def main():
inputs = np.random.random((5, 5))
autoencoder = Autoencoder([
FCLayer((5, 4), SigmoidActivationFunction(), True),
FCLayer((4, 3), SigmoidActivationFunction(), True),
FCLayer((3, 4), SigmoidActivationFunction(), True),
FCLayer((4, 5), SigmoidActivationFunction(), True)])
w = np.random.normal(size=autoencoder.net.params_number)
autoencoder.net.set_weights(w)
p = np.random.normal(size=autoencoder.net.params_number)
loss, loss_grad = autoencoder.compute_loss(inputs)
gn_grad = autoencoder.compute_gaussnewtonvec(p)
Rp_outputs = autoencoder.net.compute_Rp_outputs()
p = np.zeros_like(w)
check_gn_grad = np.zeros_like(w)
num_params = autoencoder.net.params_number
for i in range(num_params):
p[:] = 0
p[i] = 1
check_gn_grad[i] = \
check_grad(lambda x: gauss_loss_func(autoencoder, x, inputs,
Rp_outputs), w, p)
max_diff = np.abs(gn_grad - check_gn_grad).max()
min_diff = np.abs(gn_grad - check_gn_grad).min()
print("compute_gauss_newton")
print("min_diff = ", min_diff)
print("max_diff = ", max_diff)
