tensorflow 矩阵数据操作

快来打我* 2021-11-26 10:28 321阅读 0赞

1，placeholder的使用和举例：

tf.placeholder(dtype, shape=None, name=None)

dtype:数据类型;shape:数据的维度,默认为None,表示没有限制;name:名称.

feed\_dict表示参数的传递：feed\_dict = \{data1:value1,data2:value2\};

import tensorflow as tf
    data1 = tf.placeholder(tf.float32)
    data2 = tf.placeholder(tf.float32)
    dataAdd = tf.add(data1,data2)
    with tf.Session() as sess:
        dataAdd的函数参数为data1和data2,分别赋值如下data1:6,data2:2
        print(sess.run(dataAdd,feed_dict={data1:6,data2:2}))
        # 1 dataAdd 2 data (feed_dict = {1:6,2:2})
    print('end!')

2，矩阵(数组)的定义和四则运算：

#类比 数组 M行N列 []   内部[]  [里面 列数据]   [] 中括号整体 行数
    #[[6,6]] [[6,6]]
    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)# 维度
    with tf.Session() as sess:
        print(sess.run(data4)) #打印整体
        print(sess.run(data4[0]))# 打印某一行
        print(sess.run(data4[:,0]))#MN 列
        print(sess.run(data4[0,1]))# 1 1  MN = 0 32 = M012 N01

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)
    matMul2 = tf.multiply(data1,data2)
    matAdd = tf.add(data1,data3)
    with tf.Session() as sess:
        print(sess.run(matMul))#1 维 M=1 N2. 1X2(MK) 2X1(KN) = 1
        print(sess.run(matAdd))#1行2列
        print(sess.run(matMul2))# 1x2 2x1 = 2x2
        print(sess.run([matMul,matAdd]))

3，矩阵中的特殊矩阵：

import tensorflow as tf
    #普通矩阵
    mat0 = tf.constant([[0,0,0],[0,0,0]]) 
    #零矩阵
    mat1 = tf.zeros([2,3])
    #全1矩阵
    mat2 = tf.ones([3,2])
    #填充矩阵
    mat3 = tf.fill([2,3],15)
    with tf.Session() as sess:
        #print(sess.run(mat0))
        #print(sess.run(mat1))
        #print(sess.run(mat2))
        print(sess.run(mat3))

import tensorflow as tf
    mat1 = tf.constant([[2],[3],[4]])
    #以mat1为格式的构造矩阵
    mat2 = tf.zeros_like(mat1)
    #构建（0，2）为范围的11个数的矩阵
    mat3 = tf.linspace(0.0,2.0,11)
    #构建(-1,2)的[2,3]的随机矩阵
    mat4 = tf.random_uniform([2,3],-1,2)
    with tf.Session() as sess:
        print(sess.run(mat2))
        print(sess.run(mat3))
        print(sess.run(mat4))