Module dopt.core.ops.nnet
Contains common neural network operations.
These operations are currently only implemented for the CUDA backend.
Functions
Name | Description |
---|---|
convolution(features, filters, padding, stride, mod, line) | Creates a convolution operation that performs the computation required to implement a convolutional layer. |
convolutionFeaturesGrad(parentGrad, filters, featuresShape, padding, stride, mod, line) | Creates an operation representing the derivative of a convolution operation with respect to the feature maps. |
convolutionFiltersGrad(parentGrad, features, filtersShape, padding, stride, mod, line) | Creates an operation representing the derivative of a convolution operation with respect to the filters. |
convolutionTranspose(features, filters, padding, stride, mod, line) | Creates a transposed convolution operation (also known, incorrectly, as deconvolution). |
maxpool(features, dims, mod, line) | Creates a max pool operation that performs the computation required to implement a max pooling layer. |
maxpoolGrad(parentGrad, op, mod, line) | Creates an operation representing the derivative of a maxpool operation with respect to the feature maps. |
relu(inputs, mod, line) | Creates an operation representing the computation required for a ReLU layer. |
softmax(inputs, mod, line) | Creates an operation representing the computation required for a softmax layer. |
softmaxGrad(parentGrad, op, mod, line) | Creates an operation representing the gradient of the softmax function. |