1# Frequently Asked Questions 2 3If you don't find an answer to your question here, please look through our 4detailed documentation for the topic or file a 5[GitHub issue](https://github.com/tensorflow/tensorflow/issues). 6 7## Model Conversion 8 9#### What formats are supported for conversion from TensorFlow to TensorFlow Lite? 10 11The supported formats are listed [here](../models/convert/index#python_api) 12 13#### Why are some operations not implemented in TensorFlow Lite? 14 15In order to keep TFLite lightweight, only certain TF operators (listed in the 16[allowlist](op_select_allowlist)) are supported in TFLite. 17 18#### Why doesn't my model convert? 19 20Since the number of TensorFlow Lite operations is smaller than TensorFlow's, 21some models may not be able to convert. Some common errors are listed 22[here](../models/convert/index#conversion-errors). 23 24For conversion issues not related to missing operations or control flow ops, 25search our 26[GitHub issues](https://github.com/tensorflow/tensorflow/issues?q=label%3Acomp%3Alite+) 27or file a [new one](https://github.com/tensorflow/tensorflow/issues). 28 29#### How do I test that a TensorFlow Lite model behaves the same as the original TensorFlow model? 30 31The best way to test is to compare the outputs of the TensorFlow and the 32TensorFlow Lite models for the same inputs (test data or random inputs) as shown 33[here](inference#load-and-run-a-model-in-python). 34 35#### How do I determine the inputs/outputs for GraphDef protocol buffer? 36 37The easiest way to inspect a graph from a `.pb` file is to use 38[Netron](https://github.com/lutzroeder/netron), an open-source viewer for 39machine learning models. 40 41If Netron cannot open the graph, you can try the 42[summarize_graph](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/graph_transforms/README.md#inspecting-graphs) 43tool. 44 45If the summarize_graph tool yields an error, you can visualize the GraphDef with 46[TensorBoard](https://www.tensorflow.org/guide/summaries_and_tensorboard) and 47look for the inputs and outputs in the graph. To visualize a `.pb` file, use the 48[`import_pb_to_tensorboard.py`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/import_pb_to_tensorboard.py) 49script like below: 50 51```shell 52python import_pb_to_tensorboard.py --model_dir <model path> --log_dir <log dir path> 53``` 54 55#### How do I inspect a `.tflite` file? 56 57[Netron](https://github.com/lutzroeder/netron) is the easiest way to visualize a 58TensorFlow Lite model. 59 60If Netron cannot open your TensorFlow Lite model, you can try the 61[visualize.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/tools/visualize.py) 62script in our repository. 63 64If you're using TF 2.5 or a later version 65 66```shell 67python -m tensorflow.lite.tools.visualize model.tflite visualized_model.html 68``` 69 70Otherwise, you can run this script with Bazel 71 72* [Clone the TensorFlow repository](https://www.tensorflow.org/install/source) 73* Run the `visualize.py` script with bazel: 74 75```shell 76bazel run //tensorflow/lite/tools:visualize model.tflite visualized_model.html 77``` 78 79## Optimization 80 81#### How do I reduce the size of my converted TensorFlow Lite model? 82 83[Post-training quantization](../performance/post_training_quantization) can 84be used during conversion to TensorFlow Lite to reduce the size of the model. 85Post-training quantization quantizes weights to 8-bits of precision from 86floating-point and dequantizes them during runtime to perform floating point 87computations. However, note that this could have some accuracy implications. 88 89If retraining the model is an option, consider 90[Quantization-aware training](https://github.com/tensorflow/tensorflow/tree/r1.13/tensorflow/contrib/quantize). 91However, note that quantization-aware training is only available for a subset of 92convolutional neural network architectures. 93 94For a deeper understanding of different optimization methods, look at 95[Model optimization](../performance/model_optimization). 96 97#### How do I optimize TensorFlow Lite performance for my machine learning task? 98 99The high-level process to optimize TensorFlow Lite performance looks something 100like this: 101 102* *Make sure that you have the right model for the task.* For image 103 classification, check out the 104 [TensorFlow Hub](https://tfhub.dev/s?deployment-format=lite&module-type=image-classification). 105* *Tweak the number of threads.* Many TensorFlow Lite operators support 106 multi-threaded kernels. You can use `SetNumThreads()` in the 107 [C++ API](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/interpreter.h#L345) 108 to do this. However, increasing threads results in performance variability 109 depending on the environment. 110* *Use Hardware Accelerators.* TensorFlow Lite supports model acceleration for 111 specific hardware using delegates. See our 112 [Delegates](../performance/delegates) guide for information on what 113 accelerators are supported and how to use them with your model on-device. 114* *(Advanced) Profile Model.* The Tensorflow Lite 115 [benchmarking tool](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/tools/benchmark) 116 has a built-in profiler that can show per-operator statistics. If you know 117 how you can optimize an operator’s performance for your specific platform, 118 you can implement a [custom operator](ops_custom). 119 120For a more in-depth discussion on how to optimize performance, take a look at 121[Best Practices](../performance/best_practices). 122