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TrainSaverTest.cs 2.6 kB

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  1. using Microsoft.VisualStudio.TestTools.UnitTesting;
  2. using System;
  3. using System.Collections.Generic;
  4. using System.IO;
  5. using System.Text;
  6. using Tensorflow;
  7. namespace TensorFlowNET.UnitTest
  8. {
  9. [TestClass]
  10. public class TrainSaverTest : Python
  11. {
  12. public void ExportGraph()
  13. {
  14. var v = tf.Variable(0, name: "my_variable");
  15. var sess = tf.Session();
  16. tf.train.write_graph(sess.graph, "/tmp/my-model", "train1.pbtxt");
  17. }
  18. public void ImportGraph()
  19. {
  20. with<Session>(tf.Session(), sess =>
  21. {
  22. var new_saver = tf.train.import_meta_graph("C:/tmp/my-model.meta");
  23. });
  24. }
  25. public void ImportSavedModel()
  26. {
  27. with<Session>(Session.LoadFromSavedModel("mobilenet"), sess =>
  28. {
  29. });
  30. }
  31. public void ImportGraphDefFromPbFile()
  32. {
  33. var g = new Graph();
  34. var status = g.Import("mobilenet/saved_model.pb");
  35. }
  36. public void Save1()
  37. {
  38. var w1 = tf.Variable(0, name: "save1");
  39. var init_op = tf.global_variables_initializer();
  40. // Add ops to save and restore all the variables.
  41. var saver = tf.train.Saver();
  42. with<Session>(tf.Session(), sess =>
  43. {
  44. sess.run(init_op);
  45. // Save the variables to disk.
  46. var save_path = saver.save(sess, "/tmp/model1.ckpt");
  47. Console.WriteLine($"Model saved in path: {save_path}");
  48. });
  49. }
  50. public void Save2()
  51. {
  52. var v1 = tf.get_variable("v1", shape: new TensorShape(3), initializer: tf.zeros_initializer);
  53. var v2 = tf.get_variable("v2", shape: new TensorShape(5), initializer: tf.zeros_initializer);
  54. var inc_v1 = v1.assign(v1 + 1.0f);
  55. var dec_v2 = v2.assign(v2 - 1.0f);
  56. // Add an op to initialize the variables.
  57. var init_op = tf.global_variables_initializer();
  58. // Add ops to save and restore all the variables.
  59. var saver = tf.train.Saver();
  60. with<Session>(tf.Session(), sess =>
  61. {
  62. sess.run(init_op);
  63. // o some work with the model.
  64. inc_v1.op.run();
  65. dec_v2.op.run();
  66. // Save the variables to disk.
  67. var save_path = saver.save(sess, "/tmp/model2.ckpt");
  68. Console.WriteLine($"Model saved in path: {save_path}");
  69. });
  70. }
  71. }
  72. }

tensorflow框架的.NET版本,提供了丰富的特性和API,可以借此很方便地在.NET平台下搭建深度学习训练与推理流程。