Crate iron_learn Copy item path Source pub use crate::tensor::Tensor ;pub use crate::neural_network::ActivationFn ;pub use crate::neural_network::ActivationLayer ;pub use crate::neural_network::Layer ;pub use crate::neural_network::LinearLayer ;pub use crate::neural_network::LossFunction ;pub use crate::neural_network::MeanSquaredErrorLoss ;pub use crate::neural_network::NeuralNet ;pub use crate::neural_network::NeuralNetBuilder ;init neural_network read_file tensor Tensor Module - Linear Algebra Core AppContext Global application context with training configuration and GPU capabilities Complex Dataset container for double-precision (f64) examples. CpuTensor The CpuTensor structure is the backend implementaion of the Tensor trait. The implementation uses CPU for calcualations.
Although, Auto Vectorization has been used whereever possible for parallel execeution. Data DataDoublePrecision Dataset container for single-precision (f32) examples. GLOBAL_CONTEXT Global singleton instance of application context Numeric The Numeric trait defines a set of operations that numeric types must support.
It includes basic arithmetic operations and the ability to return special values like zero and one. SignedNumeric The SignedNumeric defines all the Numeric types that can be signed like i32, i64 etc. gradient_descent Perform a single gradient descent update step. init_context Initialize the global application context linear_regression Train a linear regression model using gradient descent. logistic_regression Train a logistic regression model using gradient descent. normalize_features Normalize features using provided mean and std per feature. predict_linear Predict outputs for x using linear model weights w. predict_logistic Predict binary labels for x using logistic model weights w. run_linear Run linear regression using configuration from the global context. run_logistic Run logistic regression using configuration from the global context. run_neural_net Train and evaluate a neural network using configuration from the global context.