dopt.nnet.layers

Modules

batchnorm

module dopt.nnet.layers.batchnorm

Contains an implementation of batch normalisation.

conv

module dopt.nnet.layers.conv

Contains an implementation of convolutional layers.

datasource

module dopt.nnet.layers.datasource

Allows one to provide input to a network via a dopt variable.

dense

module dopt.nnet.layers.dense

Contains an implementation of dense (i.e., fully connected) layers.

dropout

module dopt.nnet.layers.dropout

Contains an implementation of dropout.

maxpool

module dopt.nnet.layers.maxpool

Contains an implementation of max pooling.

relu

module dopt.nnet.layers.relu

Contains an implementation of the ReLU activation function.

softmax

module dopt.nnet.layers.softmax

Contains an implementation of the softmat activation function.

Public Imports

dopt.nnet.layers.batchnorm

public import dopt.nnet.layers.batchnorm;

Undocumented in source.

dopt.nnet.layers.conv

public import dopt.nnet.layers.conv;

Undocumented in source.

dopt.nnet.layers.datasource

public import dopt.nnet.layers.datasource;

Undocumented in source.

dopt.nnet.layers.dense

public import dopt.nnet.layers.dense;

Undocumented in source.

dopt.nnet.layers.dropout

public import dopt.nnet.layers.dropout;

Undocumented in source.

dopt.nnet.layers.maxpool

public import dopt.nnet.layers.maxpool;

Undocumented in source.

dopt.nnet.layers.relu

public import dopt.nnet.layers.relu;

Undocumented in source.

dopt.nnet.layers.softmax

public import dopt.nnet.layers.softmax;

Undocumented in source.

Members

Classes

Layer

class Layer

Encapsulates the expressions and parameter information that defines a network layer.

Functions

topologicalSort

Layer[] topologicalSort(Layer[] ops)

Undocumented in source. Be warned that the author may not have intended to support it.

• data
• layers
• lipschitz
• losses
• models
• networks
• parameters
• util