class ConvolutionalBlock[source]

ConvolutionalBlock(dimensions:int, in_channels:int, out_channels:int, normalization:str=None, kernel_size:int=3, activation:str='ReLU', preactivation:bool=False, use_padding:bool=True, padding_mode:str='zeros', dilation:bool=None, dropout:float=0) :: Module

This class defines a convolutional block that can be used for the construction of convolutional neural networks (CNNs).

Type Default Details
dimensions int The number of dimensions to consider. Possible options are 2 and 3.
in_channels int The number of input channels.
out_channels int The number of output channels.
normalization str None The type of normalization to use. Possible options include "batch", "layer" and "instance".
kernel_size int 3 The size of the convolutional kernel.
activation str ReLU The activation function that should be used.
preactivation bool False Whether to use preactivations.
use_padding bool True Whether to use padding.
padding_mode str zeros The type of padding to use.
dilation bool None The amount of dilation that should be used.
dropout float 0 The dropout rate.

ConvolutionalBlock.forward[source]

ConvolutionalBlock.forward(x:Tensor)

The forward pass of the convolutional block. Returns a torch.Tensor.

Type Default Details
x Tensor The input to the convolutional block.