1 矩阵的常用知识

1.1 定义矩阵[3,3]为一个3x3的矩阵，比如这种：

1.2 定义矩阵的乘法

1.2.1

[5,3]*[3,1] = [5,1]中间的直接被约掉了

1.2.2

[1,10]*0.5 = [1,10]:

1.2.3 $[1,10]^2$ ：

1.3 定义矩阵的减法

[1,10] - [1,10] = [1,10];

1.4 定义矩阵的加法

行加法：

[25,40] + [1,40] = 每一行都加上[1,40]

[25,40] + [25,1] = 每一列都加上[1,40]

[[ 1.   ]
 [ 2.625]
 [ 4.25 ]
 [ 5.875]
 [ 7.5  ]
 [ 9.125]
 [10.75 ]
 [12.375]
 [14.   ]
 [15.625]
 [17.25 ]
 [18.875]
 [20.5  ]
 [22.125]
 [23.75 ]
 [25.375]
 [27.   ]
 [28.625]
 [30.25 ]
 [31.875]
 [33.5  ]
 [35.125]
 [36.75 ]
 [38.375]
 [40.   ]]
-----------------------------------------------------------
[ 1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17. 18.
 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36.
 37. 38. 39. 40.]
----------------------------------------------------------
[ 2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19.
 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37.
 38. 39. 40. 41.]
----------------------------------------------------------
[41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58.
 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72. 73. 74. 75. 76.
 77. 78. 79. 80.]
----------------------------------------------------------
[43.625 44.625 45.625 46.625 47.625 48.625 49.625 50.625 51.625 52.625
 53.625 54.625 55.625 56.625 57.625 58.625 59.625 60.625 61.625 62.625
 63.625 64.625 65.625 66.625 67.625 68.625 69.625 70.625 71.625 72.625
 73.625 74.625 75.625 76.625 77.625 78.625 79.625 80.625 81.625 82.625]

1.5 高维矩阵的加法


import numpy as np

originData = np.linspace(1,784*32,784*32)
originData = originData.reshape([1,28,28,32])

bias = np.linspace(1,32,32)
bias = bias.reshape([1,32])

c0 = originData[0][0][0]
c1 = originData[0][0][1]
d0 = (originData+bias)[0][0][0]
d1 = (originData+bias)[0][0][1]
print(c0.shape)
print(c0)
print("............................................")
print(d0)

2 常用函数

2.1 SoftMax函数

备注：常用于分类的情况

import numpy as np
import tensorflow as tf


c = np.linspace(1,3,3)
c = c.reshape([1,3])

with tf.Session() as sess:

    answer =  sess.run( tf.nn.softmax(c))
    print(answer)

    pass

Answer：
[[0.09003057 0.24472847 0.66524096]]