placehold

import tensorflow as tf
data1=tf.placeholder(tf.float32)
data2=tf.placeholder(tf.float32)
dataAdd=tf.add(data1,data2)
with tf.Session() as sess:
    print(sess.run(dataAdd,feed_dict={data1:6,data2:2}))
    #feed_dict追加的数据
print("end!")

矩阵读取：

import tensorflow as tf
data1 =tf.constant([[6,6]])
data2 = tf.constant([[2],[2]])
data3 = tf.constant([[3,3]])
data4 = tf.constant([[1,2],[3,4],[5,6]])
print(data4.shape)
with tf.Session() as sess:
    print(sess.run(data4))
    #只打印某一行
    print(sess.run(data4[0]))
    #只打印某一列
    print(sess.run(data4[:,0]))

矩阵计算：
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矩阵乘法：

import tensorflow as tf
data1 =tf.constant([[6,6]])
data2 = tf.constant([[2],[2]])
data3 = tf.constant([[3,3]])
data4 = tf.constant([[1,2],[3,4],[5,6]])
matMul=tf.matmul(data1,data2)
matAdd=tf.add(data1,data3)
with tf.Session() as sess:
    print(sess.run(matMul))
    print(sess.run(matAdd))
    print(sess.run([matMul,matAdd]))
    #一次可以打印多个结果
print("endl!")

multiply不需要行列对照非常严格

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特殊矩阵的初始化：
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import tensorflow as tf
mat0=tf.constant([[0,0,0],[0,0,0]])
mat1=tf.zeros([2,3])
mat2=tf.ones([3,2])
mat3=tf.fill([2,3],15)
#使mat4具有和mat0一样的结构
mat4=tf.zeros_like(mat0)
#把0到2之间分为10等分
mat5=tf.linspace(0.0,2.0,11)
#产生一个随机矩阵
mat6=tf.random_uniform([2,3],-1,2)
with tf.Session() as sess:
    print(sess.run(mat0))
    print(sess.run(mat1))
    print(sess.run(mat2))
    print(sess.run(mat3))
    print(sess.run(mat5))
    print(sess.run(mat6))

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