A convolution kernel that convolves the input image with a set of filters, with each producing one feature map in the output image.


class MPSCNNConvolution : MPSCNNKernel


The attributes of a convolution operation are described by an MPSCNNConvolutionDescriptor object.



Instance Properties

var inputFeatureChannels: Int

The number of feature channels per pixel in the input image.

var outputFeatureChannels: Int

The number of feature channels per pixel in the output image.

var groups: Int

The number of groups that the input and output channels are divided into.

var neuron: MPSCNNNeuron?

The neuron filter to be applied as part of the convolution operation.

class MPSCNNNeuron

A filter that applies a neuron activation function.

enum MPSCNNNeuronType

The types of neuron filter to append to a convolution.


Inherits From

Conforms To

See Also

Convolution Layers

class MPSCNNBinaryConvolution

A convolution kernel with binary weights and an input image using binary approximations.

class MPSCNNDepthWiseConvolutionDescriptor

A description of a convolution object that does depthwise convolution.

class MPSCNNSubPixelConvolutionDescriptor

A description of a convolution object that does subpixel upsampling and reshaping.

class MPSCNNConvolutionTranspose

A transposed convolution kernel.

class MPSCNNConvolutionGradient

A gradient convolution kernel.

class MPSCNNConvolutionGradientState

An object that exposes a gradient convolution kernel's gradient with respect to weights and biases.

protocol MPSImageSizeEncodingState

A protocol for objects that contain information about an image size elsewhere in the graph.

class MPSCNNConvolutionWeightsAndBiasesState

A class that stores weights and biases.