TensorFlow自学笔记-03 基于官方文档矩阵基本操作

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布比与迈克大炮

最新推荐文章于 2025-06-28 11:26:29 发布

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分类专栏： TensorFlow 文章标签： tensorflow

本文链接：https://blog.youkuaiyun.com/bubid/article/details/108434004

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本文通过实例演示了如何使用TensorFlow进行矩阵运算，包括创建不同类型的矩阵，执行矩阵乘法、加法、减法，以及元素级别的乘法、除法和取余等操作。

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TensorFlow自学笔记-03 基于官方文档矩阵基本操作

矩阵运算，例如执行乘法、加法和减法，是任何神经网络中信号传播的重要操作。通常在计算中需要随机矩阵、零矩阵、一矩阵或者单位矩阵。

import tensorflow as tf

# 开启一个交互式会话
sess = tf.InteractiveSession()

# 创建一个对角阵
I_matrix = tf.eye(5)
print(I_matrix.eval())

# 创建一个变量
X = tf.Variable(tf.eye(10))
X.initializer.run()
print(X.eval())

# 创建一个随机矩阵
A = tf.Variable(tf.random_normal([5, 10]))
A.initializer.run()

# 矩阵乘法
product = tf.matmul(A, X)
print(product.eval())

b = tf.Variable(tf.random_uniform([5, 10], 0, 2, dtype=tf.int32))
b.initializer.run()
print(b.eval())
b_new = tf.cast(b, dtype=tf.float32)

# 矩阵相加
t_sum = tf.add(product, b_new)
t_sub = product - b_new
print("A*X+b", t_sum.eval())
print("A*X-b", t_sub.eval())

a = tf.Variable(tf.random_normal([4, 5], stddev=2))
b = tf.Variable(tf.random_normal([4, 5], stddev=2))

# 对应元素相乘
C = a * b
# 对应元素乘以2
D = tf.scalar_mul(2, C)
# 对应元素相除
E = tf.div(a, b)
# 暗元素取余
F = tf.mod(a, b)

init_op = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init_op)
    writer = tf.summary.FileWriter('graphs', sess.graph)
    a, b, C_R, D_R, E_R, F_R = sess.run([a, b, C, D, E, F])
    print("a\n", a, "\nb\n", "b", "a*b\n", C_R, "\n2*a*b\n", D_R, "\na/b\n", E_R, "\na%b\n", D_R)
writer.close()

