dimension of this layer, which must be equal to (category number + 1).
     :type size: int
     :param name: The name of this layer. It is optional.
-    :type name: basestring | None
-    :param blank: the 'blank' label used in ctc
+    :type name: basestring
+    :param blank: The 'blank' label used in ctc.
     :type blank: int
-    :param norm_by_times: Whether to normalization by times. False by default.
+    :param norm_by_times: Whether to do normalization by times. False is the default.
     :type norm_by_times: bool
-    :param layer_attr: Extra Layer config.
-    :type layer_attr: ExtraLayerAttribute | None
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
+    :type layer_attr: ExtraLayerAttribute
     :return: LayerOutput object.
     :rtype: LayerOutput
     """
@@ -5420,23 +5492,26 @@ def crf_layer(input,
                       label=label,
                       size=label_dim)
 
-    :param input: The first input layer is the feature.
+    :param input: The first input layer.
     :type input: LayerOutput
-    :param label: The second input layer is label.
+    :param label: The input label.
     :type label: LayerOutput
     :param size: The category number.
     :type size: int
-    :param weight: The third layer is "weight" of each sample, which is an
-                  optional argument.
+    :param weight: The weight layer defines a weight for each sample in the
+                   mini-batch. It is optional.
     :type weight: LayerOutput
-    :param param_attr: Parameter attribute. None means default attribute
+    :param param_attr: The parameter attribute. See ParameterAttribute for
+                       details.
     :type param_attr: ParameterAttribute
     :param name: The name of this layer. It is optional.
-    :type name: None | basestring
-    :param coeff: The coefficient affects the gradient in the backward.
+    :type name: basestring
+    :param coeff: The weight of the gradient in the back propagation.
+                  1.0 is the default value.
     :type coeff: float
-    :param layer_attr: Extra Layer config.
-    :type layer_attr: ExtraLayerAttribute | None
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
+    :type layer_attr: ExtraLayerAttribute
     :return: LayerOutput object.
     :rtype: LayerOutput
     """
@@ -5482,9 +5557,9 @@ def crf_decoding_layer(input,
     """
     A layer for calculating the decoding sequence of sequential conditional
     random field model. The decoding sequence is stored in output.ids.
-    If a second input is provided, it is treated as the ground-truth label, and
-    this layer will also calculate error. output.value[i] is 1 for incorrect
-    decoding or 0 for correct decoding.
+    If the input 'label' is provided, it is treated as the ground-truth label, and
+    this layer will also calculate error. output.value[i] is 1 for an incorrect
+    decoding and 0 for the correct.
 
     The example usage is:
 
@@ -5495,16 +5570,18 @@ def crf_decoding_layer(input,
 
     :param input: The first input layer.
     :type input: LayerOutput
-    :param size: size of this layer.
+    :param size: The dimension of this layer.
     :type size: int
-    :param label: None or ground-truth label.
-    :type label: LayerOutput or None
-    :param param_attr: Parameter attribute. None means default attribute
+    :param label: The input label.
+    :type label: LayerOutput | None
+    :param param_attr: The parameter attribute. See ParameterAttribute for
+                       details.
     :type param_attr: ParameterAttribute
     :param name: The name of this layer. It is optional.
-    :type name: None | basestring
-    :param layer_attr: Extra Layer config.
-    :type layer_attr: ExtraLayerAttribute | None
+    :type name: basestring
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
+    :type layer_attr: ExtraLayerAttribute
     :return: LayerOutput object.
     :rtype: LayerOutput
     """
@@ -5551,8 +5628,7 @@ def nce_layer(input,
               bias_attr=None,
               layer_attr=None):
     """
-    Noise-contrastive estimation. This layer implements the method in the
-    following paper:
+    Noise-contrastive estimation.
 
     Reference:
         A fast and simple algorithm for training neural probabilistic language
@@ -5568,25 +5644,27 @@ def nce_layer(input,
 
