tree: 45d945c690b94856d6225c3c4f7fc1026a379264 [path history] [tgz]
  1. c/
  2. core/
  3. delegates/
  4. examples/
  5. experimental/
  6. g3doc/
  7. java/
  8. kernels/
  9. lib_package/
  10. micro/
  11. nnapi/
  12. profiling/
  13. python/
  14. schema/
  15. testdata/
  16. testing/
  17. toco/
  18. tools/
  19. tutorials/
  20. allocation.cc
  21. allocation.h
  22. arena_planner.cc
  23. arena_planner.h
  24. arena_planner_test.cc
  25. BUILD
  26. build_def.bzl
  27. builtin_op_data.h
  28. builtin_ops.h
  29. context.h
  30. context_util.h
  31. error_reporter.h
  32. external_cpu_backend_context.cc
  33. external_cpu_backend_context.h
  34. graph_info.cc
  35. graph_info.h
  36. graph_info_test.cc
  37. interpreter.cc
  38. interpreter.h
  39. interpreter_builder.cc
  40. interpreter_builder.h
  41. interpreter_test.cc
  42. memory_planner.h
  43. minimal_logging.cc
  44. minimal_logging.h
  45. minimal_logging_android.cc
  46. minimal_logging_default.cc
  47. minimal_logging_ios.cc
  48. minimal_logging_test.cc
  49. mmap_allocation.cc
  50. mmap_allocation_disabled.cc
  51. model.h
  52. model_builder.cc
  53. model_builder.h
  54. model_flex_test.cc
  55. model_test.cc
  56. model_xnnpack_test.cc
  57. mutable_op_resolver.cc
  58. mutable_op_resolver.h
  59. mutable_op_resolver_test.cc
  60. op_resolver.h
  61. optional_debug_tools.cc
  62. optional_debug_tools.h
  63. README.md
  64. simple_memory_arena.cc
  65. simple_memory_arena.h
  66. simple_memory_arena_test.cc
  67. special_rules.bzl
  68. stderr_reporter.cc
  69. stderr_reporter.h
  70. string_type.h
  71. string_util.cc
  72. string_util.h
  73. string_util_test.cc
  74. tflite_exported_symbols.lds
  75. tflite_version_script.lds
  76. tflite_with_xnnpack.cc
  77. tflite_with_xnnpack_optional.cc
  78. tflite_with_xnnpack_optional.h
  79. type_to_tflitetype.h
  80. util.cc
  81. util.h
  82. util_test.cc
  83. version.h
tensorflow/lite/README.md

TensorFlow Lite

TensorFlow Lite is TensorFlow's lightweight solution for mobile and embedded devices. It enables low-latency inference of on-device machine learning models with a small binary size and fast performance supporting hardware acceleration.

See the documentation: https://www.tensorflow.org/lite/ Documentation edits can be made here: tensorflow/lite/g3doc