def verifGradient1d(neuralNetwork, X, y, sigma=1e-4):
w1nn, w1numerical = verifW1(neuralNetwork, X, y, sigma)
ratioW1 = utils.ratioGrad(w1nn, w1numerical)
b1nn, b1numerical = verifb1(neuralNetwork, X, y, sigma)
ratiob1 = utils.ratioGrad(b1nn, b1numerical)
w2nn, w2numerical = verifW2(neuralNetwork, X, y, sigma)
ratiow2 = utils.ratioGrad(w2nn, w2numerical)
b2nn, b2numerical = verifb2(neuralNetwork, X, y, sigma)
ratiob2 = utils.ratioGrad(b2nn, b2numerical)
return ratioW1 + ratiob1 + ratiow2 + ratiob2
def verifGradient1d(neuralNetwork, X, y, sigma=1e-4):
w1nn, w1numerical = verifW1(neuralNetwork, X, y, sigma)
ratioW1 = utils.ratioGrad(w1nn, w1numerical)
b1nn, b1numerical = verifb1(neuralNetwork, X, y, sigma)
ratiob1 = utils.ratioGrad(b1nn, b1numerical)
w2nn, w2numerical = verifW2(neuralNetwork, X, y, sigma)
ratiow2 = utils.ratioGrad(w2nn, w2numerical)
b2nn, b2numerical = verifb2(neuralNetwork, X, y, sigma)
ratiob2 = utils.ratioGrad(b2nn, b2numerical)
return ratioW1 + ratiob1 + ratiow2 + ratiob2
def verifGradientKd(neuralNetwork, Xlist, ylist, sigma=1e-4):
X = Xlist.pop()
y = ylist.pop()
w1nn, w1numerical = verifW1(neuralNetwork, X, y, sigma)
b1nn, b1numerical = verifb1(neuralNetwork, X, y, sigma)
w2nn, w2numerical = verifW2(neuralNetwork, X, y, sigma)
b2nn, b2numerical = verifb2(neuralNetwork, X, y, sigma)
w1nnRes = w1nn
w1numericalRes = w1numerical
b1nnRes = b1nn
b1numericalRes = b1numerical
w2nnRes = w2nn
w2numericalRes = w2numerical
b2nnRes = b2nn
b2numericalRes = b2numerical
for i in range(len(Xlist)):
X = Xlist[i]
y = ylist[i]
w1nn, w1numerical = verifW1(neuralNetwork, X, y, sigma)
b1nn, b1numerical = verifb1(neuralNetwork, X, y, sigma)
w2nn, w2numerical = verifW2(neuralNetwork, X, y, sigma)
b2nn, b2numerical = verifb2(neuralNetwork, X, y, sigma)
w1nnRes = np.add(w1nn, w1nnRes)
w1numericalRes = np.add(w1numerical, w1numericalRes)
b1nnRes = np.add(b1nn, b1nnRes)
b1numericalRes = np.add(b1numerical, b1numericalRes)
w2nnRes = np.add(w2nn, w2nnRes)
w2numericalRes = np.add(w2numerical, w2numericalRes)
b2nnRes = np.add(b2nn, b2nnRes)
b2numericalRes = np.add(b2numerical, b2numericalRes)
ratioW1 = utils.ratioGrad(w1nnRes, w1numericalRes)
ratiob1 = utils.ratioGrad(b1nnRes, b1numericalRes)
ratiow2 = utils.ratioGrad(w2nnRes, w2numericalRes)
ratiob2 = utils.ratioGrad(b2nnRes, b2numericalRes)
return ratioW1 + ratiob1 + ratiow2 + ratiob2
def verifGradientKd(neuralNetwork, Xlist, ylist, sigma=1e-4):
X = Xlist.pop()
y = ylist.pop()
w1nn, w1numerical = verifW1(neuralNetwork, X, y, sigma)
b1nn, b1numerical = verifb1(neuralNetwork, X, y, sigma)
w2nn, w2numerical = verifW2(neuralNetwork, X, y, sigma)
b2nn, b2numerical = verifb2(neuralNetwork, X, y, sigma)
w1nnRes = w1nn
w1numericalRes = w1numerical
b1nnRes = b1nn
b1numericalRes = b1numerical
w2nnRes = w2nn
w2numericalRes = w2numerical
b2nnRes = b2nn
b2numericalRes = b2numerical
for i in range(len(Xlist)):
X = Xlist[i]
y = ylist[i]
w1nn, w1numerical = verifW1(neuralNetwork, X, y, sigma)
b1nn, b1numerical = verifb1(neuralNetwork, X, y, sigma)
w2nn, w2numerical = verifW2(neuralNetwork, X, y, sigma)
b2nn, b2numerical = verifb2(neuralNetwork, X, y, sigma)
w1nnRes = np.add(w1nn, w1nnRes)
w1numericalRes = np.add(w1numerical, w1numericalRes)
b1nnRes = np.add(b1nn, b1nnRes)
b1numericalRes = np.add(b1numerical, b1numericalRes)
w2nnRes = np.add(w2nn, w2nnRes)
w2numericalRes = np.add(w2numerical, w2numericalRes)
b2nnRes = np.add(b2nn, b2nnRes)
b2numericalRes = np.add(b2numerical, b2numericalRes)
ratioW1 = utils.ratioGrad(w1nnRes, w1numericalRes)
ratiob1 = utils.ratioGrad(b1nnRes, b1numericalRes)
ratiow2 = utils.ratioGrad(w2nnRes, w2numericalRes)
ratiob2 = utils.ratioGrad(b2nnRes, b2numericalRes)
return ratioW1 + ratiob1 + ratiow2 + ratiob2
