23 lines
539 B
Python
23 lines
539 B
Python
# softmax squashes outputs so that the sum of the outputs equals 1 while preserving the order
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import torch
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import torch.nn as nn
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import numpy as np
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def softmax(x):
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return np.exp(x)/np.sum(np.exp(x), axis=0)
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x = np.array([2., 1., .1])
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outputs = softmax(x)
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print('inputs: ', x)
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print('softmax numpy:', outputs)
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x = torch.tensor([2., 1., .1])
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outputs = torch.softmax(x, dim=0)
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print('inputs: ', x)
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print('softmax numpy:', outputs)
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outputs2 = torch.softmax(outputs, dim=0)
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print('inputs: ', outputs)
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print('softmax numpy:', outputs2)
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