Layers¶
Data layer¶
data¶
-
class
paddle.v2.layer.
data
(name, type, **kwargs) Define DataLayer For NeuralNetwork.
The example usage is:
data = paddle.layer.data(name="input", type=paddle.data_type.dense_vector(1000))
Parameters: - name (basestring) – Name of this data layer.
- type – Data type of this data layer
- height (int|None) – Height of this data layer, used for image
- width (int|None) – Width of this data layer, used for image
- layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
Fully Connected Layers¶
fc¶
-
class
paddle.v2.layer.
fc
(*args, **kwargs) Helper for declare fully connected layer.
The example usage is:
fc = fc(input=layer, size=1024, act=paddle.v2.Activation.Linear(), bias_attr=False)
which is equal to:
with mixed(size=1024) as fc: fc += full_matrix_projection(input=layer)
Parameters: - name (basestring) – The Layer Name.
- input (paddle.v2.config_base.Layer|list|tuple) – The input layer. Could be a list/tuple of input layer.
- size (int) – The layer dimension.
- act (paddle.v2.Activation.Base) – Activation Type. Default is tanh.
- param_attr (paddle.v2.attr.ParameterAttribute) – The Parameter Attribute|list.
- bias_attr (paddle.v2.attr.ParameterAttribute|None|Any) – The Bias Attribute. If no bias, then pass False or something not type of paddle.v2.attr.ParameterAttribute. None will get a default Bias.
- layer_attr (paddle.v2.attr.ExtraAttributeNone) – Extra Layer config.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
selective_fc¶
-
class
paddle.v2.layer.
selective_fc
(*args, **kwargs) Selectived fully connected layer. Different from fc, the output of this layer maybe 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())
Parameters: - name (basestring) – The Layer Name.
- input (paddle.v2.config_base.Layer|list|tuple) – The input layer.
- select (paddle.v2.config_base.Layer) – The select layer. The output of select layer should be a sparse binary matrix, and treat as the mask of selective fc. If is None, acts exactly like fc.
- size (int) – The layer dimension.
- act (paddle.v2.Activation.Base) – Activation Type. Default is tanh.
- param_attr (paddle.v2.attr.ParameterAttribute) – The Parameter Attribute.
- bias_attr (paddle.v2.attr.ParameterAttribute|None|Any) – The Bias Attribute. If no bias, then pass False or something not type of paddle.v2.attr.ParameterAttribute. None will get a default Bias.
- layer_attr (paddle.v2.attr.ExtraAttributeNone) – Extra Layer config.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
Conv Layers¶
conv_operator¶
-
class
paddle.v2.layer.
conv_operator
(**kwargs) 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 support 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) – input image
- filter (paddle.v2.config_base.Layer) – input filter
- filter_size (int) – The x dimension of a filter kernel.
- filter_size_y (int) – The y dimension of a filter kernel. Since PaddlePaddle now supports rectangular filters, the filter’s shape can be (filter_size, filter_size_y).
- num_filters (int) – channel of output data.
- num_channels (int) – channel of input data.
- stride (int) – The x dimension of the stride.
- stride_y (int) – The y dimension of the stride.
- padding (int) – The x dimension of padding.
- padding_y (int) – The y dimension of padding.
Returns: A ConvOperator Object.
Return type: ConvOperator
conv_projection¶
-
class
paddle.v2.layer.
conv_projection
(**kwargs) Different from img_conv and conv_op, conv_projection is an Projection, which can be used in mixed and conat. It use cudnn to implement conv and only support GPU mode.
The example usage is:
proj = conv_projection(input=input1, filter_size=3, num_filters=64, num_channels=64)
Parameters: - input (paddle.v2.config_base.Layer) – input layer
- filter_size (int) – The x dimension of a filter kernel.
- filter_size_y (int) – The y dimension of a filter kernel. Since PaddlePaddle now supports rectangular filters, the filter’s shape can be (filter_size, filter_size_y).
- num_filters (int) – channel of output data.
- num_channels (int) – channel of input data.
- stride (int) – The x dimension of the stride.
- stride_y (int) – The y dimension of the stride.
- padding (int) – The x dimension of padding.
- padding_y (int) – The y dimension of padding.
- groups (int) – The group number.
- param_attr (paddle.v2.attr.ParameterAttribute) – Convolution param attribute. None means default attribute
- trans (boolean) – whether it is convTrans or conv
Returns: A DotMulProjection Object.
Return type: DotMulProjection
conv_shift¶
-
class
paddle.v2.layer.
conv_shift
(*args, **kwargs) - This layer performs cyclic convolution for two input. 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 formular:
- 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) – layer name
- a (paddle.v2.config_base.Layer) – Input layer a.
- b (paddle.v2.config_base.Layer) – input layer b.
- layer_attr (paddle.v2.attr.ExtraAttribute) – layer’s extra attribute.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
img_conv¶
-
class
paddle.v2.layer.
img_conv
(*args, **kwargs) 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 group of filter in PaddlePaddle implementation. Each group will process some channel of the inputs. For example, if an input num_channel = 256, group = 4, num_filter=32, the PaddlePaddle will create 32*4 = 128 filters to process inputs. The channels will be split into 4 pieces. First 256/4 = 64 channels will process by first 32 filters. The rest channels will be processed by rest group 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, act=paddle.v2.Activation.Relu())
Parameters: - name (basestring) – Layer name.
- input (paddle.v2.config_base.Layer) – Layer Input.
- filter_size (int|tuple|list) – The x dimension of a filter kernel. Or input a tuple for two image dimension.
- filter_size_y (int|None) – The y dimension of a filter kernel. Since PaddlePaddle currently supports rectangular filters, the filter’s shape will be (filter_size, filter_size_y).
- num_filters – Each filter group’s number of filter
- act (paddle.v2.Activation.Base) – Activation type. Default is tanh
- groups (int) – Group size of filters.
- stride (int|tuple|list) – The x dimension of the stride. Or input a tuple for two image dimension.
- stride_y (int) – The y dimension of the stride.
- padding (int|tuple|list) – The x dimension of the padding. Or input a tuple for two image dimension
- padding_y (int) – The y dimension of the padding.
- bias_attr (paddle.v2.attr.ParameterAttribute|False) – Convolution bias attribute. None means default bias. False means no bias.
- num_channels (int) – number of input channels. If None will be set automatically from previous output.
- param_attr (paddle.v2.attr.ParameterAttribute) – Convolution param attribute. None means default attribute
- shared_biases (bool) – Is biases will be shared between filters or not.
- layer_attr (paddle.v2.attr.ExtraAttribute) – Layer Extra Attribute.
- trans (bool) – true if it is a convTransLayer, false if it is a convLayer
- layer_type (String) – specify the layer_type, default is None. If trans=True, layer_type has to be “exconvt” or “cudnn_convt”, otherwise layer_type has to be either “exconv” or “cudnn_conv”
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
context_projection¶
-
class
paddle.v2.layer.
context_projection
(**kwargs) 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) – Input 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.
Returns: Projection
Return type: Projection
Image Pooling Layer¶
img_pool¶
-
class
paddle.v2.layer.
img_pool
(*args, **kwargs) Image pooling Layer.
The details of pooling layer, please refer ufldl’s pooling .
- ceil_mode=True:
\[w = 1 + int(ceil(input\_width + 2 * padding - pool\_size) / float(stride)) h = 1 + int(ceil(input\_height + 2 * padding\_y - pool\_size\_y) / float(stride\_y))\]- ceil_mode=False:
\[w = 1 + int(floor(input\_width + 2 * padding - pool\_size) / float(stride)) h = 1 + int(floor(input\_height + 2 * padding\_y - pool\_size\_y) / float(stride\_y))\]The example usage is:
maxpool = img_pool(input=conv, pool_size=3, pool_size_y=5, num_channels=8, stride=1, stride_y=2, padding=1, padding_y=2, pool_type=MaxPooling())
Parameters: - padding (int) – pooling padding width.
