1.keras-构建基本简单网络实现线性回归

构建基本简单网络实现线性回归

1.创建数据绘制散点图

import keras
import numpy as np
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense
import tensorflow as tf

# 创建数据绘制散点图
x_data = np.random.rand(100)
noise = np.random.normal(0,0.01,x_data.shape)
y_data = x_data * 0.1 + 0.2 + noise

plt.scatter(x_data,y_data)
plt.show()

2.构建模型

# 构建顺序模型
model = Sequential()
# 在模型中添加一个全连接模型
model.add(Dense(units=1,input_dim=1)) #units=1,input_dim=1输入和输出都是一维的
model.compile(optimizer='sgd',
              loss= 'mse')
for step in range(3000):
    # 每次训练一个batch
    cost = model.train_on_batch(x_data,y_data)
    if step % 500 ==0:
        print('step:',step)
        print('cost',cost)
# 打印权值和偏移项
W,b = model.layers[0].get_weights()
print('W:',W,'b',b)

out:

step: 0
cost 0.026886607
step: 500
cost 0.0005393094
step: 1000
cost 0.00020998158
step: 1500
cost 0.0001274022
step: 2000
cost 0.000106695006
step: 2500
cost 0.00010150281
W: [[0.1016009]] b [0.19795756]

3.预测并绘制预测结果

# 进行预测值
y_pred = model.predict(x_data)

# 显示随机点
plt.scatter(x_data,y_data)
plt.plot(x_data,y_pred,'r-',lw=3)
plt.show()