diff --git a/develop/doc/api/v2/config/networks.html b/develop/doc/api/v2/config/networks.html index 079ee94299dd67c2970c93a51812c651ad02dfab..19af0f2bdd5c79573418aa1820b971902299d867 100644 --- a/develop/doc/api/v2/config/networks.html +++ b/develop/doc/api/v2/config/networks.html @@ -194,39 +194,39 @@
paddle.v2.networks.sequence_conv_pool(*args, **kwargs)
-

Text convolution pooling layers helper.

+

Text convolution pooling group.

Text input => Context Projection => FC Layer => Pooling => Output.

-
Parameters:
    -
  • name (basestring) – name of output layer(pooling layer name)
    • -
  • input (LayerOutput) – name of input layer
    • +
  • name (basestring) – group name.
    • +
  • input (LayerOutput) – input layer.
  • context_len (int) – context projection length. See context_projection’s document.
  • hidden_size (int) – FC Layer size.
    • -
  • context_start (int or None) – context projection length. See +
  • context_start (int|None) – context start position. See context_projection’s context_start.
    • -
  • pool_type (BasePoolingType.) – pooling layer type. See pooling_layer’s document.
    • +
  • pool_type (BasePoolingType) – pooling layer type. See pooling_layer’s document.
  • context_proj_layer_name (basestring) – context projection layer name. None if user don’t care.
    • -
  • context_proj_param_attr (ParameterAttribute or None.) – context projection parameter attribute. -None if user don’t care.
    • +
  • context_proj_param_attr (ParameterAttribute|None) – padding parameter attribute of context projection layer. +If false, it means padding always be zero.
  • fc_layer_name (basestring) – fc layer name. None if user don’t care.
    • -
  • fc_param_attr (ParameterAttribute or None) – fc layer parameter attribute. None if user don’t care.
    • -
  • fc_bias_attr (ParameterAttribute or None) – fc bias parameter attribute. False if no bias, +
  • fc_param_attr (ParameterAttribute|None) – fc layer parameter attribute. None if user don’t care.
    • +
  • fc_bias_attr (ParameterAttribute|False|None) – fc bias parameter attribute. False if no bias, +None if user don’t care.
    • +
  • fc_act (BaseActivation) – fc layer activation type. None means tanh.
    • +
  • pool_bias_attr (ParameterAttribute|False|None) – pooling layer bias attr. False if no bias. None if user don’t care.
    • -
  • fc_act (BaseActivation) – fc layer activation type. None means tanh
    • -
  • pool_bias_attr (ParameterAttribute or None.) – pooling layer bias attr. None if don’t care. -False if no bias.
  • fc_attr (ExtraLayerAttribute) – fc layer extra attribute.
  • context_attr (ExtraLayerAttribute) – context projection layer extra attribute.
  • pool_attr (ExtraLayerAttribute) – pooling layer extra attribute.
Returns:

output layer name.

+
Returns:

layer’s output.
Return type:

LayerOutput

@@ -242,39 +242,39 @@ False if no bias.
paddle.v2.networks.text_conv_pool(*args, **kwargs)
-

Text convolution pooling layers helper.

+

Text convolution pooling group.

Text input => Context Projection => FC Layer => Pooling => Output.

-
Parameters:
    -
  • name (basestring) – name of output layer(pooling layer name)
    • -
  • input (LayerOutput) – name of input layer
    • +
  • name (basestring) – group name.
    • +
  • input (LayerOutput) – input layer.
  • context_len (int) – context projection length. See context_projection’s document.
  • hidden_size (int) – FC Layer size.
    • -
  • context_start (int or None) – context projection length. See +
  • context_start (int|None) – context start position. See context_projection’s context_start.
    • -
  • pool_type (BasePoolingType.) – pooling layer type. See pooling_layer’s document.
    • +
  • pool_type (BasePoolingType) – pooling layer type. See pooling_layer’s document.
  • context_proj_layer_name (basestring) – context projection layer name. None if user don’t care.
    • -
  • context_proj_param_attr (ParameterAttribute or None.) – context projection parameter attribute. -None if user don’t care.
    • +
  • context_proj_param_attr (ParameterAttribute|None) – padding parameter attribute of context projection layer. +If false, it means padding always be zero.
  • fc_layer_name (basestring) – fc layer name. None if user don’t care.
    • -
  • fc_param_attr (ParameterAttribute or None) – fc layer parameter attribute. None if user don’t care.
    • -
  • fc_bias_attr (ParameterAttribute or None) – fc bias parameter attribute. False if no bias, +
  • fc_param_attr (ParameterAttribute|None) – fc layer parameter attribute. None if user don’t care.
    • +
  • fc_bias_attr (ParameterAttribute|False|None) – fc bias parameter attribute. False if no bias, +None if user don’t care.
    • +
  • fc_act (BaseActivation) – fc layer activation type. None means tanh.
    • +
  • pool_bias_attr (ParameterAttribute|False|None) – pooling layer bias attr. False if no bias. None if user don’t care.
    • -
  • fc_act (BaseActivation) – fc layer activation type. None means tanh
    • -
  • pool_bias_attr (ParameterAttribute or None.) – pooling layer bias attr. None if don’t care. -False if no bias.
  • fc_attr (ExtraLayerAttribute) – fc layer extra attribute.
  • context_attr (ExtraLayerAttribute) – context projection layer extra attribute.
  • pool_attr (ExtraLayerAttribute) – pooling layer extra attribute.
Returns:

output layer name.

+
Returns:

layer’s output.
Return type:

LayerOutput

@@ -294,36 +294,37 @@ False if no bias.
paddle.v2.networks.img_conv_bn_pool(*args, **kwargs)

Convolution, batch normalization, pooling group.

+

Img input => Conv => BN => Pooling => Output.

- - - @@ -380,34 +381,34 @@ None means default attribute.
paddle.v2.networks.simple_img_conv_pool(*args, **kwargs)

Simple image convolution and pooling group.

-

Input => conv => pooling

+

Img input => Conv => Pooling => Output.

Parameters:
    -
  • name (basestring) – group name
    • -
  • input (LayerOutput) – layer’s input
    • -
  • filter_size (int) – see img_conv_layer’s document
    • -
  • num_filters (int) – see img_conv_layer’s document
    • -
  • pool_size (int) – see img_pool_layer’s document.
    • -
  • pool_type (BasePoolingType) – see img_pool_layer’s document.
    • -
  • act (BaseActivation) – see batch_norm_layer’s document.
    • -
  • groups (int) – see img_conv_layer’s document
    • -
  • conv_stride (int) – see img_conv_layer’s document.
    • -
  • conv_padding (int) – see img_conv_layer’s document.
    • -
  • conv_bias_attr (ParameterAttribute) – see img_conv_layer’s document.
    • -
  • num_channel (int) – see img_conv_layer’s document.
    • -
  • conv_param_attr (ParameterAttribute) – see img_conv_layer’s document.
    • -
  • shared_bias (bool) – see img_conv_layer’s document.
    • -
  • conv_layer_attr (ExtraLayerOutput) – see img_conv_layer’s document.
    • -
  • bn_param_attr (ParameterAttribute.) – see batch_norm_layer’s document.
    • -
  • bn_bias_attr – see batch_norm_layer’s document.
    • -
  • bn_layer_attr – ParameterAttribute.
    • -
  • pool_stride (int) – see img_pool_layer’s document.
    • -
  • pool_padding (int) – see img_pool_layer’s document.
    • -
  • pool_layer_attr (ExtraLayerAttribute) – see img_pool_layer’s document.
    • +
  • name (basestring) – group name.
    • +
  • input (LayerOutput) – input layer.
    • +
  • filter_size (int) – see img_conv_layer for details.
    • +
  • num_filters (int) – see img_conv_layer for details.
    • +
  • pool_size (int) – see img_pool_layer for details.
    • +
  • pool_type (BasePoolingType) – see img_pool_layer for details.
    • +
  • act (BaseActivation) – see batch_norm_layer for details.
    • +
  • groups (int) – see img_conv_layer for details.
    • +
  • conv_stride (int) – see img_conv_layer for details.
    • +
  • conv_padding (int) – see img_conv_layer for details.
    • +
  • conv_bias_attr (ParameterAttribute) – see img_conv_layer for details.
    • +
  • num_channel (int) – see img_conv_layer for details.
    • +
  • conv_param_attr (ParameterAttribute) – see img_conv_layer for details.
    • +
  • shared_bias (bool) – see img_conv_layer for details.
    • +
  • conv_layer_attr (ExtraLayerOutput) – see img_conv_layer for details.
    • +
  • bn_param_attr (ParameterAttribute) – see batch_norm_layer for details.
    • +
  • bn_bias_attr (ParameterAttribute) – see batch_norm_layer for details.
    • +
  • bn_layer_attr (ExtraLayerAttribute) – see batch_norm_layer for details.
    • +
  • pool_stride (int) – see img_pool_layer for details.
    • +
  • pool_padding (int) – see img_pool_layer for details.
    • +
  • pool_layer_attr (ExtraLayerAttribute) – see img_pool_layer for details.
Returns:

Layer groups output

+
Returns:

layer’s output
Return type:

LayerOutput

@@ -347,26 +348,26 @@ False if no bias.
Parameters:
  • conv_batchnorm_drop_rate (list) – if conv_with_batchnorm[i] is true, conv_batchnorm_drop_rate[i] represents the drop rate of each batch norm.
    • -
  • input (LayerOutput) – layer’s input.
    • -
  • conv_num_filter (int) – output channels num.
    • +
  • input (LayerOutput) – input layer.
    • +
  • conv_num_filter (list|tuple) – list of output channels num.
  • pool_size (int) – pooling filter size.
  • num_channels (int) – input channels num.
  • conv_padding (int) – convolution padding size.
  • conv_filter_size (int) – convolution filter size.
  • conv_act (BaseActivation) – activation funciton after convolution.
    • -
  • conv_with_batchnorm (list) – conv_with_batchnorm[i] represents -if there is a batch normalization after each convolution.
    • +
  • conv_with_batchnorm (list) – if conv_with_batchnorm[i] is true, +there is a batch normalization operation after each convolution.
  • pool_stride (int) – pooling stride size.
  • pool_type (BasePoolingType) – pooling type.
    • -
  • param_attr (ParameterAttribute) – Convolution param attribute. +
  • param_attr (ParameterAttribute) – param attribute of convolution layer, None means default attribute.
Returns:

Layer’s output

+
Returns:

layer’s output
Type:

LayerOutput

+
Return type:

LayerOutput
- - + + @@ -456,9 +460,9 @@ None means default attribute.
paddle.v2.networks.lstmemory_unit(*args, **kwargs)
-

Define calculations that a LSTM unit performs during a single time step. -This function itself is not a recurrent layer, so it can not be -directly used to process sequence inputs. This function is always used in +

lstmemory_unit defines the caculation process of a LSTM unit during a +single time step. This function is not a recurrent layer, so it can not be +directly used to process sequence input. This function is always used in recurrent_group (see layers.py for more details) to implement attention mechanism.

Please refer to Generating Sequences With Recurrent Neural Networks @@ -479,21 +483,21 @@ for more details about LSTM. The link goes as follows:

@@ -516,9 +520,9 @@ False means no bias, None means default bias.

lstm_group is a recurrent_group version of Long Short Term Memory. It does exactly the same calculation as the lstmemory layer (see lstmemory in layers.py for the maths) does. A promising benefit is that LSTM memory -cell states, or hidden states in every time step are accessible to the +cell states(or hidden states) in every time step are accessible to the user. This is especially useful in attention model. If you do not need to -access the internal states of the lstm, but merely use its outputs, +access the internal states of the lstm and merely use its outputs, it is recommended to use the lstmemory, which is relatively faster than lstmemory_group.

NOTE: In PaddlePaddle’s implementation, the following input-to-hidden @@ -540,18 +544,18 @@ full_matrix_projection must be included before lstmemory_unit is called.

Parameters:
    -
  • name (basestring) – group name
    • -
  • input (LayerOutput) – input layer name.
    • -
  • filter_size (int) – see img_conv_layer for details
    • -
  • num_filters (int) – see img_conv_layer for details
    • -
  • pool_size (int) – see img_pool_layer for details
    • -
  • pool_type (BasePoolingType) – see img_pool_layer for details
    • -
  • act (BaseActivation) – see img_conv_layer for details
    • -
  • groups (int) – see img_conv_layer for details
    • -
  • conv_stride (int) – see img_conv_layer for details
    • -
  • conv_padding (int) – see img_conv_layer for details
    • -
  • bias_attr (ParameterAttribute) – see img_conv_layer for details
    • -
  • num_channel (int) – see img_conv_layer for details
    • -
  • param_attr (ParameterAttribute) – see img_conv_layer for details
    • -
  • shared_bias (bool) – see img_conv_layer for details
    • -
  • conv_layer_attr (ExtraLayerAttribute) – see img_conv_layer for details
    • -
  • pool_stride (int) – see img_pool_layer for details
    • -
  • pool_padding (int) – see img_pool_layer for details
    • -
  • pool_layer_attr (ExtraLayerAttribute) – see img_pool_layer for details
    • +
  • name (basestring) – group name.
    • +
  • input (LayerOutput) – input layer.
    • +
  • filter_size (int) – see img_conv_layer for details.
    • +
  • num_filters (int) – see img_conv_layer for details.
    • +
  • pool_size (int) – see img_pool_layer for details.
    • +
  • pool_type (BasePoolingType) – see img_pool_layer for details.
    • +
  • act (BaseActivation) – see img_conv_layer for details.
    • +
  • groups (int) – see img_conv_layer for details.
    • +
  • conv_stride (int) – see img_conv_layer for details.
    • +
  • conv_padding (int) – see img_conv_layer for details.
    • +
  • bias_attr (ParameterAttribute) – see img_conv_layer for details.
    • +
  • num_channel (int) – see img_conv_layer for details.
    • +
  • param_attr (ParameterAttribute) – see img_conv_layer for details.
    • +
  • shared_bias (bool) – see img_conv_layer for details.
    • +
  • conv_layer_attr (ExtraLayerAttribute) – see img_conv_layer for details.
    • +
  • pool_stride (int) – see img_pool_layer for details.
    • +
  • pool_padding (int) – see img_pool_layer for details.
    • +
  • pool_layer_attr (ExtraLayerAttribute) – see img_pool_layer for details.
Returns:

Layer’s output

+
Returns:

layer’s output
Return type:

LayerOutput

@@ -432,13 +433,16 @@ None means default attribute.
Parameters:
    -
  • num_classes –
    • -
  • input_image (LayerOutput) –
    • -
  • num_channels (int) –
    • +
  • num_classes (int) – number of class.
    • +
  • input_image (LayerOutput) – input layer.
    • +
  • num_channels (int) – input channels num.
Returns:

+
Returns:

layer’s output

+
Return type:

LayerOutput
Parameters:
    -
  • input (LayerOutput) – input layer name.
    • +
  • input (LayerOutput) – input layer.
  • out_memory (LayerOutput | None) – output of previous time step
  • name (basestring) – lstmemory unit name.
  • size (int) – lstmemory unit size.
    • -
  • param_attr (ParameterAttribute) – Parameter config, None if use default.
    • -
  • act (BaseActivation) – lstm final activiation type
    • -
  • gate_act (BaseActivation) – lstm gate activiation type
    • -
  • state_act (BaseActivation) – lstm state activiation type.
    • -
  • input_proj_bias_attr (ParameterAttribute|False|None) – bias attribute for input-to-hidden projection. +
  • param_attr (ParameterAttribute) – parameter attribute, None means default attribute.
    • +
  • act (BaseActivation) – last activiation type of lstm.
    • +
  • gate_act (BaseActivation) – gate activiation type of lstm.
    • +
  • state_act (BaseActivation) – state activiation type of lstm.
    • +
  • input_proj_bias_attr (ParameterAttribute|False|None) – bias attribute for input to hidden projection. False means no bias, None means default bias.
  • input_proj_layer_attr (ExtraLayerAttribute) – extra layer attribute for input to hidden projection of the LSTM unit, such as dropout, error clipping.
    • -
  • lstm_bias_attr (ParameterAttribute|False) – bias parameter attribute of lstm layer. +
  • lstm_bias_attr (ParameterAttribute|False|None) – bias parameter attribute of lstm layer. False means no bias, None means default bias.
    • -
  • lstm_layer_attr (ExtraLayerAttribute) – lstm layer’s extra attribute.
    • +
  • lstm_layer_attr (ExtraLayerAttribute) – extra attribute of lstm layer.
Parameters:
    -
  • input (LayerOutput) – input layer name.
    • +
  • input (LayerOutput) – input layer.
  • size (int) – lstmemory group size.
    • -
  • name (basestring) – name of the lstmemory group.
    • -
  • out_memory (LayerOutput | None) – output of previous time step
    • -
  • reverse (bool) – is lstm reversed
    • -
  • param_attr (ParameterAttribute) – Parameter config, None if use default.
    • -
  • act (BaseActivation) – lstm final activiation type
    • -
  • gate_act (BaseActivation) – lstm gate activiation type
    • -
  • state_act (BaseActivation) – lstm state activiation type.
    • -
  • lstm_bias_attr (ParameterAttribute|False) – bias parameter attribute of lstm layer. +
  • name (basestring) – name of lstmemory group.
    • +
  • out_memory (LayerOutput | None) – output of previous time step.
    • +
  • reverse (bool) – process the input in a reverse order or not.
    • +
  • param_attr (ParameterAttribute) – parameter attribute, None means default attribute.
    • +
  • act (BaseActivation) – last activiation type of lstm.
    • +
  • gate_act (BaseActivation) – gate activiation type of lstm.
    • +
  • state_act (BaseActivation) – state activiation type of lstm.
    • +
  • lstm_bias_attr (ParameterAttribute|False|None) – bias parameter attribute of lstm layer. False means no bias, None means default bias.
    • -
  • input_proj_bias_attr (ParameterAttribute|False|None) – bias attribute for input-to-hidden projection. +
  • input_proj_bias_attr (ParameterAttribute|False|None) – bias attribute for input to hidden projection. False means no bias, None means default bias.
  • input_proj_layer_attr (ExtraLayerAttribute) – extra layer attribute for input to hidden projection of the LSTM unit, such as dropout, error clipping.
    • @@ -576,34 +580,34 @@ projection of the LSTM unit, such as dropout, error clipping.
    paddle.v2.networks.simple_lstm(*args, **kwargs)

    Simple LSTM Cell.

    -

    It just combine a mixed layer with fully_matrix_projection and a lstmemory -layer. The simple lstm cell was implemented as follow equations.

    +

    It just combines a mixed layer with fully_matrix_projection and a lstmemory +layer. The simple lstm cell was implemented with follow equations.

    \[ \begin{align}\begin{aligned}i_t & = \sigma(W_{xi}x_{t} + W_{hi}h_{t-1} + W_{ci}c_{t-1} + b_i)\\f_t & = \sigma(W_{xf}x_{t} + W_{hf}h_{t-1} + W_{cf}c_{t-1} + b_f)\\c_t & = f_tc_{t-1} + i_t tanh (W_{xc}x_t+W_{hc}h_{t-1} + b_c)\\o_t & = \sigma(W_{xo}x_{t} + W_{ho}h_{t-1} + W_{co}c_t + b_o)\\h_t & = o_t tanh(c_t)\end{aligned}\end{align} \]
    -

    Please refer Generating Sequences With Recurrent Neural Networks if you -want to know what lstm is. Link is here.

    +

    Please refer to Generating Sequences With Recurrent Neural Networks for more +details about lstm. Link is here.

    - - @@ -708,11 +711,11 @@ mechanism.

    does exactly the same calculation as the grumemory layer does. A promising benefit is that gru hidden states are accessible to the user. This is especially useful in attention model. If you do not need to access -any internal state, but merely use the outputs of a GRU, it is recommended +any internal state and merely use the outputs of a GRU, it is recommended to use the grumemory, which is relatively faster.

    Please see grumemory in layers.py for more detail about the maths.

    The example usage is:

    -
    gru = gur_group(input=[layer1],
+
    gru = gru_group(input=[layer1],
                 size=256,
                 act=TanhActivation(),
                 gate_act=SigmoidActivation())
@@ -723,15 +726,16 @@ to use the grumemory, which is relatively faster.
    @@ -751,11 +755,11 @@ to use the grumemory, which is relatively faster.

    paddle.v2.networks.simple_gru(*args, **kwargs)
    -

    You maybe see gru_step_layer, grumemory in layers.py, gru_unit, gru_group, +

    You may see gru_step_layer, grumemory in layers.py, gru_unit, gru_group, simple_gru in network.py. The reason why there are so many interfaces is that we have two ways to implement recurrent neural network. One way is to use one complete layer to implement rnn (including simple rnn, gru and lstm) -with multiple time steps, such as recurrent_layer, lstmemory, grumemory. But, +with multiple time steps, such as recurrent_layer, lstmemory, grumemory. But the multiplication operation \(W x_t\) is not computed in these layers. See details in their interfaces in layers.py. The other implementation is to use an recurrent group which can ensemble a @@ -785,14 +789,15 @@ gru_group, and gru_group is relatively better than simple_gru.

    @@ -812,8 +817,8 @@ gru_group, and gru_group is relatively better than simple_gru.

    paddle.v2.networks.simple_gru2(*args, **kwargs)
    -

    simple_gru2 is the same with simple_gru, but using grumemory instead -Please see grumemory in layers.py for more detail about the maths. +

    simple_gru2 is the same with simple_gru, but using grumemory instead. +Please refer to grumemory in layers.py for more detail about the math. simple_gru2 is faster than simple_gru.

    The example usage is:

    gru = simple_gru2(input=[layer1], size=256)
@@ -824,14 +829,15 @@ simple_gru2 is faster than simple_gru.
    @@ -852,7 +858,7 @@ simple_gru2 is faster than simple_gru.

    paddle.v2.networks.bidirectional_gru(*args, **kwargs)

    A bidirectional_gru is a recurrent unit that iterates over the input -sequence both in forward and bardward orders, and then concatenate two +sequence both in forward and backward orders, and then concatenate two outputs to form a final output. However, concatenation of two outputs is not the only way to form the final output, you can also, for example, just add them together.

    @@ -868,11 +874,10 @@ just add them together.

  • name (basestring) – bidirectional gru layer name.
  • input (LayerOutput) – input layer.
  • size (int) – gru layer size.
    • -
  • return_seq (bool) – If set False, outputs of the last time step are +
  • return_seq (bool) – If set False, the last time step of output are concatenated and returned. -If set True, the entire output sequences that are -processed in forward and backward directions are -concatenated and returned.
    • +If set True, the entire output sequences in forward +and backward directions are concatenated and returned. @@ -893,7 +898,7 @@ concatenated and returned.
    paddle.v2.networks.simple_attention(*args, **kwargs)
    -

    Calculate and then return a context vector by attention machanism. +

    Calculate and return a context vector with attention mechanism. Size of the context vector equals to size of the encoded_sequence.

