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API - Layers

.. automodule:: tensorlayer.layers

Layer list

.. autosummary::

   Layer

   Input

   OneHot
   Word2vecEmbedding
   Embedding
   AverageEmbedding

   Dense
   Dropout
   GaussianNoise
   DropconnectDense

   UpSampling2d
   DownSampling2d

   Conv1d
   Conv2d
   Conv3d
   DeConv2d
   DeConv3d
   DepthwiseConv2d
   SeparableConv1d
   SeparableConv2d
   DeformableConv2d
   GroupConv2d

   PadLayer
   PoolLayer
   ZeroPad1d
   ZeroPad2d
   ZeroPad3d
   MaxPool1d
   MeanPool1d
   MaxPool2d
   MeanPool2d
   MaxPool3d
   MeanPool3d
   GlobalMaxPool1d
   GlobalMeanPool1d
   GlobalMaxPool2d
   GlobalMeanPool2d
   GlobalMaxPool3d
   GlobalMeanPool3d
   CornerPool2d

   SubpixelConv1d
   SubpixelConv2d

   SpatialTransformer2dAffine
   transformer
   batch_transformer

   BatchNorm
   BatchNorm1d
   BatchNorm2d
   BatchNorm3d
   LocalResponseNorm
   InstanceNorm
   InstanceNorm1d
   InstanceNorm2d
   InstanceNorm3d
   LayerNorm
   GroupNorm
   SwitchNorm

   RNN
   SimpleRNN
   GRURNN
   LSTMRNN
   BiRNN

   retrieve_seq_length_op
   retrieve_seq_length_op2
   retrieve_seq_length_op3
   target_mask_op

   Flatten
   Reshape
   Transpose
   Shuffle

   Lambda

   Concat
   Elementwise
   ElementwiseLambda

   ExpandDims
   Tile

   Stack
   UnStack

   Sign
   Scale
   BinaryDense
   BinaryConv2d
   TernaryDense
   TernaryConv2d
   DorefaDense
   DorefaConv2d

   PRelu
   PRelu6
   PTRelu6

   flatten_reshape
   initialize_rnn_state
   list_remove_repeat

Base Layer

.. autoclass:: Layer

Input Layers

Input Layer

.. autofunction:: Input

One-hot Layer

.. autoclass:: OneHot

Word2Vec Embedding Layer

.. autoclass:: Word2vecEmbedding

Embedding Layer

.. autoclass:: Embedding

Average Embedding Layer

.. autoclass:: AverageEmbedding

Activation Layers

PReLU Layer

.. autoclass:: PRelu

PReLU6 Layer

.. autoclass:: PRelu6

PTReLU6 Layer

.. autoclass:: PTRelu6

Convolutional Layers

Convolutions

Conv1d

.. autoclass:: Conv1d

Conv2d

.. autoclass:: Conv2d

Conv3d

.. autoclass:: Conv3d

Deconvolutions

DeConv2d

.. autoclass:: DeConv2d

DeConv3d

.. autoclass:: DeConv3d

Deformable Convolutions

DeformableConv2d

.. autoclass:: DeformableConv2d

Depthwise Convolutions

DepthwiseConv2d

.. autoclass:: DepthwiseConv2d

Group Convolutions

GroupConv2d

.. autoclass:: GroupConv2d

Separable Convolutions

SeparableConv1d

.. autoclass:: SeparableConv1d

SeparableConv2d

.. autoclass:: SeparableConv2d

SubPixel Convolutions

SubpixelConv1d

.. autoclass:: SubpixelConv1d

SubpixelConv2d

.. autoclass:: SubpixelConv2d

Dense Layers

Dense Layer

.. autoclass:: Dense

Drop Connect Dense Layer

.. autoclass:: DropconnectDense

Dropout Layers

.. autoclass:: Dropout

Extend Layers

Expand Dims Layer

.. autoclass:: ExpandDims

Tile layer

.. autoclass:: Tile

Image Resampling Layers

2D UpSampling

.. autoclass:: UpSampling2d

2D DownSampling

.. autoclass:: DownSampling2d

Lambda Layers

Lambda Layer

.. autoclass:: Lambda

ElementWise Lambda Layer

.. autoclass:: ElementwiseLambda

Merge Layers

Concat Layer

.. autoclass:: Concat

ElementWise Layer

.. autoclass:: Elementwise

Noise Layer

.. autoclass:: GaussianNoise

Normalization Layers

Batch Normalization

.. autoclass:: BatchNorm

Batch Normalization 1D

.. autoclass:: BatchNorm1d

Batch Normalization 2D

.. autoclass:: BatchNorm2d

Batch Normalization 3D

.. autoclass:: BatchNorm3d

Local Response Normalization

.. autoclass:: LocalResponseNorm

Instance Normalization

.. autoclass:: InstanceNorm

Instance Normalization 1D

.. autoclass:: InstanceNorm1d

Instance Normalization 2D

.. autoclass:: InstanceNorm2d

Instance Normalization 3D

.. autoclass:: InstanceNorm3d

Layer Normalization

.. autoclass:: LayerNorm

Group Normalization

.. autoclass:: GroupNorm

Switch Normalization

.. autoclass:: SwitchNorm

Padding Layers

Pad Layer (Expert API)

Padding layer for any modes.

