quantization

问题 Recently, I've started creating neural networks with Tensorflow + Keras and I would like to try the quantization feature available in Tensorflow. So far, experimenting with examples from TF tutorials worked just fine and I have this basic working example (from https://www.tensorflow.org/tutorials/keras/basic_classification): import tensorflow as tf from tensorflow import keras fashion_mnist = keras.datasets.fashion_mnist (train_images, train_labels), (test_images, test_labels) = fashion_mnist

问题 Recently, I've started creating neural networks with Tensorflow + Keras and I would like to try the quantization feature available in Tensorflow. So far, experimenting with examples from TF tutorials worked just fine and I have this basic working example (from https://www.tensorflow.org/tutorials/keras/basic_classification): import tensorflow as tf from tensorflow import keras fashion_mnist = keras.datasets.fashion_mnist (train_images, train_labels), (test_images, test_labels) = fashion_mnist

问题 I am trying to convert a trained model from checkpoint file to tflite . I am using tf.lite.LiteConverter . The float conversion went fine with reasonable inference speed. But the inference speed of the INT8 conversion is very slow. I tried to debug by feeding in a very small network. I found that inference speed for INT8 model is generally slower than float model. In the INT8 tflite file, I found some tensors called ReadVariableOp, which doesn't exist in TensorFlow's official mobilenet tflite

问题 I've been studying quantization using Tensorflow's TFLite. As far as I understand it is possible to quantize my model weights (so that they will be stored using 4x less memory) but it doesn't necessary implies that the model won't convert it back to floats to run it. I've also understood that to run my model only using int I need to set the following parameters: converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] converter.inference_input_type = tf.uint8 converter

来源： https://stackoverflow.com/questions/9275754/jpeg-file-quantization-table-definition

来源： https://stackoverflow.com/questions/63830570/full-quatization-does-not-except-int8-data-to-change-model-input-layer-to-int8

来源： https://stackoverflow.com/questions/63108767/create-training-graph-failed-when-converted-mobilefacenet-to-quantize-aware-mo

问题 I have to reduce the bit-depth of a digital audio signal from 24 to 16 bit. Taking only the 16 most significant bits (i.e. truncating) of each sample is equivalent to doing a proportional calculation (out = in * 0xFFFF / 0xFFFFFF)? 回答1: I assume you mean (in * 0xFFFF) / 0xFFFFFF , in which case, yes. 回答2: You'll get better sounding results by adding a carefully crafted noise signal to the original signal, just below the truncating threshold, before truncating (a.k.a. dithering). 回答3: x *

问题 I'm trying to use UINT8 quantization while converting tensorflow model to tflite model: If use post_training_quantize = True , model size is x4 lower then original fp32 model, so I assume that model weights are uint8, but when I load model and get input type via interpreter_aligner.get_input_details()[0]['dtype'] it's float32. Outputs of the quantized model are about the same as original model. converter = tf.contrib.lite.TFLiteConverter.from_frozen_graph( graph_def_file='tflite-models/tf

问题 I'm trying to use UINT8 quantization while converting tensorflow model to tflite model: If use post_training_quantize = True , model size is x4 lower then original fp32 model, so I assume that model weights are uint8, but when I load model and get input type via interpreter_aligner.get_input_details()[0]['dtype'] it's float32. Outputs of the quantized model are about the same as original model. converter = tf.contrib.lite.TFLiteConverter.from_frozen_graph( graph_def_file='tflite-models/tf