Softmax2d#
- class brainpy.dnn.Softmax2d(name=None, mode=None)[source]#
Applies SoftMax over features to each spatial location.
When given an image of
Channels x Height x Width
, it will apply Softmax to each location \((Channels, h_i, w_j)\)- Shape:
Input: \((N, C, H, W)\) or \((C, H, W)\).
Output: \((N, C, H, W)\) or \((C, H, W)\) (same shape as input)
- Returns:
a Tensor of the same dimension and shape as the input with values in the range [0, 1]
Examples:
>>> import brainpy as bp >>> import brainpy.math as bm >>> m = bp.dnn.Softmax2d() >>> # you softmax over the 2nd dimension >>> input = bm.random.randn(2, 3, 12, 13) >>> output = m(input)