使用Keras实现简单线性回归模型操作

import numpy as np
np.random.seed(1337) 
from keras.models import Sequential
from keras.layers import Dense
import matplotlib.pyplot as plt
 
# 生成数据
X = np.linspace(-1, 1, 200) #在返回（-1, 1）范围内的等差序列
np.random.shuffle(X) # 打乱顺序
Y = 0.5 * X + 2 + np.random.normal(0, 0.05, (200, )) #生成Y并添加噪声
# plot
plt.scatter(X, Y)
plt.show()
 
X_train, Y_train = X[:160], Y[:160]  # 前160组数据为训练数据集
X_test, Y_test = X[160:], Y[160:]  #后40组数据为测试数据集
 
# 构建神经网络模型
model = Sequential()
model.add(Dense(input_dim=1, units=1))
 
# 选定loss函数和优化器
model.compile(loss='mse', optimizer='sgd')
 
# 训练过程
print('Training -----------')
for step in range(501):
 cost = model.train_on_batch(X_train, Y_train)
 if step % 50 == 0:
  print("After %d trainings, the cost: %f" % (step, cost))
 
# 测试过程
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)
 
# 将训练结果绘出
Y_pred = model.predict(X_test)
plt.scatter(X_test, Y_test)
plt.plot(X_test, Y_pred)
plt.show()

>>> np.linspace(2.0, 3.0, num=5)
array([ 2. , 2.25, 2.5 , 2.75, 3. ])
>>> np.linspace(2.0, 3.0, num=5, endpoint=False)
array([ 2. , 2.2, 2.4, 2.6, 2.8])
>>> np.linspace(2.0, 3.0, num=5, retstep=True)
(array([ 2. , 2.25, 2.5 , 2.75, 3. ]), 0.25)