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

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))

model.compile(loss='mse',optimizer='sgd')


print("=====training=======")

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



Y_pred = model.predict(X_test)
plt.scatter(X_test, Y_test)
plt.plot(X_test, Y_pred)
plt.show()
