def relu_prime(z):
z[z < 0] = 0
z[z > 0] = 1
return z
def loss(y_true, y_pred):
return 0.5 * (y_pred - y_true)**2
def loss_prime(y_true, y_pred):
return y_pred - y_true
x_train = np.array([[[0, 0]], [[0, 1]], [[1, 0]], [[1, 1]]])
y_train = np.array([[[0]], [[1]], [[1]], [[0]]])
net = Network()
net.add(FCLayer((1, 2), (1, 3)))
net.add(ActivationLayer((1, 3), (1, 3), relu, relu_prime))
net.add(FCLayer((1, 3), (1, 1)))
net.add(ActivationLayer((1, 3), (1, 3), relu, relu_prime))
net.setup_loss(loss, loss_prime)
net.fit(x_train, y_train, epochs=1000, learning_rate=0.01)
out = net.predict([[0, 1]])
print(out)
def relu(z):
return np.maximum(0, z)
def relu_prime(z):
z[z < 0] = 0
z[z > 0] = 1
return z
def loss(y_true, y_pred):
return 0.5 * (y_pred - y_true)**2
def loss_prime(y_true, y_pred):
return (y_pred - y_true)
x_train = np.array([[[0, 0]], [[0, 1]], [[1, 0]], [[1, 1]]])
y_train = np.array([[[0]], [[1]], [[1]], [[0]]])
net = Network()
net.add(FullConnectedLayer((1, 2), (1, 3)))
net.add(ActivationLayer((1, 3), (1, 3), relu, relu_prime))
net.add(FullConnectedLayer((1, 3), (1, 1)))
net.add(ActivationLayer((1, 1), (1, 1), relu, relu_prime))
net.setup_loss(loss, loss_prime)
net.fit(x_train, y_train, 0.01, 1000)
output = net.predict([[0, 1]])
print(output)