# 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)``
``71def softmax(x, dim):72    return _softmax(x, dim)``
``49def softmax(x, temp):50    x = x / temp51    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_data43    y = out_data44    y[:] = np.exp(x - x.max(axis=1).reshape((x.shape, 1)))45    y /= y.sum(axis=1).reshape((x.shape, 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 inplace31    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)``