Exploring Tensorflow Lite and Tensorflow.js

In this unit, we will delve into Tensorflow Lite and Tensorflow.js, two powerful tools that allow us to run Tensorflow models on mobile, edge devices, and in the browser.

Tensorflow Lite

Tensorflow Lite is a set of tools provided by Google to run machine learning models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and a small binary size.

Converting Tensorflow Models to Tensorflow Lite

The first step to using Tensorflow Lite is converting your Tensorflow model into a format that Tensorflow Lite can use. This is done using the Tensorflow Lite Converter. The converter supports SavedModel directories, tf.keras models, and concrete functions.

Here is a simple example of how to convert a model:

import tensorflow as tf

# Load your model
model = tf.keras.models.load_model('my_model.h5')

# Convert the model
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()

# Save the model
with open('model.tflite', 'wb') as f:
  f.write(tflite_model)

Running Tensorflow Lite Models on Mobile and Edge Devices

Once you have your .tflite file, you can load it onto your device and use it for inference. Tensorflow Lite provides APIs for different languages including Java for Android, Swift and Objective-C for iOS, and C++ for edge devices.

Tensorflow.js

Tensorflow.js is a library for machine learning in JavaScript. It allows you to run existing models, retrain existing models, and develop machine learning models right in the browser or on Node.js.

Running Tensorflow Models in the Browser

To run a model in the browser, you first need to convert it to the Tensorflow.js format. This can be done using the tensorflowjs_converter tool. Once converted, you can load the model using the tf.loadLayersModel function.

Here is an example of how to load a model:

const model = await tf.loadLayersModel('https://path/to/my-model.json');

Converting Tensorflow Models to Tensorflow.js

To convert a Tensorflow model to Tensorflow.js, you can use the tensorflowjs_converter tool. This tool supports SavedModel directories, tf.keras HDF5 models, and tf.keras SavedModel format.

Here is an example of how to convert a model:

tensorflowjs_converter --input_format=keras_saved_model /path/to/my_model /path/to/tfjs_model

In conclusion, Tensorflow Lite and Tensorflow.js are powerful tools that allow us to run our Tensorflow models on a variety of platforms. Whether you're developing a mobile app, an IoT device, or a web app, these tools have you covered.