Package deepnetts.net.layers

Neural network layers, which are main building blocks of a neural network.

See: Description

Interface Summary

Interface	Description
Layer	Common base interface for all types of neural network layers.

Class Summary

Class	Description
AbstractLayer	Base class for different types of layers.
ConvolutionalLayer	Convolutional layer performs image convolution operation on outputs of a previous layer using filters.
Filter	Settings of a convolutional filter which is used to learn to detect pixel patterns.
FlattenLayer	Just transform outputs from prev 3d layer into flat 1d tensor in formward pass backward pass will be same as backward for fc, and for 3d layers backwardFromFc Automaticly add after 2d or 3d layer to transition to fc layers.
FullyConnectedLayer	Fully connected layer is used as hidden layer in the neural network, and it has a single row of units/nodes/neurons connected to all neurons in previous and next layer.
InputLayer	Input layer in a neural network.
MaxPoolingLayer	This layer performs max pooling operation in convolutional neural network, which scales down output from previous layer by taking max outputs from small predefined filter areas.
OutputLayer	Output layer of a neural network.
SoftmaxOutputLayer	Output layer with softmax activation function.

Enum Summary

Enum	Description
LayerType	Supported types of layers.

Package deepnetts.net.layers Description

Neural network layers, which are main building blocks of a neural network. Every neural network consists of a sequence of layers.