     :param name: The name of this layer. It is optional.
     :type name: basestring
-    :param input: The input layers. It should be a LayerOutput or a list/tuple
-                  of LayerOutput.
+    :param input: The first input of this layer.
     :type input: LayerOutput | list | tuple | collections.Sequence
-    :param label: The ground truth.
+    :param label: The input label.
     :type label: LayerOutput
     :param weight: The weight layer defines a weight for each sample in the
-                   mini-batch. The default value is None.
+                   mini-batch. It is optional.
     :type weight: LayerOutput
-    :param num_classes: The class number.
+    :param num_classes: The number of classes.
     :type num_classes: int
-    :param param_attr: The parameter attributes.
-    :type param_attr: ParameterAttribute|list
-    :param num_neg_samples: The number of sampled negative labels. The default
-                            value is 10.
+    :param act: Activation type. SigmoidActivation is the default activation.
+    :type act: BaseActivation
+    :param param_attr: The parameter attribute. See ParameterAttribute for
+                       details.
+    :type param_attr: ParameterAttribute
+    :param num_neg_samples: The number of sampled negative labels. 10 is the
+                            default value.
     :type num_neg_samples: int
     :param neg_distribution: The discrete noisy distribution over the output
                              space from which num_neg_samples negative labels
                              are sampled. If this parameter is not set, a
-                             uniform distribution will be used. A user defined
+                             uniform distribution will be used. A user-defined
                              distribution is a list whose length must be equal
                              to the num_classes. Each member of the list defines
                              the probability of a class given input x.
@@ -5596,9 +5674,10 @@ def nce_layer(input,
                       no bias is defined. If this parameter is set to True,
                       the bias is initialized to zero.
     :type bias_attr: ParameterAttribute | None | bool | Any
-    :param layer_attr: Extra Layer Attribute.
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
     :type layer_attr: ExtraLayerAttribute
-    :return: The LayerOutput object.
+    :return: LayerOutput object.
     :rtype: LayerOutput
     """
     if isinstance(input, LayerOutput):
@@ -5665,11 +5744,11 @@ def rank_cost(left,
               coeff=1.0,
               layer_attr=None):
     """
-    A cost Layer for learning to rank using gradient descent. Details can refer
-    to `papers <http://research.microsoft.com/en-us/um/people/cburges/papers/
-    ICML_ranking.pdf>`_.
-    This layer contains at least three inputs. The weight is an optional
-    argument, which affects the cost.
+    A cost Layer for learning to rank using gradient descent.
+
+    Reference:
+        Learning to Rank using Gradient Descent
+        http://research.microsoft.com/en-us/um/people/cburges/papers/ICML_ranking.pdf
 
     .. math::
 
@@ -5700,14 +5779,16 @@ def rank_cost(left,
     :type right: LayerOutput
     :param label: Label is 1 or 0, means positive order and reverse order.
     :type label: LayerOutput
-    :param weight: The weight affects the cost, namely the scale of cost.
-                   It is an optional argument.
+    :param weight: The weight layer defines a weight for each sample in the
+                   mini-batch. It is optional.
     :type weight: LayerOutput
     :param name: The name of this layer. It is optional.
-    :type name: None | basestring
-    :param coeff: The coefficient affects the gradient in the backward.
+    :type name: basestring
+    :param coeff: The weight of the gradient in the back propagation.
+                  1.0 is the default value.
     :type coeff: float
-    :param layer_attr: Extra Layer Attribute.
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
     :type layer_attr: ExtraLayerAttribute
     :return: LayerOutput object.
     :rtype: LayerOutput
@@ -5752,25 +5833,25 @@ def lambda_cost(input,
                          NDCG_num=8,
                          max_sort_size=-1)
 
