def test_gradient():
nn = NeuralNetwork([2, 3, 1])
X = np.arange(-1, 1, 0.01)
y = np.power(X, 2)
error1 = np.abs(compute_activations(X[0], nn)[-1] - y[0])
derivs = network_gradient(X[0], y[0], nn)
nn.weights = modify_weights(nn, derivs, 0.1)
error2 = np.abs(compute_activations(X[0], nn)[-1] - y[0])
print(error1, error2)
def x_squared_plot():
layer_sizes = [2, 6, 6, 1]
nn = NeuralNetwork(layer_sizes)
X = np.arange(-1, 1, 0.05)
y = np.power(X, 2)
nn = SGD(X, y, nn, max_iter = 1500, printing=True)
out = [compute_activations(x, nn)[-1] for x in X]
plt.scatter(X, y)
plt.plot(X, out, c='r')
plt.grid()
plt.show()
def test_backprop():
x = np.array([1, 2])
y = -2
activations = ne.compute_activations(x, network)
expected_activations = [np.array([1, 1, 2]),
np.array([ 0.76159416, 0.76159416, -0.76159416]),
-0.90925167399694251]
for a, b in zip(activations, expected_activations):
assert np.allclose(a, b, atol=0.001)
deltas = ne.compute_deltas(y, activations, network.weights,
network.activation_deriv)
expected_deltas = [np.array([ 0, -0, -1.37425893]),
np.array([-0.45808631, 0.91617262,-0.45808631]),
-1.0907483260030575]
for a, b in zip(deltas, expected_deltas):
assert np.allclose(a, b, atol=0.001)
def plot_decision_fn(X):
out = [compute_activations(x, nn)[-1] for x in X]
return out