    \[ \begin{align}\begin{aligned}a(s_{i-1},h_{j}) & = v_{a}f(W_{a}s_{t-1} + U_{a}h_{j})\\e_{i,j} & = a(s_{i-1}, h_{j})\\a_{i,j} & = \frac{exp(e_{i,j})}{\sum_{k=1}^{T_x}{exp(e_{i,k})}}\\c_{i} & = \sum_{j=1}^{T_{x}}a_{i,j}h_{j}\end{aligned}\end{align} \]
    @@ -917,8 +922,8 @@ Align and Translate for more details. The link is as follows:
    Parameters:
    • name (basestring) – lstm layer name.
      • -
    • input (LayerOutput) – input layer name.
      • +
    • input (LayerOutput) – layer’s input.
    • size (int) – lstm layer size.
      • -
    • reverse (bool) – whether to process the input data in a reverse order
      • -
    • mat_param_attr (ParameterAttribute) – mixed layer’s matrix projection parameter attribute.
      • +
    • reverse (bool) – process the input in a reverse order or not.
      • +
    • mat_param_attr (ParameterAttribute) – parameter attribute of matrix projection in mixed layer.
    • bias_param_attr (ParameterAttribute|False) – bias parameter attribute. False means no bias, None means default bias.
      • -
    • inner_param_attr (ParameterAttribute) – lstm cell parameter attribute.
      • -
    • act (BaseActivation) – lstm final activiation type
      • -
    • gate_act (BaseActivation) – lstm gate activiation type
      • -
    • state_act (BaseActivation) – lstm state activiation type.
      • -
    • mixed_layer_attr (ExtraLayerAttribute) – mixed layer’s extra attribute.
      • -
    • lstm_cell_attr (ExtraLayerAttribute) – lstm layer’s extra attribute.
      • +
    • inner_param_attr (ParameterAttribute) – parameter attribute of lstm cell.
      • +
    • act (BaseActivation) – last activiation type of lstm.
      • +
    • gate_act (BaseActivation) – gate activiation type of lstm.
      • +
    • state_act (BaseActivation) – state activiation type of lstm.
      • +
    • mixed_layer_attr (ExtraLayerAttribute) – extra attribute of mixed layer.
      • +
    • lstm_cell_attr (ExtraLayerAttribute) – extra attribute of lstm.
    Returns:

    lstm layer name.

    +
    Returns:

    layer’s output.
    Return type:

    LayerOutput

    @@ -620,8 +624,8 @@ means default bias.
    paddle.v2.networks.bidirectional_lstm(*args, **kwargs)

    A bidirectional_lstm is a recurrent unit that iterates over the input -sequence both in forward and bardward orders, and then concatenate two -outputs form a final output. However, concatenation of two outputs +sequence both in forward and backward orders, and then concatenate two +outputs to form a final output. However, concatenation of two outputs is not the only way to form the final output, you can also, for example, just add them together.

    Please refer to Neural Machine Translation by Jointly Learning to Align @@ -640,15 +644,14 @@ The link goes as follows:

  • name (basestring) – bidirectional lstm layer name.
  • input (LayerOutput) – input layer.
  • size (int) – lstm layer size.
    • -
  • return_seq (bool) – If set False, outputs of the last time step are +
  • return_seq (bool) – If set False, the last time step of output are concatenated and returned. -If set True, the entire output sequences that are -processed in forward and backward directions are -concatenated and returned.
    • +If set True, the entire output sequences in forward +and backward directions are concatenated and returned.
    Returns:

    LayerOutput object accroding to the return_seq.

    +
    Returns:

    LayerOutput object.
    Return type:

    LayerOutput

    @@ -667,9 +670,9 @@ concatenated and returned.
    paddle.v2.networks.gru_unit(*args, **kwargs)
    -

    Define calculations that a gated recurrent unit performs in a single time -step. This function itself is not a recurrent layer, so it can not be -directly used to process sequence inputs. This function is always used in +

    gru_unit defines the calculation process of a gated recurrent unit during a single +time step. This function is not a recurrent layer, so it can not be +directly used to process sequence input. This function is always used in the recurrent_group (see layers.py for more details) to implement attention mechanism.

    Please see grumemory in layers.py for the details about the maths.

    @@ -678,13 +681,13 @@ mechanism.
    Parameters:
      -
    • input (LayerOutput) – input layer name.
      • +
    • input (LayerOutput) – input layer.
    • memory_boot (LayerOutput | None) – the initialization state of the LSTM cell.
    • name (basestring) – name of the gru group.
    • size (int) – hidden size of the gru.
      • -
    • act (BaseActivation) – type of the activation
      • -
    • gate_act (BaseActivation) – type of the gate activation
      • -
    • gru_layer_attr (ParameterAttribute|False) – Extra parameter attribute of the gru layer.
      • +
    • act (BaseActivation) – activation type of gru
      • +
    • gate_act (BaseActivation) – gate activation type or gru
      • +
    • gru_layer_attr (ExtraLayerAttribute) – Extra attribute of the gru layer.
    Parameters:
      -
    • input (LayerOutput) – input layer name.
      • +
    • input (LayerOutput) – input layer.
    • memory_boot (LayerOutput | None) – the initialization state of the LSTM cell.
    • name (basestring) – name of the gru group.
    • size (int) – hidden size of the gru.
      • -
    • reverse (bool) – whether to process the input data in a reverse order
      • -
    • act (BaseActivation) – type of the activiation
      • -
    • gate_act (BaseActivation) – type of the gate activiation
      • -
    • gru_bias_attr (ParameterAttribute|False) – bias. False means no bias, None means default bias.
      • -
    • gru_layer_attr (ParameterAttribute|False) – Extra parameter attribute of the gru layer.
      • +
    • reverse (bool) – process the input in a reverse order or not.
      • +
    • act (BaseActivation) – activiation type of gru
      • +
    • gate_act (BaseActivation) – gate activiation type of gru
      • +
    • gru_bias_attr (ParameterAttribute|False|None) – bias parameter attribute of gru layer, +False means no bias, None means default bias.
      • +
    • gru_layer_attr (ExtraLayerAttribute) – Extra attribute of the gru layer.
    Parameters:
      -
    • input (LayerOutput) – input layer name.
      • +
    • input (LayerOutput) – input layer.
    • name (basestring) – name of the gru group.
    • size (int) – hidden size of the gru.
      • -
    • reverse (bool) – whether to process the input data in a reverse order
      • -
    • act (BaseActivation) – type of the activiation
      • -
    • gate_act (BaseActivation) – type of the gate activiation
      • -
    • gru_bias_attr (ParameterAttribute|False) – bias. False means no bias, None means default bias.
      • -
    • gru_layer_attr (ParameterAttribute|False) – Extra parameter attribute of the gru layer.
      • +
    • reverse (bool) – process the input in a reverse order or not.
      • +
    • act (BaseActivation) – activiation type of gru
      • +
    • gate_act (BaseActivation) – gate activiation type of gru
      • +
    • gru_bias_attr (ParameterAttribute|False|None) – bias parameter attribute of gru layer, +False means no bias, None means default bias.
      • +
    • gru_layer_attr (ExtraLayerAttribute) – Extra attribute of the gru layer.
    Parameters:
      -
    • input (LayerOutput) – input layer name.
      • +
    • input (LayerOutput) – input layer.
    • name (basestring) – name of the gru group.
    • size (int) – hidden size of the gru.
      • -
    • reverse (bool) – whether to process the input data in a reverse order
      • -
    • act (BaseActivation) – type of the activiation
      • -
    • gate_act (BaseActivation) – type of the gate activiation
      • -
    • gru_bias_attr (ParameterAttribute|False) – bias. False means no bias, None means default bias.
      • -
    • gru_layer_attr (ParameterAttribute|False) – Extra parameter attribute of the gru layer.
      • +
    • reverse (bool) – process the input in a reverse order or not.
      • +
    • act (BaseActivation) – activiation type of gru
      • +
    • gate_act (BaseActivation) – gate activiation type of gru
      • +
    • gru_bias_attr (ParameterAttribute|False|None) – bias parameter attribute of gru layer, +False means no bias, None means default bias.
      • +
    • gru_layer_attr (ExtraLayerAttribute) – Extra attribute of the gru layer.
    Parameters:
    • name (basestring) – name of the attention model.
    • softmax_param_attr (ParameterAttribute) – parameter attribute of sequence softmax -that is used to produce attention weight
      • -
    • weight_act (Activation) – activation of the attention model
      • +that is used to produce attention weight. +
    • weight_act (BaseActivation) – activation of the attention model.
    • encoded_sequence (LayerOutput) – output of the encoder
    • encoded_proj (LayerOutput) – attention weight is computed by a feed forward neural network which has two inputs : decoder’s hidden state diff --git a/develop/doc/searchindex.js b/develop/doc/searchindex.js index aa32a122aca8cb6613300c4695e2630a814574d7..b9e2b10c6269769eec458ac35a0216c2c461d252 100644 --- a/develop/doc/searchindex.js +++ b/develop/doc/searchindex.js @@ -1 +1 @@ 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\ No newline at end of file diff --git a/develop/doc_cn/api/v2/config/networks.html b/develop/doc_cn/api/v2/config/networks.html index ee21be7bd1a2b4d83895a34da8c896f507071a30..f4840621cc374f68edf297307167183b6d672d2e 100644 --- a/develop/doc_cn/api/v2/config/networks.html +++ b/develop/doc_cn/api/v2/config/networks.html @@ -201,39 +201,39 @@
      paddle.v2.networks.sequence_conv_pool(*args, **kwargs)
      -

      Text convolution pooling layers helper.

