H5 is a file format used for fast I/O processing and large data storage. H5 is suitable for machine learning models. To be able to preserve the architecture, optimizer state and model weights we can save the hard model in .h5 file format.One of the most popular libraries for running neural networks on Android is Tensorflow Lite. To use it, we need to convert a Keras .h5 file to a Tensorflow .tflite file. Then, how to convert .h5 files to .tflite files?Here is the code for converting h5 files to tflite files.
tflite_path = "model.tflite"
#Convert the model
converter = tf.lite.TFLiteConverter.from_keras_model(saved_model_dir)
tflite_model = converter.convert()
#Save the model
with open(tflite_path, 'wb') as f:
f.write(tflite_model)
TFLiteConverter function is used to convert .h5 files into .tflite files. The tflite model will be saved with the name model.tflite. ‘wb’ means file writes(w) and writes are done in binary(b) mode.Tflite is a binary file written in C language format, therefore it can be used in various languages. As like the
documentation, There are many ways to use it, but you must first understand the concept of how it works if you wanna using it in tensorflow lite. TensorFlow lite inference typically follows the following steps:
- Loading a model : You must load the .tflite model into memory, which contains the model’s execution graph.
- Transforming data: Raw input data for the model generally does not match the input data format expected by the model. For example, you might need to resize an image or change the image format to be compatible with the model.
- Running inference: This step involves using the TensorFlow Lite API to execute the model. It involves a few steps such as building the interpreter, and allocating tensors, as described in the following sections.
- Interpreting output: When you receive results from the model inference, you must interpret the tensors in a meaningful way that’s useful in your application. For example, a model might return only a list of probabilities. It’s up to you to map the probabilities to relevant categories and present it to your end-user.