.. autoclass:: PadLayer

1D Zero padding

.. autoclass:: ZeroPad1d

2D Zero padding

.. autoclass:: ZeroPad2d

3D Zero padding

.. autoclass:: ZeroPad3d

Pooling Layers

Pool Layer (Expert API)

Pooling layer for any dimensions and any pooling functions.

.. autoclass:: PoolLayer

1D Max pooling

.. autoclass:: MaxPool1d

1D Mean pooling

.. autoclass:: MeanPool1d

2D Max pooling

.. autoclass:: MaxPool2d

2D Mean pooling

.. autoclass:: MeanPool2d

3D Max pooling

.. autoclass:: MaxPool3d

3D Mean pooling

.. autoclass:: MeanPool3d

1D Global Max pooling

.. autoclass:: GlobalMaxPool1d

1D Global Mean pooling

.. autoclass:: GlobalMeanPool1d

2D Global Max pooling

.. autoclass:: GlobalMaxPool2d

2D Global Mean pooling

.. autoclass:: GlobalMeanPool2d

3D Global Max pooling

.. autoclass:: GlobalMaxPool3d

3D Global Mean pooling

.. autoclass:: GlobalMeanPool3d

2D Corner pooling

.. autoclass:: CornerPool2d

Quantized Nets

This is an experimental API package for building Quantized Neural Networks. We are using matrix multiplication rather than add-minus and bit-count operation at the moment. Therefore, these APIs would not speed up the inferencing, for production, you can train model via TensorLayer and deploy the model into other customized C/C++ implementation (We probably provide users an extra C/C++ binary net framework that can load model from TensorLayer).

Note that, these experimental APIs can be changed in the future.

Sign

.. autoclass:: Sign

Scale

.. autoclass:: Scale

Binary Dense Layer

.. autoclass:: BinaryDense

Binary (De)Convolutions

BinaryConv2d

.. autoclass:: BinaryConv2d

Ternary Dense Layer

TernaryDense

.. autoclass:: TernaryDense

Ternary Convolutions

TernaryConv2d

.. autoclass:: TernaryConv2d

DoReFa Convolutions

DorefaConv2d

.. autoclass:: DorefaConv2d

DoReFa Convolutions

DorefaConv2d

.. autoclass:: DorefaConv2d

Recurrent Layers

Common Recurrent layer

All recurrent layers can implement any type of RNN cell by feeding different cell function (LSTM, GRU etc).

RNN layer

.. autoclass:: RNN

RNN layer with Simple RNN Cell

.. autoclass:: SimpleRNN

RNN layer with GRU Cell

.. autoclass:: GRURNN

RNN layer with LSTM Cell

.. autoclass:: LSTMRNN

Bidirectional layer

.. autoclass:: BiRNN

Advanced Ops for Dynamic RNN

These operations usually be used inside Dynamic RNN layer, they can compute the sequence lengths for different situation and get the last RNN outputs by indexing.

Compute Sequence length 1

.. autofunction:: retrieve_seq_length_op

Compute Sequence length 2

.. autofunction:: retrieve_seq_length_op2

Compute Sequence length 3

.. autofunction:: retrieve_seq_length_op3

Compute mask of the target sequence

.. autofunction:: target_mask_op

Shape Layers

Flatten Layer

.. autoclass:: Flatten

Reshape Layer

.. autoclass:: Reshape

Transpose Layer

.. autoclass:: Transpose

Shuffle Layer

.. autoclass:: Shuffle

Spatial Transformer

2D Affine Transformation

.. autoclass:: SpatialTransformer2dAffine

2D Affine Transformation function

.. autofunction:: transformer

Batch 2D Affine Transformation function

.. autofunction:: batch_transformer

Stack Layer

Stack Layer

.. autoclass:: Stack

Unstack Layer

.. autoclass:: UnStack

Helper Functions

Flatten tensor

.. autofunction:: flatten_reshape

Initialize RNN state

.. autofunction:: initialize_rnn_state

Remove repeated items in a list

.. autofunction:: list_remove_repeat