finish chapter 12

11_01_softmax.py

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