      +

      Text convolution pooling group.

      Text input => Context Projection => FC Layer => Pooling => Output.

      -
      参数:
        -
      • name (basestring) – name of output layer(pooling layer name)
        • -
      • input (LayerOutput) – name of input layer
        • +
      • name (basestring) – group name.
        • +
      • input (LayerOutput) – input layer.
      • context_len (int) – context projection length. See context_projection’s document.
      • hidden_size (int) – FC Layer size.
        • -
      • context_start (int or None) – context projection length. See +
      • context_start (int|None) – context start position. See context_projection’s context_start.
        • -
      • pool_type (BasePoolingType.) – pooling layer type. See pooling_layer’s document.
        • +
      • pool_type (BasePoolingType) – pooling layer type. See pooling_layer’s document.
      • context_proj_layer_name (basestring) – context projection layer name. None if user don’t care.
        • -
      • context_proj_param_attr (ParameterAttribute or None.) – context projection parameter attribute. -None if user don’t care.
        • +
      • context_proj_param_attr (ParameterAttribute|None) – padding parameter attribute of context projection layer. +If false, it means padding always be zero.
      • fc_layer_name (basestring) – fc layer name. None if user don’t care.
        • -
      • fc_param_attr (ParameterAttribute or None) – fc layer parameter attribute. None if user don’t care.
        • -
      • fc_bias_attr (ParameterAttribute or None) – fc bias parameter attribute. False if no bias, +
      • fc_param_attr (ParameterAttribute|None) – fc layer parameter attribute. None if user don’t care.
        • +
      • fc_bias_attr (ParameterAttribute|False|None) – fc bias parameter attribute. False if no bias, +None if user don’t care.
        • +
      • fc_act (BaseActivation) – fc layer activation type. None means tanh.
        • +
      • pool_bias_attr (ParameterAttribute|False|None) – pooling layer bias attr. False if no bias. None if user don’t care.
        • -
      • fc_act (BaseActivation) – fc layer activation type. None means tanh
        • -
      • pool_bias_attr (ParameterAttribute or None.) – pooling layer bias attr. None if don’t care. -False if no bias.
      • fc_attr (ExtraLayerAttribute) – fc layer extra attribute.
      • context_attr (ExtraLayerAttribute) – context projection layer extra attribute.
      • pool_attr (ExtraLayerAttribute) – pooling layer extra attribute.
      返回:

      output layer name.

      +
      返回:

      layer’s output.
      返回类型:

      LayerOutput

      @@ -249,39 +249,39 @@ False if no bias.
      paddle.v2.networks.text_conv_pool(*args, **kwargs)
      -

      Text convolution pooling layers helper.

      +

      Text convolution pooling group.

      Text input => Context Projection => FC Layer => Pooling => Output.

      -
      参数:
        -
      • name (basestring) – name of output layer(pooling layer name)
        • -
      • input (LayerOutput) – name of input layer
        • +
      • name (basestring) – group name.
        • +
      • input (LayerOutput) – input layer.
      • context_len (int) – context projection length. See context_projection’s document.
      • hidden_size (int) – FC Layer size.
        • -
      • context_start (int or None) – context projection length. See +
      • context_start (int|None) – context start position. See context_projection’s context_start.
        • -
      • pool_type (BasePoolingType.) – pooling layer type. See pooling_layer’s document.
        • +
      • pool_type (BasePoolingType) – pooling layer type. See pooling_layer’s document.
      • context_proj_layer_name (basestring) – context projection layer name. None if user don’t care.
        • -
      • context_proj_param_attr (ParameterAttribute or None.) – context projection parameter attribute. -None if user don’t care.
        • +
      • context_proj_param_attr (ParameterAttribute|None) – padding parameter attribute of context projection layer. +If false, it means padding always be zero.
      • fc_layer_name (basestring) – fc layer name. None if user don’t care.
        • -
      • fc_param_attr (ParameterAttribute or None) – fc layer parameter attribute. None if user don’t care.
        • -
      • fc_bias_attr (ParameterAttribute or None) – fc bias parameter attribute. False if no bias, +
      • fc_param_attr (ParameterAttribute|None) – fc layer parameter attribute. None if user don’t care.
        • +
      • fc_bias_attr (ParameterAttribute|False|None) – fc bias parameter attribute. False if no bias, +None if user don’t care.
        • +
      • fc_act (BaseActivation) – fc layer activation type. None means tanh.
        • +
      • pool_bias_attr (ParameterAttribute|False|None) – pooling layer bias attr. False if no bias. None if user don’t care.
        • -
      • fc_act (BaseActivation) – fc layer activation type. None means tanh
        • -
      • pool_bias_attr (ParameterAttribute or None.) – pooling layer bias attr. None if don’t care. -False if no bias.
      • fc_attr (ExtraLayerAttribute) – fc layer extra attribute.
      • context_attr (ExtraLayerAttribute) – context projection layer extra attribute.
      • pool_attr (ExtraLayerAttribute) – pooling layer extra attribute.
      返回:

      output layer name.

      +
      返回:

      layer’s output.
      返回类型:

      LayerOutput

      @@ -301,36 +301,37 @@ False if no bias.
      paddle.v2.networks.img_conv_bn_pool(*args, **kwargs)

      Convolution, batch normalization, pooling group.

      +

      Img input => Conv => BN => Pooling => Output.

      - - - @@ -387,34 +388,34 @@ None means default attribute.
      paddle.v2.networks.simple_img_conv_pool(*args, **kwargs)

      Simple image convolution and pooling group.

      -

      Input => conv => pooling

      +

      Img input => Conv => Pooling => Output.

      参数:
        -
      • name (basestring) – group name
        • -
      • input (LayerOutput) – layer’s input
        • -
      • filter_size (int) – see img_conv_layer’s document
        • -
      • num_filters (int) – see img_conv_layer’s document
        • -
      • pool_size (int) – see img_pool_layer’s document.
        • -
      • pool_type (BasePoolingType) – see img_pool_layer’s document.
        • -
      • act (BaseActivation) – see batch_norm_layer’s document.
        • -
      • groups (int) – see img_conv_layer’s document
        • -
      • conv_stride (int) – see img_conv_layer’s document.
        • -
      • conv_padding (int) – see img_conv_layer’s document.
        • -
      • conv_bias_attr (ParameterAttribute) – see img_conv_layer’s document.
        • -
      • num_channel (int) – see img_conv_layer’s document.
        • -
      • conv_param_attr (ParameterAttribute) – see img_conv_layer’s document.
        • -
      • shared_bias (bool) – see img_conv_layer’s document.
        • -
      • conv_layer_attr (ExtraLayerOutput) – see img_conv_layer’s document.
        • -
      • bn_param_attr (ParameterAttribute.) – see batch_norm_layer’s document.
        • -
      • bn_bias_attr – see batch_norm_layer’s document.
        • -
      • bn_layer_attr – ParameterAttribute.
        • -
      • pool_stride (int) – see img_pool_layer’s document.
        • -
      • pool_padding (int) – see img_pool_layer’s document.
        • -
      • pool_layer_attr (ExtraLayerAttribute) – see img_pool_layer’s document.
        • +
      • name (basestring) – group name.
        • +
      • input (LayerOutput) – input layer.
        • +
      • filter_size (int) – see img_conv_layer for details.
        • +
      • num_filters (int) – see img_conv_layer for details.
        • +
      • pool_size (int) – see img_pool_layer for details.
        • +
      • pool_type (BasePoolingType) – see img_pool_layer for details.
        • +
      • act (BaseActivation) – see batch_norm_layer for details.
        • +
      • groups (int) – see img_conv_layer for details.
        • +
      • conv_stride (int) – see img_conv_layer for details.
        • +
      • conv_padding (int) – see img_conv_layer for details.
        • +
      • conv_bias_attr (ParameterAttribute) – see img_conv_layer for details.
        • +
      • num_channel (int) – see img_conv_layer for details.
        • +
      • conv_param_attr (ParameterAttribute) – see img_conv_layer for details.
        • +
      • shared_bias (bool) – see img_conv_layer for details.
        • +
      • conv_layer_attr (ExtraLayerOutput) – see img_conv_layer for details.
        • +
      • bn_param_attr (ParameterAttribute) – see batch_norm_layer for details.
        • +
      • bn_bias_attr (ParameterAttribute) – see batch_norm_layer for details.
        • +
      • bn_layer_attr (ExtraLayerAttribute) – see batch_norm_layer for details.
        • +
      • pool_stride (int) – see img_pool_layer for details.
        • +
      • pool_padding (int) – see img_pool_layer for details.
        • +
      • pool_layer_attr (ExtraLayerAttribute) – see img_pool_layer for details.
      返回:

      Layer groups output

      +
      返回:

      layer’s output
      返回类型:

      LayerOutput

      @@ -354,26 +355,26 @@ False if no bias.
      参数:
      • conv_batchnorm_drop_rate (list) – if conv_with_batchnorm[i] is true, conv_batchnorm_drop_rate[i] represents the drop rate of each batch norm.
        • -
      • input (LayerOutput) – layer’s input.
        • -
      • conv_num_filter (int) – output channels num.
        • +
      • input (LayerOutput) – input layer.
        • +
      • conv_num_filter (list|tuple) – list of output channels num.
      • pool_size (int) – pooling filter size.
      • num_channels (int) – input channels num.
      • conv_padding (int) – convolution padding size.
      • conv_filter_size (int) – convolution filter size.
      • conv_act (BaseActivation) – activation funciton after convolution.
        • -
      • conv_with_batchnorm (list) – conv_with_batchnorm[i] represents -if there is a batch normalization after each convolution.
        • +
      • conv_with_batchnorm (list) – if conv_with_batchnorm[i] is true, +there is a batch normalization operation after each convolution.
      • pool_stride (int) – pooling stride size.
      • pool_type (BasePoolingType) – pooling type.
        • -
      • param_attr (ParameterAttribute) – Convolution param attribute. +
      • param_attr (ParameterAttribute) – param attribute of convolution layer, None means default attribute.
      返回:

      Layer’s output

      +
      返回:

      layer’s output
      Type:

      LayerOutput

      +
      返回类型:

      LayerOutput
      - - + + @@ -463,9 +467,9 @@ None means default attribute.
      paddle.v2.networks.lstmemory_unit(*args, **kwargs)
      -

      Define calculations that a LSTM unit performs during a single time step. -This function itself is not a recurrent layer, so it can not be -directly used to process sequence inputs. This function is always used in +

      lstmemory_unit defines the caculation process of a LSTM unit during a +single time step. This function is not a recurrent layer, so it can not be +directly used to process sequence input. This function is always used in recurrent_group (see layers.py for more details) to implement attention mechanism.

      Please refer to Generating Sequences With Recurrent Neural Networks @@ -486,21 +490,21 @@ for more details about LSTM. The link goes as follows:

      @@ -523,9 +527,9 @@ False means no bias, None means default bias.

      lstm_group is a recurrent_group version of Long Short Term Memory. It does exactly the same calculation as the lstmemory layer (see lstmemory in layers.py for the maths) does. A promising benefit is that LSTM memory -cell states, or hidden states in every time step are accessible to the +cell states(or hidden states) in every time step are accessible to the user. This is especially useful in attention model. If you do not need to -access the internal states of the lstm, but merely use its outputs, +access the internal states of the lstm and merely use its outputs, it is recommended to use the lstmemory, which is relatively faster than lstmemory_group.

      NOTE: In PaddlePaddle’s implementation, the following input-to-hidden @@ -547,18 +551,18 @@ full_matrix_projection must be included before lstmemory_unit is called.

      参数:
        -
      • name (basestring) – group name
        • -
      • input (LayerOutput) – input layer name.
        • -
      • filter_size (int) – see img_conv_layer for details
        • -
      • num_filters (int) – see img_conv_layer for details
        • -
      • pool_size (int) – see img_pool_layer for details
        • -
      • pool_type (BasePoolingType) – see img_pool_layer for details
        • -
      • act (BaseActivation) – see img_conv_layer for details
        • -
      • groups (int) – see img_conv_layer for details
        • -
      • conv_stride (int) – see img_conv_layer for details
        • -
      • conv_padding (int) – see img_conv_layer for details
        • -
      • bias_attr (ParameterAttribute) – see img_conv_layer for details
        • -
      • num_channel (int) – see img_conv_layer for details
        • -
      • param_attr (ParameterAttribute) – see img_conv_layer for details
        • -
      • shared_bias (bool) – see img_conv_layer for details
        • -
      • conv_layer_attr (ExtraLayerAttribute) – see img_conv_layer for details
        • -
      • pool_stride (int) – see img_pool_layer for details
        • -
      • pool_padding (int) – see img_pool_layer for details
        • -
      • pool_layer_attr (ExtraLayerAttribute) – see img_pool_layer for details
        • +
      • name (basestring) – group name.
        • +
      • input (LayerOutput) – input layer.
        • +
      • filter_size (int) – see img_conv_layer for details.
        • +
      • num_filters (int) – see img_conv_layer for details.
        • +
      • pool_size (int) – see img_pool_layer for details.
        • +
      • pool_type (BasePoolingType) – see img_pool_layer for details.
        • +
      • act (BaseActivation) – see img_conv_layer for details.
        • +
      • groups (int) – see img_conv_layer for details.
        • +
      • conv_stride (int) – see img_conv_layer for details.
        • +
      • conv_padding (int) – see img_conv_layer for details.
        • +
      • bias_attr (ParameterAttribute) – see img_conv_layer for details.
        • +
      • num_channel (int) – see img_conv_layer for details.
        • +
      • param_attr (ParameterAttribute) – see img_conv_layer for details.
        • +
      • shared_bias (bool) – see img_conv_layer for details.
        • +
      • conv_layer_attr (ExtraLayerAttribute) – see img_conv_layer for details.
        • +
      • pool_stride (int) – see img_pool_layer for details.
        • +
      • pool_padding (int) – see img_pool_layer for details.
        • +
      • pool_layer_attr (ExtraLayerAttribute) – see img_pool_layer for details.
      返回:

      Layer’s output

      +
      返回:

      layer’s output
      返回类型:

      LayerOutput

      @@ -439,13 +440,16 @@ None means default attribute.
      参数:
        -
      • num_classes –
        • -
      • input_image (LayerOutput) –
        • -
      • num_channels (int) –
        • +
      • num_classes (int) – number of class.
        • +
      • input_image (LayerOutput) – input layer.
        • +
      • num_channels (int) – input channels num.
      返回:

      +
      返回:

      layer’s output

      +
      返回类型:

