import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, InputLayer, Dropout, Conv1D, Conv2D, Flatten, Reshape, MaxPooling1D, MaxPooling2D, AveragePooling2D, BatchNormalization, Permute, ReLU, Softmax from tensorflow.keras.optimizers.legacy import Adam EPOCHS = args.epochs or 40 LEARNING_RATE = args.learning_rate or 0.0005 # If True, non-deterministic functions (e.g. shuffling batches) are not used. # This is False by default. ENSURE_DETERMINISM = args.ensure_determinism # this controls the batch size, or you can manipulate the tf.data.Dataset objects yourself BATCH_SIZE = args.batch_size or 32 if not ENSURE_DETERMINISM: train_dataset = train_dataset.shuffle(buffer_size=BATCH_SIZE*4) train_dataset=train_dataset.batch(BATCH_SIZE, drop_remainder=False) validation_dataset = validation_dataset.batch(BATCH_SIZE, drop_remainder=False) # model architecture model = Sequential() model.add(Conv2D(48, kernel_size=3, strides=2, kernel_constraint=tf.keras.constraints.MaxNorm(1), padding='same')) model.add(ReLU()) model.add(Conv2D(48, kernel_size=3, strides=2, kernel_constraint=tf.keras.constraints.MaxNorm(1), padding='same')) model.add(ReLU()) model.add(Flatten()) model.add(Dropout(0.35)) model.add(Dense(classes, name='y_pred')) model.add(Softmax()) # this controls the learning rate opt = Adam(learning_rate=LEARNING_RATE, beta_1=0.9, beta_2=0.999) callbacks.append(BatchLoggerCallback(BATCH_SIZE, train_sample_count, epochs=EPOCHS, ensure_determinism=ENSURE_DETERMINISM)) # train the neural network model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy']) model.fit(train_dataset, epochs=EPOCHS, validation_data=validation_dataset, verbose=2, callbacks=callbacks) import tensorflow as tf def akida_quantize_model( keras_model, weight_quantization: int = 4, activ_quantization: int = 4, input_weight_quantization: int = 8, ): import cnn2snn print("Performing post-training quantization...") akida_model = cnn2snn.quantize( keras_model, weight_quantization=weight_quantization, activ_quantization=activ_quantization, input_weight_quantization=input_weight_quantization, ) print("Performing post-training quantization OK") print("") return akida_model def akida_perform_qat( akida_model, train_dataset: tf.data.Dataset, validation_dataset: tf.data.Dataset, optimizer: str, fine_tune_loss: str, fine_tune_metrics: "list[str]", callbacks, stopping_metric: str = "val_accuracy", fit_verbose: int = 2, qat_epochs: int = 30, ): early_stopping = tf.keras.callbacks.EarlyStopping( monitor=stopping_metric, mode="max", verbose=1, min_delta=0, patience=10, restore_best_weights=True, ) callbacks.append(early_stopping) print("Running quantization-aware training...") akida_model.compile( optimizer=optimizer, loss=fine_tune_loss, metrics=fine_tune_metrics ) akida_model.fit( train_dataset, epochs=qat_epochs, verbose=fit_verbose, validation_data=validation_dataset, callbacks=callbacks, ) print("Running quantization-aware training OK") print("") return akida_model akida_model = akida_quantize_model(model) akida_model = akida_perform_qat( akida_model, train_dataset=train_dataset, validation_dataset=validation_dataset, optimizer=opt, fine_tune_loss='categorical_crossentropy', fine_tune_metrics=['accuracy'], callbacks=callbacks)