08、TF2.0之实战tf.constant和tf.strings与ragged tensor

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08、TF2.0之实战tf.constant和tf.strings与ragged tensor

1. # -*- coding: utf-8 -*-
   
2. __author__ = u'东方耀 微信：dfy_88888'
   
3. __date__ = '2019/10/30 20:54'
   
4. __product__ = 'PyCharm'
   
5. __filename__ = 'tf_base_api'
   
6. 
   
7. import numpy as np
   
8. import matplotlib as mpl
   
9. import matplotlib.pyplot as plt
   
10. import sklearn
    
11. import pandas as pd
    
12. import os
    
13. import sys
    
14. import time
    
15. import tensorflow as tf
    
16. from tensorflow import keras
    
17. 
    
18. print(sys.version_info)
    
19. for module in mpl, np, pd, sklearn, tf, keras:
    
20.     print(module.__name__, module.__version__)
    
21. 
    
22. # 实战tf.constant和tf.strings与ragged tensor
    
23. 
    
24. t = tf.constant(value=np.arange(1, 7).reshape(2, 3), dtype=tf.float32)
    
25. print(t)
    
26. # index
    
27. print(t[:, 1:])
    
28. print(t[:, 1])
    
29. print(t[..., 1])
    
30. 
    
31. # op
    
32. print(t+10)
    
33. print(tf.square(t))
    
34. print(tf.exp(t))
    
35. print(tf.pow(t, 2))
    
36. # t @ tf.transpose(t)  t.dot(t.T)
    
37. print(t @ tf.transpose(t))
    
38. print(tf.matmul(t, tf.transpose(t)))
    
39. 
    
40. # tf直接转numpy对象
    
41. print(t.numpy())
    
42. print(np.square(t))
    
43. 
    
44. # scalars 0维tensor 标量
    
45. t0 = tf.constant(value=3.14)
    
46. print(t0.numpy())
    
47. print(type(t0))
    
48. print(t0.shape)
    
49. 
    
50. # strings
    
51. str1 = tf.constant(value='dfy_88888')
    
52. print(str1)
    
53. print(type(str1))
    
54. print(tf.strings.length(str1))
    
55. print(tf.strings.length(str1, unit='BYTE'))
    
56. print(tf.strings.length(str1, unit='UTF8_CHAR'))
    
57. print(tf.strings.unicode_decode(str1, 'UTF8'))
    
58. 
    
59. 
    
60. # strings array
    
61. str2 = tf.constant(value=['caffe', 'cafe', 'dfy', '咖啡'])
    
62. print(tf.strings.length(str2, unit='BYTE'))
    
63. print(tf.strings.length(str2, unit='UTF8_CHAR'))
    
64. # tf.RaggedTensor 不规则的数据 2.0新加的
    
65. print(tf.strings.unicode_decode(str2, 'UTF8'))
    
66. 
    
67. r1 = tf.ragged.constant([[1, 2], [3], [4, 5, 6]])
    
68. # RaggedTensor
    
69. print(r1)
    
70. print(r1.shape)
    
71. # Tensor
    
72. print(r1[1])
    
73. print(r1[1:3])
    
74. 
    
75. # ops on ragged tensor：concat 拼接
    
76. r2 = tf.ragged.constant([[51, 52], [], [71], [99, 80, 100]])
    
77. r3 = tf.concat([r1, r2], axis=0)
    
78. print(r3)
    
79. print(r3.shape)
    
80. 
    
81. # r4 = tf.concat([r1, r2], axis=1)
    
82. r = tf.ragged.constant([[], [0], [100]])
    
83. r4 = tf.concat([r1, r], axis=1)
    
84. print(r4)
    
85. print(r4.shape)
    
86. 
    
87. # ragged_tensor 转换为 普通tensor 用0补齐
    
88. print(r4.to_tensor())
    

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1. sys.version_info(major=3, minor=6, micro=8, releaselevel='final', serial=0)
   
2. matplotlib 3.1.1
   
3. numpy 1.17.3
   
4. pandas 0.25.2
   
5. sklearn 0.21.3
   
6. tensorflow 2.0.0
   
7. tensorflow_core.keras 2.2.4-tf
   
8. 2019-11-04 11:14:50.026692: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
   
9. 2019-11-04 11:14:50.199491: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
   
