# Layers¶

## Data layer¶

### data¶

paddle.v2.layer.data

alias of name

## Fully Connected Layers¶

### fc¶

class paddle.v2.layer.fc

Helper for declare fully connected layer.

The example usage is:

fc = fc(input=layer,
size=1024,
bias_attr=False)


which is equal to:

with mixed(size=1024) as fc:
fc += full_matrix_projection(input=layer)

Parameters: name (basestring) – The name of this layer. It is optional. input (paddle.v2.config_base.Layer | list | tuple) – The input of this layer. size (int) – The layer dimension. act (paddle.v2.activation.Base) – Activation Type. paddle.v2.activation.Tanh is the default activation. param_attr (paddle.v2.attr.ParameterAttribute) – The Parameter Attribute|list. bias_attr (paddle.v2.attr.ParameterAttribute | None | bool | Any) – The bias attribute. If the parameter is set to False or an object whose type is not paddle.v2.attr.ParameterAttribute, no bias is defined. If the parameter is set to True, the bias is initialized to zero. layer_attr (paddle.v2.attr.ExtraAttribute | None) – Extra Layer config. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### selective_fc¶

class paddle.v2.layer.selective_fc

Selectived fully connected layer. Different from fc, the output 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 acts exactly like fc.

The simple usage is:

sel_fc = selective_fc(input=input, size=128, act=paddle.v2.activation.Tanh())


## Conv Layers¶

### conv_operator¶

class paddle.v2.layer.conv_operator

Different from img_conv, conv_op is an Operator, which can be used in mixed. And conv_op takes two inputs to perform convolution. The first input is the image and the second is filter kernel. It only supports GPU mode.

The example usage is:

op = conv_operator(img=input1,
filter=input2,
filter_size=3,
num_filters=64,
num_channels=64)

Parameters: img (paddle.v2.config_base.Layer) – The input image. filter (paddle.v2.config_base.Layer) – The input filter. filter_size (int) – The dimension of the filter kernel on the x axis. filter_size_y (int) – 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. num_filters (int) – The number of the output channels. num_channels (int) – 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’. stride (int) – The stride on the x axis. stride_y (int) – The stride on the y axis. If the parameter is not set or set to None, it will be set to ‘stride’ automatically. padding (int) – The padding size on the x axis. padding_y (int) – The padding size on the y axis. If the parameter is not set or set to None, it will be set to ‘padding’ automatically. A ConvOperator Object. ConvOperator

### conv_projection¶

class paddle.v2.layer.conv_projection

Different from img_conv and conv_op, conv_projection is a Projection, which can be used in mixed and concat. It uses cudnn to implement convolution and only supports GPU mode.

The example usage is:

proj = conv_projection(input=input1,
filter_size=3,
num_filters=64,
num_channels=64)


### conv_shift¶

class paddle.v2.layer.conv_shift
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.
$c[i] = \sum_{j=-(N-1)/2}^{(N-1)/2}a_{i+j} * b_{j}$
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 the right size (which is the end of array) to the left.

The example usage is:

conv_shift = conv_shift(a=layer1, b=layer2)

Parameters: name (basestring) – The name of this layer. It is optional. a (paddle.v2.config_base.Layer) – The first input of this layer. b (paddle.v2.config_base.Layer) – The second input of this layer. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### img_conv¶

class paddle.v2.layer.img_conv

Convolution layer for image. Paddle can support both square and non-square input currently.

The details of convolution layer, please refer UFLDL’s convolution .

Convolution Transpose (deconv) layer for image. Paddle can support both square and non-square input currently.

The details of convolution transpose layer, please refer to the following explanation and references therein <http://datascience.stackexchange.com/questions/6107/ what-are-deconvolutional-layers/>_ . The num_channel means input image’s channel number. It may be 1 or 3 when input is raw pixels of image(mono or RGB), or it may be the previous layer’s num_filters * num_group.

There are several groups of filters in PaddlePaddle implementation. Each group will process some channels of the input. For example, if num_channel = 256, group = 4, num_filter=32, the PaddlePaddle will create 32*4 = 128 filters to process the input. The channels will be split into 4 pieces. First 256/4 = 64 channels will be processed by first 32 filters. The rest channels will be processed by the rest groups of filters.

The example usage is:

conv = img_conv(input=data, filter_size=1, filter_size_y=1,
num_channels=8,
num_filters=16, stride=1,
bias_attr=False,


### context_projection¶

class paddle.v2.layer.context_projection

Context Projection.

It just simply reorganizes input sequence, combines “context_len” sequence to one context from context_start. “context_start” will be set to -(context_len - 1) / 2 by default. If context position out of sequence length, padding will be filled as zero if padding_attr = False, otherwise it is trainable.

For example, origin sequence is [A B C D E F G], context len is 3, then after context projection and not set padding_attr, sequence will be [ 0AB ABC BCD CDE DEF EFG FG0 ].

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer, which should be a sequence. context_len (int) – context length. context_start (int) – context start position. Default is -(context_len - 1)/2 padding_attr (bool | paddle.v2.attr.ParameterAttribute) – Padding Parameter Attribute. If false, it means padding always be zero. Otherwise Padding is learnable, and parameter attribute is set by this parameter. Projection Projection

### row_conv¶

class paddle.v2.layer.row_conv

The row convolution is called lookahead convolution. It is firstly introduced in paper of Deep Speech 2: End-to-End Speech Recognition in English and Mandarin .

The bidirectional RNN that learns representation for a sequence by performing a forward and a backward pass through the entire sequence. However, unlike unidirectional RNNs, bidirectional RNNs are challenging to deploy in an online and low-latency setting. The lookahead convolution incorporates information from future subsequences in a computationally efficient manner to improve unidirectional RNNs.

The connection of row convolution is different from the 1D sequence convolution. Assumed that, the future context-length is k, that is to say, it can get the output at timestep t by using the the input feature from t-th timestep to (t+k+1)-th timestep. Assumed that the hidden dim of input activations are d, the activations r_t for the new layer at time-step t are:

$r_{t,r} = \sum_{j=1}^{k + 1} {w_{i,j}h_{t+j-1, i}} \quad ext{for} \quad (1 \leq i \leq d)$

Note

The context_len is k + 1. That is to say, the lookahead step number plus one equals context_len.

row_conv = row_conv(input=input, context_len=3)

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. context_len (int) – The context length equals the lookahead step number plus one. act (paddle.v2.activation.Base) – Activation Type. paddle.v2.activation.Linear is the default activation. param_attr (paddle.v2.attr.ParameterAttribute) – The parameter attribute. See paddle.v2.attr.ParameterAttribute for details. layer_attr (paddle.v2.attr.ExtraAttribute | None) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

## Image Pooling Layer¶

### img_pool¶

class paddle.v2.layer.img_pool

Image pooling Layer.

The details of pooling layer, please refer to ufldl’s pooling .

• ceil_mode=True:
\begin{align}\begin{aligned}w & = 1 + \frac{ceil(input\_width + 2 * padding - pool\_size)}{stride}\\h & = 1 + \frac{ceil(input\_height + 2 * padding\_y - pool\_size\_y)}{stride\_y}\end{aligned}\end{align}
• ceil_mode=False:
\begin{align}\begin{aligned}w & = 1 + \frac{floor(input\_width + 2 * padding - pool\_size)}{stride}\\h & = 1 + \frac{floor(input\_height + 2 * padding\_y - pool\_size\_y)}{stride\_y}\end{aligned}\end{align}

The example usage is:

maxpool = img_pool(input=conv,
pool_size=3,
pool_size_y=5,
num_channels=8,
stride=1,
stride_y=2,
pool_type=MaxPooling())


### spp¶

class paddle.v2.layer.spp

A layer performs spatial pyramid pooling.

Reference:
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

The example usage is:

spp = spp(input=data,
pyramid_height=2,
num_channels=16,
pool_type=MaxPooling())

Parameters: name (basestring) – The name of this layer. It is optional. input (paddle.v2.config_base.Layer) – The input of this layer. num_channels (int) – 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. pool_type – Pooling type. MaxPooling is the default pooling. pyramid_height (int) – The pyramid height of this pooling. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### maxout¶

class paddle.v2.layer.maxout
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 be devided by groups.

Reference:
Maxout Networks Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
\begin{align}\begin{aligned}& out = \max_k (in[n, k, o_c , s])\\& out_{i * s + j} = \max_k in_{ k * o_{c} * s + i * s + j}\\& s = \frac{input.size}{ num\_channels}\\& o_{c} = \frac{num\_channels}{groups}\\& 0 \le i < o_{c}\\& 0 \le j < s\\& 0 \le k < groups\end{aligned}\end{align}

The simple usage is:

maxout = maxout(input,
num_channels=128,
groups=4)

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. num_channels (int) – 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. groups (int) – The group number of input layer. name (basestring) – The name of this layer. It is optional. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### roi_pool¶

class paddle.v2.layer.roi_pool

A layer used by Fast R-CNN to extract feature maps of ROIs from the last feature map.

Parameters: name (basestring) – The Layer Name. input (paddle.v2.config_base.Layer.) – The input layer. rois (paddle.v2.config_base.Layer.) – The input ROIs’ data. pooled_width (int) – The width after pooling. pooled_height (int) – The height after pooling. spatial_scale (float) – The spatial scale between the image and feature map. num_channels (int) – number of input channel. paddle.v2.config_base.Layer

## Norm Layer¶

### img_cmrnorm¶

class paddle.v2.layer.img_cmrnorm

Response normalization across feature maps.

Reference:
ImageNet Classification with Deep Convolutional Neural Networks

The example usage is:

norm = img_cmrnorm(input=net, size=5)

Parameters: name (basestring) – The name of this layer. It is optional. input (paddle.v2.config_base.Layer) – The input of this layer. size (int) – Normalize in number of $size$ feature maps. scale (float) – The hyper-parameter. power (float) – The hyper-parameter. 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. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attributes. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### batch_norm¶

class paddle.v2.layer.batch_norm

Batch Normalization Layer. The notation of this layer is as follows.

$x$ is the input features over a mini-batch.

$\begin{split}\mu_{\beta} &\gets \frac{1}{m} \sum_{i=1}^{m} x_i \qquad &//\ \ mini-batch\ mean \\ \sigma_{\beta}^{2} &\gets \frac{1}{m} \sum_{i=1}^{m}(x_i - \ \mu_{\beta})^2 \qquad &//\ mini-batch\ variance \\ \hat{x_i} &\gets \frac{x_i - \mu_\beta} {\sqrt{\ \sigma_{\beta}^{2} + \epsilon}} \qquad &//\ normalize \\ y_i &\gets \gamma \hat{x_i} + \beta \qquad &//\ scale\ and\ shift\end{split}$
Reference:
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

The example usage is:

norm = batch_norm(input=net, act=paddle.v2.activation.Relu())

Parameters: name (basestring) – The name of this layer. It is optional. input (paddle.v2.config_base.Layer) – This layer’s input which is to be performed batch normalization on. batch_norm_type (None | string, None or "batch_norm" or "cudnn_batch_norm" or "mkldnn_batch_norm") – We have batch_norm, mkldnn_batch_norm and cudnn_batch_norm. batch_norm supports CPU, MKLDNN and GPU. cudnn_batch_norm requires cuDNN version greater or equal to v4 (>=v4). But cudnn_batch_norm is faster and needs less memory than batch_norm. mkldnn_batch_norm requires use_mkldnn is enabled. By default (None), we will automatically select cudnn_batch_norm for GPU, mkldnn_batch_norm for MKLDNN and batch_norm for CPU. Users can specify the batch norm type. If you use cudnn_batch_norm, we suggested you use latest version, such as v5.1. act (paddle.v2.activation.Base) – Activation type. paddle.v2.activation.Relu is the default activation. num_channels (int) – 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. bias_attr (paddle.v2.attr.ParameterAttribute | None | bool | Any) – $\beta$. The bias attribute. If the parameter is set to False or an object whose type is not paddle.v2.attr.ParameterAttribute, no bias is defined. If the parameter is set to True, the bias is initialized to zero. param_attr (paddle.v2.attr.ParameterAttribute) – $\gamma$. The parameter attribute. See paddle.v2.attr.ParameterAttribute for details. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. use_global_stats (bool | None.) – Whether use moving mean/variance statistics during testing peroid. If the parameter is set to None or True, it will use moving mean/variance statistics during testing. If the parameter is set to False, it will use the mean and variance of the current batch of test data. epsilon (float.) – The small constant added to the variance to improve numeric stability. moving_average_fraction (float.) – Factor used in the moving average computation. $runningMean = newMean*(1-factor) + runningMean*factor$ mean_var_names (string list) – [mean name, variance name] paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### sum_to_one_norm¶

class paddle.v2.layer.sum_to_one_norm

A layer for sum-to-one normalization, which is used in NEURAL TURING MACHINE.

$out[i] = \frac {in[i]} {\sum_{k=1}^N in[k]}$

where $in$ is a (batchSize x dataDim) input vector, and $out$ is a (batchSize x dataDim) output vector.

The example usage is:

sum_to_one_norm = sum_to_one_norm(input=layer)

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. name (basestring) – The name of this layer. It is optional. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### cross_channel_norm¶

class paddle.v2.layer.cross_channel_norm

Normalize a layer’s output. This layer is necessary for ssd. This layer applys normalize across the channels of each sample to a conv layer’s output and scale the output by a group of trainable factors which dimensions equal to the channel’s number.

Parameters: name (basestring) – The name of this layer. It is optional. input (paddle.v2.config_base.Layer) – The input of this layer. param_attr (paddle.v2.attr.ParameterAttribute) – The Parameter Attribute|list. paddle.v2.config_base.Layer

### row_l2_norm¶

class paddle.v2.layer.row_l2_norm

A layer for L2-normalization in each row.

$out[i] = \frac{in[i]} {\sqrt{\sum_{k=1}^N in[k]^{2}}}$

where the size of $in$ is (batchSize x dataDim) , and the size of $out$ is a (batchSize x dataDim) .

The example usage is:

row_l2_norm = row_l2_norm(input=layer)

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. name (basestring) – The name of this layer. It is optional. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

## Recurrent Layers¶

### recurrent¶

class paddle.v2.layer.recurrent

Simple recurrent unit layer. It is just a fully connect layer through both time and neural network.

For each sequence [start, end] it performs the following computation:

$\begin{split}out_{i} = act(in_{i}) \ \ \text{for} \ i = start \\ out_{i} = act(in_{i} + out_{i-1} * W) \ \ \text{for} \ start < i <= end\end{split}$

If reversed is true, the order is reversed:

$\begin{split}out_{i} = act(in_{i}) \ \ \text{for} \ i = end \\ out_{i} = act(in_{i} + out_{i+1} * W) \ \ \text{for} \ start <= i < end\end{split}$

### lstmemory¶

class paddle.v2.layer.lstmemory

Long Short-term Memory Cell.

The memory cell was implemented as 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}

NOTE: In PaddlePaddle’s implementation, the multiplications $W_{xi}x_{t}$ , $W_{xf}x_{t}$, $W_{xc}x_t$, $W_{xo}x_{t}$ are not done in the lstmemory layer, so an additional mixed with full_matrix_projection or a fc must be included in the configuration file to complete the input-to-hidden mappings before lstmemory is called.

NOTE: This is a low level user interface. You can use network.simple_lstm to config a simple plain lstm layer.

Reference:
Generating Sequences With Recurrent Neural Networks

### grumemory¶

class paddle.v2.layer.grumemory

Gate Recurrent Unit Layer.

The memory cell was implemented as follow equations.

1. update gate $z$: defines how much of the previous memory to keep around or the unit updates its activations. The update gate is computed by:

$z_t = \sigma(W_{z}x_{t} + U_{z}h_{t-1} + b_z)$

2. reset gate $r$: determines how to combine the new input with the previous memory. The reset gate is computed similarly to the update gate:

$r_t = \sigma(W_{r}x_{t} + U_{r}h_{t-1} + b_r)$

3. The candidate activation $\tilde{h_t}$ is computed similarly to that of the traditional recurrent unit:

${\tilde{h_t}} = tanh(W x_{t} + U (r_{t} \odot h_{t-1}) + b)$

4. The hidden activation $h_t$ of the GRU at time t is a linear interpolation between the previous activation $h_{t-1}$ and the candidate activation $\tilde{h_t}$:

$h_t = (1 - z_t) h_{t-1} + z_t {\tilde{h_t}}$

NOTE: In PaddlePaddle’s implementation, the multiplication operations $W_{r}x_{t}$, $W_{z}x_{t}$ and $W x_t$ are not performed in gate_recurrent layer. Consequently, an additional mixed with full_matrix_projection or a fc must be included before grumemory is called.