- padding_y (int|None) – pooling padding height. It’s equal to padding by default.
- name (basestring.) – name of pooling layer
- input (paddle.v2.config_base.Layer) – layer’s input
- pool_size (int) – pooling window width
- pool_size_y (int|None) – pooling window height. It’s eaqual to pool_size by default.
- num_channels (int) – number of input channel.
- pool_type (BasePoolingType) – pooling type. MaxPooling or AvgPooling. Default is MaxPooling.
- stride (int) – stride width of pooling.
- stride_y (int|None) – stride height of pooling. It is equal to stride by default.
- layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer attribute.
- ceil_mode (bool) – Wether to use ceil mode to calculate output height and with. Defalut is True. If set false, Otherwise use floor.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
spp¶
-
class
paddle.v2.layer.
spp
(*args, **kwargs) Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. The details please refer to Kaiming He’s paper.
The example usage is:
spp = spp(input=data, pyramid_height=2, num_channels=16, pool_type=MaxPooling())
Parameters: - name (basestring) – layer name.
- input (paddle.v2.config_base.Layer) – layer’s input.
- num_channels (int) – number of input channel.
- pool_type – Pooling type. MaxPooling or AveragePooling. Default is MaxPooling.
- pyramid_height (int) – pyramid height.
- layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
maxout¶
-
class
paddle.v2.layer.
maxout
(*args, **kwargs) - A layer to do max out on conv layer output.
- Input: output of a conv layer.
- Output: feature map size same as input. Channel is (input channel) / groups.
So groups should be larger than 1, and the num of channels should be able to devided by groups.
- Please refer to Paper:
- Maxout Networks: http://www.jmlr.org/proceedings/papers/v28/goodfellow13.pdf
- Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks: https://arxiv.org/pdf/1312.6082v4.pdf
The simple usage is:
maxout = maxout(input, num_channels=128, groups=4)
Parameters: - input (paddle.v2.config_base.Layer) – The input layer.
- num_channels (int|None) – The channel number of input layer. If None will be set automatically from previous output.
- groups (int) – The group number of input layer.
- name (None|basestring.) – The name of this layer, which can not specify.
- layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer attribute.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
Norm Layer¶
img_cmrnorm¶
-
class
paddle.v2.layer.
img_cmrnorm
(*args, **kwargs) Response normalization across feature maps. The details please refer to Alex’s paper.
The example usage is:
norm = img_cmrnorm(input=net, size=5)
Parameters: - name (None|basestring) – layer name.
- input (paddle.v2.config_base.Layer) – layer’s input.
- size (int) – Normalize in number of \(size\) feature maps.
- scale (float) – The hyper-parameter.
- power (float) – The hyper-parameter.
- num_channels – input layer’s filers number or channels. If num_channels is None, it will be set automatically.
- layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
batch_norm¶
-
class
paddle.v2.layer.
batch_norm
(*args, **kwargs) Batch Normalization Layer. The notation of this layer as follow.
\(x\) is the input features over a mini-batch.
\[\begin{split}\mu_{\beta} &\gets \frac{1}{m} \sum_{i=1}^{m} x_i \qquad &//\ \ mini-batch\ mean \\ \sigma_{\beta}^{2} &\gets \frac{1}{m} \sum_{i=1}^{m}(x_i - \ \mu_{\beta})^2 \qquad &//\ mini-batch\ variance \\ \hat{x_i} &\gets \frac{x_i - \mu_\beta} {\sqrt{\ \sigma_{\beta}^{2} + \epsilon}} \qquad &//\ normalize \\ y_i &\gets \gamma \hat{x_i} + \beta \qquad &//\ scale\ and\ shift\end{split}\]The details of batch normalization please refer to this paper.
The example usage is:
norm = batch_norm(input=net, act=paddle.v2.Activation.Relu())
Parameters: - name (basestring) – layer name.
- input (paddle.v2.config_base.Layer) – batch normalization input. Better be linear activation. Because there is an activation inside batch_normalization.
- batch_norm_type (None|string, None or "batch_norm" or "cudnn_batch_norm") – We have batch_norm and cudnn_batch_norm. batch_norm supports both CPU 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. By default (None), we will automaticly select cudnn_batch_norm for GPU and batch_norm for CPU. Otherwise, select batch norm type based on the specified type. If you use cudnn_batch_norm, we suggested you use latest version, such as v5.1.
- act (paddle.v2.Activation.Base) – Activation Type. Better be relu. Because batch normalization will normalize input near zero.
- num_channels (int) – num of image channels or previous layer’s number of filters. None will automatically get from layer’s input.
- bias_attr (paddle.v2.attr.ParameterAttribute) – \(\beta\), better be zero when initialize. So the initial_std=0, initial_mean=1 is best practice.
- param_attr (paddle.v2.attr.ParameterAttribute) – \(\gamma\), better be one when initialize. So the initial_std=0, initial_mean=1 is best practice.
- layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
- use_global_stats (bool|None.) – whether use moving mean/variance statistics during testing peroid. If None or True, it will use moving mean/variance statistics during testing. If False, it will use the mean and variance of current batch of test data for testing.
- moving_average_fraction (float.) – Factor used in the moving average computation, referred to as facotr, \(runningMean = newMean*(1-factor) + runningMean*factor\)
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
sum_to_one_norm¶
-
class
paddle.v2.layer.
sum_to_one_norm
(*args, **kwargs) 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) – Input layer.
- name (basestring) – Layer name.
- layer_attr (paddle.v2.attr.ExtraAttribute) – extra layer attributes.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
cross_channel_norm¶
-
class
paddle.v2.layer.
cross_channel_norm
(*args, **kwargs) 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 Layer Name.
- input (paddle.v2.config_base.Layer) – The input layer.
- param_attr (paddle.v2.attr.ParameterAttribute) – The Parameter Attribute|list.
Returns: paddle.v2.config_base.Layer
Recurrent Layers¶
recurrent¶
-
class
paddle.v2.layer.
recurrent
(*args, **kwargs) 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:
\[\begin{split}out_{i} = act(in_{i}) \ \ \text{for} \ i = end \\ out_{i} = act(in_{i} + out_{i+1} * W) \ \ \text{for} \ start <= i < end\end{split}\]Parameters: - input (paddle.v2.config_base.Layer) – Input Layer
- act (paddle.v2.Activation.Base) – activation.
- bias_attr (paddle.v2.attr.ParameterAttribute) – bias attribute.
- param_attr (paddle.v2.attr.ParameterAttribute) – parameter attribute.
- name (basestring) – name of the layer
- layer_attr (paddle.v2.attr.ExtraAttribute) – Layer Attribute.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
lstmemory¶
-
class
paddle.v2.layer.
lstmemory
(*args, **kwargs) 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.
Please refer to Generating Sequences With Recurrent Neural Networks for more details about LSTM.
Link goes as below.
Parameters: - name (basestring) – The lstmemory layer name.
- input (paddle.v2.config_base.Layer) – input layer name.
- reverse (bool) – is sequence process reversed or not.
- act (paddle.v2.Activation.Base) – activation type, paddle.v2.Activation.Tanh by default. \(h_t\)
- gate_act (paddle.v2.Activation.Base) – gate activation type, paddle.v2.Activation.Sigmoid by default.
- state_act (paddle.v2.Activation.Base) – state activation type, paddle.v2.Activation.Tanh by default.
- bias_attr (paddle.v2.attr.ParameterAttribute|None|False) – Bias attribute. None means default bias. False means no bias.
- param_attr (paddle.v2.attr.ParameterAttribute|None|False) – Parameter Attribute.
