Convolutional Classifier

The classifier accepts any batchShape and number of spatial axes. Convolution and pooling use the same vector-valued configuration for signals, images, volumes, and higher-dimensional data.

structure TorchLean.nn.models.CNN.Config (d : ℕ) : Type

Configuration for a convolutional classifier over d spatial axes.

• inputChannels : ℕ

  Number of channels in each input sample.

• spatial : Tensor ℕ [d]

  Extent of each spatial axis.

• convolution : Convolution.Config d

  Convolution applied before activation and pooling.

• pooling : Pooling.Config d

  Pooling applied after the convolutional activation.

• classCount : ℕ

  Number of classifier logits per sample.

def TorchLean.nn.models.CNN.Config.validate {d : ℕ} (config : Config d) : Except String Unit

Validate the complete classifier geometry before allocating convolution or head parameters.

@[reducible, inline]

abbrev TorchLean.nn.models.CNN.Config.inputShape {d : ℕ} (config : Config d) (batchShape : Shape := []) : Shape

Input tensor shape after prepending an arbitrary batch shape.

@[reducible, inline]

abbrev TorchLean.nn.models.CNN.Config.outputShape {d : ℕ} (config : Config d) (batchShape : Shape := []) : Shape

Classifier output shape with the same batch shape as the input.

def TorchLean.nn.models.cnn {d : ℕ} (config : CNN.Config d) (batchShape : Shape := []) : Builder (Sequential (config.inputShape batchShape) (config.outputShape batchShape))

Build convolution -> activation -> max pool -> flatten -> linear.