from keras.models import Sequential
from keras.layers import Dense
from keras.layers.core import Activation, Dense

# define base mode
def baseline_model():
    # create model
    model = Sequential()
    model.add(Dense(1, input_dim=1, init='normal', activation='relu'))
    model.add(Dense(1, init='normal'))
    model.compile(loss='mean_squared_error', optimizer='adam')
    return model

regr = baseline_model()
regr.fit(X_train, Y_train,
          nb_epoch=200, validation_split=0.2, verbose = 1) #batch_size=5,

Epoch 197/200
64/64 [==============================] - 0s - loss: 34810.5195 - val_loss: 131652.9375
Epoch 198/200
64/64 [==============================] - 0s - loss: 34809.8574 - val_loss: 131651.5000
Epoch 199/200
64/64 [==============================] - 0s - loss: 34809.2266 - val_loss: 131650.0781
Epoch 200/200
64/64 [==============================] - 0s - loss: 34808.5801 - val_loss: 131648.6406

plt.scatter(X_test, Y_test,  color='black')
plt.plot(X_test, regr.predict(X_test), color='blue',
         linewidth=3)

plt.xticks()
plt.yticks()

plt.show()