¿Cómo se optimiza un modelo TFLite?

How do you optimize a TFLite model?

Model optimization

1. Quantization.
2. Full integer quantization with int16 activations and int8 weights.
3. Pruning.
4. Clustering.

TfLite models are also quantized, due to which they are not as accurate as the original models. To solve this issue, quantization aware training can be used.

Using TensorFlow Lite we see a considerable speed increase when compared with the original results from our previous benchmarks using full TensorFlow. We see an approximately ×2 increase in inferencing speed between the original TensorFlow figures and the new results using TensorFlow Lite.

1. Overview.
2. Setup.
3. Generate a TensorFlow Model.
4. Convert to a TensorFlow Lite model. Convert using dynamic range quantization. Convert using float fallback quantization. Convert using integer-only quantization. Save the models as files.
5. Run the TensorFlow Lite models. Test the models on one image. Evaluate the models on all images.

A quantized model executes some or all of the operations on tensors with integers rather than floating point values. This allows for a more compact model representation and the use of high performance vectorized operations on many hardware platforms.

For the ease of transmission, the sampled signal is further processed, i.e., converted into digital form. For conversion into digital the sampled signal is quantized. In this process each sample is compared to a standard scale of discrete values and is given a binary number representing its approximate amplitude.)

This provides several advantages over TensorFlow’s protocol buffer model format such as reduced size (small code footprint) and faster inference (data is directly accessed without an extra parsing/unpacking step) that enables TensorFlow Lite to execute efficiently on devices with limited compute and memory resources.

Tensorflow Lite Converter converts a Tensorflow model to Tensorflow Lite flat buffer file (.tflite). Tensorflow Lite flat buffer file is deployed to the client, which in our cases can be a mobile device running on iOS or Android or an embedded device.

You can quantize an already-trained float TensorFlow model when you convert it to TensorFlow Lite format using the TensorFlow Lite Converter. Note: The procedures on this page require TensorFlow 1.15 or higher. There are several post-training quantization options to choose from. Here is a summary table of the choices and the benefits they provide:

On Linux platforms (including Raspberry Pi ), you can run inferences using TensorFlow Lite APIs available in C++ and Python , as shown in the following sections. Running a TensorFlow Lite model involves a few simple steps: Load the model into memory.