Created
December 7, 2020 10:30
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import numpy as np | |
X = [[2], | |
[3]] | |
Y = [[1], | |
[2]] | |
W_0 = [[0.30,0.53,0.94], | |
[0.46,0.80,0.21]] | |
W_1 = [[0.89,0.39], | |
[0.74,0.41], | |
[0.24,0.06]] | |
W_0nump = np.array(W_0) | |
W_0T = W_0nump.transpose() | |
x_nump = np.array(X) | |
temp = np.matmul(W_0T,x_nump) | |
h = np.tanh(temp) | |
z = np.arctanh(h) | |
W_1nump = np.array(W_1) | |
W_1T = np.transpose(W_1nump) | |
Y_hat = np.matmul(W_1T,h) | |
Y_hat2 = np.matmul(W_1T,temp) | |
h_nump = np.array(h) | |
z = np.arctanh(h_nump) | |
bias = .208194682 | |
yhat1 = 1.83209 | |
yhat2 = .84381 | |
yres1 = 1 | |
yres2 = 2 | |
hspec1 = .96258698 | |
hspec2 = .99802629 | |
hspec3 = .98687761 | |
hspec = hspec1 | |
yres = yres1 | |
yhat = yhat1 | |
weightspec = .89 | |
errorDeriv = (2*((yhat-yres)))*(1-np.tanh(np.tanh((weightspec*hspec)+bias)))*(hspec) | |
print(errorDeriv) |
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