Reference:
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

The simple usage is:

gru = grumemory(input)

Parameters: name (basestring) – The name of this layer. It is optional. input (paddle.v2.config_base.Layer.) – The input of this layer. size (int) – DEPRECATED. The dimension of the gru cell. reverse (bool) – Whether the input sequence is processed in a reverse order. act (paddle.v2.activation.Base) – Activation type, paddle.v2.activation.Tanh is the default. This activation affects the ${\tilde{h_t}}$. gate_act (paddle.v2.activation.Base) – Activation type of this layer’s two gates. paddle.v2.activation.Sigmoid is the default activation. This activation affects the $z_t$ and $r_t$. It is the $\sigma$ in the above formula. bias_attr (paddle.v2.attr.ParameterAttribute | None | bool | Any) – The bias attribute. If the parameter is set to False or an object whose type is not paddle.v2.attr.ParameterAttribute, no bias is defined. If the parameter is set to True, the bias is initialized to zero. param_attr (paddle.v2.attr.ParameterAttribute) – The parameter attribute. See paddle.v2.attr.ParameterAttribute for details. layer_attr (paddle.v2.attr.ExtraAttribute | None) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

## Recurrent Layer Group¶

### memory¶

class paddle.v2.layer.memory

The memory takes a layer’s output at previous time step as its own output.

If boot_bias, the activation of the bias is the initial value of the memory.

If boot_with_const_id is set, then the memory’s output at the first time step is a IndexSlot, the Arguments.ids()[0] is this cost_id.

If boot is specified, the memory’s output at the first time step will be the boot’s output.

In other case, the default memory’s output at the first time step is zero.

mem = memory(size=256, name='state')
state = fc(input=mem, size=256, name='state')


If you do not want to specify the name, you can also use set_input() to specify the layer to be remembered as the following:

mem = memory(size=256)
state = fc(input=mem, size=256)
mem.set_input(mem)

Parameters: name (basestring) – The name of the layer which this memory remembers. If name is None, user should call set_input() to specify the name of the layer which this memory remembers. size (int) – The dimensionality of memory. memory_name (basestring) – The name of the memory. It is ignored when name is provided. is_seq (bool) – DEPRECATED. is sequence for boot boot (paddle.v2.config_base.Layer | None) – This parameter specifies memory’s output at the first time step and the output is boot’s output. boot_bias (paddle.v2.attr.ParameterAttribute | None) – The bias attribute of memory’s output at the first time step. If the parameter is set to False or an object whose type is not paddle.v2.attr.ParameterAttribute, no bias is defined. If the parameter is set to True, the bias is initialized to zero. boot_bias_active_type (paddle.v2.activation.Base) – Activation type for memory’s bias at the first time step. paddle.v2.activation.Linear is the default activation. boot_with_const_id (int) – This parameter specifies memory’s output at the first time step and the output is an index. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### recurrent_group¶

class paddle.v2.layer.recurrent_group

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 useful for attention-based models, or Neural Turning Machine like models.

The basic usage (time steps) is:

def step(input):
output = fc(input=layer,
size=1024,
bias_attr=False)
return output

group = recurrent_group(input=layer,
step=step)


You can see following configs for further usages:

• time steps: lstmemory_group, paddle/gserver/tests/sequence_group.conf, demo/seqToseq/seqToseq_net.py
Parameters: step (callable) – 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 a time step result. Then gather outputs of each time step into layer group’s output. name (basestring) – The recurrent_group’s name. It is optional. input (paddle.v2.config_base.Layer | StaticInput | SubsequenceInput | list | tuple) – Input links array. paddle.v2.config_base.Layer 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 over time. It’s a mechanism to access layer outside step function. reverse (bool) – If reverse is set to True, the recurrent unit will process the input sequence in a reverse order. targetInlink (paddle.v2.config_base.Layer | SubsequenceInput) – DEPRECATED. The input layer which share info with layer group’s output Param input specifies multiple input layers. For SubsequenceInput inputs, config should assign one input layer that share info(the number of sentences and the number of words in each sentence) with all layer group’s outputs. targetInlink should be one of the layer group’s input. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### lstm_step¶

class paddle.v2.layer.lstm_step

LSTM Step Layer. This function is used only in recurrent_group. The lstm equations are shown as follows.

\begin{align}\begin{aligned}i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + W_{c_i}c_{t-1} + b_i)\\f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + W_{c_f}c_{t-1} + b_f)\\c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t+W_{h_c}h_{t-1} + b_c)\\o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + W_{c_o}c_t + b_o)\\h_t & = o_t tanh(c_t)\end{aligned}\end{align}

The input of lstm step is $Wx_t + Wh_{t-1}$, and user should use mixed and full_matrix_projection to calculate these input vectors.

The state of lstm step is $c_{t-1}$. And lstm step layer will do

\begin{align}\begin{aligned}i_t = \sigma(input + W_{ci}c_{t-1} + b_i)\\...\end{aligned}\end{align}

This layer has two outputs. The default output is $h_t$. The other output is $o_t$, whose name is ‘state’ and users can use get_output to extract this output.

### gru_step¶

class paddle.v2.layer.gru_step

### get_output¶

class paddle.v2.layer.get_output

Get layer’s output by name. In PaddlePaddle, a layer might return multiple values, but returns one layer’s output. If the user wants to use another output besides the default one, please use get_output first to get the output from input.

Parameters: name (basestring) – The name of this layer. It is optional. input (paddle.v2.config_base.Layer) – The input layer. And this layer should contain multiple outputs. arg_name (basestring) – The name of the output to be extracted from the input layer. layer_attr – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

## Mixed Layer¶

### mixed¶

class paddle.v2.layer.mixed

Mixed Layer. A mixed layer will add all inputs together, then activate. Each inputs is a projection or operator.

There are two styles of usages.

1. When not set inputs parameter, use mixed like this:
with mixed(size=256) as m:
m += full_matrix_projection(input=layer1)
m += identity_projection(input=layer2)

1. You can also set all inputs when invoke mixed as follows:
m = mixed(size=256,
input=[full_matrix_projection(input=layer1),
full_matrix_projection(input=layer2)])

Parameters: name (basestring) – mixed layer name. Can be referenced by other layer. size (int) – layer size. input – The input of this layer. It is an optional parameter. If set, then this function will just return layer’s name. act (paddle.v2.activation.Base) – Activation Type. paddle.v2.activation.Linear is the default activation. bias_attr (paddle.v2.attr.ParameterAttribute | None | bool | Any) – The bias attribute. If the parameter is set to False or an object whose type is not paddle.v2.attr.ParameterAttribute, no bias is defined. If the parameter is set to True, the bias is initialized to zero. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer config. Default is None. MixedLayerType object can add inputs or layer name. MixedLayerType

### embedding¶

class paddle.v2.layer.embedding

Define a embedding Layer.

Parameters: name (basestring) – The name of this layer. It is optional. input (paddle.v2.config_base.Layer) – The input of this layer, which must be Index Data. size (int) – The embedding dimension. param_attr (paddle.v2.attr.ParameterAttribute | None) – The embedding parameter attribute. See paddle.v2.attr.ParameterAttribute for details. layer_attr (paddle.v2.attr.ExtraAttribute | None) – Extra layer Config. Default is None. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### scaling_projection¶

class paddle.v2.layer.scaling_projection

scaling_projection multiplies the input with a scalar parameter and add to the output.

$out += w * in$

The example usage is:

proj = scaling_projection(input=layer)

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. param_attr (paddle.v2.attr.ParameterAttribute) – Parameter config, None if use default. A ScalingProjection object ScalingProjection

### dotmul_projection¶

class paddle.v2.layer.dotmul_projection

DotMulProjection with a layer as input. It performs element-wise multiplication with weight.

$out.row[i] += in.row[i] .* weight$

where $.*$ means element-wise multiplication.

The example usage is:

proj = dotmul_projection(input=layer)

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. param_attr (paddle.v2.attr.ParameterAttribute) – Parameter config, None if use default. A DotMulProjection Object. DotMulProjection

### dotmul_operator¶

class paddle.v2.layer.dotmul_operator

DotMulOperator takes two inputs and performs element-wise multiplication:

$out.row[i] += scale * (a.row[i] .* b.row[i])$

where $.*$ means element-wise multiplication, and scale is a config scalar, its default value is one.

