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)
loss, loss_grad = autoencoder.compute_loss(inputs)
num_params = autoencoder.net.params_number
p = np.zeros((autoencoder.net.params_number))
check_loss_grad = np.zeros((autoencoder.net.params_number))
for i in range(num_params):
p[:] = 0
p[i] = 1
check_loss_grad[i] = \
check_grad(lambda x: loss_func(autoencoder, x, inputs), w, p)
max_diff = np.abs(loss_grad - check_loss_grad).max()
min_diff = np.abs(loss_grad - check_loss_grad).min()
print("compute_loss")
print("min_diff = ", min_diff)
print("max_diff = ", max_diff)
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)
loss, loss_grad = autoencoder.compute_loss(inputs)
p = np.random.normal(size=autoencoder.net.params_number)
Rp_loss_grad = autoencoder.compute_hessvec(p)
hess = np.zeros((autoencoder.net.params_number))
check_Rp_loss_grad = \
check_grad(lambda x: loss_func_grad(autoencoder, x, inputs), w, p)
max_diff = np.abs(Rp_loss_grad - check_Rp_loss_grad).max()
min_diff = np.abs(Rp_loss_grad - check_Rp_loss_grad).min()
print("compute_hessvec")
print("min_diff = ", min_diff)
print("max_diff = ", max_diff)