- layer_attr (paddle.v2.attr.ExtraAttributeNone) – Extra Layer attribute
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
grumemory¶
-
class
paddle.v2.layer.
grumemory
(*args, **kwargs) 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 computed in gate_recurrent layer. Consequently, an additional mixed with full_matrix_projection or a fc must be included before grumemory is called.
More details can be found by referring to Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.
The simple usage is:
gru = grumemory(input)
Parameters: - name (None|basestring) – The gru layer name.
- input (paddle.v2.config_base.Layer.) – input layer.
- reverse (bool) – Whether sequence process is reversed or not.
- act (paddle.v2.Activation.Base) – activation type, paddle.v2.Activation.Tanh by default. This activation affects the \({\tilde{h_t}}\).
- gate_act (paddle.v2.Activation.Base) – gate activation type, paddle.v2.Activation.Sigmoid by default. This activation affects the \(z_t\) and \(r_t\). It is the \(\sigma\) in the above formula.
- bias_attr (paddle.v2.attr.ParameterAttribute|None|False) – Bias attribute. None means default bias. False means no bias.
- param_attr (paddle.v2.attr.ParameterAttribute|None|False) – Parameter Attribute.
- layer_attr (paddle.v2.attr.ExtraAttributeNone) – Extra Layer attribute
- size (None) – Stub parameter of size, but actually not used. If set this size will get a warning.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
Recurrent Layer Group¶
memory¶
-
class
paddle.v2.layer.
memory
(name, extra_input=None, **kwargs) The memory layers is a layer cross each time step. Reference this output as previous time step layer
name
‘s output.The default memory is zero in first time step, previous time step’s output in the rest time steps.
If boot_bias, the first time step value is this bias and with activation.
If boot_with_const_id, then the first time stop is a IndexSlot, the Arguments.ids()[0] is this
cost_id
.If boot_layer is not null, the memory is just the boot_layer’s output. Set
is_seq
is true boot layer is sequence.The same name layer in recurrent group will set memory on each time step.
mem = memory(size=256, name='state') state = fc_layer(input=mem, size=256, name='state')
If you do not want to specify the name, you can equivalently use set_input() to specify the layer needs to be remembered as the following:
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) – size of memory.
- memory_name (basestring) – the name of the memory. It is ignored when name is provided.
- is_seq (bool) – is sequence for boot_layer
- boot_layer (LayerOutput|None) – boot layer of memory.
- boot_bias (ParameterAttribute|None) – boot layer’s bias
- boot_bias_active_type (BaseActivation) – boot layer’s active type.
- boot_with_const_id (int) – boot layer’s id.
Returns: LayerOutput object which is a memory.
Return type: LayerOutput
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 extremely usefull for attention based model, or Neural Turning Machine like models.
The basic usage (time steps) is:
def step(input): output = fc_layer(input=layer, size=1024, act=LinearActivation(), 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_layer_group.conf, demo/seqToseq/seqToseq_net.py
- sequence steps: paddle/gserver/tests/sequence_nest_layer_group.conf
Parameters: - step (callable) –
recurrent one time step function.The input of this function is input of the group. The return of this function will be recurrent group’s return value.
The recurrent group scatter a sequence into time steps. And for each time step, will invoke step function, and return a time step result. Then gather each time step of output into layer group’s output.
- name (basestring) – recurrent_group’s name.
- input (LayerOutput|StaticInput|SubsequenceInput|list|tuple) –
Input links array.
LayerOutput will be scattered into time steps. SubsequenceInput will be scattered into sequence steps. StaticInput will be imported to each time step, and doesn’t change through time. It’s a mechanism to access layer outside step function.
- reverse (bool) – If reverse is set true, the recurrent unit will process the input sequence in a reverse order.
- targetInlink (LayerOutput|SubsequenceInput) –
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.
- is_generating – If is generating, none of input type should be LayerOutput; else, for training or testing, one of the input type must be LayerOutput.
: type is_generating: bool
Returns: LayerOutput object. Return type: LayerOutput
lstm_step¶
-
class
paddle.v2.layer.
lstm_step
(*args, **kwargs) LSTM Step Layer. It used in recurrent_group. The lstm equations are shown as follow.
\[ \begin{align}\begin{aligned}i_t & = \sigma(W_{xi}x_{t} + W_{hi}h_{t-1} + W_{ci}c_{t-1} + b_i)\\f_t & = \sigma(W_{xf}x_{t} + W_{hf}h_{t-1} + W_{cf}c_{t-1} + b_f)\\c_t & = f_tc_{t-1} + i_t tanh (W_{xc}x_t+W_{hc}h_{t-1} + b_c)\\o_t & = \sigma(W_{xo}x_{t} + W_{ho}h_{t-1} + W_{co}c_t + b_o)\\h_t & = o_t tanh(c_t)\end{aligned}\end{align} \]The input of lstm step is \(Wx_t + Wh_{t-1}\), and user should use
mixed
andfull_matrix_projection
to calculate these input vector.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 contains two outputs. Default output is \(h_t\). The other output is \(o_t\), which name is ‘state’ and can use
get_output
to extract this output.Parameters: - name (basestring) – Layer’s name.
- size (int) – Layer’s size. NOTE: lstm layer’s size, should be equal as
input.size/4
, and should be equal asstate.size
. - input (paddle.v2.config_base.Layer) – input layer. \(Wx_t + Wh_{t-1}\)
- state (paddle.v2.config_base.Layer) – State Layer. \(c_{t-1}\)
- act (paddle.v2.Activation.Base) – Activation type. Default is tanh
- gate_act (paddle.v2.Activation.Base) – Gate Activation Type. Default is sigmoid, and should be sigmoid only.
- state_act (paddle.v2.Activation.Base) – State Activation Type. Default is sigmoid, and should be sigmoid only.
- bias_attr (paddle.v2.attr.ParameterAttribute) – Bias Attribute.
- layer_attr (paddle.v2.attr.ExtraAttribute) – layer’s extra attribute.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
gru_step¶
-
class
paddle.v2.layer.
gru_step
(*args, **kwargs) Parameters: - input (paddle.v2.config_base.Layer) –
- output_mem –
- size –
- act –
- name –
- gate_act –
- bias_attr –
- param_attr – the parameter_attribute for transforming the output_mem from previous step.
- layer_attr –
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
beam_search¶
-
class
paddle.v2.layer.
beam_search
Beam search is a heuristic search algorithm used in sequence generation. It explores a graph by expanding the most promising nodes in a limited set to maintain tractability.
The example usage is:
def rnn_step(input): last_time_step_output = memory(name='rnn', size=512) with mixed_layer(size=512, name='rnn') as simple_rnn: simple_rnn += full_matrix_projection(input) simple_rnn += last_time_step_output return simple_rnn beam_gen = beam_search(name="decoder", step=rnn_step, input=[StaticInput(encoder_last)], bos_id=0, eos_id=1, beam_size=5)
Please see the following demo for more details:
- machine translation : demo/seqToseq/translation/gen.conf demo/seqToseq/seqToseq_net.py
Parameters: - name (base string) – Name of the recurrent unit that generates sequences.
- step (callable) –
A callable function that defines the calculation in a time step, and it is applied to sequences with arbitrary length by sharing a same set of weights.
You can refer to the first parameter of recurrent_group, or demo/seqToseq/seqToseq_net.py for more details.
- input (list) – Input data for the recurrent unit
- bos_id (int) – Index of the start symbol in the dictionary. The start symbol is a special token for NLP task, which indicates the beginning of a sequence. In the generation task, the start symbol is essential, since it is used to initialize the RNN internal state.
- eos_id (int) – Index of the end symbol in the dictionary. The end symbol is a special token for NLP task, which indicates the end of a sequence. The generation process will stop once the end symbol is generated, or a pre-defined max iteration number is exceeded.