      LayerOutput
      参数:
        -
      • input (LayerOutput) – input layer name.
        • +
      • input (LayerOutput) – input layer.
      • out_memory (LayerOutput | None) – output of previous time step
      • name (basestring) – lstmemory unit name.
      • size (int) – lstmemory unit size.
        • -
      • param_attr (ParameterAttribute) – Parameter config, None if use default.
        • -
      • act (BaseActivation) – lstm final activiation type
        • -
      • gate_act (BaseActivation) – lstm gate activiation type
        • -
      • state_act (BaseActivation) – lstm state activiation type.
        • -
      • input_proj_bias_attr (ParameterAttribute|False|None) – bias attribute for input-to-hidden projection. +
      • param_attr (ParameterAttribute) – parameter attribute, None means default attribute.
        • +
      • act (BaseActivation) – last activiation type of lstm.
        • +
      • gate_act (BaseActivation) – gate activiation type of lstm.
        • +
      • state_act (BaseActivation) – state activiation type of lstm.
        • +
      • input_proj_bias_attr (ParameterAttribute|False|None) – bias attribute for input to hidden projection. False means no bias, None means default bias.
      • input_proj_layer_attr (ExtraLayerAttribute) – extra layer attribute for input to hidden projection of the LSTM unit, such as dropout, error clipping.
        • -
      • lstm_bias_attr (ParameterAttribute|False) – bias parameter attribute of lstm layer. +
      • lstm_bias_attr (ParameterAttribute|False|None) – bias parameter attribute of lstm layer. False means no bias, None means default bias.
        • -
      • lstm_layer_attr (ExtraLayerAttribute) – lstm layer’s extra attribute.
        • +
      • lstm_layer_attr (ExtraLayerAttribute) – extra attribute of lstm layer.
      参数:
        -
      • input (LayerOutput) – input layer name.
        • +
      • input (LayerOutput) – input layer.
      • size (int) – lstmemory group size.
        • -
      • name (basestring) – name of the lstmemory group.
        • -
      • out_memory (LayerOutput | None) – output of previous time step
        • -
      • reverse (bool) – is lstm reversed
        • -
      • param_attr (ParameterAttribute) – Parameter config, None if use default.
        • -
      • act (BaseActivation) – lstm final activiation type
        • -
      • gate_act (BaseActivation) – lstm gate activiation type
        • -
      • state_act (BaseActivation) – lstm state activiation type.
        • -
      • lstm_bias_attr (ParameterAttribute|False) – bias parameter attribute of lstm layer. +
      • name (basestring) – name of lstmemory group.
        • +
      • out_memory (LayerOutput | None) – output of previous time step.
        • +
      • reverse (bool) – process the input in a reverse order or not.
        • +
      • param_attr (ParameterAttribute) – parameter attribute, None means default attribute.
        • +
      • act (BaseActivation) – last activiation type of lstm.
        • +
      • gate_act (BaseActivation) – gate activiation type of lstm.
        • +
      • state_act (BaseActivation) – state activiation type of lstm.
        • +
      • lstm_bias_attr (ParameterAttribute|False|None) – bias parameter attribute of lstm layer. False means no bias, None means default bias.
        • -
      • input_proj_bias_attr (ParameterAttribute|False|None) – bias attribute for input-to-hidden projection. +
      • input_proj_bias_attr (ParameterAttribute|False|None) – bias attribute for input to hidden projection. False means no bias, None means default bias.
      • input_proj_layer_attr (ExtraLayerAttribute) – extra layer attribute for input to hidden projection of the LSTM unit, such as dropout, error clipping.
        • @@ -583,34 +587,34 @@ projection of the LSTM unit, such as dropout, error clipping.
        paddle.v2.networks.simple_lstm(*args, **kwargs)

        Simple LSTM Cell.

        -

        It just combine a mixed layer with fully_matrix_projection and a lstmemory -layer. The simple lstm cell was implemented as follow equations.

        +

        It just combines a mixed layer with fully_matrix_projection and a lstmemory +layer. The simple lstm cell was implemented with follow equations.

        \[ \begin{align}\begin{aligned}i_t & = \sigma(W_{xi}x_{t} + W_{hi}h_{t-1} + W_{ci}c_{t-1} + b_i)\\f_t & = \sigma(W_{xf}x_{t} + W_{hf}h_{t-1} + W_{cf}c_{t-1} + b_f)\\c_t & = f_tc_{t-1} + i_t tanh (W_{xc}x_t+W_{hc}h_{t-1} + b_c)\\o_t & = \sigma(W_{xo}x_{t} + W_{ho}h_{t-1} + W_{co}c_t + b_o)\\h_t & = o_t tanh(c_t)\end{aligned}\end{align} \]
        -

        Please refer Generating Sequences With Recurrent Neural Networks if you -want to know what lstm is. Link is here.

        +

        Please refer to Generating Sequences With Recurrent Neural Networks for more +details about lstm. Link is here.

        - - @@ -715,11 +718,11 @@ mechanism.

        does exactly the same calculation as the grumemory layer does. A promising benefit is that gru hidden states are accessible to the user. This is especially useful in attention model. If you do not need to access -any internal state, but merely use the outputs of a GRU, it is recommended +any internal state and merely use the outputs of a GRU, it is recommended to use the grumemory, which is relatively faster.

        Please see grumemory in layers.py for more detail about the maths.

        The example usage is:

        -
        gru = gur_group(input=[layer1],
+
        gru = gru_group(input=[layer1],
                 size=256,
                 act=TanhActivation(),
                 gate_act=SigmoidActivation())
@@ -730,15 +733,16 @@ to use the grumemory, which is relatively faster.
        @@ -758,11 +762,11 @@ to use the grumemory, which is relatively faster.

        paddle.v2.networks.simple_gru(*args, **kwargs)
        -

        You maybe see gru_step_layer, grumemory in layers.py, gru_unit, gru_group, +

        You may see gru_step_layer, grumemory in layers.py, gru_unit, gru_group, simple_gru in network.py. The reason why there are so many interfaces is that we have two ways to implement recurrent neural network. One way is to use one complete layer to implement rnn (including simple rnn, gru and lstm) -with multiple time steps, such as recurrent_layer, lstmemory, grumemory. But, +with multiple time steps, such as recurrent_layer, lstmemory, grumemory. But the multiplication operation \(W x_t\) is not computed in these layers. See details in their interfaces in layers.py. The other implementation is to use an recurrent group which can ensemble a @@ -792,14 +796,15 @@ gru_group, and gru_group is relatively better than simple_gru.

        @@ -819,8 +824,8 @@ gru_group, and gru_group is relatively better than simple_gru.

        paddle.v2.networks.simple_gru2(*args, **kwargs)
        -

        simple_gru2 is the same with simple_gru, but using grumemory instead -Please see grumemory in layers.py for more detail about the maths. +

        simple_gru2 is the same with simple_gru, but using grumemory instead. +Please refer to grumemory in layers.py for more detail about the math. simple_gru2 is faster than simple_gru.

        The example usage is:

        gru = simple_gru2(input=[layer1], size=256)
@@ -831,14 +836,15 @@ simple_gru2 is faster than simple_gru.
        @@ -859,7 +865,7 @@ simple_gru2 is faster than simple_gru.

        paddle.v2.networks.bidirectional_gru(*args, **kwargs)

        A bidirectional_gru is a recurrent unit that iterates over the input -sequence both in forward and bardward orders, and then concatenate two +sequence both in forward and backward orders, and then concatenate two outputs to form a final output. However, concatenation of two outputs is not the only way to form the final output, you can also, for example, just add them together.

        @@ -875,11 +881,10 @@ just add them together.

      • name (basestring) – bidirectional gru layer name.
      • input (LayerOutput) – input layer.
      • size (int) – gru layer size.
        • -
      • return_seq (bool) – If set False, outputs of the last time step are +
      • return_seq (bool) – If set False, the last time step of output are concatenated and returned. -If set True, the entire output sequences that are -processed in forward and backward directions are -concatenated and returned.
        • +If set True, the entire output sequences in forward +and backward directions are concatenated and returned. @@ -900,7 +905,7 @@ concatenated and returned.
        paddle.v2.networks.simple_attention(*args, **kwargs)
        -

        Calculate and then return a context vector by attention machanism. +

        Calculate and return a context vector with attention mechanism. Size of the context vector equals to size of the encoded_sequence.

