Tensor1-TensorFlow

# https://www.w3cschool.cn/tensorflow_python/tensorflow_python-bm7y28si.html
import tensorflow as tf
import numpy as np

#生成随机数据，共100个点
x_data = np.float32(np.random.rand(2,100))       #随机输入,[0,1)之间  ,randn--正态分布
y_data = np.dot([0.100,0.200],x_data) + 0.300    #shape--[1*100] , <class 'numpy.ndarray'>

#构造线性模型
b = tf.Variable(tf.zeros([1]))     #创建变量
W = tf.Variable(tf.random_uniform([1,2],-1.0,1.0))   #均匀分布，在[-1,1]之间
y = tf.matmul(W,x_data) + b

#最小化方差
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)

#常量-constant
input1 = tf.constant(3.)
input2 = tf.constant(2.)
add = tf.add(input1,input2)
mul = tf.multiply(input1,input2)

#Feed-使用一个 tensor 值临时替换一个操作的输出结果
input3 = tf.placeholder(tf.float32)
input4 = tf.placeholder(tf.float32)
output = tf.multiply(input3,input4)

#初始化变量
init = tf.global_variables_initializer()   #新形式
#启动图
sess = tf.Session()
sess.run(init)

# with tf.device("/gpu:1"):    #指定GPU
Y = sess.run([output], feed_dict={input3:[6.],input4:[8.]})
Z = sess.run([mul,add])
# print(Y)

for step in range(0,201):
    sess.run(train)
    if step%20 ==0:
        print(step, sess.run(W), sess.run(b))
sess.close()    #释放源：source op