- max_length (int) – Max generated sequence length.
- beam_size (int) – Beam search for sequence generation is an iterative search algorithm. To maintain tractability, every iteration only only stores a predetermined number, called the beam_size, of the most promising next words. The greater the beam size, the fewer candidate words are pruned.
- num_results_per_sample (int) – Number of the generated results per input sequence. This number must always be less than beam size.
Returns: The generated word index.
Return type: LayerOutput
get_output¶
-
class
paddle.v2.layer.
get_output
(*args, **kwargs) 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) – Layer’s name.
- input (paddle.v2.config_base.Layer) – get output layer’s input. And this layer should contains multiple outputs.
- arg_name (basestring) – Output name from input.
- layer_attr – Layer’s extra attribute.
Returns: paddle.v2.config_base.Layer object.
Return type: 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.
- When not set inputs parameter, use mixed_layer like this:
with mixed_layer(size=256) as m: m += full_matrix_projection(input=layer1) m += identity_projection(input=layer2)
- You can also set all inputs when invoke mixed_layer as follows:
m = mixed_layer(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 – inputs layer. It is an optional parameter. If set, then this function will just return layer’s name.
- act (BaseActivation) – Activation Type.
- bias_attr (ParameterAttribute or None or bool) – The Bias Attribute. If no bias, then pass False or something not type of ParameterAttribute. None will get a default Bias.
- layer_attr (ExtraLayerAttribute) – The extra layer config. Default is None.
Returns: MixedLayerType object can add inputs or layer name.
Return type: MixedLayerType
embedding¶
-
class
paddle.v2.layer.
embedding
(*args, **kwargs) Define a embedding Layer.
Parameters: - name (basestring) – Name of this embedding layer.
- input (paddle.v2.config_base.Layer) – The input layer for this embedding. NOTE: 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.ExtraAttributeNone) – Extra layer Config. Default is None.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
scaling_projection¶
-
class
paddle.v2.layer.
scaling_projection
(**kwargs) 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) – Input Layer.
- param_attr (paddle.v2.attr.ParameterAttribute) – Parameter config, None if use default.
Returns: A ScalingProjection object
Return type: ScalingProjection
dotmul_projection¶
-
class
paddle.v2.layer.
dotmul_projection
(**kwargs) 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) – Input layer.
- param_attr (paddle.v2.attr.ParameterAttribute) – Parameter config, None if use default.
Returns: A DotMulProjection Object.
Return type: DotMulProjection
dotmul_operator¶
-
class
paddle.v2.layer.
dotmul_operator
(**kwargs) 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.
Returns: A DotMulOperator Object.
Return type: DotMulOperator
full_matrix_projection¶
-
class
paddle.v2.layer.
full_matrix_projection
(**kwargs) Full Matrix Projection. It performs full matrix multiplication.
\[out.row[i] += in.row[i] * weight\]There are two styles of usage.
- When used in mixed like this, you can only set the input:
with mixed(size=100) as m: m += full_matrix_projection(input=layer)
- 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) – input layer
- size (int) – The parameter size. Means the width of parameter.
- param_attr (paddle.v2.attr.ParameterAttribute) – Parameter config, None if use default.
Returns: A FullMatrixProjection Object.
Return type: FullMatrixProjection
identity_projection¶
-
class
paddle.v2.layer.
identity_projection
(**kwargs) - 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) – Input Layer.
- offset (int) – Offset, None if use default.
Returns: A IdentityProjection or IdentityOffsetProjection object
Return type: IdentityProjection or IdentityOffsetProjection
table_projection¶
-
class
paddle.v2.layer.
table_projection
(**kwargs) 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.
- When used in mixed like this, you can only set the input:
with mixed(size=100) as m: m += table_projection(input=layer)
- 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) – Input 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.
Returns: A TableProjection Object.
Return type: TableProjection
trans_full_matrix_projection¶
-
class
paddle.v2.layer.
trans_full_matrix_projection
(**kwargs) 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) – input layer
- size (int) – The parameter size. Means the width of parameter.
- param_attr (paddle.v2.attr.ParameterAttribute) – Parameter config, None if use default.
Returns: A TransposedFullMatrixProjection Object.
Return type: TransposedFullMatrixProjection
Aggregate Layers¶
pooling¶
-
class
paddle.v2.layer.
pooling
(*args, **kwargs) Pooling layer for sequence inputs, not used for Image.
The example usage is:
seq_pool = pooling(input=layer, pooling_type=AvgPooling(), agg_level=AggregateLevel.EACH_SEQUENCE)
Parameters: - agg_level (AggregateLevel) – AggregateLevel.EACH_TIMESTEP or AggregateLevel.EACH_SEQUENCE
- name (basestring) – layer name.
- input (paddle.v2.config_base.Layer) – input layer name.
- pooling_type (BasePoolingType|None) – Type of pooling, MaxPooling(default), AvgPooling, SumPooling, SquareRootNPooling.
- bias_attr (paddle.v2.attr.ParameterAttribute|None|False) – Bias parameter attribute. False if no bias.
- layer_attr (paddle.v2.attr.ExtraAttributeNone) – The Extra Attributes for layer, such as dropout.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
last_seq¶
-
class
paddle.v2.layer.
last_seq
(*args, **kwargs) Get Last Timestamp Activation of a sequence.
If stride > 0, this layer slides a window whose size is determined by stride, and return the last value of the window as the output. Thus, a long sequence will be shorten. 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 – Aggregated level
- name (basestring) – Layer name.
- input (paddle.v2.config_base.Layer) – Input layer name.
- stride (Int) – window size.
- layer_attr (paddle.v2.attr.ExtraAttribute) – extra layer attributes.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
first_seq¶
-
class
paddle.v2.layer.
first_seq
(*args, **kwargs) Get First Timestamp Activation of a sequence.
If stride > 0, this layer slides a window whose size is determined by stride, and return the first value of the window as the output. Thus, a long sequence will be shorten. 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 – aggregation level
- name (basestring) – Layer name.
- input (paddle.v2.config_base.Layer) – Input layer name.
- stride (Int) – window size.
- layer_attr (paddle.v2.attr.ExtraAttribute) – extra layer attributes.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
concat¶
-
class
paddle.v2.layer.
concat
(*args, **kwargs) Concat all input vector into one huge vector. Inputs can be list of paddle.v2.config_base.Layer or list of projection.
The example usage is:
concat = concat(input=[layer1, layer2])
Parameters: - name (basestring) – Layer name.
- input (list|tuple|collections.Sequence) – input layers or projections
- act (paddle.v2.Activation.Base) – Activation type.
- layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
seq_concat¶
-
class
paddle.v2.layer.
seq_concat
(*args, **kwargs) Concat sequence a with sequence b.
- Inputs:
- a = [a1, a2, ..., an]
- b = [b1, b2, ..., bn]
- Note that the length of a and b should be the same.
Output: [a1, b1, a2, b2, ..., an, bn]
The example usage is:
concat = seq_concat(a=layer1, b=layer2)
Parameters: - name (basestring) – Layer name.
- a (paddle.v2.config_base.Layer) – input sequence layer
- b (paddle.v2.config_base.Layer) – input sequence layer
- act (paddle.v2.Activation.Base) – Activation type.
- layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
- bias_attr (paddle.v2.attr.ParameterAttribute or None or bool) – The Bias Attribute. If no bias, then pass False or something not type of paddle.v2.attr.ParameterAttribute. None will get a default Bias.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
Reshaping Layers¶
block_expand¶
-
class
paddle.v2.layer.
block_expand
(*args, **kwargs) - 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 expand method is the same with ExpandConvLayer, but saved the transposed value. After expanding, output.sequenceStartPositions will store timeline. The number of time steps are outputH * outputW and the dimension of each time step is block_y * block_x * num_channels. This layer can be used after convolution 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 layer.
