Expand description
axonml-nn - Neural Network Module Library
§File
crates/axonml-nn/src/lib.rs
§Author
Andrew Jewell Sr - AutomataNexus
§Updated
March 8, 2026
§Disclaimer
Use at own risk. This software is provided “as is”, without warranty of any kind, express or implied. The author and AutomataNexus shall not be held liable for any damages arising from the use of this software.
Re-exports§
pub use module::Module;pub use module::ModuleList;pub use parameter::Parameter;pub use sequential::Sequential;pub use layers::AdaptiveAvgPool2d;pub use layers::AvgPool1d;pub use layers::AvgPool2d;pub use layers::BatchNorm1d;pub use layers::BatchNorm2d;pub use layers::Conv1d;pub use layers::Conv2d;pub use layers::ConvTranspose2d;pub use layers::CrossAttention;pub use layers::DifferentialAttention;pub use layers::Dropout;pub use layers::Embedding;pub use layers::Expert;pub use layers::FFT1d;pub use layers::GATConv;pub use layers::GCNConv;pub use layers::GRU;pub use layers::GRUCell;pub use layers::GroupNorm;pub use layers::GroupSparsity;pub use layers::InstanceNorm2d;pub use layers::LSTM;pub use layers::LSTMCell;pub use layers::LayerNorm;pub use layers::Linear;pub use layers::LotteryTicket;pub use layers::MaxPool1d;pub use layers::MaxPool2d;pub use layers::MoELayer;pub use layers::MoERouter;pub use layers::MultiHeadAttention;pub use layers::PackedTernaryWeights;pub use layers::RNN;pub use layers::RNNCell;pub use layers::ResidualBlock;pub use layers::STFT;pub use layers::Seq2SeqTransformer;pub use layers::SparseLinear;pub use layers::TernaryLinear;pub use layers::TransformerDecoder;pub use layers::TransformerDecoderLayer;pub use layers::TransformerEncoder;pub use layers::TransformerEncoderLayer;pub use activation::ELU;pub use activation::Flatten;pub use activation::GELU;pub use activation::Identity;pub use activation::LeakyReLU;pub use activation::LogSoftmax;pub use activation::ReLU;pub use activation::SiLU;pub use activation::Sigmoid;pub use activation::Softmax;pub use activation::Tanh;pub use loss::BCELoss;pub use loss::BCEWithLogitsLoss;pub use loss::CrossEntropyLoss;pub use loss::L1Loss;pub use loss::MSELoss;pub use loss::NLLLoss;pub use loss::Reduction;pub use loss::SmoothL1Loss;pub use init::InitMode;pub use init::constant;pub use init::diag;pub use init::eye;pub use init::glorot_normal;pub use init::glorot_uniform;pub use init::he_normal;pub use init::he_uniform;pub use init::kaiming_normal;pub use init::kaiming_uniform;pub use init::normal;pub use init::ones;pub use init::orthogonal;pub use init::randn;pub use init::sparse;pub use init::uniform;pub use init::uniform_range;pub use init::xavier_normal;pub use init::xavier_uniform;pub use init::zeros;
Modules§
- activation
- Activation Modules - Non-linear Activation Functions
- functional
- Functional API - Stateless Neural Network Operations
- init
- Weight Initialization - Parameter Initialization Strategies
- layers
- Neural Network Layers
- loss
- Loss Functions - Training Objectives
- module
- Module Trait - Neural Network Module Interface
- parameter
- Parameter - Learnable Parameter Wrapper
- prelude
- Common imports for neural network development.
- sequential
- Sequential - Sequential Container for Modules