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Module: oryx.experimental.nn

Module for neural network layers.

Modules

base module: Contains the Template and Layer API for Oryx.

combinator module: Contains combinator layers.

convolution module: Contains building blocks for convolutional neural networks.

core module: Contains important layers for neural network construction.

normalization module: Contains building blocks for normalization layers.

pooling module: Contains building blocks for pooling layers used for neural networks.

reshape module: Contains layers that reshape arrays.

Classes

class AvgPooling: Average pooling layer, computes the average within the window.

class BatchNorm: Layer for Batch Normalization.

class Conv: Neural network layer for 2D convolution.

class Deconv: Neural network layer for 2D transposed convolution.

class Dense: Dense layer used for building neural networks.

class Dropout: Dropout layer used for building neural networks.

class Flatten: Flattens the inputs collapsing all ending dimensions.

class Layer: Base class for neural network layers.

class LayerParams: LayerParams holds params and info of Layers.

class LogSoftmax: Parent abstract class for activation functions.

class MaxPooling: Max pooling layer, computes the maximum within the window.

class Relu: Parent abstract class for activation functions.

class Reshape: Reshape the inputs to a new compatatible shape.

class Serial: Layer that executes a sequence of child layers.

class Softmax: Parent abstract class for activation functions.

class Softplus: Parent abstract class for activation functions.

class SumPooling: Sum pooling layer, computes the sum within the window.

class Tanh: Parent abstract class for activation functions.

class Template: Template class used by neural network layers.