- num_channels (int|None) – The channel number of input layer.
- 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 (None|basestring.) – The name of this layer, which can not specify.
- layer_attr (paddle.v2.attr.ExtraAttributeNone) – Extra Layer config.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
expand¶
-
class
paddle.v2.layer.
expand
(*args, **kwargs) A layer for “Expand 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_TIMESTEP)
Parameters: - input (paddle.v2.config_base.Layer) – Input layer
- expand_as (paddle.v2.config_base.Layer) – Expand as this layer’s sequence info.
- name (basestring) – Layer name.
- bias_attr (paddle.v2.attr.ParameterAttribute|None|False) – Bias attribute. None means default bias. False means no bias.
- expand_level (ExpandLevel) – whether input layer is timestep(default) or sequence.
- layer_attr (paddle.v2.attr.ExtraAttribute) – extra layer attributes.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
repeat¶
-
class
paddle.v2.layer.
repeat
(*args, **kwargs) A layer for repeating the input for num_repeats times. This is equivalent to apply concat() with num_repeats same input.
\[y = [x, x, \cdots, x]\]The example usage is:
expand = repeat(input=layer, num_repeats=4)
Parameters: - input (paddle.v2.config_base.Layer) – Input layer
- num_repeats (int) – Repeat the input so many times
- name (basestring) – Layer name.
- layer_attr (paddle.v2.attr.ExtraAttribute) – extra layer attributes.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
rotate¶
-
class
paddle.v2.layer.
rotate
(*args, **kwargs) 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) – Input layer.
- height (int) – The height of the sample matrix
- name (basestring) – Layer name.
- layer_attr (paddle.v2.attr.ExtraAttribute) – extra layer attributes.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
seq_reshape¶
-
class
paddle.v2.layer.
seq_reshape
(*args, **kwargs) 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) – Input layer.
- reshape_size (int) – the size of reshaped sequence.
- name (basestring) – Layer name.
- act (paddle.v2.Activation.Base) – Activation type.
- layer_attr (paddle.v2.attr.ExtraAttribute) – extra layer attributes.
- bias_attr (paddle.v2.attr.ParameterAttribute or None or bool) – The Bias Attribute. If no bias, then pass False or something not type of paddle.v2.attr.ParameterAttribute. None will get a default Bias.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
Math Layers¶
addto¶
-
class
paddle.v2.layer.
addto
(*args, **kwargs) AddtoLayer.
\[y = f(\sum_{i} x_i + b)\]where \(y\) is output, \(x\) is input, \(b\) is bias, and \(f\) is activation function.
The example usage is:
addto = addto(input=[layer1, layer2], act=paddle.v2.Activation.Relu(), bias_attr=False)
This layer just simply add all input layers together, then activate the sum inputs. Each input of this layer should be the same size, which is also the output size of this layer.
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.
It is a very good way to set dropout outside the layers. Since not all PaddlePaddle layer support dropout, you can add an add_to layer, set dropout here. Please refer to dropout for details.
Parameters: - name (basestring) – Layer name.
- input (paddle.v2.config_base.Layer|list|tuple) – Input layers. It could be a paddle.v2.config_base.Layer or list/tuple of paddle.v2.config_base.Layer.
- act (paddle.v2.Activation.Base) – Activation Type, default is tanh.
- bias_attr (paddle.v2.attr.ParameterAttribute|bool) – Bias attribute. If False, means no bias. None is default bias.
- layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer attribute.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
linear_comb¶
-
class
paddle.v2.layer.
linear_comb
(*args, **kwargs) - 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 Layer Name.
- layer_attr (paddle.v2.attr.ExtraAttributeNone) – Extra Layer config.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
interpolation¶
-
class
paddle.v2.layer.
interpolation
(*args, **kwargs) This layer is for linear interpolation with 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) – Input layer.
- weight (paddle.v2.config_base.Layer) – Weight layer.
- name (basestring) – Layer name.
- layer_attr (paddle.v2.attr.ExtraAttribute) – extra layer attributes.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
bilinear_interp¶
-
class
paddle.v2.layer.
bilinear_interp
(*args, **kwargs) This layer is to implement bilinear interpolation on conv layer output.
Please refer to Wikipedia: https://en.wikipedia.org/wiki/Bilinear_interpolation
The simple usage is:
bilinear = bilinear_interp(input=layer1, out_size_x=64, out_size_y=64)
Parameters: - input (paddle.v2.config_base.Layer.) – A input layer.
- out_size_x (int|None) – bilinear interpolation output width.
- out_size_y (int|None) – bilinear interpolation output height.
- name (None|basestring) – The layer’s name, which cna not be specified.
- layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer attribute.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
power¶
-
class
paddle.v2.layer.
power
(*args, **kwargs) This layer applies a power function to a vector element-wise, which is used in NEURAL TURING MACHINE.
\[y = x^w\]where \(x\) is a input vector, \(w\) is scalar weight, and \(y\) is a output vector.
The example usage is:
power = power(input=layer1, weight=layer2)
Parameters: - input (paddle.v2.config_base.Layer) – Input layer.
- weight (paddle.v2.config_base.Layer) – Weight layer.
- name (basestring) – Layer name.
- layer_attr (paddle.v2.attr.ExtraAttribute) – extra layer attributes.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
scaling¶
-
class
paddle.v2.layer.
scaling
(*args, **kwargs) 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) – Input layer.
- weight (paddle.v2.config_base.Layer) – Weight layer.
- name (basestring) – Layer name.
- layer_attr (paddle.v2.attr.ExtraAttribute) – extra layer attributes.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
slope_intercept¶
-
class
paddle.v2.layer.
slope_intercept
(*args, **kwargs) This layer for applying a slope and an intercept to the input element-wise. There is no activation and weight.
\[y = slope * x + intercept\]The simple usage is:
scale = slope_intercept(input=input, slope=-1.0, intercept=1.0)
Parameters: - input (paddle.v2.config_base.Layer) – The input layer.
- name (basestring) – The Layer Name.
- slope (float.) – the scale factor.
- intercept (float.) – the offset.
- layer_attr (paddle.v2.attr.ExtraAttributeNone) – Extra Layer config.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
tensor¶
-
class
paddle.v2.layer.
tensor
(*args, **kwargs) This layer performs tensor operation for two input. For example, each sample:
\[y_{i} = a * W_{i} * {b^\mathrm{T}}, i=0,1,...,K-1\]- 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)
Parameters: - name (basestring) – layer name
- a (paddle.v2.config_base.Layer) – Input layer a.
- b (paddle.v2.config_base.Layer) – input layer b.
- size (int.) – the layer dimension.
- act (paddle.v2.Activation.Base) – Activation Type. Default is tanh.
- param_attr (paddle.v2.attr.ParameterAttribute) – The Parameter Attribute.
- bias_attr (paddle.v2.attr.ParameterAttribute|None|Any) – The Bias Attribute. If no bias, then pass False or something not type of paddle.v2.attr.ParameterAttribute. None will get a default Bias.
- layer_attr (paddle.v2.attr.ExtraAttributeNone) – Extra Layer config.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
cos_sim¶
-
class
paddle.v2.layer.
cos_sim
(*args, **kwargs) 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) – layer name
- a (paddle.v2.config_base.Layer) – input layer a
- b (paddle.v2.config_base.Layer) – input layer b
- scale (float) – scale for cosine value. default is 5.
- size (int) – layer size. NOTE size_a * size should equal size_b.
- layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
trans¶
-
class
paddle.v2.layer.
trans
(*args, **kwargs) 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) – Input layer.
- name (basestring) – Layer name.
