10 examples of 'scipy softmax' in Python

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20def _softmax(logits):
21 """Apply softmax non-linearity on inputs."""
22 return np.exp(logits) / np.sum(np.exp(logits), axis=-1, keepdims=True)
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71def softmax(x, dim):
72 return _softmax(x, dim)
49def softmax(x, temp):
50 x = x / temp
51 e_x = np.exp(x - np.max(x))
52 return e_x / e_x.sum()
4def softmax(X):
5 e = np.exp(X - np.amax(X, axis=1, keepdims=True))
6 return e/np.sum(e, axis=1, keepdims=True)
41def forward(self, in_data, out_data):
42 x = in_data[0]
43 y = out_data[0]
44 y[:] = np.exp(x - x.max(axis=1).reshape((x.shape[0], 1)))
45 y /= y.sum(axis=1).reshape((x.shape[0], 1))
298def np_softmax(x):
299 """Compute softmax values for each sets of scores in x."""
300 e_x = np.exp(x - np.max(x))
301 return e_x / e_x.sum()
14def softmax(x):
15 probs = np.exp(x - np.max(x))
16 probs /= np.sum(probs)
17 return probs
29def softmax(x): # with numerical stability, as suggested in Karpathy's notes.
30 x = x - np.max(x, axis=0) # -= would make it inplace
31 return np.exp(x) / np.sum(np.exp(x), axis=0)
6def softmax(x):
7 exp_out = np.exp(x - np.max(x, axis=-1)[..., None])
8 return exp_out / np.sum(exp_out, axis=-1)[..., None]
523def softmax(x):
524 """Compute softmax values for each sets of scores in x."""
525 return np.exp(x) / np.sum(np.exp(x), axis=0)

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