自己構建一些資料，來求Weights和Biases

#create data

import tensorflow as tf
import numpy as np

x_data = np.random.rand(100).astype(np.float32)
y_data = x_data*0.1 + 0.3

#create tensorflow structure

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)

 

with tf.Session() as sess:
    sess.run(init)

    for step in range(201):
        sess.run(train)
        if step % 20 == 0:
            print(step, sess.run(weights), sess.run(biases))

輸出結果：

0 [-0.53141755] [0.01215398]
20 [-0.42150587] [0.19936536]
40 [-0.3514395] [0.31025246]
60 [-0.30549625] [0.37518197]
80 [-0.27419537] [0.41245845]
100 [-0.2518188] [0.4331152]
120 [-0.23491696] [0.44380206]
140 [-0.22140765] [0.4485264]
160 [-0.21003328] [0.44970292]
180 [-0.20003326] [0.44878575]
200 [-0.19094667] [0.44665036]