48 lines
1.6 KiB
Python
48 lines
1.6 KiB
Python
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#!/usr/bin/env python
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import numpy as np
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import random
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# First implement a gradient checker by filling in the following functions
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def gradcheck_naive(f, x, gradientText):
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""" Gradient check for a function f.
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Arguments:
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f -- a function that takes a single argument and outputs the
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loss and its gradients
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x -- the point (numpy array) to check the gradient at
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gradientText -- a string detailing some context about the gradient computation
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"""
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rndstate = random.getstate()
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random.setstate(rndstate)
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fx, grad = f(x) # Evaluate function value at original point
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h = 1e-4 # Do not change this!
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# Iterate over all indexes ix in x to check the gradient.
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it = np.nditer(x, flags=['multi_index'], op_flags=['readwrite'])
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while not it.finished:
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ix = it.multi_index
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x[ix] += h # increment by h
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random.setstate(rndstate)
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fxh, _ = f(x) # evalute f(x + h)
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x[ix] -= 2 * h # restore to previous value (very important!)
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random.setstate(rndstate)
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fxnh, _ = f(x)
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x[ix] += h
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numgrad = (fxh - fxnh) / 2 / h
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# Compare gradients
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reldiff = abs(numgrad - grad[ix]) / max(1, abs(numgrad), abs(grad[ix]))
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if reldiff > 1e-5:
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print("Gradient check failed for %s." % gradientText)
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print("First gradient error found at index %s in the vector of gradients" % str(ix))
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print("Your gradient: %f \t Numerical gradient: %f" % (
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grad[ix], numgrad))
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return
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it.iternext() # Step to next dimension
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print("Gradient check passed!")
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