Скачать или смотреть mobile and embedded tensorflow tensorflow dev summit 2017

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the tensorflow dev summit 2017 included a number of talks and tutorials about tensorflow, including how to use tensorflow for mobile and embedded devices. this tutorial will summarize key points from the summit regarding tensorflow lite, which is specifically designed for mobile and embedded machine learning applications.

what is tensorflow lite?

tensorflow lite is a lightweight version of tensorflow optimized for mobile and embedded devices. it allows developers to run machine learning models on android and ios devices with low latency and a small binary size. tensorflow lite supports a variety of hardware platforms and is designed to work efficiently on resources-constrained devices.

key features of tensorflow lite

1. **model optimization**: tensorflow lite allows you to convert tensorflow models into a format that is optimized for mobile devices. this includes techniques like quantization, which reduces the precision of the weights and activations in the model.

2. **interpreter**: tensorflow lite provides an interpreter that runs the optimized models on devices. this interpreter is designed to be lightweight and efficient.

3. **support for various platforms**: tensorflow lite supports android, ios, raspberry pi, microcontrollers, and other platforms.

steps to use tensorflow lite

1. **train your model**: you can train your model using the standard tensorflow framework.

2. **convert the model**: use the tensorflow lite converter to convert your model to a tensorflow lite format.

3. **load the model**: in your mobile application, load the tensorflow lite model using the interpreter.

4. **run inference**: use the interpreter to run inference with the model on new data.

code example: using tensorflow lite on android

here's a simple example of how to use tensorflow lite in an android application.

step 1: train a model

for this example, let’s assume you have a trained tensorflow model saved as `model.h5`. you can convert it to tensorflow lite form ...

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