API documentation
| Module | Description |
|---|---|
| dopt.core.ops.basic | Contains functions for creating variable nodes and subsequently manipulating their shapes. |
| dopt.core.ops.math | Contains common maths operations. |
| dopt.core.ops.nnet | Contains common neural network operations. |
| dopt.core.ops.random | Contains functions for generating random numbers. |
| dopt.core.grads | Contains the automatic differentiation framework. |
| dopt.core.ops | This package facilitates the construction of various nodes in the operation graph. |
| dopt.nnet.layers.batchnorm | Contains an implementation of batch normalisation. |
| dopt.nnet.layers.conv | Contains an implementation of convolutional layers. |
| dopt.nnet.layers.datasource | Allows one to provide input to a network via a dopt variable. |
| dopt.nnet.layers.dense | Contains an implementation of dense (i.e., fully connected) layers. |
| dopt.nnet.layers.dropout | Contains an implementation of dropout. |
| dopt.nnet.layers.maxpool | Contains an implementation of max pooling. |
| dopt.nnet.layers.relu | Contains an implementation of the ReLU activation function. |
| dopt.nnet.layers.softmax | Contains an implementation of the softmat activation function. |
| dopt.nnet.layers | Contains generic utilities for working with Layer objects.
|
| dopt.nnet.losses | Contains some utilities for constructing graphs for common loss functions. |
| dopt.nnet.networks | Provides a useful tools for constructing neural networks. |
| dopt.nnet.parameters | This module contains methods for initialising the parameters of neural networks. |
| dopt.online.adam | Contains an implementation of ADAM that relies on automatic differentiation |
| dopt.online.amsgrad | Contains an implementation of AMSGrad that relies on automatic differentiation |
| dopt.online.sgd | Contains an implementation of stochastic gradient descent that relies on automatic differentiation |
| dopt.core | This package contains the framework for constructing and executing operation graphs. |
| dopt.cpu | This module enables operation graphs to be evaluated using CPU kernels. |
| dopt.cuda | This is the main interface for the dopt CUDA backend. |
| dopt.nnet | This package contains a deep learning API backed by dopt. |
| dopt.online | This package contains implementations of common online optimisation algorithms, with a particular bias towards those commonly used in large scale machine learning/deep learning. |