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de2bc5da
编写于
11月 14, 2017
作者:
R
ranqiu
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电子邮件补丁
差异文件
Update annotations of layers.py according to comments
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1baeebc8
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1
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1 changed file
with
54 addition
and
50 deletion
+54
-50
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+54
-50
未找到文件。
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
de2bc5da
...
...
@@ -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
...
...
@@ -3253,7 +3253,7 @@ 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
...
...
@@ -3311,7 +3311,7 @@ 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
...
...
@@ -3406,7 +3406,7 @@ 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
...
...
@@ -3572,7 +3572,7 @@ def lstm_step_layer(input,
...
This layer has two outputs.
D
efault output is :math:`h_t`. The other
This layer has two outputs.
The d
efault 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.
...
...
@@ -3583,13 +3583,15 @@ def lstm_step_layer(input,
:type size: int
:param input: The input of this layer.
:type input: LayerOutput
:param state: The state of
a lstm
.
: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: Activation type of the gate. SigmoidActivation is the default.
:param gate_act: Activation type of the gate. SigmoidActivation is the
default activation.
:type gate_act: BaseActivation
:param state_act: Activation type of the state. 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 bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
...
...
@@ -3648,12 +3650,13 @@ def gru_step_layer(input,
: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.
Sigmoid
Activation
is the default.
:param act: Activation type of this layer's output.
Tanh
Activation
is the default
activation
.
:type act: BaseActivation
:param name: The name of this layer. It is optional.
:type name: basestring
: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 bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
...
...
@@ -3707,10 +3710,10 @@ def gru_step_naive_layer(input,
param_attr
=
None
,
layer_attr
=
None
):
"""
GRU Step Layer,
but using MixedLayer to generate
. It supports ERROR_CLIPPING
GRU Step Layer,
which is realized using PaddlePaddle API
. It supports ERROR_CLIPPING
and DROPOUT.
:param input: The input of this layer, whose dimension can be divided by 3.
:param input: The input of this layer, whose dimension
ality
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
...
...
@@ -3719,11 +3722,11 @@ def gru_step_naive_layer(input,
:type size: int
:param name: The name of this layer. It is optional.
:type name: basestring
:param act: Activation type of this layer's output.
Sigmoid
Activation
is the default.
:param act: Activation type of this layer's output.
Tanh
Activation
is the default
activation
.
:type act: BaseActivation
:param gate_act: Activation type of this layer's two gates.
Tanh
Activation
is the default.
:param gate_act: Activation type of this layer's two gates.
Sigmoid
Activation
is the default
activation
.
:type gate_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
...
...
@@ -3798,7 +3801,7 @@ def get_output_layer(input, arg_name, name=None, layer_attr=None):
:param input: The input layer. And this layer should contain
multiple outputs.
:type input: LayerOutput
:param arg_name: The name of the output
of
the input layer.
:param arg_name: The name of the output
to be extracted from
the input layer.
:type arg_name: basestring
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
...
...
@@ -3858,7 +3861,7 @@ 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 bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
...
...
@@ -3928,8 +3931,8 @@ 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 useful for attention-based models, 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:
...
...
@@ -3951,9 +3954,8 @@ 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: A step function which will be executed every step. The input
of this function is the 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 scatters a sequence into time steps. And
for each time step, it will invoke step function, and return
...
...
@@ -4251,8 +4253,8 @@ def beam_search(step,
- machine translation : demo/seqToseq/translation/gen.conf
\
demo/seqToseq/seqToseq_net.py
:param name: The name of the recurrent unit that
generates sequences.
It is optional.
: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
...
...
@@ -4386,7 +4388,7 @@ def square_error_cost(input,
mini-batch. It is optional.
:type weight: LayerOutput
: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.
...
...
@@ -4435,7 +4437,7 @@ def classification_cost(input,
details.
:type layer_attr: ExtraLayerAttribute
: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
:return: LayerOutput object.
:rtype: LayerOutput
...
...
@@ -4878,7 +4880,7 @@ def tensor_layer(a,
:type b: LayerOutput
:param size: The dimension of this layer.
:type size: 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.
...
...
@@ -4946,7 +4948,7 @@ def selective_fc_layer(input,
: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 pass_generation: The flag which indicates whether it is during generation.
:type pass_generation: bool
...
...
@@ -5498,7 +5500,7 @@ def crf_layer(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.
...
...
@@ -5644,12 +5646,13 @@ def nce_layer(input,
:type weight: LayerOutput
:param num_classes: The number of classes.
:type num_classes: int
:param act: Activation type. SigmoidActivation is the default.
: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.
: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
...
...
@@ -5775,7 +5778,7 @@ def rank_cost(left,
: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.
...
...
@@ -5886,7 +5889,7 @@ 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 weight layer defines a weight for each sample in the
mini-batch. It is optional.
...
...
@@ -5934,7 +5937,7 @@ 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
...
...
@@ -6024,7 +6027,7 @@ 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.
...
...
@@ -6074,7 +6077,7 @@ 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.
...
...
@@ -6119,7 +6122,7 @@ 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.
...
...
@@ -6290,7 +6293,7 @@ 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.
...
...
@@ -6442,7 +6445,7 @@ 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.
...
...
@@ -6564,7 +6567,8 @@ 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
...
...
@@ -6945,7 +6949,7 @@ 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
...
...
@@ -7137,7 +7141,7 @@ 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
...
...
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