Module nn

candle-nn

Other Crates

Candle consists of a number of crates. This crate holds structs and functions that allow you to build and train neural nets. You may wish to look at the docs for the other crates which can be found here:

• candle-core. Core Datastructures and DataTypes.
• candle-nn. Building blocks for Neural Nets.
• candle-datasets. Rust access to commonly used Datasets like MNIST.
• candle-examples. Examples of Candle in Use.
• candle-onnx. Loading and using ONNX models.
• candle-pyo3. Access to Candle from Python.
• candle-transformers. Candle implemntation of many published transformer models.

Re-exports

• pub use activation::prelu;
• pub use activation::Activation;
• pub use activation::PReLU;
• pub use attention::scaled_dot_product_attention;
• pub use batch_norm::batch_norm;
• pub use batch_norm::BatchNorm;
• pub use batch_norm::BatchNormConfig;
• pub use conv::conv1d;
• pub use conv::conv1d_no_bias;
• pub use conv::conv2d;
• pub use conv::conv2d_no_bias;
• pub use conv::conv_transpose1d;
• pub use conv::conv_transpose1d_no_bias;
• pub use conv::conv_transpose2d;
• pub use conv::conv_transpose2d_no_bias;
• pub use conv::Conv1d;
• pub use conv::Conv1dConfig;
• pub use conv::Conv2d;
• pub use conv::Conv2dConfig;
• pub use conv::ConvTranspose1d;
• pub use conv::ConvTranspose1dConfig;
• pub use conv::ConvTranspose2d;
• pub use conv::ConvTranspose2dConfig;
• pub use embedding::embedding;
• pub use embedding::Embedding;
• pub use func::func;
• pub use func::func_t;
• pub use func::Func;
• pub use func::FuncT;
• pub use group_norm::group_norm;
• pub use group_norm::GroupNorm;
• pub use init::Init;
• pub use layer_norm::layer_norm;
• pub use layer_norm::rms_norm_non_quant;
• pub use layer_norm::rms_norm_quant;
• pub use layer_norm::LayerNorm;
• pub use layer_norm::LayerNormConfig;
• pub use layer_norm::RmsNorm;
• pub use linear::linear;
• pub use linear::linear_b;
• pub use linear::linear_no_bias;
• pub use linear::Linear;
• pub use ops::kvconcat;
• pub use ops::Dropout;
• pub use optim::AdamW;
• pub use optim::Optimizer;
• pub use optim::ParamsAdamW;
• pub use optim::SGD;
• pub use rnn::gru;
• pub use rnn::lstm;
• pub use rnn::GRUConfig;
• pub use rnn::LSTMConfig;
• pub use rnn::GRU;
• pub use rnn::LSTM;
• pub use rnn::RNN;
• pub use rope::RotaryEmbedding;
• pub use sequential::seq;
• pub use sequential::Sequential;
• pub use var_builder::VarBuilder;
• pub use var_map::VarMap;
• pub use crate::core::Module;
• pub use crate::core::ModuleT;

Modules

• activation
  Activation Functions
• attention
• batch_norm
  Batch Normalization.
• conv
  Convolution Layers.
• embedding
  Embedding Layer.
• encoding
  Encoding Utilities. (e.g., one-hot/cold encoding)
• func
  Layers defined by closures.
• group_norm
  Group Normalization.
• init
  Variable initialization.
• kv_cache
  Cache Implementations
• layer_norm
  Layer Normalization.
• linear
  Linear layer
• loss
  Loss Calculations
• ops
  Tensor ops.
• optim
  Various optimization algorithms.
• rnn
  Recurrent Neural Networks
• rope
• rotary_emb
  Rotary Embeddings
• sequential
  Sequential Layer
• var_builder
  A VarBuilder is used to retrieve variables used by a model. These variables can either come from a pre-trained checkpoint, e.g. using VarBuilder::from_mmaped_safetensors, or initialized for training, e.g. using VarBuilder::from_varmap.
• var_map