QAT, PTQ .. 모델 경량화

QAT - Quantization Aware Training

양자화 하지 않고 학습후

# Load MNIST dataset mnist = keras.datasets.mnist (train_images, train_labels), (test_images, test_labels) = mnist.load_data() # Normalize the input image so that each pixel value is between 0 to 1. train_images = train_images / 255.0 test_images = test_images / 255.0 # Define the model architecture. model = keras.Sequential([   keras.layers.InputLayer(input_shape=(28, 28)),   keras.layers.Reshape(target_shape=(28, 28, 1)),   keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation='relu'),   keras.layers.MaxPooling2D(pool_size=(2, 2)),   keras.layers.Flatten(),   keras.layers.Dense(10) ]) # Train the digit classification model model.compile(optimizer='adam',               loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),               metrics=['accuracy']) model.fit(   train_images,   train_labels,   epochs=1,   validation_split=0.1, )

 

양자화 모델로 변환하고, 파인튜닝

import tensorflow_model_optimization as tfmot quantize_model = tfmot.quantization.keras.quantize_model # q_aware stands for for quantization aware. q_aware_model = quantize_model(model) # `quantize_model` requires a recompile. q_aware_model.compile(optimizer='adam',               loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),               metrics=['accuracy']) q_aware_model.summary() train_images_subset = train_images[0:1000] # out of 60000 train_labels_subset = train_labels[0:1000] q_aware_model.fit(train_images_subset, train_labels_subset,                   batch_size=500, epochs=1, validation_split=0.1)

[링크 : https://www.tensorflow.org/model_optimization/guide/quantization/training?hl=ko]

 

PTQ - Post Training Quantization

[링크 : https://www.tensorflow.org/model_optimization/guide/quantization/post_training?hl=ko]

 

pruning - 0에 가까운 애들 없애기 (가지치기)

quantization (양자화)

distillation (증류)

low rank factorization (NxM -> Xxk kxM)

[링크 : https://u-b-h.tistory.com/13]

[링크 : https://asidefine.tistory.com/318]