extract_features_for_cifar100.py

# check gpu devices
import tensorflow as tf
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical

# load cifar10 datasets
from lib.data_loader import load_cifar100
from lib.feature_extractor import NASNetLargeExtractor

devices = tf.config.list_physical_devices('GPU')
if len(devices) < 1:
    raise AttributeError("No GPU found!")
else:
    print(devices)
    print()

batch_size = 128

# download google nasnet large pre-trained model
model = NASNetLargeExtractor(32, 100, model_path="models/cifar100", data_path="datasets/cifar100")
print("Pre-trained NASNetLarge is loaded.")

# preprocess the dataset
(x_train, y_train), (x_test, y_test) = load_cifar100()

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')


def preprocess_data(data_set):
    data_set /= 255.0
    return data_set


x_train = preprocess_data(x_train)
x_test = preprocess_data(x_test)

y_train = to_categorical(y_train, num_classes=100)
y_test = to_categorical(y_test, num_classes=100)

# split a validation set
x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=0.2, random_state=42)

print("There are {} training samples and {} validation samples".format(x_train.shape[0], x_valid.shape[0]))
print("There are {} test samples.".format(x_test.shape[0]))

# extract features
# features_train = model.extract(x_train, batch_size=batch_size)
# print("The shape of the extracted training sample features is: ", features_train.shape)

# save features
# model.save_features()

# load features
model.load_features()
model.load_extractor()
model.load_classifier()

# use dense layer to test feature quality
# history = model.train_classifier(y_train, epochs=100, batch_size=batch_size, validation_data=(x_valid, y_valid))
# model.save_history(history, name="train_classifier_his_3")

# save trained model
# model.save_classifier()
# model.save_extractor()

# model.load_classifier()
# model.load_extractor()
model.extract(model.features, y_train, batch_size=batch_size, compression=True)

model.save_features()

# Random Search for best fine tine hyper parameters
# rds = RandomSearch(model)
# best_dict, history_dict = rds(x_train, y_train, validation_data=(x_valid, y_valid), batch_size=batch_size)
#
# model.save_history(history_dict, name="random_search_his")

# fine-tune the network
# print("Start to fine tune the network and extract compressed features.")
# history = model.fine_tune_features(x_train, y_train, learning_rate=best_dict["learning_rate"],
#                                    weight_decay=best_dict["weight_decay"], batch_size=batch_size, epochs=128,
#                                    validation_data=(x_valid, y_valid), early_stop=True)
# features = model.extract(x_train, compression=True)

# save results
# model.save_classifier()
# model.save_extractor()
#
# model.save_features()
#
# model.save_history(history, "fine_tune_his")