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.

Re-exports

• pub use init::f_init;
• pub use init::init;
• pub use init::Init;

Modules

• init
  Variable initialization.

Structs

• Adam
  Parameters for the Adam optimizer.
• AdamW
  Parameters for the AdamW 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.
• GroupNorm
  A group-normalization layer.
• GroupNormConfig
  Group-normalization config.
• 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

Enums

• PaddingMode
  How padding is performed by convolution operations on the edge of the input tensor.

Traits

• 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.

Functions

• adam
  Creates the configuration for the Adam optimizer.
• adamw
  Creates the configuration for the AdamW 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
• group_norm
• gru
  Creates a new GRU layer.
• 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.

Type Definitions

• 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.