The example usage is:

op = dotmul_operator(a=layer1, b=layer2, scale=0.5)

Parameters: a (paddle.v2.config_base.Layer) – Input layer1 b (paddle.v2.config_base.Layer) – Input layer2 scale (float) – config scalar, default value is one. A DotMulOperator Object. DotMulOperator

### full_matrix_projection¶

class paddle.v2.layer.full_matrix_projection

Full Matrix Projection. It performs full matrix multiplication.

$out.row[i] += in.row[i] * weight$

There are two styles of usage.

1. When used in mixed like this, you can only set the input:
with mixed(size=100) as m:
m += full_matrix_projection(input=layer)

1. When used as an independant object like this, you must set the size:
proj = full_matrix_projection(input=layer,
size=100,
param_attr=ParamAttr(name='_proj'))

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. size (int) – The parameter size. Means the width of parameter. param_attr (paddle.v2.attr.ParameterAttribute) – Parameter config, None if use default. A FullMatrixProjection Object. FullMatrixProjection

### identity_projection¶

class paddle.v2.layer.identity_projection
1. IdentityProjection if offset=None. It performs:
$out.row[i] += in.row[i]$

The example usage is:

proj = identity_projection(input=layer)


2. IdentityOffsetProjection if offset!=None. It likes IdentityProjection, but layer size may be smaller than input size. It select dimesions [offset, offset+layer_size) from input:

$out.row[i] += in.row[i + \textrm{offset}]$

The example usage is:

proj = identity_projection(input=layer,
offset=10)


Note that both of two projections should not have any parameter.

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. offset (int) – Offset, None if use default. A IdentityProjection or IdentityOffsetProjection object IdentityProjection or IdentityOffsetProjection

### slice_projection¶

class paddle.v2.layer.slice_projection

slice_projection can slice the input value into multiple parts, and then select some of them to merge into a new output.

$output = [input.slices()]$

The example usage is:

proj = slice_projection(input=layer, slices=[(0, 10), (20, 30)])


Note that slice_projection should not have any parameter.

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. slices (pair of int) – An array of slice parameters. Each slice contains the start and end offsets based on the input. A SliceProjection object SliceProjection

### table_projection¶

class paddle.v2.layer.table_projection

Table Projection. It selects rows from parameter where row_id is in input_ids.

$out.row[i] += table.row[ids[i]]$

where $out$ is output, $table$ is parameter, $ids$ is input_ids, and $i$ is row_id.

There are two styles of usage.

1. When used in mixed like this, you can only set the input:
with mixed(size=100) as m:
m += table_projection(input=layer)

1. When used as an independant object like this, you must set the size:
proj = table_projection(input=layer,
size=100,
param_attr=ParamAttr(name='_proj'))

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer, which must contains id fields. size (int) – The parameter size. Means the width of parameter. param_attr (paddle.v2.attr.ParameterAttribute) – Parameter config, None if use default. A TableProjection Object. TableProjection

### trans_full_matrix_projection¶

class paddle.v2.layer.trans_full_matrix_projection

Different from full_matrix_projection, this projection performs matrix multiplication, using transpose of weight.

$out.row[i] += in.row[i] * w^\mathrm{T}$

$w^\mathrm{T}$ means transpose of weight. The simply usage is:

proj = trans_full_matrix_projection(input=layer,
size=100,
param_attr=ParamAttr(
name='_proj',
initial_mean=0.0,
initial_std=0.01))

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. size (int) – The parameter size. Means the width of parameter. param_attr (paddle.v2.attr.ParameterAttribute) – Parameter config, None if use default. A TransposedFullMatrixProjection Object. TransposedFullMatrixProjection

## Aggregate Layers¶

### AggregateLevel¶

class paddle.v2.layer.AggregateLevel

• SequenceType.NO_SEQUENCE means the sample is not a sequence.
• SequenceType.SEQUENCE means the sample is a sequence.
• SequenceType.SUB_SEQUENCE means the sample is a nested sequence, each timestep of which is also a sequence.

Accordingly, AggregateLevel supports two modes:

• AggregateLevel.TO_NO_SEQUENCE means the aggregation acts on each timestep of a sequence, both SUB_SEQUENCE and SEQUENCE will be aggregated to NO_SEQUENCE.
• AggregateLevel.TO_SEQUENCE means the aggregation acts on each sequence of a nested sequence, SUB_SEQUENCE will be aggregated to SEQUENCE.

### pooling¶

class paddle.v2.layer.pooling

Pooling layer for sequence inputs, not used for Image.

If stride > 0, this layer slides a window whose size is determined by stride, and return the pooling value of the window as the output. Thus, a long sequence will be shorten.

The parameter stride specifies the intervals at which to apply the pooling operation. Note that for sequence with sub-sequence, the default value of stride is -1.

The example usage is:

seq_pool = pooling(input=layer,
pooling_type=AvgPooling(),
agg_level=AggregateLevel.TO_NO_SEQUENCE)

Parameters: agg_level (AggregateLevel) – AggregateLevel.TO_NO_SEQUENCE or AggregateLevel.TO_SEQUENCE name (basestring) – The name of this layer. It is optional. input (paddle.v2.config_base.Layer) – The input of this layer. pooling_type (BasePoolingType | None) – Type of pooling, MaxPooling(default), AvgPooling, SumPooling, SquareRootNPooling. stride (Int) – The step size between successive pooling regions. bias_attr (paddle.v2.attr.ParameterAttribute | None | bool | Any) – The bias attribute. If the parameter is set to False or an object whose type is not paddle.v2.attr.ParameterAttribute, no bias is defined. If the parameter is set to True, the bias is initialized to zero. layer_attr (paddle.v2.attr.ExtraAttribute | None) – The Extra Attributes for layer, such as dropout. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### last_seq¶

class paddle.v2.layer.last_seq

Get Last Timestamp Activation of a sequence.

If stride > 0, this layer will slide a window whose size is determined by stride, and return the last value of the sequence in the window as the output. Thus, a long sequence will be shortened. Note that for sequence with sub-sequence, the default value of stride is -1.

The simple usage is:

seq = last_seq(input=layer)

Parameters: agg_level (AggregateLevel) – Aggregated level name (basestring) – The name of this layer. It is optional. input (paddle.v2.config_base.Layer) – The input of this layer. stride (int) – The step size between successive pooling regions. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### first_seq¶

class paddle.v2.layer.first_seq

Get First Timestamp Activation of a sequence.

If stride > 0, this layer will slide a window whose size is determined by stride, and return the first value of the sequence in the window as the output. Thus, a long sequence will be shortened. Note that for sequence with sub-sequence, the default value of stride is -1.

The simple usage is:

seq = first_seq(input=layer)

Parameters: agg_level (AggregateLevel) – aggregation level name (basestring) – The name of this layer. It is optional. input (paddle.v2.config_base.Layer) – The input of this layer. stride (int) – The step size between successive pooling regions. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### concat¶

class paddle.v2.layer.concat

Concatenate all input vectors to one vector. Inputs can be a list of paddle.v2.config_base.Layer or a list of projection.

The example usage is:

concat = concat(input=[layer1, layer2])

Parameters: name (basestring) – The name of this layer. It is optional. input (list | tuple | collections.Sequence) – The input layers or projections act (paddle.v2.activation.Base) – Activation type. paddle.v2.activation.Identity is the default activation. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### seq_concat¶

class paddle.v2.layer.seq_concat

Concatenate sequence a and sequence b.

Inputs:
• a = [a1, a2, ..., am]
• b = [b1, b2, ..., bn]

Output: [a1, ..., am, b1, ..., bn]

Note that the above computation is for one sample. Multiple samples are processed in one batch.

The example usage is:

concat = seq_concat(a=layer1, b=layer2)

Parameters: name (basestring) – The name of this layer. It is optional. a (paddle.v2.config_base.Layer) – The first input sequence layer b (paddle.v2.config_base.Layer) – The second input sequence layer act (paddle.v2.activation.Base) – Activation type. paddle.v2.activation.Identity is the default activation. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. bias_attr (paddle.v2.attr.ParameterAttribute | None | bool | Any) – The bias attribute. If the parameter is set to False or an object whose type is not paddle.v2.attr.ParameterAttribute, no bias is defined. If the parameter is set to True, the bias is initialized to zero. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### seq_slice¶

class paddle.v2.layer.seq_slice

seq_slice will return one or several sub-sequences from the input sequence layer given start and end indices.

