Deploy ML on mobile, microcontrollers and other edge devices
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Deploy ML on mobile, microcontrollers and other edge devices
Install TensorFlow 2
Deploy ML on mobile, microcontrollers and other edge devices
TensorFlow.js | Machine Learning for JavaScript Developers
Deploy ML on mobile, microcontrollers and other edge devices
Post-training quantization | TensorFlow Lite
The following decision tree can help determine which post-training quantization method is best for your use case: Dynamic range quantization Dynamic range quantization is a recommended starting point because it provides reduced memory usage and faster computation without you having to provide a representative dataset for calibration. This type of quantization, statically quantizes only the weights
Better performance with tf.function | TensorFlow Core
In TensorFlow 2, eager execution is turned on by default. The user interface is intuitive and flexible (running one-off operations is much easier and faster), but this can come at the expense of performance and deployability. You can use tf.function to make graphs out of your programs. It is a transformation tool that creates Python-independent dataflow graphs out of your Python code. This will he
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Deploy ML on mobile, microcontrollers and other edge devices
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