-    :param input: Samples of the same query should be loaded as sequence.
+    :param input: The first input of this layer, which is often a document
+                  samples list of the same query and whose type must be sequence.
     :type input: LayerOutput
-    :param score: The 2nd input. Score of each sample.
+    :param score: The scores of the samples.
     :type input: LayerOutput
     :param NDCG_num: The size of NDCG (Normalized Discounted Cumulative Gain),
                      e.g., 5 for NDCG@5. It must be less than or equal to the
-                     minimum size of lists.
+                     minimum size of the list.
     :type NDCG_num: int
-    :param max_sort_size: The size of partial sorting in calculating gradient.
-                          If max_sort_size = -1, then for each list, the
-                          algorithm will sort the entire list to get gradient.
-                          In other cases, max_sort_size must be greater than or
-                          equal to NDCG_num. And if max_sort_size is greater
-                          than the size of a list, the algorithm will sort the
-                          entire list of get gradient.
+    :param max_sort_size: The size of partial sorting in calculating gradient. If
+                          max_sort_size is equal to -1 or greater than the number
+                          of the samples in the list, then the algorithm will sort
+                          the entire list to compute the gradient. In other cases,
+                          max_sort_size must be greater than or equal to NDCG_num.
     :type max_sort_size: int
     :param name: The name of this layer. It is optional.
-    :type name: None | basestring
-    :param layer_attr: Extra Layer Attribute.
+    :type name: basestring
+    :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
+                       details.
     :type layer_attr: ExtraLayerAttribute
     :return: LayerOutput object.
     :rtype: LayerOutput
@@ -5815,11 +5896,10 @@ def cross_entropy(input,
     :param name: The name of this layer. It is optional.
     :type name: basestring
     :param coeff: The weight of the gradient in the back propagation.
-                  1.0 is the default.
+                  1.0 is the default value.
     :type coeff: float
-    :param weight: The cost of each sample is multiplied with each weight.
-                   The weight should be a layer with size=1. Note that gradient
-                   will not be calculated for weight.
+    :param weight: The weight layer defines a weight for each sample in the
+                   mini-batch. It is optional.
     :type weight: LayerOutout
     :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
                        details.
@@ -5864,7 +5944,7 @@ def cross_entropy_with_selfnorm(input,
     :param name: The name of this layer. It is optional.
     :type name: basestring
     :param coeff: The weight of the gradient in the back propagation.
-                  1.0 is the default.
+                  1.0 is the default value.
     :type coeff: float
     :param softmax_selfnorm_alpha: The scale factor affects the cost.
     :type softmax_selfnorm_alpha: float
@@ -5954,7 +6034,7 @@ def huber_regression_cost(input,
     :param delta: The difference between the observed and predicted values.
     :type delta: float
     :param coeff: The weight of the gradient in the back propagation.
-                  1.0 is the default.
+                  1.0 is the default value.
     :type coeff: float
     :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
                        details.
@@ -6004,7 +6084,7 @@ def huber_classification_cost(input,
     :param name: The name of this layer. It is optional.
     :type name: basestring
     :param coeff: The weight of the gradient in the back propagation.
-                  1.0 is the default.
+                  1.0 is the default value.
     :type coeff: float
     :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
                        details.
@@ -6049,7 +6129,7 @@ def multi_binary_label_cross_entropy(input,
     :param name: The name of this layer. It is optional.
     :type name: basestring
     :param coeff: The weight of the gradient in the back propagation.
-                  1.0 is the default.
+                  1.0 is the default value.
     :type coeff: float
     :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
                        details.
@@ -6220,7 +6300,7 @@ def smooth_l1_cost(input, label, name=None, coeff=1.0, layer_attr=None):
     :param name: The name of this layer. It is optional.
     :type name: basestring
     :param coeff: The weight of the gradient in the back propagation.
-                  1.0 is the default.
+                  1.0 is the default value.
     :type coeff: float
     :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
                        details.
@@ -6372,7 +6452,7 @@ def row_conv_layer(input,
     :param context_len: The context length equals the lookahead step number
                         plus one.
     :type context_len: int
-    :param act: Activation Type. LinearActivation is the default.
+    :param act: Activation Type. LinearActivation is the default activation.
     :type act: BaseActivation
     :param param_attr: The parameter attribute. See ParameterAttribute for
                        details.
@@ -6494,7 +6574,8 @@ def gated_unit_layer(input,
     :type input: LayerOutput
     :param size: The dimension of this layer's output.
     :type size: int
-    :param act: Activation type of the projection. LinearActivation is the default.
+    :param act: Activation type of the projection. LinearActivation is the default
+                activation.
     :type act: BaseActivation
     :param name: The name of this layer. It is optional.
     :type name: basestring
@@ -6875,7 +6956,7 @@ def img_conv3d_layer(input,
     :type filter_size: int | tuple | list
     :param num_filters: The number of filters in each group.
     :type num_filters: int
-    :param act: Activation type. ReluActivation is the default.
+    :param act: Activation type. ReluActivation is the default activation.
     :type act: BaseActivation
     :param groups: The number of the filter groups.
     :type groups: int
@@ -6890,8 +6971,8 @@ def img_conv3d_layer(input,
                       parameter is set to True, the bias is initialized to zero.
     :type bias_attr: ParameterAttribute | None | bool | Any
     :param num_channels: The number of input channels. If the parameter is not set or
-                         set to None,  its actual value will be automatically set to
-                         the channels number of the input .
+                         set to None, its actual value will be automatically set to
+                         the channels number of the input.
     :type num_channels: int
     :param param_attr: The parameter attribute of the convolution. See ParameterAttribute for
                        details.
@@ -7067,7 +7148,7 @@ def sub_seq_layer(input, offsets, sizes, act=None, bias_attr=None, name=None):
     :type offsets: LayerOutput
     :param sizes: The sizes of the sub-sequences, which should be sequence type.
     :type sizes: LayerOutput
-    :param act: Activation type, LinearActivation is the default.
+    :param act: Activation type, LinearActivation is the default activation.
     :type act: BaseActivation.
     :param bias_attr: The bias attribute. If the parameter is set to False or an object
                       whose type is not ParameterAttribute, no bias is defined. If the