Module modules
Expand description
Neural network modules implementations.
Modules§
- attention
- Attention module
- basic
- Basic RNN.
- cache
- Cache module
- conv
- Convolution module
- gru
- Gated Recurrent Unit module.
- interpolate
- Interpolate module
- lstm
- Long Short-Term Memory module.
- norm
- Normalization Layers
- pool
- Pooling module
- transformer
- Transformer module
Structs§
- Batch
Norm - Applies Batch Normalization over a tensor.
- Batch
Norm Config BatchNormConfiguration.- Batch
Norm Record - The record type for the module.
- Batch
Norm Record Item - The record item type for the module.
- BiGru
- The BiGru module. This implementation is for Bidirectional GRU.
- BiGru
Config - Configuration to create a BiGru module using the init function.
- BiGru
Record - The record type for the module.
- BiGru
Record Item - The record item type for the module.
- BiLstm
- The BiLstm module. This implementation is for Bidirectional LSTM.
- BiLstm
Config - Configuration to create a BiLstm module using the init function.
- BiLstm
Record - The record type for the module.
- BiLstm
Record Item - The record item type for the module.
- BiRnn
- The BiRnn module. This implementation is for Bidirectional RNN. Should be created with BiRnnConfig.
- BiRnn
Config - Configuration to create a BiRnn module using the init function.
- BiRnn
Record - The record type for the module.
- BiRnn
Record Item - The record item type for the module.
- Dropout
- Set at random some elements of the input tensor to zero during training.
- Dropout
Config - Configuration to create a Dropout layer using the init function.
- Embedding
- Lookup table to store a fix number of vectors.
- Embedding
Config - Configuration to create an Embedding layer using the init function.
- Embedding
Record - The record type for the module.
- Embedding
Record Item - The record item type for the module.
- Gate
Controller - A GateController represents a gate in an LSTM cell. An LSTM cell generally contains three gates: an input gate, forget gate, and output gate. Additionally, cell gate is just used to compute the cell state.
- Gate
Controller Record - The record type for the module.
- Gate
Controller Record Item - The record item type for the module.
- Gaussian
Noise - Add pseudorandom Gaussian noise to an arbitrarily shaped tensor.
- Gaussian
Noise Config - Configuration to create a GaussianNoise layer using the init function.
- Group
Norm - Applies Group Normalization over a mini-batch of inputs as described in the paper Group Normalization.
- Group
Norm Config - Configuration to create a GroupNorm layer using the init function.
- Group
Norm Record - The record type for the module.
- Group
Norm Record Item - The record item type for the module.
- Gru
- The Gru (Gated recurrent unit) module. This implementation is for a unidirectional, stateless, Gru.
- GruConfig
- Configuration to create a gru module using the init function.
- GruRecord
- The record type for the module.
- GruRecord
Item - The record item type for the module.
- Instance
Norm - Applies Instance Normalization over a tensor as described in the paper Instance Normalization
- Instance
Norm Config - Configuration to create a InstanceNorm layer using the init function.
- Instance
Norm Record - The record type for the module.
- Instance
Norm Record Item - The record item type for the module.
- Layer
Norm - Applies Layer Normalization over an input tensor as described in the paper Layer Normalization.
- Layer
Norm Config - Configuration to create a LayerNorm layer using the init function.
- Layer
Norm Record - The record type for the module.
- Layer
Norm Record Item - The record item type for the module.
- Linear
- Applies a linear transformation to the input tensor.
- Linear
Config - Configuration to create a
Linearlayer using the init function. - Linear
Record - The record type for the module.
- Linear
Record Item - The record item type for the module.
- Local
Response Norm - Applies Local Response Normalization as described in ImageNet Classification with Deep Convolutional Neural Networks.
- Local
Response Norm Config - Configuration to create a LocalResponseNorm layer using the init function.
- Lstm
- The Lstm module. This implementation is for a unidirectional, stateless, Lstm.
- Lstm
Config - Configuration to create a Lstm module using the init function.
- Lstm
Record - The record type for the module.
- Lstm
Record Item - The record item type for the module.
- Lstm
State - A LstmState is used to store cell state and hidden state in LSTM.
- Positional
Encoding - Positional encoding layer for transformer models.
- Positional
Encoding Config - Configuration to create a PositionalEncoding layer using the init function.
- Positional
Encoding Record - The record type for the module.
- Positional
Encoding Record Item - The record item type for the module.
- RmsNorm
- Applies RMS Normalization over an input tensor along the last dimension.
- RmsNorm
Config - Configuration to create a RMS Norm layer using the init function.
- RmsNorm
Record - The record type for the module.
- RmsNorm
Record Item - The record item type for the module.
- Rnn
- The Rnn module. This implementation is for a unidirectional, stateless, Rnn. Should be created with RnnConfig.
- RnnConfig
- Configuration to create a Rnn module using the init function.
- RnnRecord
- The record type for the module.
- RnnRecord
Item - The record item type for the module.
- RnnState
- A RnnState is used to store hidden state in RNN.
- Rotary
Encoding - A module that applies rotary positional encoding to a tensor. Rotary Position Encoding or Embedding (RoPE), is a type of position embedding which encodes absolute positional information with rotation matrix and naturally incorporates explicit relative position dependency in self-attention formulation.
- Rotary
Encoding Config - Configuration to create a RotaryEncoding layer using the init function.
- Rotary
Encoding Record - The record type for the module.
- Rotary
Encoding Record Item - The record item type for the module.
- Unfold4d
- Four-dimensional unfolding.
- Unfold4d
Config - Configuration to create an unfold 4d layer using the init function.
Enums§
- Linear
Layout - The layout in which the linear parameters are stored.
Functions§
- generate_
sinusoids - Returns sinusoids for positional embedding introduced in Attention is all you need.