• If only start indices are given, and end indices are set to None, this layer slices the input sequence from the given start indices to its end.
• If only end indices are given, and start indices are set to None, this layer slices the input sequence from its beginning to the given end indices.
• If start and end indices are both given, they should have the same number of elements.

If start or end indices contains more than one elements, the input sequence will be sliced for multiple times.

seq_silce = seq_slice(input=input_seq,
starts=start_pos, ends=end_pos)

Parameters: name (basestring) – The name of this layer. It is optional. input (paddle.v2.config_base.Layer) – The input of this layer, which should be a sequence. starts (paddle.v2.config_base.Layer | None) – The start indices to slice the input sequence. ends (paddle.v2.config_base.Layer | None) – The end indices to slice the input sequence. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### sub_nested_seq¶

class paddle.v2.layer.sub_nested_seq

The sub_nested_seq accepts two inputs: the first one is a nested sequence; the second one is a set of selceted indices in the nested sequence.

Then sub_nest_seq trims the first nested sequence input according to the selected indices to form a new output. This layer is useful in beam training.

The example usage is:

sub_nest_seq = sub_nested_seq(input=data, selected_indices=selected_ids)

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. It is a nested sequence. selected_indices – A set of sequence indices in the nested sequence. name (basestring) – The name of this layer. It is optional. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

## Reshaping Layers¶

### block_expand¶

class paddle.v2.layer.block_expand
Expand feature map to minibatch matrix.
• matrix width is: block_y * block_x * num_channels
• matirx height is: outputH * outputW
\begin{align}\begin{aligned}outputH = 1 + (2 * padding_y + imgSizeH - block_y + stride_y - 1) / stride_y\\outputW = 1 + (2 * padding_x + imgSizeW - block_x + stride_x - 1) / stride_x\end{aligned}\end{align}

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 is outputH * outputW and the dimension of each time step is block_y * block_x * num_channels. This layer can be used after convolutional neural network, and before recurrent neural network.

The simple usage is:

block_expand = block_expand(input=layer,
num_channels=128,
stride_x=1,
stride_y=1,
block_x=1,
block_x=3)

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. num_channels (int) – 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. block_x (int) – The width of sub block. block_y (int) – The width of sub block. stride_x (int) – The stride size in horizontal direction. stride_y (int) – The stride size in vertical direction. padding_x (int) – The padding size in horizontal direction. padding_y (int) – The padding size in vertical direction. name (basestring.) – The name of this layer. It is optional. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### ExpandLevel¶

class paddle.v2.layer.ExpandLevel

ExpandLevel supports two modes:

• ExpandLevel.FROM_NO_SEQUENCE means the expansion acts on NO_SEQUENCE, which will be expanded to SEQUENCE or SUB_SEQUENCE.
• ExpandLevel.FROM_SEQUENCE means the expansion acts on SEQUENCE, which will be expanded to SUB_SEQUENCE.

### expand¶

class paddle.v2.layer.expand

A layer for expanding dense data or (sequence data where the length of each sequence is one) to sequence data.

The example usage is:

expand = expand(input=layer1,
expand_as=layer2,
expand_level=ExpandLevel.FROM_NO_SEQUENCE)

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. expand_as (paddle.v2.config_base.Layer) – Expand the input according to this layer’s sequence infomation. And after the operation, the input expanded will have the same number of elememts as this layer. name (basestring) – The name of this layer. It is optional. bias_attr (paddle.v2.attr.ParameterAttribute | None | bool | Any) – The bias attribute. If the parameter is set to False or an object whose type is not paddle.v2.attr.ParameterAttribute, no bias is defined. If the parameter is set to True, the bias is initialized to zero. expand_level (ExpandLevel) – Whether the input layer is a sequence or the element of a sequence. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### repeat¶

class paddle.v2.layer.repeat

A layer for repeating the input for num_repeats times.

If as_row_vector:

$y = [x_1,\cdots, x_n, \cdots, x_1, \cdots, x_n]$

If not as_row_vector:

$y = [x_1,\cdots, x_1, \cdots, x_n, \cdots, x_n]$

The example usage is:

expand = repeat(input=layer, num_repeats=4)

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. num_repeats (int) – The times of repeating the input. name (basestring) – The name of this layer. It is optional. as_row_vector (bool) – Whether to treat the input as row vectors or not. If the parameter is set to True, the repeating operation will be performed in the column direction. Otherwise, it will be performed in the row direction. act (paddle.v2.activation.Base) – Activation type. paddle.v2.activation.Identity is the default activation. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### rotate¶

class paddle.v2.layer.rotate

A layer for rotating 90 degrees (clock-wise) for each feature channel, usually used when the input sample is some image or feature map.

$y(j,i,:) = x(M-i-1,j,:)$

where $x$ is (M x N x C) input, and $y$ is (N x M x C) output.

The example usage is:

rot = rotate(input=layer,
height=100,
width=100)

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. height (int) – The height of the sample matrix. width (int) – The width of the sample matrix. name (basestring) – The name of this layer. It is optional. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### seq_reshape¶

class paddle.v2.layer.seq_reshape

A layer for reshaping the sequence. Assume the input sequence has T instances, the dimension of each instance is M, and the input reshape_size is N, then the output sequence has T*M/N instances, the dimension of each instance is N.

Note that T*M/N must be an integer.

The example usage is:

reshape = seq_reshape(input=layer, reshape_size=4)

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. reshape_size (int) – The dimension of the reshaped sequence. name (basestring) – The name of this layer. It is optional. act (paddle.v2.activation.Base) – Activation type. paddle.v2.activation.Identity is the default activation. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. bias_attr (paddle.v2.attr.ParameterAttribute | None | bool | Any) – The bias attribute. If the parameter is set to False or an object whose type is not paddle.v2.attr.ParameterAttribute, no bias is defined. If the parameter is set to True, the bias is initialized to zero. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

## Math Layers¶

class paddle.v2.layer.addto

$y = f(\sum_{i} x_i + b)$

where $y$ is output, $x$ is input, $b$ is bias, and $f$ is activation function.

The example usage is:

addto = addto(input=[layer1, layer2],
bias_attr=False)


This layer just simply adds all input layers together, then activates the sum. All inputs should share the same dimension, which is also the dimension of this layer’s output.

There is no weight matrix for each input, because it just a simple add operation. If you want a complicated operation before add, please use mixed.

### linear_comb¶

class paddle.v2.layer.linear_comb
A layer for weighted sum of vectors takes two inputs.
• Input: size of weights is M
size of vectors is M*N
• Output: a vector of size=N
$z(i) = \sum_{j=0}^{M-1} x(j) y(i+Nj)$

where $0 \le i \le N-1$

Or in the matrix notation:

$z = x^\mathrm{T} Y$
In this formular:
• $x$: weights
• $y$: vectors.
• $z$: the output.

Note that the above computation is for one sample. Multiple samples are processed in one batch.

The simple usage is:

linear_comb = linear_comb(weights=weight, vectors=vectors,
size=elem_dim)

Parameters: weights (paddle.v2.config_base.Layer) – The weight layer. vectors (paddle.v2.config_base.Layer) – The vector layer. size (int) – The dimension of this layer. name (basestring) – The name of this layer. It is optional. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### interpolation¶

class paddle.v2.layer.interpolation

This layer performs linear interpolation on two inputs, which is used in NEURAL TURING MACHINE.

$y.row[i] = w[i] * x_1.row[i] + (1 - w[i]) * x_2.row[i]$

where $x_1$ and $x_2$ are two (batchSize x dataDim) inputs, $w$ is (batchSize x 1) weight vector, and $y$ is (batchSize x dataDim) output.

The example usage is:

interpolation = interpolation(input=[layer1, layer2], weight=layer3)

Parameters: input (list | tuple) – The input of this layer. weight (paddle.v2.config_base.Layer) – Weight layer. name (basestring) – The name of this layer. It is optional. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### bilinear_interp¶

class paddle.v2.layer.bilinear_interp

This layer implements bilinear interpolation on convolutional layer’s output.

The simple usage is:

bilinear = bilinear_interp(input=layer1, out_size_x=64, out_size_y=64)

Parameters: input (paddle.v2.config_base.Layer.) – The input of this layer. out_size_x (int) – The width of the output. out_size_y (int) – The height of the output. name (basestring) – The name of this layer. It is optional. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### dot_prod¶

class paddle.v2.layer.dot_prod

A layer for computing the dot product of two vectors.

