کتابخانه TensorFlow Lite Model Maker فرآیند آموزش یک مدل TensorFlow Lite را با استفاده از مجموعه داده های سفارشی ساده می کند. از یادگیری انتقال برای کاهش داده های آموزشی مورد نیاز و کوتاه کردن زمان آموزش استفاده می کند.
وظایف پشتیبانی شده
کتابخانه Model Maker در حال حاضر از وظایف ML زیر پشتیبانی می کند. برای راهنمایی در مورد نحوه آموزش مدل روی لینک های زیر کلیک کنید.
اگر وظایف شما پشتیبانی نمی‌شود، لطفاً ابتدا از TensorFlow برای آموزش مجدد مدل TensorFlow با یادگیری انتقال (به دنبال راهنماهایی مانند تصاویر ، متن ، صدا ) استفاده کنید یا آن را از ابتدا آموزش دهید و سپس آن را به مدل TensorFlow Lite تبدیل کنید .
مثال سرتاسری
Model Maker به شما امکان می دهد یک مدل TensorFlow Lite را با استفاده از مجموعه داده های سفارشی تنها در چند خط کد آموزش دهید. به عنوان مثال، در اینجا مراحل آموزش یک مدل طبقه بندی تصویر آورده شده است.
fromtflite_model_makerimportimage_classifierfromtflite_model_maker.image_classifierimportDataLoader# Load input data specific to an on-device ML app.data=DataLoader.from_folder('flower_photos/')train_data,test_data=data.split(0.9)# Customize the TensorFlow model.model=image_classifier.create(train_data)# Evaluate the model.loss,accuracy=model.evaluate(test_data)# Export to Tensorflow Lite model and label file in `export_dir`.model.export(export_dir='/tmp/')
تاریخ آخرین بهروزرسانی 2025-01-14 بهوقت ساعت هماهنگ جهانی.
[[["درک آسان","easyToUnderstand","thumb-up"],["مشکلم را برطرف کرد","solvedMyProblem","thumb-up"],["غیره","otherUp","thumb-up"]],[["اطلاعاتی که نیاز دارم وجود ندارد","missingTheInformationINeed","thumb-down"],["بیشازحد پیچیده/ مراحل بسیار زیاد","tooComplicatedTooManySteps","thumb-down"],["قدیمی","outOfDate","thumb-down"],["مشکل ترجمه","translationIssue","thumb-down"],["مشکل کد / نمونهها","samplesCodeIssue","thumb-down"],["غیره","otherDown","thumb-down"]],["تاریخ آخرین بهروزرسانی 2025-01-14 بهوقت ساعت هماهنگ جهانی."],[],[],null,["# TensorFlow Lite Model Maker\n\nOverview\n--------\n\nThe TensorFlow Lite Model Maker library simplifies the process of training a\nTensorFlow Lite model using custom dataset. It uses transfer learning to reduce\nthe amount of training data required and shorten the training time.\n\nSupported Tasks\n---------------\n\nThe Model Maker library currently supports the following ML tasks. Click the\nlinks below for guides on how to train the model.\n\n| Supported Tasks | Task Utility |\n|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------|\n| Image Classification: [tutorial](./image_classification), [api](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker/image_classifier) | Classify images into predefined categories. |\n| Object Detection: [tutorial](./object_detection), [api](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker/object_detector) | Detect objects in real time. |\n| Text Classification: [tutorial](./text_classification), [api](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker/text_classifier) | Classify text into predefined categories. |\n| BERT Question Answer: [tutorial](./question_answer), [api](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker/question_answer) | Find the answer in a certain context for a given question with BERT. |\n| Audio Classification: [tutorial](./audio_classification), [api](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker/audio_classifier) | Classify audio into predefined categories. |\n| Recommendation: [demo](https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/demo/recommendation_demo.py), [api](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker/recommendation) | Recommend items based on the context information for on-device scenario. |\n| Searcher: [tutorial](./text_searcher), [api](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker/searcher) | Search for similar text or image in a database. |\n\nIf your tasks are not supported, please first use\n[TensorFlow](https://www.tensorflow.org/guide) to retrain a TensorFlow model\nwith transfer learning (following guides like\n[images](https://www.tensorflow.org/tutorials/images/transfer_learning),\n[text](https://www.tensorflow.org/official_models/fine_tuning_bert),\n[audio](https://www.tensorflow.org/tutorials/audio/transfer_learning_audio)) or\ntrain it from scratch, and then [convert](../../models/convert) it to TensorFlow\nLite model.\n\nEnd-to-End Example\n------------------\n\nModel Maker allows you to train a TensorFlow Lite model using custom datasets in\njust a few lines of code. For example, here are the steps to train an image\nclassification model. \n\n from tflite_model_maker import image_classifier\n from tflite_model_maker.image_classifier import DataLoader\n\n # Load input data specific to an on-device ML app.\n data = DataLoader.from_folder('flower_photos/')\n train_data, test_data = data.split(0.9)\n\n # Customize the TensorFlow model.\n model = image_classifier.create(train_data)\n\n # Evaluate the model.\n loss, accuracy = model.evaluate(test_data)\n\n # Export to Tensorflow Lite model and label file in `export_dir`.\n model.export(export_dir='/tmp/')\n\nFor more details, see the [image classification guide](./image_classification).\n\nInstallation\n------------\n\nThere are two ways to install Model Maker.\n\n- Install a prebuilt pip package.\n\n pip install tflite-model-maker\n\nIf you want to install nightly version, please follow the command: \n\n pip install tflite-model-maker-nightly\n\n- Clone the source code from GitHub and install.\n\n git clone https://github.com/tensorflow/examples\n cd examples/tensorflow_examples/lite/model_maker/pip_package\n pip install -e .\n\nTensorFlow Lite Model Maker depends on TensorFlow [pip\npackage](https://www.tensorflow.org/install/pip). For GPU drivers, please refer\nto TensorFlow's [GPU guide](https://www.tensorflow.org/install/gpu) or\n[installation guide](https://www.tensorflow.org/install).\n\nPython API Reference\n--------------------\n\nYou can find out Model Maker's public APIs in [API\nreference](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker)."]]
