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标题: 08、TF2.0之实战tf.constant和tf.strings与ragged tensor [打印本页]
作者: 东方耀    时间: 2019-11-4 11:17
标题: 08、TF2.0之实战tf.constant和tf.strings与ragged tensor
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'

  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

  18. print(sys.version_info)
  19. for module in mpl, np, pd, sklearn, tf, keras:
  20.     print(module.__name__, module.__version__)

  22. # 实战tf.constant和tf.strings与ragged tensor

  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])

  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)))

  40. # tf直接转numpy对象
  41. print(t.numpy())
  42. print(np.square(t))

  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)

  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'))


  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'))

  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])

  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)

  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)

  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)

  80. Process finished with exit code 0
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