Hype
Hype Namespace
| Type | Description |
| Batch | Training batch configuration |
| Classifier | Base type for classifiers |
| Dataset | Dataset for holding training data |
| EarlyStopping | Early stopping configuration |
| GradientClipping | Gradient clipping configuration |
| LearningRate | Learning rate schemes |
| LogisticClassifier | Classifier for binary classification |
| Loss | Loss function configuration |
| Method | Gradient-based optimization methods |
| Momentum | Momentum configuration |
| Optimize | Main optimization module |
| Params | Record type holding optimization or training parameters |
| Regularization | Regularization configuration |
| Rnd | Random number generator |
| SoftmaxClassifier | Classifier for softmax classification |
| Util | Various utility functions |
| Module | Description |
| Params |
Hype.Inference Namespace
| Type | Description |
| HMCSampler | Hamiltonian MCMC sampler |
Hype.NLP Namespace
| Type | Description |
| Language | Language model |
Hype.Neural Namespace
| Type | Description |
| Activation | Activation layer with custom functions |
| FeedForward | Feedforward sequence of layers |
| GRU | Gated recurrent unit layer |
| Initializer | Initialization schemes for neural layer weights |
| LSTM | Long short-term memory layer |
| LSTMAlt | Long short-term memory layer (alternative implementation) |
| Layer | Base type for neural layers |
| Linear | Linear layer |
| LinearNoBias | Linear layer with no bias |
| Recurrent | Vanilla RNN layer |
