使用keras进行Regressor

本文内容比较简单，没那么多文字解释，直接上代码。

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

#创建一些数据
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:]

#建立模型
model=Sequential()
model.add(Dense(units=1,input_dim=1))

#选择损失函数和优化方法
model.compile(loss='mse',optimizer='sgd')

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

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

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