The example usage is:

dot_prod = dot_prod(input1=vec1, input2=vec2)

Parameters: name (basestring) – The name of this layer. It is optional. input1 (paddle.v2.config_base.Layer) – The first input layer. input2 (paddle.v2.config_base.Layer) – The second input layer. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### out_prod¶

class paddle.v2.layer.out_prod

A layer for computing the outer product of two vectors The result is a matrix of size(input1) x size(input2)

The example usage is:

out_prod = out_prod(input1=vec1, input2=vec2)

Parameters: name (basestring) – The name of this layer. It is optional. input1 – The first input layer. input2 (paddle.v2.config_base.Layer) – The second input layer. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### power¶

class paddle.v2.layer.power

This layer applies a power function to a vector element-wise, which is used in NEURAL TURING MACHINE.

$y = x^w$

where $x$ is an input vector, $w$ is a scalar exponent, and $y$ is an output vector.

The example usage is:

power = power(input=layer1, weight=layer2)

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. weight (paddle.v2.config_base.Layer) – The exponent of the power. name (basestring) – The name of this layer. It is optional. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### scaling¶

class paddle.v2.layer.scaling

A layer for multiplying input vector by weight scalar.

$y = w x$

where $x$ is size=dataDim input, $w$ is size=1 weight, and $y$ is size=dataDim output.

Note that the above computation is for one sample. Multiple samples are processed in one batch.

The example usage is:

scale = scaling(input=layer1, weight=layer2)

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. weight (paddle.v2.config_base.Layer) – The weight of each sample. name (basestring) – The name of this layer. It is optional. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### clip¶

class paddle.v2.layer.clip

A layer for clipping the input value by the threshold.

$out[i] = \min (\max (in[i],p_{1} ),p_{2} )$
clip = clip(input=input, min=-10, max=10)

Parameters: name (basestring) – The name of this layer. It is optional. input (paddle.v2.config_base.Layer.) – The input of this layer. min (float) – The lower threshold for clipping. max (float) – The upper threshold for clipping. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### resize¶

class paddle.v2.layer.resize

The resize layer resizes the input matrix with a shape of [Height, Width] into the output matrix with a shape of [Height x Width / size, size], where size is the parameter of this layer indicating the output dimension.

Parameters: input (paddle.v2.config_base.Layer.) – The input of this layer. name (basestring) – The name of this layer. It is optional. size (int) – The resized output dimension of this layer. A paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### slope_intercept¶

class paddle.v2.layer.slope_intercept

This layer for applying a slope and an intercept to the input.

$y = slope * x + intercept$

The simple usage is:

scale = slope_intercept(input=input, slope=-1.0, intercept=1.0)

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. name (basestring) – The name of this layer. It is optional. slope (float) – The scale factor. intercept (float) – The offset. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### tensor¶

class paddle.v2.layer.tensor

This layer performs tensor operation on two inputs. For example:

$y_{i} = a * W_{i} * {b^\mathrm{T}}, i=0,1,...,K-1$
In this formular:
• $a$: the first input contains M elements.
• $b$: the second input contains N elements.
• $y_{i}$: the i-th element of y.
• $W_{i}$: the i-th learned weight, shape if [M, N]
• $b^\mathrm{T}$: the transpose of $b_{2}$.

The simple usage is:

tensor = tensor(a=layer1, b=layer2, size=1000)


### cos_sim¶

class paddle.v2.layer.cos_sim

Cosine Similarity Layer. The cosine similarity equation is here.

$similarity = cos(\theta) = {\mathbf{a} \cdot \mathbf{b} \over \|\mathbf{a}\| \|\mathbf{b}\|}$

The size of a is M, size of b is M*N, Similarity will be calculated N times by step M. The output size is N. The scale will be multiplied to similarity.

Note that the above computation is for one sample. Multiple samples are processed in one batch.

The example usage is:

cos = cos_sim(a=layer1, b=layer2, size=3)

Parameters: name (basestring) – The name of this layer. It is optional. a (paddle.v2.config_base.Layer) – The first input of this layer. b (paddle.v2.config_base.Layer) – The second input of this layer. scale (float) – The scale of the cosine similarity. 1 is the default value. size (int) – The dimension of this layer. NOTE size_a * size should equal size_b. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### l2_distance¶

class paddle.v2.layer.l2_distance

This layer calculates and returns the Euclidean distance between two input vectors x and y. The equation is as follows:

$l2_distance(\mathbf{x}, \mathbf{y}) = \sqrt{\sum_{i=1}^D(x_i - y_i)}$

The output size of this layer is fixed to be 1. Note that the above computation is for one sample. Multiple samples are processed in one batch.

The example usage is:

l2_sim = l2_distance(x=layer1, y=layer2)

Parameters: name (basestring) – The name of this layer. It is optional. x (paddle.v2.config_base.Layer) – The first input x for this layer, whose output is a matrix with dimensionality N x D. N is the sample number in a mini-batch. D is the dimensionality of x’s output. y (paddle.v2.config_base.Layer) – The second input y for this layer, whose output is a matrix with dimensionality N x D. N is the sample number in a mini-batch. D is the dimensionality of y’s output. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attributes, for example, drop rate. See paddle.v2.attr.ExtraAttribute for more details. The returned paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### trans¶

class paddle.v2.layer.trans

A layer for transposing a minibatch matrix.

$y = x^\mathrm{T}$

where $x$ is (M x N) input, and $y$ is (N x M) output.

The example usage is:

trans = trans(input=layer)

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. name (basestring) – The name of this layer. It is optional. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### scale_shift¶

class paddle.v2.layer.scale_shift

A layer applies a linear transformation to each element in each row of the input matrix. For each element, the layer first re-scales it and then adds a bias to it.

This layer is very like the SlopeInterceptLayer, except the scale and bias are trainable.

$y = w * x + b$
scale_shift = scale_shift(input=input, bias_attr=False)

Parameters: name (basestring) – The name of this layer. It is optional. input (paddle.v2.config_base.Layer) – The input of this layer. param_attr (paddle.v2.attr.ParameterAttribute) – The parameter attribute of scaling. See paddle.v2.attr.ParameterAttribute for details. bias_attr (paddle.v2.attr.ParameterAttribute | None | bool | Any) – The bias attribute. If the parameter is set to False or an object whose type is not paddle.v2.attr.ParameterAttribute, no bias is defined. If the parameter is set to True, the bias is initialized to zero. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

## Sampling Layers¶

### maxid¶

class paddle.v2.layer.max_id

A layer for finding the id which has the maximal value for each sample. The result is stored in output.ids.

The example usage is:

maxid = maxid(input=layer)

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. name (basestring) – The name of this layer. It is optional. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### sampling_id¶

class paddle.v2.layer.sampling_id

A layer for sampling id from a multinomial distribution from the input layer. Sampling one id for one sample.

The simple usage is:

samping_id = sampling_id(input=input)

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. name (basestring) – The name of this layer. It is optional. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### multiplex¶

class paddle.v2.layer.multiplex

This layer multiplex multiple layers according to the indexes, which are provided by the first input layer. inputs[0]: the indexes of the layers to form the output of size batchSize. inputs[1:N]; the candidate output data. For each index i from 0 to batchSize - 1, the i-th row of the output is the the same to the i-th row of the (index[i] + 1)-th layer.

For each i-th row of output: .. math:

y[i][j] = x_{x_{0}[i] + 1}[i][j], j = 0,1, ... , (x_{1}.width - 1)


where, y is output. $x_{k}$ is the k-th input layer and $k = x_{0}[i] + 1$.

The example usage is:

maxid = multiplex(input=layers)

Parameters: input (list of paddle.v2.config_base.Layer) – Input layers. name (basestring) – The name of this layer. It is optional. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

## Factorization Machine Layer¶

### factorization_machine¶

class paddle.v2.layer.factorization_machine

The Factorization Machine models pairwise feature interactions as inner product of the learned latent vectors corresponding to each input feature. The Factorization Machine can effectively capture feature interactions especially when the input is sparse.

This implementation only consider the 2-order feature interactions using Factorization Machine with the formula:

$y = \sum_{i=1}^{n-1}\sum_{j=i+1}^n\langle v_i, v_j$

angle x_i x_j

Note:
X is the input vector with size n. V is the factor matrix. Each row of V is the latent vector corresponding to each input dimesion. The size of each latent vector is k.

