X = digits.data
Y = digits.target
Y_classes = np.zeros((X.shape[0], 10))
for i in range(Y.shape[0]):
Y_classes[i, Y[i]] = 1
Y = Y_classes
X_train, X_test, y_train, y_test = train_test_split(X, Y)
nn = NeuralNetwork(X_train, y_train, X_train.shape[1], 0.01, 0.1, 1000, 100,
100, y_train.shape[1])
# nn.train_neural_network()
# Save theta values.
# nn.save_theta()
# This is executed once we have trained the neural network.
index = random.randrange(0, X_test.shape[0])
nn.load_theta('theta0.csv', 'theta1.csv', 'theta2.csv')
prediction = np.argmax(nn.predict(X_test[index, :].reshape((-1, 1))))
label = np.argmax(y_test[index])
plt.gray()
plt.matshow(X_test[index, :].reshape((8, 8)))
plt.xlabel("Prediction: " + str(prediction) + " Label: " + str(label))
plt.show()