Tensorflow线性回归实例

代码

import tensorflow as tf
import numpy as np
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data*0.5 + 0.7

Weights = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
Biases = tf.Variable(tf.zeros([1]))

y = Weights * x_data + Biases
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)

init = tf.global_variables_initializer()

sess = tf.Session()
sess.run(init)

for step in range(200):
    sess.run(train)
    if step % 20 == 0:
        print(step, sess.run(Weights), sess.run(Biases))

效果