saved_bias = saved_weights(bias_weights)
layer = Dense(2, 2, kernel_initializer=saved_kernel, bias_initializer=saved_bias)
sigmoid = Sigmoid()
X = np.array([[0, 0],
[0, 1],
[1, 0],
[1, 1]])
y = np.array([[0, 1],
[1, 0],
[1, 0],
[1, 0]])
print("Round 1")
z = layer.predict(X)
print("Z", z)
a = sigmoid.predict(z)
print("a", a)
error_signal = a - y
delta = layer.backprop(error_signal)
print("delta", delta)
layer.update()
print("Round 2")
z = layer.predict(X)
print("z", z)
a = sigmoid.predict(z)
print("a", a)
