10 examples of 'scipy softmax' in Python

20
def _softmax(logits):
21
    """Apply softmax non-linearity on inputs."""
22
    return np.exp(logits) / np.sum(np.exp(logits), axis=-1, keepdims=True)

71
def softmax(x, dim):
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    return _softmax(x, dim)

49
def softmax(x, temp):
50
    x = x / temp
51
    e_x = np.exp(x - np.max(x))
52
    return e_x / e_x.sum()

4
def softmax(X):
5
    e = np.exp(X - np.amax(X, axis=1, keepdims=True))
6
    return e/np.sum(e, axis=1, keepdims=True)

41
def forward(self, in_data, out_data):
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    x = in_data[0]
43
    y = out_data[0]
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    y[:] = np.exp(x - x.max(axis=1).reshape((x.shape[0], 1)))
45
    y /= y.sum(axis=1).reshape((x.shape[0], 1))

298
def 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()

14
def softmax(x):
15
    probs = np.exp(x - np.max(x))
16
    probs /= np.sum(probs)
17
    return probs

29
def 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)

6
def 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]

523
def 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)