Expand description
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
Modules
Variable initialization.
Structs
Parameters for the Adam optimizer.
Parameters for the AdamW optimizer.
A batch-normalization layer.
Batch-normalization config.
A N-dimensional convolution layer.
Generic convolution config.
A generic transposed convolution configuration.
A generic transposed convolution layer.
An embedding layer.
Configuration option for an embedding layer.
A layer defined by a simple closure.
A layer defined by a closure with an additional training parameter.
A Gated Recurrent Unit (GRU) layer.
A GRU state, this contains a single tensor.
A group-normalization layer.
Group-normalization config.
An identity layer. This just propagates its tensor input as output.
A Long Short-Term Memory (LSTM) layer.
The state for a LSTM network, this contains two tensors.
A layer-normalization layer.
Layer-normalization config.
A linear fully-connected layer.
Configuration for a linear layer.
An optimizer to run gradient descent.
A variable store with an associated path for variables naming.
Configuration for the GRU and LSTM layers.
Parameters for the RmsProp optimizer.
A sequential layer combining multiple other layers.
A sequential layer combining new layers with support for a training mode.
Parameters for the SGD optimizer.
A VarStore is used to store variables used by one or multiple layers.
It specifies a single device where all variables are stored.
Enums
How padding is performed by convolution operations
on the edge of the input tensor.
Traits
The simplest module trait, defining a forward function.
Module trait with an additional train parameter.
Optimizer configurations. These configs can be used to build optimizer.
Trait for Recurrent Neural Networks.
Functions
Creates the configuration for the Adam optimizer.
Creates the configuration for the AdamW optimizer.
Applies Batch Normalization over a three dimension input.
Applies Batch Normalization over a four dimension input.
Applies Batch Normalization over a five dimension input.
Creates a new convolution layer for any number of dimensions.
Creates a new one dimension convolution layer.
Creates a new two dimension convolution layer.
Creates a new three dimension convolution layer.
Creates a one dimension transposed convolution layer.
Creates a two dimension transposed convolution layer.
Creates a three dimension transposed convolution layer.
Creates a new GRU layer.
Creates a new linear layer.
Creates a LSTM layer.
The default convolution config without bias.
Creates the configuration for the RmsProp optimizer.
Creates a new empty sequential layer.
Creates a new empty sequential layer.
Creates the configuration for a Stochastic Gradient Descent (SGD) optimizer.
Type Definitions
One dimension convolution layer.
Two dimensions convolution layer.
Three dimensions convolution layer.
Convolution config using the same parameters on all dimensions.
A one dimension transposed convolution layer.
A two dimension transposed convolution layer.
A three dimension transposed convolution layer.
A transposed convolution configuration using the same values on each dimension.