- layer_attr (paddle.v2.attr.ExtraAttribute) – extra layer attributes.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
Sampling Layers¶
maxid¶
-
class
paddle.v2.layer.
max_id
(*args, **kwargs) 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) – Input layer name.
- name (basestring) – Layer name.
- layer_attr (paddle.v2.attr.ExtraAttribute) – extra layer attributes.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
sampling_id¶
-
class
paddle.v2.layer.
sampling_id
(*args, **kwargs) A layer for sampling id from 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 layer.
- name (basestring) – The Layer Name.
- layer_attr (paddle.v2.attr.ExtraAttributeNone) – Extra Layer config.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
Slicing and Joining Layers¶
pad¶
-
class
paddle.v2.layer.
pad
(*args, **kwargs) This operation pads zeros to the input data according to pad_c,pad_h and pad_w. pad_c, pad_h, pad_w specifies the which dimension and size of padding. And the input data shape is NCHW.
For example, pad_c=[2,3] means padding 2 zeros before the input data and 3 zeros after the input data in channel dimension. pad_h means padding zeros in height dimension. pad_w means padding zeros in width dimension.
For example,
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]] ] ] pad_c=[1,1], pad_h=[0,0], pad_w=[0,0] 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, pad_c=[4,4], pad_h=[0,0], pad_w=[2,2])
Parameters: - input (paddle.v2.config_base.Layer) – layer’s input.
- pad_c (list|None) – padding size in channel dimension.
- pad_h (list|None) – padding size in height dimension.
- pad_w (list|None) – padding size in width dimension.
- layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
- name (basestring) – layer name.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
Cost Layers¶
cross_entropy_cost¶
-
class
paddle.v2.layer.
cross_entropy_cost
(*args, **kwargs) A loss layer for multi class entropy.
cost = cross_entropy(input=input, label=label)
Parameters: - input (paddle.v2.config_base.Layer.) – The first input layer.
- label – The input label.
- name (None|basestring.) – The name of this layers. It is not necessary.
- coeff (float.) – The cost is multiplied with coeff. The coefficient affects the gradient in the backward.
- weight (LayerOutout) – The cost of each sample is multiplied with each weight. The weight should be a layer with size=1. Note that gradient will not be calculated for weight.
- layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer.
cross_entropy_with_selfnorm_cost¶
-
class
paddle.v2.layer.
cross_entropy_with_selfnorm_cost
(*args, **kwargs) A loss layer for multi class entropy with selfnorm. Input should be a vector of positive numbers, without normalization.
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 (None|basestring.) – The name of this layers. It is not necessary.
- coeff (float.) – The coefficient affects the gradient in the backward.
- softmax_selfnorm_alpha (float.) – The scale factor affects the cost.
- layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer.
multi_binary_label_cross_entropy_cost¶
-
class
paddle.v2.layer.
multi_binary_label_cross_entropy_cost
(*args, **kwargs) A loss layer for multi binary label cross entropy.
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.
- type (basestring) – The type of cost.
- name (None|basestring) – The name of this layers. It is not necessary.
- coeff (float) – The coefficient affects the gradient in the backward.
- layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
huber_cost¶
-
class
paddle.v2.layer.
huber_cost
(*args, **kwargs) A loss layer for huber loss.
cost = huber_cost(input=input, label=label)
Parameters: - input (paddle.v2.config_base.Layer.) – The first input layer.
- label – The input label.
- name (None|basestring.) – The name of this layers. It is not necessary.
- coeff (float.) – The coefficient affects the gradient in the backward.
- layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer.
lambda_cost¶
-
class
paddle.v2.layer.
lambda_cost
(*args, **kwargs) lambdaCost for lambdaRank LTR approach.
The simple usage:
cost = lambda_cost(input=input, score=score, NDCG_num=8, max_sort_size=-1)
Parameters: - input (paddle.v2.config_base.Layer) – Samples of the same query should be loaded as sequence.
- score – The 2nd input. Score of each sample.
- NDCG_num (int) – The size of NDCG (Normalized Discounted Cumulative Gain), e.g., 5 for NDCG@5. It must be less than for equal to the minimum size of lists.
- max_sort_size (int) – The size of partial sorting in calculating gradient. If max_sort_size = -1, then for each list, the algorithm will sort the entire list to get gradient. In other cases, max_sort_size must be greater than or equal to NDCG_num. And if max_sort_size is greater than the size of a list, the algorithm will sort the entire list of get gradient.
- name (None|basestring) – The name of this layers. It is not necessary.
- layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
mse_cost¶
-
class
paddle.v2.layer.
mse_cost
(*args, **kwargs) mean squared error cost:
\[\]rac{1}{N}sum_{i=1}^N(t_i-y_i)^2
param name: layer name. type name: basestring param input: Network prediction. type input: paddle.v2.config_base.Layer param label: Data label. type label: paddle.v2.config_base.Layer param weight: The weight affects the cost, namely the scale of cost. It is an optional argument. type weight: paddle.v2.config_base.Layer param layer_attr: layer’s extra attribute. type layer_attr: paddle.v2.attr.ExtraAttribute return: paddle.v2.config_base.Layer object. rtype: paddle.v2.config_base.Layer
rank_cost¶
-
class
paddle.v2.layer.
rank_cost
(*args, **kwargs) A cost Layer for learning to rank using gradient descent. Details can refer to papers. This layer contains at least three inputs. The weight is an optional argument, which affects the cost.
\[ \begin{align}\begin{aligned}C_{i,j} & = -\tilde{P_{ij}} * o_{i,j} + log(1 + e^{o_{i,j}})\\o_{i,j} & = o_i - o_j\\\tilde{P_{i,j}} & = \{0, 0.5, 1\} \ or \ \{0, 1\}\end{aligned}\end{align} \]- 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 simple usage:
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 affects the cost, namely the scale of cost. It is an optional argument.
- name (None|basestring) – The name of this layers. It is not necessary.
- coeff (float) – The coefficient affects the gradient in the backward.
- layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
sum_cost¶
-
class
paddle.v2.layer.
sum_cost
(*args, **kwargs) A loss layer which calculate the sum of the input as loss
cost = sum_cost(input=input)
Parameters: - input (paddle.v2.config_base.Layer.) – The first input layer.
- name (None|basestring.) – The name of this layers. It is not necessary.
- layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer.
crf¶
-
class
paddle.v2.layer.
crf
(*args, **kwargs) A layer for calculating the cost of sequential conditional random field model.
The simple usage:
crf = crf(input=input, label=label, size=label_dim)
Parameters: - input (paddle.v2.config_base.Layer) – The first input layer is the feature.
- label (paddle.v2.config_base.Layer) – The second input layer is label.
- size (int) – The category number.
- weight (paddle.v2.config_base.Layer) – The third layer is “weight” of each sample, which is an optional argument.
- param_attr (paddle.v2.attr.ParameterAttribute) – Parameter attribute. None means default attribute
- name (None|basestring) – The name of this layers. It is not necessary.
- layer_attr (paddle.v2.attr.ExtraAttributeNone) – Extra Layer config.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
crf_decoding¶
-
class
paddle.v2.layer.
crf_decoding
(*args, **kwargs) A layer for calculating the decoding sequence of sequential conditional random field model. The decoding sequence is stored in output.ids. If a second input is provided, it is treated as the ground-truth label, and this layer will also calculate error. output.value[i] is 1 for incorrect decoding or 0 for correct decoding.
The simple usage:
crf_decoding = crf_decoding(input=input, size=label_dim)
Parameters: - input (paddle.v2.config_base.Layer) – The first input layer.
- size (int) – size of this layer.
- label (paddle.v2.config_base.Layer or None) – None or ground-truth label.
- param_attr (paddle.v2.attr.ParameterAttribute) – Parameter attribute. None means default attribute
- name (None|basestring) – The name of this layers. It is not necessary.
