TensorFlow的一个训练权重和偏置的小例子

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import tensorflow as tf
import numpy as np
"""
本例子是用来演示利用TensorFlow训练出假设的权重和偏置
"""

# set data
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_pred = Weights*x_data+biases

loss = tf.reduce_mean(tf.square(y_pred-y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)

init = tf.initialize_all_variables()

# define the session
sess = tf.Session()
sess.run(init) # 激活神经网络

# 开始训练
for step in range(201):
    sess.run(train)
    if step%20 ==0:
        print(step,sess.run(Weights),sess.run(biases))

输出结果为：

0 [-0.43459344] [0.7752902]
20 [-0.05772059] [0.38117662]
40 [0.05961926] [0.3207834]
60 [0.08966146] [0.30532113]
80 [0.09735306] [0.30136237]
100 [0.09932232] [0.30034882]
120 [0.09982649] [0.3000893]
140 [0.0999556] [0.30002287]
160 [0.09998864] [0.30000585]
180 [0.09999712] [0.3000015]
200 [0.09999926] [0.3000004]

经过201次迭代得到训练的权重和偏置近似于我们预先的假设函数的设置。
注意：
TensorFlow和Numpy的构造方法千万不要混杂，tf.Variable里面一定用tf的方法不能用np的方法，否则类型不匹配