Created
October 27, 2018 00:59
-
-
Save stephentu/bb16ce04da83997f738a4d76794fcbaa to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import autograd.numpy as np | |
from autograd import grad | |
def main(): | |
def relu(x): | |
return np.maximum(x, np.zeros_like(x)) | |
def drelu(x): | |
ret = np.zeros_like(x) | |
ret[x > 0] = 1.0 | |
return ret | |
d = 5 | |
n1 = 10 | |
n2 = 15 | |
xpt = np.random.randn(d) | |
ypt = np.random.randn(n2) | |
def net(args): | |
W1, W2, b1 = args | |
z = np.dot(W2, relu(np.dot(W1, xpt) + b1)) - ypt | |
return 0.5 * np.dot(z, z) | |
g = grad(net) | |
W1 = np.random.randn(n1, d) | |
W2 = np.random.randn(n2, n1) | |
b1 = np.random.randn(n1) | |
ret1 = g((W1, W2, b1)) | |
def compute_grad(args): | |
"""The backprop algorithm described in the notes | |
""" | |
W1, W2, b1 = args | |
z1 = np.dot(W1, xpt) + b1 | |
z2 = relu(z1) | |
z3 = np.dot(W2, z2) | |
z4 = z3 - ypt | |
f = 0.5 * np.dot(z4, z4) | |
Df_z4 = z4.reshape((1, n2)) | |
Df_z3 = Df_z4 | |
Df_z2 = np.dot(Df_z3, W2) | |
Df_z1 = np.dot(Df_z2, np.diag(drelu(z1))) | |
Df_b1 = Df_z1 | |
return (np.outer(Df_z1.flatten(), xpt), | |
np.outer(Df_z3.flatten(), z2), | |
Df_b1.flatten()) | |
ret2 = compute_grad((W1, W2, b1)) | |
# compare our answers with the autograd package | |
assert np.allclose(ret1[0], ret2[0]) | |
assert np.allclose(ret1[1], ret2[1]) | |
assert np.allclose(ret1[2], ret2[2]) | |
if __name__ == '__main__': | |
main() |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment