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Numerically stable softmax

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AntreasAntoniou 2017-11-13 23:52:39 +00:00
parent 6a0cdbea3a
commit b9147c269c
1 changed files with 3 additions and 3 deletions
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6
mlp/errors.py
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@ -154,9 +154,9 @@ class CrossEntropySoftmaxError(object):
Returns: Returns:
Scalar error function value. Scalar error function value.
""" """
probs = np.exp(outputs - outputs.max(-1)[:, None]) normOutputs = outputs - outputs.max(-1)[:, None]
probs /= probs.sum(-1)[:, None] logProb = normOutputs - np.log(np.sum(np.exp(normOutputs))(-1)[:, None])
return -np.mean(np.sum(targets * np.log(probs), axis=1)) return -np.mean(np.sum(targets * logProb, axis=1))
def grad(self, outputs, targets): def grad(self, outputs, targets):
"""Calculates gradient of error function with respect to outputs. """Calculates gradient of error function with respect to outputs.