import math from pathlib import Path import tensorflow as tf from tensorflow.keras import Model from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, InputLayer, Dropout, Conv1D, Flatten, Reshape, MaxPooling1D, BatchNormalization, Conv2D, GlobalMaxPooling2D, Lambda from tensorflow.keras.optimizers import Adam, Adadelta from tensorflow.keras.losses import categorical_crossentropy sys.path.append('./resources/libraries') import ei_tensorflow.training WEIGHTS_PATH = './transfer-learning-weights/keras/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_0.35_160.h5' INPUT_SHAPE = (160, 160, 3) base_model = tf.keras.applications.MobileNetV2( input_shape = INPUT_SHAPE, alpha=0.35, weights = WEIGHTS_PATH ) base_model.trainable = False model = Sequential() model.add(InputLayer(input_shape=INPUT_SHAPE, name='x_input')) # Don't include the base model's top layers last_layer_index = -3 model.add(Model(inputs=base_model.inputs, outputs=base_model.layers[last_layer_index].output)) model.add(Reshape((-1, model.layers[-1].output.shape[3]))) model.add(Dense(16, activation='relu')) model.add(Dropout(0.5)) model.add(Flatten()) model.add(Dense(classes, activation='softmax')) model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0003), loss='categorical_crossentropy', metrics=['accuracy']) # Implements the data augmentation policy def augment_image(image, label): # Flips the image randomly image = tf.image.random_flip_left_right(image) # Increase the image size, then randomly crop it down to # the original dimensions resize_factor = random.uniform(1, 1.3) new_height = math.floor(resize_factor * INPUT_SHAPE[0]) new_width = math.floor(resize_factor * INPUT_SHAPE[1]) image = tf.image.resize_with_crop_or_pad(image, new_height, new_width) image = tf.image.random_crop(image, size=INPUT_SHAPE) # Vary the brightness of the image image = tf.image.random_brightness(image, max_delta=0.2) return image, label train_dataset = train_dataset.map(augment_image, num_parallel_calls=tf.data.AUTOTUNE) BATCH_SIZE = 32 EPOCHS = 30 train_dataset = train_dataset.batch(BATCH_SIZE, drop_remainder=False) validation_dataset = validation_dataset.batch(BATCH_SIZE, drop_remainder=False) callbacks.append(BatchLoggerCallback(BATCH_SIZE, train_sample_count, epochs=EPOCHS)) model.fit(train_dataset, validation_data=validation_dataset, epochs=EPOCHS, verbose=2, callbacks=callbacks, class_weight=ei_tensorflow.training.get_class_weights(Y_train)) print('') print('Initial training done.', flush=True) # How many epochs we will fine tune the model FINE_TUNE_EPOCHS = 10 # What percentage of the base model's layers we will fine tune FINE_TUNE_PERCENTAGE = 65 print('Fine-tuning best model for {} epochs...'.format(FINE_TUNE_EPOCHS), flush=True) # Load best model from initial training model = ei_tensorflow.training.load_best_model(BEST_MODEL_PATH) # Determine which layer to begin fine tuning at model_layer_count = len(model.layers) fine_tune_from = math.ceil(model_layer_count * ((100 - FINE_TUNE_PERCENTAGE) / 100)) # Allow the entire base model to be trained model.trainable = True # Freeze all the layers before the 'fine_tune_from' layer for layer in model.layers[:fine_tune_from]: layer.trainable = False model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.000045), loss='categorical_crossentropy', metrics=['accuracy']) model.fit(train_dataset, epochs=FINE_TUNE_EPOCHS, verbose=2, validation_data=validation_dataset, callbacks=callbacks, class_weight=ei_tensorflow.training.get_class_weights(Y_train) )