- layer_attr (paddle.v2.attr.ExtraAttributeNone) – Extra Layer config.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
ctc¶
-
class
paddle.v2.layer.
ctc
(*args, **kwargs) Connectionist Temporal Classification (CTC) is designed for temporal classication task. That is, for sequence labeling problems where the alignment between the inputs and the target labels is unknown.
More details can be found by referring to Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks
Note
Considering the ‘blank’ label needed by CTC, you need to use (num_classes + 1) as the input size. num_classes is the category number. And the ‘blank’ is the last category index. So the size of ‘input’ layer, such as fc with softmax activation, should be num_classes + 1. The size of ctc should also be num_classes + 1.
The simple usage:
ctc = ctc(input=input, label=label, size=9055, norm_by_times=True)
Parameters: - input (paddle.v2.config_base.Layer) – The input layer.
- label (paddle.v2.config_base.Layer) – The data layer of label with variable length.
- size (int) – category numbers + 1.
- name (basestring|None) – The name of this layer
- norm_by_times (bool) – Whether to normalization by times. False by default.
- layer_attr (paddle.v2.attr.ExtraAttributeNone) – Extra Layer config.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
warp_ctc¶
-
class
paddle.v2.layer.
warp_ctc
(*args, **kwargs) A layer intergrating the open-source warp-ctc <https://github.com/baidu-research/warp-ctc> library, which is used in Deep Speech 2: End-toEnd Speech Recognition in English and Mandarin <https://arxiv.org/pdf/1512.02595v1.pdf>, to compute Connectionist Temporal Classification (CTC) loss.
More details of CTC can be found by referring to Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks
Note
- Let num_classes represent the category number. Considering the ‘blank’ label needed by CTC, you need to use (num_classes + 1) as the input size. Thus, the size of both warp_ctc and ‘input’ layer should be set to num_classes + 1.
- You can set ‘blank’ to any value ranged in [0, num_classes], which should be consistent as that used in your labels.
- As a native ‘softmax’ activation is interated to the warp-ctc library, ‘linear’ activation is expected instead in the ‘input’ layer.
The simple usage:
ctc = warp_ctc(input=input, label=label, size=1001, blank=1000, norm_by_times=False)
Parameters: - input (paddle.v2.config_base.Layer) – The input layer.
- label (paddle.v2.config_base.Layer) – The data layer of label with variable length.
- size (int) – category numbers + 1.
- name (basestring|None) – The name of this layer, which can not specify.
- blank (int) – the ‘blank’ label used in ctc
- norm_by_times (bool) – Whether to normalization by times. False by default.
- layer_attr (paddle.v2.attr.ExtraAttributeNone) – Extra Layer config.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
nce¶
-
class
paddle.v2.layer.
nce
(*args, **kwargs) Noise-contrastive estimation. Implements the method in the following paper: A fast and simple algorithm for training neural probabilistic language models.
The example usage is:
cost = nce(input=layer1, label=layer2, weight=layer3, num_classes=3, neg_distribution=[0.1,0.3,0.6])
Parameters: - name (basestring) – layer name
- input (paddle.v2.config_base.Layer|list|tuple|collections.Sequence) – input layers. It could be a paddle.v2.config_base.Layer of list/tuple of paddle.v2.config_base.Layer.
- label (paddle.v2.config_base.Layer) – label layer
- weight (paddle.v2.config_base.Layer) – weight layer, can be None(default)
- num_classes (int) – number of classes.
- act (paddle.v2.Activation.Base) – Activation, default is Sigmoid.
- num_neg_samples (int) – number of negative samples. Default is 10.
- neg_distribution (list|tuple|collections.Sequence|None) – The distribution for generating the random negative labels. A uniform distribution will be used if not provided. If not None, its length must be equal to num_classes.
- bias_attr (paddle.v2.attr.ParameterAttribute|None|False) – Bias parameter attribute. True if no bias.
- layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
Returns: layer name.
Return type: paddle.v2.config_base.Layer
hsigmoid¶
-
class
paddle.v2.layer.
hsigmoid
(*args, **kwargs) 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. This idea is from “F. Morin, Y. Bengio (AISTATS 05): 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) – Input layers. It could be a paddle.v2.config_base.Layer or list/tuple of paddle.v2.config_base.Layer.
- label (paddle.v2.config_base.Layer) – Label layer.
- num_classes (int|None) – number of classes.
- name (basestring) – layer name
- bias_attr (paddle.v2.attr.ParameterAttribute|False) – Bias attribute. None means default bias. False means no bias.
- param_attr (paddle.v2.attr.ParameterAttribute|None) – Parameter Attribute. None means default parameter.
- layer_attr (paddle.v2.attr.ExtraAttribute) – Extra Layer Attribute.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer
Check Layer¶
eos¶
-
class
paddle.v2.layer.
eos
(*args, **kwargs) 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) – Layer name.
- input (paddle.v2.config_base.Layer) – Input layer name.
- eos_id (int) – end id of sequence
- layer_attr (paddle.v2.attr.ExtraAttribute) – extra layer attributes.
Returns: paddle.v2.config_base.Layer object.
Return type: paddle.v2.config_base.Layer


| Methods' Details |
createMark
- Description
- creates a mark of the current postion and returns an identifier to it.
|
|
deleteMark
- Description
- deletes the mark that you previously created with
XMarkableStream::createMark.
It is an error to delete a mark if other marks after this
exist. In this case, for reasons of robustness, the
implementation must delete this mark and all others after
this mark.
|
|
jumpToMark
- Description
- jumps to a previously created mark.
|
|
jumpToFurthest
- Description
- jumps to the furthest position of the stream.
In the inputstream case, a subsequent read call returns
data, that was never read or skipped over before. In the
outputstream case, a subsequent write call will add
new data at the end of the stream without overwriting existing data.
|
|
offsetToMark
- Returns
- the offset from the current stream position to the
mark ("current position" - "mark position").
- Parameter nMark
- identifies the mark which is used as a base
to calculate the offset of the current position.
- Throws
- IllegalArgumentException
if the mark does not exist or is deleted.
- Throws
- IOException
if an I/O error has occurred.
|
|
Copyright © 2013, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


黒の手番です。63 はどう動かしますか？
Unlimited
pips Black=146 White=147
(CorrectAnswer= 0/0, ErrorScore= 0)
1. Bar/22 24/18 eq:-0.507 37.06% (G: 9.93% B:0.48%) 62.94% (G:20.89% B:0.91%) 2. Bar/22 9/3 eq:-0.536 (-0.029) 36.21% (G:11.47% B:0.45%) 63.79% (G:19.92% B:0.94%) 3. Bar/16 eq:-0.580 (-0.073) 35.37% (G: 9.60% B:0.37%) 64.63% (G:20.13% B:0.97%) 4. Bar/22 8/2 eq:-0.666 (-0.158) 33.81% (G:10.88% B:0.42%) 66.19% (G:22.87% B:1.71%) 5. Bar/22 13/7 eq:-0.972 (-0.464) 30.59% (G: 9.35% B:0.50%) 69.41% (G:27.60% B:3.14%)


:: com :: sun :: star :: text ::
|
| enum WrapTextMode |
- Description
- enumeration values specify the text wrap around objects in a text.
|
| Values |
| NONE |
text does not flow around the object.
|
| THROUGHT |
text flow ignores the object.
|
| PARALLEL |
text flows to the left and right of the object.
|
| DYNAMIC |
text flow depends on the situation. The text formatting
decides the best way.
|
| LEFT |
text flows to the left side of the object.
|
| RIGHT |
text flows to the right side of the object.
|
| Values' Details |
NONE
NONE,
- Description
- text does not flow around the object.
