import numpy as np 
np.random.seed(1337)
from keras.models import Sequential
from keras.layers import Dense
import matplotlib.pyplot as plt 

# Keras regression example

X = np.linspace(-1, 1, 200)
np.random.shuffle(X)
Y = 0.5*X + 2 + np.random.normal(0, 0.05, (200, ))

plt.scatter(X, Y)
plt.show()


X_train, Y_train = X[:160], Y[:160]    
X_test, Y_test = X[160:], Y[160:]   


model = Sequential()
model.add(Dense(output_dim=1, input_dim=1))
# Thoose loss function and optimizing method
model.compile(loss='mse', optimizer='sgd') #mean square error 


# Training
print('Training -----------')
for step in range(301):
    cost = model.train_on_batch(X_train, Y_train)
    if step % 100 == 0:
        print('train cost: ', cost)

# Test
print('\nTesting ------------')
cost = model.evaluate(X_test, Y_test, batch_size=40)
print('test cost:', cost)
W, b = model.layers[0].get_weights()
print('Weights=', W, '\nbiases=', b)


# plotting the prediction
Y_pred = model.predict(X_test)
plt.scatter(X_test, Y_test)
plt.plot(X_test, Y_pred)
plt.show()




