机器学习~TensorFlow实现线性回归

import numpy as np
import tensorflow as tf

# 模型参数
W = tf.Variable([.3], dtype=tf.float32)
b = tf.Variable([-.3], dtype=tf.float32)

# 模型的输入和输出
x = tf.placeholder(tf.float32)
linear_model = W * x + b
y = tf.placeholder(tf.float32)

# 定义损失函数
loss = tf.reduce_sum(tf.square(linear_model - y)) # 均方偏差

# 优化算法
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)

# 训练数据
x_train = [1,2,3,4]
y_train = [0,-1,-2,-3]

# 训练过程
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

for i in range(100):
sess.run(train, {x:x_train, y:y_train})

# 训练偏差
curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x:x_train, y:y_train})
print("W: %s b: %s loss: %s"%(curr_W, curr_b, curr_loss))

========================================================================

[-0.84079814] b: [ 0.53192717] loss: 0.146364