tensorflow 的矩阵运算

输出矩阵的基本信息

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]])
# 输出矩阵的维度
print("矩阵的维度：", data4.shape)
with tf.Session() as sess:
    print("----------------------")
    print("运行结果")
    # 打印整个矩阵
    print(sess.run(data4))
    # 打印第一行
    print(sess.run(data4[0]))
    # 打印第一列
    print(sess.run(data4[:,0]))
    # 打印第一行第一列
    print(sess.run(data4[0,0]))

矩阵的乘法

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]])
# 输出矩阵的维度
print("矩阵的维度：", data4.shape)

matMul = tf.matmul(data1,data2)    # 矩阵1 乘以 矩阵2

matMul2 = tf.multiply(data1,data2)    # 将矩阵中各个元素相乘

matAdd = tf.add(data1,data3)    # 矩阵相加
with tf.Session() as sess:
    print("运算结果")
    print(sess.run(matMul))
    print(sess.run(matAdd))
    print(sess.run(matMul2))
    print("中括号一次打印多个内容")
    print(sess.run([matMul, matAdd]))

矩阵的维度： (3, 2)
运算结果
[[24]]
[[9 9]]
[[12 12]
 [12 12]]
中括号一次打印多个内容
[array([[24]]), array([[9, 9]])]