def feedforward(self, a):
"""Returns the output the network if a is the input. a must be a vector
with the length of 784 which is the amount of pixels in a picture in
the MNIST dataset"""
for b, w in zip(self.biases, self.weights):
a = sigmoid(np.dot(w, a) + b)
return a
def backpropagate(network, x, y):
"""Calculates the gradient for the cost function"""
nabla_b = [np.zeros(b.shape) for b in network.biases]
nabla_w = [np.zeros(w.shape) for w in network.weights]
activation = x
activations = [x]
zs = []
"""Forward"""
for b, w in zip(network.biases, network.weights):
z = np.dot(w, activation) + b
zs.append(z)
activation = sigmoid(z)
activations.append(activation)
"""Backward"""
delta = network.cost.delta(zs[-1], activations[-1], y)
nabla_b[-1] = delta
nabla_w[-1] = np.dot(delta, activations[-2].transpose())
for i in range(2, network.num_layers):
z = zs[-i]
sp = sigmoid_prime(z)
delta = np.dot(network.weights[-i + 1].transpose(), delta) * sp
nabla_b[-i] = delta
nabla_w[-i] = np.dot(delta, activations[-i - 1].transpose())
return nabla_b, nabla_w
def test_sigmoid_function_returns_correct_value_on_zero():
assert sigmoid(0) == 0.5
def test_sigmoid_function_returns_correct_value_on_upper_bound():
assert sigmoid(37) == 1.0
def test_sigmoid_function_returns_correct_value_on_lower_bound():
assert sigmoid(-10) < 0.01