|
|
THROUGHT
THROUGHT,
- Description
- text flow ignores the object.
|
|
PARALLEL
PARALLEL,
- Description
- text flows to the left and right of the object.
|
|
DYNAMIC
DYNAMIC,
- Description
- text flow depends on the situation. The text formatting
decides the best way.
|
|
LEFT
LEFT,
- Description
- text flows to the left side of the object.
|
|
RIGHT
RIGHT,
- Description
- text flows to the right side of the object.
|
|
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Copyright © 2013, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


A unified JavaScript layer for Apache Cordova projects.
cordova-js
|
|-build/
| Will contain any build modules (currently nothing here as it is all
| hacked into the JakeFile)
|
|-lib
| |-cordova.js
| | Common Cordova stuff such as callback handling and
| | window/document add/removeEventListener hijacking
| |
| |-common/
| | Contains the common-across-platforms base modules
| |
| |-common/builder.js
| | Injects in our classes onto window and navigator (or wherever else
| | is needed)
| |
| |-common/channel.js
| | A pub/sub implementation to handle custom framework events
| |
| |-common/common.js
| | Common locations to add Cordova objects to browser globals.
| |
| |-common/exec.js
| | Stub for platform's specific version of exec.js
| |
| |-common/platform.js
| | Stub for platform's specific version of platform.js
| |
| |-common/utils.js
| | General purpose JS utility stuff: closures, uuids, object
| | cloning, extending prototypes
| |
| |-common/plugin
| | Contains the common-across-platforms plugin modules
| |
| |-scripts/
| | Contains non-module JavaScript source that gets added to the
| | resulting cordova.<platform>.js files closures, uuids, object
| |
| |-scripts/bootstrap.js
| | Code to bootstrap the Cordova platform, inject APIs and fire events
| |
| |-scripts/require.js
| | Our own module definition and require implementation.
| |
| |-<platform>/
| | Contains the platform-specific base modules.
| |
| |-<platform>/plugin/<platform>
| | Contains the platform-specific plugin modules.
The way the resulting cordova.<platform>.js
files will be built is by combining the scripts in the lib/scripts
directory with modules from the lib/common
and lib/<platform>
directories. For cases where there is the same named module in lib/common
and lib/<platform>/plugin/<platform>
, the lib/<platform>
version wins. For instance, every lib/<platform>
includes an exec.js
, and there is also a version in lib/common
, so the lib/<platform>
version will always be used. In fact, the lib/common
one will throw errors, so if you build a new platform and forget exec.js
, the resulting cordova.<platform>.js
file will also throw errors.
Make sure you have node.js installed. It should come pre-installed with npm - but if you install node and can’t run npm
then head over to the website and install it yourself. Make sure you have all of the node dependencies installed by running the following command from the repository root:
npm install
All of the build tasks can be run via the jake
node module. Install it globally first by running:
sudo npm install -g jake
Every build also runs the scripts through JSHint. It is best installed globally, but it is not necessary for building cordova-js (you just won’t get syntax and style hints when you build):
sudo npm install -g jshint
Then from the repository root run:
jake
This will run the build
, hint
and test
tasks by default. All of the available tasks are:
build
: creates platform versions of cordova-js and builds them into the pkg/
directorytest
: runs all of the unit tests inside nodebtest
: creates a server so you can run the tests inside a browserclean
: cleans out the pkg/
directoryhint
: runs all of the script files through JSHintfixwhitespace
: converts all tabs to four spaces, removes carriage returns and cuts out trailing whitespace within the script filesnpm install
. Using node v0.6.6 works, though.npm install
, you may get errors regarding contextify. This is necessary for running the tests. Make sure you are running node v0.6.15 at the least (and npm v1.1.16 which should come bundled with node 0.6.15). Also, install Python 2.7.x and Visual C++ 2010 Express. When that is done, run npm install
again and it should build contextify natively on Windows.The build/packager.js
tool is a node.js script that concatenates all of the core Cordova plugins in this repository into a cordova.<platform>.js
file under the pkg/
folder. It also wraps the plugins with a RequireJS-compatible module syntax that works in both browser and node environments. We end up with a cordova.js file that wraps each Cordova plugin into its own module.
Cordova defines a channel
module under lib/common/channel.js
, which is a publish/subscribe implementation that the project uses for event management.
The Cordova native-to-webview bridge is initialized in lib/scripts/bootstrap.js
. This file attaches the boot
function to the channel.onNativeReady
event - fired by native with a call to:
cordova.require('cordova/channel).onNativeReady.fire()
The boot
method does all the work. First, it grabs the common platform definition (under lib/common/common.js
) and injects all of the objects defined there onto window
and other global namespaces. Next, it grabs all of the platform-specific object definitions (as defined under lib/<platform>/platform.js
) and overrides those onto window
. Finally, it calls the platform-specific initialize
function (located in the platform definition). At this point, Cordova is fully initialized and ready to roll. Last thing we do is wait for the DOMContentLoaded
event to fire to make sure the page has loaded properly. Once that is done, Cordova fires the deviceready
event where you can safely attach functions that consume the Cordova APIs.
Tests run in node or the browser. To run the tests in node:
jake test
To run them in the browser:
jake btest
Final testing should always be done with the Mobile Spec test application.
Build the .js file and drop it in as a replacement for cordova.js.
Load this in Ripple to play with it. You will have to use the cordova prototype branch to better simulate the phone environment and use this javascript rather than Ripple’s emulated code.
git clone git@github.com:blackberry-webworks/Ripple-UI.git
git checkout winnie.the.pooh
./configure
jake
and then load the unpacked extension in chrome in the pkg/chromium folder. Use the cordova.proto platform in ripple.
lib
folder.exec
method and call it exec.js. The exec
method is a JavaScript function that enables communication from the platform’s JavaScript environment into the platform’s native environment. Each platform uses a different mechanism to enable this bridge. We recommend you check out the other platform exec
definitions for inspiration. Drop this into the lib/<platform>
folder you created in step 1. The exec
method has the following method signature: function(success, fail, service, action, args)
, with the following parameters:success
: a success function callbackfail
: a failure function callbackservice
: a string identifier that the platform can resolve to a native classaction
: a string identifier that the platform can resolve to a specific method inside the class pointed to by service
args
: an array of parameters to pass to the native method invoked by the exec
call It is required that new platform additions be as consistent as possible with the existing service
and action
labels.Define your platform definition object and name it platform.js. Drop this into the lib/<platform>
folder. This file should contain a JSON object with the following properties:
id
: a string representing the platform. This should be the same name the .js file hasobjects
: the property names defined as children of this property are injected into window
, and also overrides any existing properties. Each property can have the following child properties:
path
: a string representing the module ID that will define this object. For example, the file lib/plugin/accelerometer.js
can be accessed as "cordova/plugin/accelerometer"
. More details on how the module IDs are defined are above under the “How It Works” section.children
: in a recursive fashion, can have path
and children
properties of its own that are defined as children of the parent property objectmerges
: similar to the above objects
property, this one will not clobber existing objects, instead it will recursively merge this object into the specific targetinitialize
: a function that fires immediately after the objects
(see above) are defined in the global scopeThe following is a simple example of a platform definition:
{ id:"atari", initialize:function(){ console.log('firing up cordova in my atari, yo.'); }, objects:{ cordova:{ path:"cordova", children:{ joystick:{ path:"cordova/plugin/atari/joystick" } } } } }
packager.bundle('<platform>')
call to the Jakefile
under the build
task.require('cordova/channel').onNativeReady.fire()
.require('cordova/channel').onCordovaInfoReady.fire()
(for device information) and require('cordova/channel').OnCordovaConnectionReady.fire()
(for network information).Last but certainly not least: add yourself to the contributors list! It’s in the package.json
file in the root of this repository. You deserve it!