Layers

Core modules

Buffer	Class representing a non-learnable state array (e.g. running statistics).
Module	Base abstract class for all the modules.
Parameter	Class representing a learnable parameter.

Layers

Layers are basic building blocks of neural networks.

The following table lists the available layers:

AvgPool1D	Apply a 1D average pooling.
AvgPool2D	Apply a 2D average pooling.
BatchNorm	Apply Batch Normalization over the channels of the input.
Conv1D	Apply a 1D convolution.
Conv2D	Apply a 2D convolution.
Dropout	Randomly zero some of the input values during training.
Flatten	Flatten a contiguous range of dimensions into a single dimension.
GlobalAvgPool	Apply a global average pooling over the spatial dimensions of the input.
GlobalMaxPool	Apply a global max pooling over the spatial dimensions of the input.
GroupNorm	Apply Group Normalization over the channels of the input.
GRU	Apply a multi-layer Gated Recurrent Unit (GRU) to an input sequence.
GRUCell	Apply a Gated Recurrent Unit (GRU) cell.
Identity	Identity operation.
InstanceNorm	Apply Instance Normalization over the spatial dimensions of the input.
LayerNorm	Apply Layer Normalization over the trailing dimensions of the input.
Linear	Apply a linear transformation over the final dimension of the input.
LSTM	Apply a multi-layer Long Short-Term Memory (LSTM) to an input sequence.
LSTMCell	Apply a Long Short-Term Memory (LSTM) cell.
MaxPool1D	Apply a 1D max pooling.
MaxPool2D	Apply a 2D max pooling.
RMSNorm	Apply Root Mean Square (RMS) Normalization over the trailing dimensions of the input.
RNN	Apply a multi-layer Elman's Recurrent Neural Network (RNN) to an input sequence.
RNNCell	Apply a Elman's Recurrent Neural Network (RNN) cell.
Sequential	Apply callable modules (e.g. layers, activation functions, etc.) connected in a cascading sequence.