[−][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 |
Enums
Init | Variable initializations. |
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. |
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. |
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. |