def main():
args = get_args()
input_path = args.input
batch_size = args.batch_size
nb_epochs = args.nb_epochs
depth = args.depth
k = args.width
validation_split = args.validation_split
use_augmentation = args.aug
logging.debug("Loading data...")
image, gender, age, _, image_size, _ = load_data(input_path)
X_data = image
y_data_a = np_utils.to_categorical(age, 101)
#custom parameters
nb_class = 2
hidden_dim = 512
vgg_model = VGGFace(include_top=False, input_shape=(224, 224, 3))
last_layer = vgg_model.get_layer('pool5').output
x = Flatten(name='flatten')(last_layer)
x = Dense(hidden_dim, activation='relu', name='fc6')(x)
x = Dense(hidden_dim, activation='relu', name='fc7')(x)
out = Dense(nb_class, activation='softmax', name='fc8')(x)
model = Model(vgg_model.input, out)
sgd = SGD(lr=0.1, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd,
loss=["categorical_crossentropy"],
metrics=['accuracy'])
logging.debug("Model summary...")
model.count_params()
model.summary()
logging.debug("Saving model...")
mk_dir("models")
with open(os.path.join("models", "vgg_{}_{}.json".format(depth, k)),
"w") as f:
f.write(model.to_json())
mk_dir("checkpoints")
callbacks = [
LearningRateScheduler(schedule=Schedule(nb_epochs)),
ModelCheckpoint("checkpoints/weights.{epoch:02d}-{val_loss:.2f}.hdf5",
monitor="val_loss",
verbose=1,
save_best_only=True,
mode="auto")
]
logging.debug("Running training...")
data_num = len(X_data)
indexes = np.arange(data_num)
np.random.shuffle(indexes)
X_data = X_data[indexes]
y_data_g = y_data_g[indexes]
y_data_a = y_data_a[indexes]
train_num = int(data_num * (1 - validation_split))
X_train = X_data[:train_num]
X_test = X_data[train_num:]
y_train_g = y_data_g[:train_num]
y_test_g = y_data_g[train_num:]
y_train_a = y_data_a[:train_num]
y_test_a = y_data_a[train_num:]
if use_augmentation:
datagen = ImageDataGenerator(width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=True,
preprocessing_function=get_random_eraser(
v_l=0, v_h=255))
training_generator = MixupGenerator(X_train, [y_train_g, y_train_a],
batch_size=batch_size,
alpha=0.2,
datagen=datagen)()
hist = model.fit_generator(generator=training_generator,
steps_per_epoch=train_num // batch_size,
validation_data=(X_test,
[y_test_g, y_test_a]),
epochs=nb_epochs,
verbose=1,
callbacks=callbacks)
else:
hist = model.fit(X_train, [y_train_g, y_train_a],
batch_size=batch_size,
epochs=nb_epochs,
callbacks=callbacks,
validation_data=(X_test, [y_test_g, y_test_a]))
logging.debug("Saving weights...")
model.save_weights(os.path.join("models", "vgg_{}_{}.h5".format(depth, k)),
overwrite=True)
pd.DataFrame(hist.history).to_hdf(
os.path.join("models", "history_{}_{}.h5".format(depth, k)), "history")
input_shape = (32, 32, 3)
if __name__ == "__main__":
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
x_train, x_test = normalize(x_train, x_test)
y_train = keras.utils.to_categorical(y_train, num_classes=10)
y_test = keras.utils.to_categorical(y_test, num_classes=10)
# MUNET model
inputs = Input(input_shape)
prediction = MUNET(inputs)
model_star = Model(inputs=[inputs], outputs=[prediction])
model_star.summary()
params = model_star.count_params()
params /= 1E6
print()
print("Number of params of model: %f M" % (params))
# optimization details
sgd = optimizers.SGD(lr=lrf, decay=lr_decay, momentum=0.9, nesterov=True)
model_star.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
# data augmentation
datagen = ImageDataGenerator(
width_shift_range=4. /
32, # randomly shift images horizontally (fraction of total width)
height_shift_range=4. /
