X, y = datasets.make_moons(n_samples, shuffle=shuffle, noise=noise)
return X, y
X_train, y_train = generate_halfmoon_dataset(noise=0.1)
X_test, y_test = generate_halfmoon_dataset(noise=0.1)
nn = NeuralNetwork([2, 4, 2, 1], 0.03)
if (not os.path.isfile("nn_halfmoon_noise_0.1_tanh.npy")):
train = [X_train, y_train]
nn.train_network(train, n_epochs=0, threshold=0.001)
np.save("nn_halfmoon_noise_0.1_tanh", nn.get_network())
else:
W = np.load("nn_halfmoon_noise_0.1_tanh.npy")
print("loaded weight matrix W = %s\n" % (W))
nn.load_network(W)
y_test_test = []
for i in range(len(y_test)):
y_test_test.append(np.around(np.squeeze(nn.predict(X_test[i]))))
y_train_test = []
for j in range(len(y_test)):
y_train_test.append(np.around(np.squeeze(nn.predict(X_train[j]))))
plt.subplot(221)
plt.title("Train Data")
plt.scatter(X_test[:, 0], X_test[:, 1], c=y_train, s=40)
plt.subplot(222)
plt.title("Testing Training Data")