For details of Factorization Machine, please refer to the paper: Factorization machines.

param input: type input: The input layer. Supported input types: all input data types on CPU, and only dense input types on GPU. paddle.v2.config_base.Layer The hyperparameter that defines the dimensionality of the latent vector size. int Activation Type. Default is linear activation. paddle.v2.activation.Base The parameter attribute. See paddle.v2.attr.ParameterAttribute for details. paddle.v2.attr.ParameterAttribute Extra Layer config. paddle.v2.attr.ExtraAttributeNone paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

## Slicing and Joining Layers¶

class paddle.v2.layer.pad

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,

input(2,2,2,3)  = [
[ [[1,2,3], [3,4,5]],
[[2,3,5], [1,6,7]] ],
[ [[4,3,1], [1,8,7]],
[[3,8,9], [2,3,5]] ]
]

output(2,4,2,3) = [
[ [[0,0,0], [0,0,0]],
[[1,2,3], [3,4,5]],
[[2,3,5], [1,6,7]],
[[0,0,0], [0,0,0]] ],
[ [[0,0,0], [0,0,0]],
[[4,3,1], [1,8,7]],
[[3,8,9], [2,3,5]],
[[0,0,0], [0,0,0]] ]
]


The simply usage is:

pad = pad(input=ipt,


## Cost Layers¶

### cross_entropy_cost¶

class paddle.v2.layer.cross_entropy_cost

A loss layer for multi class entropy.

The example usage is:

cost = cross_entropy(input=input,
label=label)

Parameters: input (paddle.v2.config_base.Layer) – The first input layer. label – The input label. name (basestring) – The name of this layer. It is optional. coeff (float) – The weight of the gradient in the back propagation. 1.0 is the default value. weight (LayerOutout) – The weight layer defines a weight for each sample in the mini-batch. It is optional. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### cross_entropy_with_selfnorm_cost¶

class paddle.v2.layer.cross_entropy_with_selfnorm_cost

A loss layer for multi class entropy with selfnorm. Input should be a vector of positive numbers, without normalization.

The example usage is:

cost = cross_entropy_with_selfnorm(input=input,
label=label)

Parameters: input (paddle.v2.config_base.Layer) – The first input layer. label – The input label. name (basestring) – The name of this layer. It is optional. coeff (float) – The weight of the gradient in the back propagation. 1.0 is the default value. softmax_selfnorm_alpha (float) – The scale factor affects the cost. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### multi_binary_label_cross_entropy_cost¶

class paddle.v2.layer.multi_binary_label_cross_entropy_cost

A loss layer for multi binary label cross entropy.

The example usage is:

cost = multi_binary_label_cross_entropy(input=input,
label=label)

Parameters: input (paddle.v2.config_base.Layer) – The first input layer. label – The input label. name (basestring) – The name of this layer. It is optional. coeff (float) – The weight of the gradient in the back propagation. 1.0 is the default value. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### huber_regression_cost¶

class paddle.v2.layer.huber_regression_cost

In statistics, the Huber loss is a loss function used in robust regression, that is less sensitive to outliers in data than the squared error loss. Given a prediction f(x), a label y and $\delta$, the loss function is defined as:

\begin{align}\begin{aligned}loss = 0.5*(y-f(x))^{2}, | y-f(x) | < \delta\\loss = \delta | y-f(x) | - 0.5 \delta ^2, otherwise\end{aligned}\end{align}

The example usage is:

cost = huber_regression_cost(input=input, label=label)

Parameters: input (paddle.v2.config_base.Layer) – The first input layer. label – The input label. name (basestring) – The name of this layer. It is optional. delta (float) – The difference between the observed and predicted values. coeff (float) – The weight of the gradient in the back propagation. 1.0 is the default value. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer.

### huber_classification_cost¶

class paddle.v2.layer.huber_classification_cost

For classification purposes, a variant of the Huber loss called modified Huber is sometimes used. Given a prediction f(x) (a real-valued classifier score) and a true binary class label $y\in \{-1, 1 \}$, the modified Huber loss is defined as:

The example usage is:

cost = huber_classification_cost(input=input, label=label)

Parameters: input (paddle.v2.config_base.Layer) – The first input layer. label – The input label. name (basestring) – The name of this layer. It is optional. coeff (float) – The weight of the gradient in the back propagation. 1.0 is the default value. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### lambda_cost¶

class paddle.v2.layer.lambda_cost

lambdaCost for lambdaRank LTR approach.

The example usage is:

cost = lambda_cost(input=input,
score=score,
NDCG_num=8,
max_sort_size=-1)

Parameters: input (paddle.v2.config_base.Layer) – The first input of this layer, which is often a document samples list of the same query and whose type must be sequence. score – The scores of the samples. NDCG_num (int) – 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 the list. max_sort_size (int) – 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. name (basestring) – The name of this layer. It is optional. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### square_error_cost¶

class paddle.v2.layer.square_error_cost

sum of square error cost:

$cost = \sum_{i=1}^N(t_i-y_i)^2$
Parameters: name (basestring) – The name of this layer. It is optional. input (paddle.v2.config_base.Layer) – The first input layer. label (paddle.v2.config_base.Layer) – The input label. weight (paddle.v2.config_base.Layer) – The weight layer defines a weight for each sample in the mini-batch. It is optional. coeff (float) – The weight of the gradient in the back propagation. 1.0 is the default value. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### rank_cost¶

class paddle.v2.layer.rank_cost

A cost Layer for learning to rank using gradient descent.

Reference:
Learning to Rank using Gradient Descent
\begin{align}\begin{aligned}C_{i,j} & = -\tilde{P_{ij}} * o_{i,j} + log(1 + e^{o_{i,j}})\\o_{i,j} & = o_i - o_j\\\tilde{P_{i,j}} & = \{0, 0.5, 1\} \ or \ \{0, 1\}\end{aligned}\end{align}
In this formula:
• $C_{i,j}$ is the cross entropy cost.
• $\tilde{P_{i,j}}$ is the label. 1 means positive order and 0 means reverse order.
• $o_i$ and $o_j$: the left output and right output. Their dimension is one.

The example usage is:

cost = rank_cost(left=out_left,
right=out_right,
label=label)

Parameters: left (paddle.v2.config_base.Layer) – The first input, the size of this layer is 1. right (paddle.v2.config_base.Layer) – The right input, the size of this layer is 1. label (paddle.v2.config_base.Layer) – Label is 1 or 0, means positive order and reverse order. weight (paddle.v2.config_base.Layer) – The weight layer defines a weight for each sample in the mini-batch. It is optional. name (basestring) – The name of this layer. It is optional. coeff (float) – The weight of the gradient in the back propagation. 1.0 is the default value. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### sum_cost¶

class paddle.v2.layer.sum_cost

A loss layer which calculates the sum of the input as loss.

The example usage is:

cost = sum_cost(input=input)

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. name (basestring) – The name of this layer. It is optional. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer.

### crf¶

class paddle.v2.layer.crf

A layer for calculating the cost of sequential conditional random field model.

The example usage is:

crf = crf(input=input,
label=label,
size=label_dim)

Parameters: input (paddle.v2.config_base.Layer) – The first input layer. label (paddle.v2.config_base.Layer) – The input label. size (int) – The category number. weight (paddle.v2.config_base.Layer) – The weight layer defines a weight for each sample in the mini-batch. It is optional. param_attr (paddle.v2.attr.ParameterAttribute) – The parameter attribute. See paddle.v2.attr.ParameterAttribute for details. name (basestring) – The name of this layer. It is optional. coeff (float) – The weight of the gradient in the back propagation. 1.0 is the default value. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### crf_decoding¶

class paddle.v2.layer.crf_decoding

A layer for calculating the decoding sequence of sequential conditional random field model. The decoding sequence is stored in output.ids. 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:

crf_decoding = crf_decoding(input=input,
size=label_dim)

Parameters: input (paddle.v2.config_base.Layer) – The first input layer. size (int) – The dimension of this layer. label (paddle.v2.config_base.Layer | None) – The input label. param_attr (paddle.v2.attr.ParameterAttribute) – The parameter attribute. See paddle.v2.attr.ParameterAttribute for details. name (basestring) – The name of this layer. It is optional. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### ctc¶

class paddle.v2.layer.ctc

Connectionist Temporal Classification (CTC) is designed for temporal classication task. e.g. sequence labeling problems where the alignment between the inputs and the target labels is unknown.

