2D Zero padding

This block is used in Convolutional Neural Networks; it represents a padding operation, which means adding zeros “around” the input to get a larger tensor.

This block is typically placed before a 2D Convolutional layer.

2D Convolutional layers, return by default a smaller image than the input. The output size is given by:

Output size

where: o = output size, i = input size, k = filter (kernel) size, s = stride, p = padding size.

The 2D Zero padding block allows you to manually specify the amount of horizontal and vertical padding you would like to add to your input.

Parameters

Horizontal padding: The amount of zero-filled columns to add to the left and right of the input. Default: 1

Vertical padding: The amount of zero-filled rows to add to the left and right of the input. Default: 1

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