completed part 1 of deep learning
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import numpy as np
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# Write a function that takes as input a list of numbers, and returns
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# the list of values given by the softmax function.
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def softmax(L):
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expL = np.exp(L)
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sumExpL = sum(expL)
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result = []
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for i in expL:
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result.append(i * 1.0 / sumExpL)
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return result
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# Note: The function np.divide can also be used here, as follows:
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# def softmax(L):
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# expL = np.exp(L)
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# return np.divide (expL, expL.sum())
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