Reference:
Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks

Note

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 with softmax activation) should be (num_classes + 1). The size of ctc should also be (num_classes + 1).

The example usage is:

ctc = ctc(input=input,
label=label,
size=9055,
norm_by_times=True)

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. label (paddle.v2.config_base.Layer) – The input label. size (int) – The dimension of this layer, which must be equal to (category number + 1). name (basestring) – The name of this layer. It is optional. norm_by_times (bool) – Whether to do normalization by times. False is the default. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### warp_ctc¶

class paddle.v2.layer.warp_ctc

A layer intergrating the open-source warp-ctc library, which is used in Deep Speech 2: End-toEnd Speech Recognition in English and Mandarin, to compute Connectionist Temporal Classification (CTC) loss. Besides, another warp-ctc repository, which is forked from the official one, is maintained to enable more compiling options. During the building process, PaddlePaddle will clone the source codes, build and install it to third_party/install/warpctc directory.

Reference:
Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks

Note

• 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 with those used in your labels.
• As a native ‘softmax’ activation is interated to the warp-ctc library, ‘linear’ activation is expected to be used instead in the ‘input’ layer.

The example usage is:

ctc = warp_ctc(input=input,
label=label,
size=1001,
blank=1000,
norm_by_times=False)

Parameters: input (paddle.v2.config_base.Layer) – The input of this layer. label (paddle.v2.config_base.Layer) – The input label. size (int) – The dimension of this layer, which must be equal to (category number + 1). name (basestring) – The name of this layer. It is optional. blank (int) – The ‘blank’ label used in ctc. norm_by_times (bool) – Whether to do normalization by times. False is the default. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### nce¶

class paddle.v2.layer.nce

Noise-contrastive estimation.

Reference:
A fast and simple algorithm for training neural probabilistic language models.

The example usage is:

cost = nce(input=[layer1, layer2], label=layer2,
param_attr=[attr1, attr2], weight=layer3,
num_classes=3, neg_distribution=[0.1,0.3,0.6])


### hsigmoid¶

class paddle.v2.layer.hsigmoid

Organize the classes into a binary tree. At each node, a sigmoid function is used to calculate the probability of belonging to the right branch.

Reference:
Hierarchical Probabilistic Neural Network Language Model

The example usage is:

cost = hsigmoid(input=[layer1, layer2],
label=data)

Parameters: input (paddle.v2.config_base.Layer | list | tuple) – The input of this layer. label (paddle.v2.config_base.Layer) – The input label. num_classes (int) – The number of classes. And it should be larger than 2. If the parameter is not set or set to None, its actual value will be automatically set to the number of labels. name (basestring) – The name of this layer. It is optional. bias_attr (paddle.v2.attr.ParameterAttribute | None | bool | Any) – The bias attribute. If the parameter is set to False or an object whose type is not paddle.v2.attr.ParameterAttribute, no bias is defined. If the parameter is set to True, the bias is initialized to zero. param_attr (paddle.v2.attr.ParameterAttribute) – The parameter attribute. See paddle.v2.attr.ParameterAttribute for details. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### smooth_l1_cost¶

class paddle.v2.layer.smooth_l1_cost

This is a L1 loss but more smooth. It requires that the sizes of input and label are equal. The formula is as follows,

$L = \sum_{i} smooth_{L1}(input_i - label_i)$

in which

$\begin{split}smooth_{L1}(x) = \begin{cases} 0.5x^2& \text{if} \ |x| < 1 \\ |x|-0.5& \text{otherwise} \end{cases}\end{split}$
Reference:
Fast R-CNN

The example usage is:

cost = smooth_l1_cost(input=input,
label=label)

Parameters: input (paddle.v2.config_base.Layer) – The input layer. label – The input label. name (basestring) – The name of this layer. It is optional. coeff (float) – The weight of the gradient in the back propagation. 1.0 is the default value. layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### multibox_loss¶

class paddle.v2.layer.multibox_loss

Compute the location loss and the confidence loss for ssd.

Parameters: name (basestring) – The name of this layer. It is optional. input_loc (paddle.v2.config_base.Layer | List of paddle.v2.config_base.Layer) – The input predict locations. input_conf (paddle.v2.config_base.Layer | List of paddle.v2.config_base.Layer) – The input priorbox confidence. priorbox (paddle.v2.config_base.Layer) – The input priorbox location and the variance. label (paddle.v2.config_base.Layer) – The input label. num_classes (int) – The number of the classification. overlap_threshold (float) – The threshold of the overlap. neg_pos_ratio (float) – The ratio of the negative bbox to the positive bbox. neg_overlap (float) – The negative bbox overlap threshold. background_id (int) – The background class index. paddle.v2.config_base.Layer

## Check Layer¶

### eos¶

class paddle.v2.layer.eos

A layer for checking EOS for each sample: - output_id = (input_id == conf.eos_id)

The result is stored in output_.ids. It is used by recurrent layer group.

The example usage is:

eos = eos(input=layer, eos_id=id)

Parameters: name (basestring) – The name of this layer. It is optional. input (paddle.v2.config_base.Layer) – The input of this layer. eos_id (int) – End id of sequence layer_attr (paddle.v2.attr.ExtraAttribute) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

## Miscs¶

### dropout¶

class paddle.v2.layer.dropout

The example usage is:

dropout = dropout(input=input, dropout_rate=0.5)

Parameters: name (basestring) – The name of this layer. It is optional. input (paddle.v2.config_base.Layer) – The input of this layer. dropout_rate (float) – The probability of dropout. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

## Activation with learnable parameter¶

### prelu¶

class paddle.v2.layer.prelu

The Parametric Relu activation that actives outputs with a learnable weight.

Reference:
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
$\begin{split}z_i &\quad if \quad z_i > 0 \\ a_i * z_i &\quad \mathrm{otherwise}\end{split}$

The example usage is:

prelu = prelu(input=layers, partial_sum=1)

Parameters: name (basestring) – The name of this layer. It is optional. input (paddle.v2.config_base.Layer) – The input of this layer. partial_sum (int) – this parameter makes a group of inputs share the same weight. partial_sum = 1, indicates the element-wise activation: each element has a weight. partial_sum = number of elements in one channel, indicates the channel-wise activation, elements in a channel share the same weight. partial_sum = number of outputs, indicates all elements share the same weight. channel_shared (bool) – whether or not the parameter are shared across channels. channel_shared = True, we set the partial_sum to the number of outputs. channel_shared = False, we set the partial_sum to the number of elements in one channel. num_channels (int) – number of input channel. param_attr (paddle.v2.attr.ParameterAttribute) – The parameter attribute. See paddle.v2.attr.ParameterAttribute for details. layer_attr (paddle.v2.attr.ExtraAttribute | None) – The extra layer attribute. See paddle.v2.attr.ExtraAttribute for details. paddle.v2.config_base.Layer object. paddle.v2.config_base.Layer

### gated_unit¶

class paddle.v2.layer.gated_unit

The gated unit layer implements a simple gating mechanism over the input. The input $X$ is first projected into a new space $X'$, and it is also used to produce a gate weight $\sigma$. Element-wise product between :match:X’ and $\sigma$ is finally returned.

Reference:
Language Modeling with Gated Convolutional Networks
$y=\text{act}(X \cdot W + b)\otimes \sigma(X \cdot V + c)$

The example usage is:

## Detection output Layer¶

### detection_output¶

class paddle.v2.layer.detection_output`

Apply the NMS to the output of network and compute the predict bounding box location. The output’s shape of this layer could be zero if there is no valid bounding box.

Parameters: name (basestring) – The name of this layer. It is optional. input_loc (paddle.v2.config_base.Layer | List of paddle.v2.config_base.Layer.) – The input predict locations. input_conf (paddle.v2.config_base.Layer | List of paddle.v2.config_base.Layer.) – The input priorbox confidence. priorbox (paddle.v2.config_base.Layer) – The input priorbox location and the variance. num_classes (int) – The number of the classification. nms_threshold (float) – The Non-maximum suppression threshold. nms_top_k (int) – The bbox number kept of the NMS’s output keep_top_k (int) – The bbox number kept of the layer’s output confidence_threshold (float) – The classification confidence threshold background_id (int) – The background class index. paddle.v2.config_base.Layer