TensorFlow Lite library

It is based on the ResNet architecture and is fully convolutional.

TensorFlow also has its architecture TPU, which performs computations faster than GPU and CPU . Therefore, models built using TPU can be easily deployed on a cloud at a cheaper rate and executed at a faster rate. However, TensorFlow’s architecture TPU only allows the execution of a model, not to train it.

Utilization rate hits 270% with the . ckpt file, but stays at around 100% with the . tflite file. One hypothesis is that tensorflow lite is not configured for multithreading, and another is that tensorflow lite is optimized for ARM processors (rather than an Intel one that my computer runs on) and thus it is slower.

Model optimization aims to create smaller models that are generally faster and more energy efficient , so that they can be deployed on mobile devices. TensorFlow Lite supports multiple optimization techniques, such as quantization. Check out our model optimization docs for details.28 Oca 2021

TensorFlow Lite takes existing models and converts them into an optimized version within the sort of . tflite file. … TensorFlow Lite Vs. TensorFlow Mobile. TensorFlow LiteTensorFlow MobileLess Binary File Size.Max Binary File Size.Better Performance.Good PerformanceIntroduction to TensorFlow Lite – GeeksforGeeks www.geeksforgeeks.org › introduction-to-tensorflow-lite