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visualize.rst 1.7 kB

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  1. API - Visualization
  2. ================================
  3. TensorFlow provides `TensorBoard <https://www.tensorflow.org/get_started/summaries_and_tensorboard>`_
  4. to visualize the model, activations etc. Here we provide more functions for data visualization.
  5. .. automodule:: tensorlayer.visualize
  6. .. autosummary::
  7. read_image
  8. read_images
  9. save_image
  10. save_images
  11. draw_boxes_and_labels_to_image
  12. draw_mpii_pose_to_image
  13. draw_weights
  14. CNN2d
  15. frame
  16. images2d
  17. tsne_embedding
  18. draw_boxes_and_labels_to_image_with_json
  19. Save and read images
  20. ----------------------
  21. Read one image
  22. ^^^^^^^^^^^^^^^^^
  23. .. autofunction:: read_image
  24. Read multiple images
  25. ^^^^^^^^^^^^^^^^^^^^^^^^^^
  26. .. autofunction:: read_images
  27. Save one image
  28. ^^^^^^^^^^^^^^^^^^^^^^^^^^
  29. .. autofunction:: save_image
  30. Save multiple images
  31. ^^^^^^^^^^^^^^^^^^^^^^^^^^
  32. .. autofunction:: save_images
  33. Save image for object detection
  34. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  35. .. autofunction:: draw_boxes_and_labels_to_image
  36. Save image for object detection with json
  37. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  38. .. autofunction:: draw_boxes_and_labels_to_image_with_json
  39. Save image for pose estimation (MPII)
  40. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  41. .. autofunction:: draw_mpii_pose_to_image
  42. Visualize model parameters
  43. ------------------------------
  44. Visualize CNN 2d filter
  45. ^^^^^^^^^^^^^^^^^^^^^^^^^^
  46. .. autofunction:: CNN2d
  47. Visualize weights
  48. ^^^^^^^^^^^^^^^^^^^^^^^^^^
  49. .. autofunction:: draw_weights
  50. Visualize images
  51. -----------------
  52. Image by matplotlib
  53. ^^^^^^^^^^^^^^^^^^^^^
  54. .. autofunction:: frame
  55. Images by matplotlib
  56. ^^^^^^^^^^^^^^^^^^^^^
  57. .. autofunction:: images2d
  58. Visualize embeddings
  59. --------------------
  60. .. autofunction:: tsne_embedding

TensorLayer3.0 是一款兼容多种深度学习框架为计算后端的深度学习库。计划兼容TensorFlow, Pytorch, MindSpore, Paddle.