[−][src]Module tch::nn

A small neural-network library based on Torch.

This library tries to stay as close as possible to the original Python and C++ implementations.

Structs

Adam	Parameters for the Adam optimizer.
BatchNorm	A batch-normalization layer.
BatchNormConfig	Batch-normalization config.
Conv	A N-dimensional convolution layer.
ConvConfigND	Generic convolution config.
ConvTransposeConfigND	A generic transposed convolution configuration.
ConvTransposeND	A generic transposed convolution layer.
Embedding	An embedding layer.
EmbeddingConfig	Configuration option for an embedding layer.
Func	A layer defined by a simple closure.
FuncT	A layer defined by a closure with an additional training parameter.
GRU	A Gated Recurrent Unit (GRU) layer.
GRUState	A GRU state, this contains a single tensor.
Id	An identity layer. This just propagates its tensor input as output.
LSTM	A Long Short-Term Memory (LSTM) layer.
LSTMState	The state for a LSTM network, this contains two tensors.
LayerNorm	A layer-normalization layer.
LayerNormConfig	Layer-normalization config.
Linear	A linear fully-connected layer.
LinearConfig	Configuration for a linear layer.
Optimizer	An optimizer to run gradient descent.
Path	A variable store with an associated path for variables naming.
RNNConfig	Configuration for the GRU and LSTM layers.
RmsProp	Parameters for the RmsProp optimizer.
Sequential	A sequential layer combining multiple other layers.
SequentialT	A sequential layer combining new layers with support for a training mode.
Sgd	Parameters for the SGD optimizer.
VarStore	A VarStore is used to store variables used by one or multiple layers. It specifies a single device where all variables are stored.
Variables

Init	Variable initializations.

Module	The simplest module trait, defining a forward function.
ModuleT	Module trait with an additional train parameter.
OptimizerConfig	Optimizer configurations. These configs can be used to build optimizer.
RNN	Trait for Recurrent Neural Networks.

adam	Creates the configuration for the Adam optimizer.
batch_norm1d	Applies Batch Normalization over a three dimension input.
batch_norm2d	Applies Batch Normalization over a four dimension input.
batch_norm3d	Applies Batch Normalization over a five dimension input.
conv	Creates a new convolution layer for any number of dimensions.
conv1d	Creates a new one dimension convolution layer.
conv2d	Creates a new two dimension convolution layer.
conv3d	Creates a new three dimension convolution layer.
conv_transpose1d	Creates a one dimension transposed convolution layer.
conv_transpose2d	Creates a two dimension transposed convolution layer.
conv_transpose3d	Creates a three dimension transposed convolution layer.
embedding
func
func_t
gru	Creates a new GRU layer.
init	Creates a new float tensor with the specified shape, device, and initialization.
layer_norm
linear	Creates a new linear layer.
lstm	Creates a LSTM layer.
no_bias	The default convolution config without bias.
rms_prop	Creates the configuration for the RmsProp optimizer.
seq	Creates a new empty sequential layer.
seq_t	Creates a new empty sequential layer.
sgd	Creates the configuration for a Stochastic Gradient Descent (SGD) optimizer.

Conv1D	One dimension convolution layer.
Conv2D	Two dimensions convolution layer.
Conv3D	Three dimensions convolution layer.
ConvConfig	Convolution config using the same parameters on all dimensions.
ConvTranspose1D	A one dimension transposed convolution layer.
ConvTranspose2D	A two dimension transposed convolution layer.
ConvTranspose3D	A three dimension transposed convolution layer.
ConvTransposeConfig	A transposed convolution configuration using the same values on each dimension.