1 import tensorflow as tf
 2 import numpy as np
 3
 4 if __name__ == '__main__':
 5     # 创建训练数据
 6     x_data = np.random.rand(100).astype(np.float32)
 7     y_data = x_data * 0.5 + 0.1
 8     # 随机产生一个W,B的值
 9     Weights = tf.Variable(tf.random_uniform([1], -10, 10))
10     Baises = tf.Variable(tf.random_uniform([1], -10, 10))
11
12     y = Weights * x_data + Baises
13     #创建TF训练模型结构
14     loss = tf.reduce_mean(tf.square(y - y_data))
15     optimizer = tf.train.GradientDescentOptimizer(0.5)
16     train = optimizer.minimize(loss)
17     init = tf.initialize_all_variables()
18     #初始化运行
19     sess = tf.Session()
20     sess.run(init)
21     #实时查看
22     for step in range(201):
23         sess.run(train)
24         if step%20 ==0:
25             print(step,sess.run(Weights),sess.run(Baises))

 1 import tensorflow as tf
 2 import numpy as np
 3
 4 if __name__ == '__main__':
 5     matrax1 = tf.constant([[1,2,3]])#相当于np.array(),建立一行三列数据
 6     matrax2 = tf.constant([[1],[2],[3]])
 7     project = tf.matmul(matrax1,matrax2)#相乘
 8     sess = tf.Session()#建立一个Session
 9     result = sess.run(project)#用Session去运行matmul乘法
10     print(result)
11     sess.close()

 1 import tensorflow as tf
 2 import numpy as np
 3
 4 if __name__ == '__main__':
 5     matrax1 = tf.constant([[1,2,3]])#相当于np.array(),建立一行三列数据
 6     matrax2 = tf.constant([[1],[2],[3]])
 7     project = tf.matmul(matrax1,matrax2)#相乘
 8
 9     with tf.Session() as sess:#相当于 sess = tf.Session(),读取结束之后自动关闭==sess.close()
10         print(sess.run(project))

 1 import tensorflow as tf
 2 import numpy as np
 3
 4 if __name__ == '__main__':
 5
 6     state = tf.Variable(0,name="count")     #定义一个变量值为0，名字是count，相当于字典
 7     ones = tf.constant(1)                   #常量const
 8     new_value = tf.add(state,ones)          #相加
 9     up_data = tf.assign(state,new_value)    #更新state，不是C语言的指针不能直接更新，相当于等于号
10     init = tf.global_variables_initializer()    #只要有Variable变量，就需要初始化
11     with tf.Session() as sess:
12         sess.run(init)
13         for _ in range(3):
14             print(sess.run(up_data))

 1 import tensorflow as tf
 2 import numpy as np
 3
 4 if __name__ == '__main__':
 5
 6     input1 = tf.placeholder(tf.float32)
 7     input2 = tf.placeholder(tf.float32)
 8     output = tf.multiply(input1,input2)
 9     with tf.Session() as sess:
10         print(sess.run(output,feed_dict={input1:[7.],input2:[10.]}))