10. name: GeForce GTX 750 major: 5 minor: 0 memoryClockRate(GHz): 1.0845
    
11. pciBusID: 0000:01:00.0
    
12. 2019-11-04 11:14:50.199987: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
    
13. 2019-11-04 11:14:50.203068: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
    
14. 2019-11-04 11:14:50.203699: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
    
15. 2019-11-04 11:14:50.207506: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
    
16. name: GeForce GTX 750 major: 5 minor: 0 memoryClockRate(GHz): 1.0845
    
17. pciBusID: 0000:01:00.0
    
18. 2019-11-04 11:14:50.207999: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
    
19. 2019-11-04 11:14:50.211351: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
    
20. tf.Tensor(
    
21. [[1. 2. 3.]
    
22. [4. 5. 6.]], shape=(2, 3), dtype=float32)
    
23. 2019-11-04 11:14:51.901068: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
    
24. 2019-11-04 11:14:51.901409: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0
    
25. 2019-11-04 11:14:51.901622: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N
    
26. 2019-11-04 11:14:51.905666: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 508 MB memory) -> physical GPU (device: 0, name: GeForce GTX 750, pci bus id: 0000:01:00.0, compute capability: 5.0)
    
27. tf.Tensor(
    
28. [[2. 3.]
    
29. [5. 6.]], shape=(2, 2), dtype=float32)
    
30. tf.Tensor([2. 5.], shape=(2,), dtype=float32)
    
31. tf.Tensor([2. 5.], shape=(2,), dtype=float32)
    
32. tf.Tensor(
    
33. [[11. 12. 13.]
    
34. [14. 15. 16.]], shape=(2, 3), dtype=float32)
    
35. tf.Tensor(
    
36. [[ 1.  4.  9.]
    
37. [16. 25. 36.]], shape=(2, 3), dtype=float32)
    
38. tf.Tensor(
    
39. [[  2.7182817   7.389056   20.085537 ]
    
40. [ 54.59815   148.41316   403.4288   ]], shape=(2, 3), dtype=float32)
    
41. tf.Tensor(
    
42. [[ 1.        4.        9.      ]
    
43. [16.       24.999998 36.      ]], shape=(2, 3), dtype=float32)
    
44. 2019-11-04 11:14:51.978749: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_100.dll
    
45. tf.Tensor(
    
46. [[14. 32.]
    
47. [32. 77.]], shape=(2, 2), dtype=float32)
    
48. tf.Tensor(
    
49. [[14. 32.]
    
50. [32. 77.]], shape=(2, 2), dtype=float32)
    
51. [[1. 2. 3.]
    
52. [4. 5. 6.]]
    
53. [[ 1.  4.  9.]
    
54. [16. 25. 36.]]
    
55. 3.14
    
56. <class 'tensorflow.python.framework.ops.EagerTensor'>
    
57. ()
    
58. tf.Tensor(b'dfy_88888', shape=(), dtype=string)
    
59. <class 'tensorflow.python.framework.ops.EagerTensor'>
    
60. tf.Tensor(9, shape=(), dtype=int32)
    
61. tf.Tensor(9, shape=(), dtype=int32)
    
62. tf.Tensor(9, shape=(), dtype=int32)
    
63. tf.Tensor([100 102 121  95  56  56  56  56  56], shape=(9,), dtype=int32)
    
64. tf.Tensor([5 4 3 6], shape=(4,), dtype=int32)
    
65. tf.Tensor([5 4 3 2], shape=(4,), dtype=int32)
    
66. <tf.RaggedTensor [[99, 97, 102, 102, 101], [99, 97, 102, 101], [100, 102, 121], [21654, 21857]]>
    
67. <tf.RaggedTensor [[1, 2], [3], [4, 5, 6]]>
    
68. (3, None)
    
69. tf.Tensor([3], shape=(1,), dtype=int32)
    
70. <tf.RaggedTensor [[3], [4, 5, 6]]>
    
71. <tf.RaggedTensor [[1, 2], [3], [4, 5, 6], [51, 52], [], [71], [99, 80, 100]]>
    
72. (7, None)
    
73. <tf.RaggedTensor [[1, 2], [3, 0], [4, 5, 6, 100]]>
    
74. (3, None)
    
75. tf.Tensor(
    
76. [[  1   2   0   0]
    
77. [  3   0   0   0]
    
78. [  4   5   6 100]], shape=(3, 4), dtype=int32)
    
79. 
    
80. Process finished with exit code 0

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