Crate dfdx

Ergonomics & safety focused deep learning in Rust. Main features include:

1. Tensor library, complete with const generic shapes, activation functions, and more.
2. Safe & Easy to use neural network building blocks.
3. Standard deep learning optimizers such as Sgd and Adam.
4. Reverse mode auto differentiation[1] implementation.

Modules

devices

Provides implementations for modifying Nd arrays on the CPU.

diff_fns

losses

Standard loss functions such as mse, mae, cross entropy, and more.

nn

Neural network building blocks such as Linear and impls for tuples as feedforward networks.

numpy

Provides some generic functions to save Nd arrays in the .npy format.

optim

Optimizers such as Sgd and Adam that can optimize neural networks.

prelude

tensor_ops

Implementations of all operations for tensors, including activations, binary operations, and other methods.