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 75 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(Dense(20, activation='relu', activity_regularizer=tf.keras.regularizers.l1(0.00001))) model.add(Dense(10, activation='relu', activity_regularizer=tf.keras.regularizers.l1(0.00001))) model.add(Dense(classes, name='y_pred', activation='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, class_weight=ei_tensorflow.training.get_class_weights(Y_train)) # Use this flag to disable per-channel quantization for a model. # This can reduce RAM usage for convolutional models, but may have # an impact on accuracy. disable_per_channel_quantization = False