A tensor containing a batch of input feature maps.
A tensor containing the filters that will be convolved with the feature maps.
An operation representing convolutions of input imgs with some kernels.
import dopt.core : evaluate; auto features = float32([1, 1, 3, 5], [ 1.0f, 1.0f, 1.0f, 0.0f, 0.0f, 1.0f, 1.0f, 1.0f, 0.0f, 0.0f, 1.0f, 1.0f, 1.0f, 0.0f, 0.0f ]); auto filters = float32([1, 1, 1, 2], [ -1.0f, 1.0f ]); auto result = convolution(features, filters); auto edges = result.evaluate().get!float; assert(edges == [ 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f ]);
Creates a convolution operation that performs the computation required to implement a convolutional layer.
Currently this operation only implements 2D convolutions.