TensorFlow Lite 实现边缘智能 目录 使⽤ TensorFlow 2 实现图像数据增强 使⽤ TensorFlow 2 实现分布式训练 使⽤ TensorFlow Hub 迁移学习 7 8 9 11 12 13 使⽤ @tf.function 提升性能 使⽤ TensorFlow Serving 部署云端服务 使⽤ TensorFlow Lite 实现边缘智能 TensorFlow TensorFlow Lite 官网 https://www.tensorflow.org/lite/examples TensorFlow Lite Examples 搭建 TensorFlow Lite 运行环境 (Android) https://developer.android.com/studio Step 1：下载 TensorFlow examples 项目 $ git clone clone https://github.com/tensorflow/examples Step 2：在 Android Studio 中加载 examples 项目 项目路径：examples/lite/examples/image_classification/android Step 2：在 Android Studio 中加载 examples 项目 Step 3：在 Android Studio

applying quantization in practical situations using popular frameworks like Tensorflow and Tensorflow Lite. An Overview of Compression One of the simplest approaches towards efficiency is compression to the resource constraints that we initially outlined. As mentioned in Chapter 1, TFLite (Tensorflow Lite) helps to convert and deploy tensorflow models to IoT and edge devices. It is optimized for ARM based """Evaluate the generated TFLite model.""" # Load the TFLite model and allocate tensors. interpreter = tf.lite.Interpreter(model_content=model_content) interpreter.allocate_tensors() # Get input and output tensors

Release GPU & more TensorBoard 1.0 Release XLA, New APIs High-Level APIs tf.keras tf.data TF Lite Eager 2.0 Preview 15,000,000 Tracked by https://www.timqian.com/star-history/#tensorflow/tensorflow

specifically for deploying efficient models. For example, tensorflow has a tight integration with Tensorflow Lite (TFLite) and related libraries, which allow exporting and running models on mobile devices. Similarly

include_optimizer=False) clustered_tflite_file = '/tmp/clustered_speech.tflite' converter = tf.lite.TFLiteConverter.from_keras_model(final_model) tflite_clustered_model = converter.convert() with