运行结果：

[[1. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0.]
 [0. 0. 1. 0. 0.]
 [0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 1.]]
[[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
 [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]]
[[-0.32839566  0.70773065  1.566395   -0.38252068 -0.7719653  -0.5209189
   0.85864055  2.096747   -0.82279724 -0.6647939 ]
 [ 0.23995121  0.6506701   0.5211475   1.0676296   0.40433612  1.0071797
  -0.7312989   0.33244786  1.2448255  -1.5655426 ]
 [ 0.16492666 -1.3693453   0.5388546   0.5675936   0.4816498  -0.6679784
  -0.49331537  0.8492635  -0.5760576  -1.6270505 ]
 [ 1.2073728  -1.0819451   0.39811417 -0.06598005 -0.41204235  3.3360064
  -0.63800156  0.8687895   0.531367   -0.5185357 ]
 [-0.9538085   0.9306115  -1.1394081   2.7497325   1.0245194   1.7426227
  -1.6228052  -1.207261   -0.81112486  1.0767409 ]]
[[1 1 0 1 0 1 1 1 0 1]
 [0 1 0 1 0 0 1 0 1 0]
 [0 0 1 1 0 0 1 1 0 1]
 [1 1 0 0 0 0 0 1 0 1]
 [0 1 0 0 1 0 1 0 0 1]]
A*X+b [[ 0.67160434  1.7077307   1.566395    0.6174793  -0.7719653   0.4790811
   1.8586406   3.096747   -0.82279724  0.3352061 ]
 [ 0.23995121  1.65067     0.5211475   2.0676296   0.40433612  1.0071797
   0.26870108  0.33244786  2.2448254  -1.5655426 ]
 [ 0.16492666 -1.3693453   1.5388546   1.5675936   0.4816498  -0.6679784
   0.50668466  1.8492634  -0.5760576  -0.6270505 ]
 [ 2.2073727  -0.08194506  0.39811417 -0.06598005 -0.41204235  3.3360064
  -0.63800156  1.8687894   0.531367    0.48146433]
 [-0.9538085   1.9306115  -1.1394081   2.7497325   2.0245194   1.7426227
  -0.62280524 -1.207261   -0.81112486  2.0767407 ]]
A*X-b [[-1.3283956  -0.29226935  1.566395   -1.3825207  -0.7719653  -1.5209188
  -0.14135945  1.0967469  -0.82279724 -1.664794  ]
 [ 0.23995121 -0.3493299   0.5211475   0.06762958  0.40433612  1.0071797
  -1.7312989   0.33244786  0.24482548 -1.5655426 ]
 [ 0.16492666 -1.3693453  -0.4611454  -0.43240643  0.4816498  -0.6679784
  -1.4933153  -0.15073651 -0.5760576  -2.6270504 ]
 [ 0.20737278 -2.081945    0.39811417 -0.06598005 -0.41204235  3.3360064
  -0.63800156 -0.1312105   0.531367   -1.5185356 ]
 [-0.9538085  -0.06938851 -1.1394081   2.7497325   0.02451944  1.7426227
  -2.622805   -1.207261   -0.81112486  0.07674086]]
WARNING:tensorflow:From D:/pythontest/picture/test7.py:42: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Deprecated in favor of operator or tf.math.divide.
a
 [[ 0.82497346  1.3807483  -0.7351806  -1.2301304   0.42804122]
 [ 2.3935492   0.81827664 -0.5669892  -1.9985942   0.97212964]
 [-0.27384508 -1.987736   -0.49118406 -1.6789733   2.4437551 ]
 [ 0.46587303  2.7760673  -0.3990933   1.9804771  -2.0173335 ]] 
b
 b a*b
 [[-1.821212    0.9518396  -1.7641987  -2.8179405  -0.775763  ]
 [ 4.738205   -1.3682789   0.5364556  -0.64807075 -1.0987829 ]
 [ 0.4054848  -7.618403    0.30937684  2.0860884   0.03083204]
 [ 0.12988795 -2.7496135  -0.8516452   3.0710206   0.79247415]] 
2*a*b
 [[ -3.642424     1.9036793   -3.5283973   -5.635881    -1.551526  ]
 [  9.47641     -2.7365577    1.0729111   -1.2961415   -2.1975658 ]
 [  0.8109696  -15.236806     0.6187537    4.172177     0.06166408]
 [  0.2597759   -5.499227    -1.7032903    6.142041     1.5849483 ]] 
a/b
 [[-3.7369683e-01  2.0029275e+00 -3.0636603e-01 -5.3699535e-01
  -2.3617947e-01]
 [ 1.2091241e+00 -4.8935688e-01  5.9926069e-01 -6.1634917e+00
  -8.6007529e-01]
 [ 1.8494189e-01 -5.1862502e-01  7.7983141e-01  1.3513097e+00
   1.9369264e+02]
 [ 1.6709609e+00 -2.8027756e+00 -1.8702093e-01  1.2771941e+00
   5.1353531e+00]] 
a%b
 [[ -3.642424     1.9036793   -3.5283973   -5.635881    -1.551526  ]
 [  9.47641     -2.7365577    1.0729111   -1.2961415   -2.1975658 ]
 [  0.8109696  -15.236806     0.6187537    4.172177     0.06166408]
 [  0.2597759   -5.499227    -1.7032903    6.142041     1.5849483 ]]

Process finished with exit code 0