        \[ \begin{align}\begin{aligned}a(s_{i-1},h_{j}) & = v_{a}f(W_{a}s_{t-1} + U_{a}h_{j})\\e_{i,j} & = a(s_{i-1}, h_{j})\\a_{i,j} & = \frac{exp(e_{i,j})}{\sum_{k=1}^{T_x}{exp(e_{i,k})}}\\c_{i} & = \sum_{j=1}^{T_{x}}a_{i,j}h_{j}\end{aligned}\end{align} \]
        @@ -924,8 +929,8 @@ Align and Translate for more details. The link is as follows:
        参数:
        • name (basestring) – lstm layer name.
          • -
        • input (LayerOutput) – input layer name.
          • +
        • input (LayerOutput) – layer’s input.
        • size (int) – lstm layer size.
          • -
        • reverse (bool) – whether to process the input data in a reverse order
          • -
        • mat_param_attr (ParameterAttribute) – mixed layer’s matrix projection parameter attribute.
          • +
        • reverse (bool) – process the input in a reverse order or not.
          • +
        • mat_param_attr (ParameterAttribute) – parameter attribute of matrix projection in mixed layer.
        • bias_param_attr (ParameterAttribute|False) – bias parameter attribute. False means no bias, None means default bias.
          • -
        • inner_param_attr (ParameterAttribute) – lstm cell parameter attribute.
          • -
        • act (BaseActivation) – lstm final activiation type
          • -
        • gate_act (BaseActivation) – lstm gate activiation type
          • -
        • state_act (BaseActivation) – lstm state activiation type.
          • -
        • mixed_layer_attr (ExtraLayerAttribute) – mixed layer’s extra attribute.
          • -
        • lstm_cell_attr (ExtraLayerAttribute) – lstm layer’s extra attribute.
          • +
        • inner_param_attr (ParameterAttribute) – parameter attribute of lstm cell.
          • +
        • act (BaseActivation) – last activiation type of lstm.
          • +
        • gate_act (BaseActivation) – gate activiation type of lstm.
          • +
        • state_act (BaseActivation) – state activiation type of lstm.
          • +
        • mixed_layer_attr (ExtraLayerAttribute) – extra attribute of mixed layer.
          • +
        • lstm_cell_attr (ExtraLayerAttribute) – extra attribute of lstm.
        返回:

        lstm layer name.

        +
        返回:

        layer’s output.
        返回类型:

        LayerOutput

        @@ -627,8 +631,8 @@ means default bias.
        paddle.v2.networks.bidirectional_lstm(*args, **kwargs)

        A bidirectional_lstm is a recurrent unit that iterates over the input -sequence both in forward and bardward orders, and then concatenate two -outputs form a final output. However, concatenation of two outputs +sequence both in forward and backward orders, and then concatenate two +outputs to form a final output. However, concatenation of two outputs is not the only way to form the final output, you can also, for example, just add them together.

        Please refer to Neural Machine Translation by Jointly Learning to Align @@ -647,15 +651,14 @@ The link goes as follows:

      • name (basestring) – bidirectional lstm layer name.
      • input (LayerOutput) – input layer.
      • size (int) – lstm layer size.
        • -
      • return_seq (bool) – If set False, outputs of the last time step are +
      • return_seq (bool) – If set False, the last time step of output are concatenated and returned. -If set True, the entire output sequences that are -processed in forward and backward directions are -concatenated and returned.
        • +If set True, the entire output sequences in forward +and backward directions are concatenated and returned.
        返回:

        LayerOutput object accroding to the return_seq.

        +
        返回:

        LayerOutput object.
        返回类型:

        LayerOutput

        @@ -674,9 +677,9 @@ concatenated and returned.
        paddle.v2.networks.gru_unit(*args, **kwargs)
        -

        Define calculations that a gated recurrent unit performs in a single time -step. This function itself is not a recurrent layer, so it can not be -directly used to process sequence inputs. This function is always used in +

        gru_unit defines the calculation process of a gated recurrent unit during a single +time step. This function is not a recurrent layer, so it can not be +directly used to process sequence input. This function is always used in the recurrent_group (see layers.py for more details) to implement attention mechanism.

        Please see grumemory in layers.py for the details about the maths.

        @@ -685,13 +688,13 @@ mechanism.
        参数:
          -
        • input (LayerOutput) – input layer name.
          • +
        • input (LayerOutput) – input layer.
        • memory_boot (LayerOutput | None) – the initialization state of the LSTM cell.
        • name (basestring) – name of the gru group.
        • size (int) – hidden size of the gru.
          • -
        • act (BaseActivation) – type of the activation
          • -
        • gate_act (BaseActivation) – type of the gate activation
          • -
        • gru_layer_attr (ParameterAttribute|False) – Extra parameter attribute of the gru layer.
          • +
        • act (BaseActivation) – activation type of gru
          • +
        • gate_act (BaseActivation) – gate activation type or gru
          • +
        • gru_layer_attr (ExtraLayerAttribute) – Extra attribute of the gru layer.
        参数:
          -
        • input (LayerOutput) – input layer name.
          • +
        • input (LayerOutput) – input layer.
        • memory_boot (LayerOutput | None) – the initialization state of the LSTM cell.
        • name (basestring) – name of the gru group.
        • size (int) – hidden size of the gru.
          • -
        • reverse (bool) – whether to process the input data in a reverse order
          • -
        • act (BaseActivation) – type of the activiation
          • -
        • gate_act (BaseActivation) – type of the gate activiation
          • -
        • gru_bias_attr (ParameterAttribute|False) – bias. False means no bias, None means default bias.
          • -
        • gru_layer_attr (ParameterAttribute|False) – Extra parameter attribute of the gru layer.
          • +
        • reverse (bool) – process the input in a reverse order or not.
          • +
        • act (BaseActivation) – activiation type of gru
          • +
        • gate_act (BaseActivation) – gate activiation type of gru
          • +
        • gru_bias_attr (ParameterAttribute|False|None) – bias parameter attribute of gru layer, +False means no bias, None means default bias.
          • +
        • gru_layer_attr (ExtraLayerAttribute) – Extra attribute of the gru layer.
        参数:
          -
        • input (LayerOutput) – input layer name.
          • +
        • input (LayerOutput) – input layer.
        • name (basestring) – name of the gru group.
        • size (int) – hidden size of the gru.
          • -
        • reverse (bool) – whether to process the input data in a reverse order
          • -
        • act (BaseActivation) – type of the activiation
          • -
        • gate_act (BaseActivation) – type of the gate activiation
          • -
        • gru_bias_attr (ParameterAttribute|False) – bias. False means no bias, None means default bias.
          • -
        • gru_layer_attr (ParameterAttribute|False) – Extra parameter attribute of the gru layer.
          • +
        • reverse (bool) – process the input in a reverse order or not.
          • +
        • act (BaseActivation) – activiation type of gru
          • +
        • gate_act (BaseActivation) – gate activiation type of gru
          • +
        • gru_bias_attr (ParameterAttribute|False|None) – bias parameter attribute of gru layer, +False means no bias, None means default bias.
          • +
        • gru_layer_attr (ExtraLayerAttribute) – Extra attribute of the gru layer.
        参数:
          -
        • input (LayerOutput) – input layer name.
          • +
        • input (LayerOutput) – input layer.
        • name (basestring) – name of the gru group.
        • size (int) – hidden size of the gru.
          • -
        • reverse (bool) – whether to process the input data in a reverse order
          • -
        • act (BaseActivation) – type of the activiation
          • -
        • gate_act (BaseActivation) – type of the gate activiation
          • -
        • gru_bias_attr (ParameterAttribute|False) – bias. False means no bias, None means default bias.
          • -
        • gru_layer_attr (ParameterAttribute|False) – Extra parameter attribute of the gru layer.
          • +
        • reverse (bool) – process the input in a reverse order or not.
          • +
        • act (BaseActivation) – activiation type of gru
          • +
        • gate_act (BaseActivation) – gate activiation type of gru
          • +
        • gru_bias_attr (ParameterAttribute|False|None) – bias parameter attribute of gru layer, +False means no bias, None means default bias.
          • +
        • gru_layer_attr (ExtraLayerAttribute) – Extra attribute of the gru layer.
        参数:
        • name (basestring) – name of the attention model.
        • softmax_param_attr (ParameterAttribute) – parameter attribute of sequence softmax -that is used to produce attention weight
          • -
        • weight_act (Activation) – activation of the attention model
          • +that is used to produce attention weight. +
        • weight_act (BaseActivation) – activation of the attention model.
        • encoded_sequence (LayerOutput) – output of the encoder
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