def main():
# This main will first load the datasets then start the training for both, age as well as gender estimation
# with the default amount of epochs with the value 312 and an early stopping callback with the patience of 4.
# You can change the amount of epochs by typing "epochs = X" into the Trainer constructor.
# If you want to train a model by loading the weights of an allready trained model,
# use the load_model method from the OurModel class.
# After the training is done, the models will be saved and evulation files (.csv) will be created.
# Use the parsers to interpret the .csv files
dataset = Dataset()
dataset.createFolder("../data/classification")
dataset.createClassificationFolders("../data/classification/age/Train")
dataset.createClassificationFolders("../data/classification/age/Valid")
dataset.createClassificationFolders("../data/classification/age/Test")
#FGNET
dataset.downloadAndUnzip("http://yanweifu.github.io/FG_NET_data/FGNET.zip",
"../data/FGNET.zip", "../data/FGNET")
dataset.readAndPrintDataImagesFGNET("../data/FGNET",
"../data/classification/age")
# Start training for age estimation
AgeModel = OurModel(0)
AgeModel = Trainer(AgeModel.model,
filepath_age + "Train",
filepath_age + "Valid",
filepath_age + "Test",
identifier=0)
AgeModel.train()
def main():
# This main will first load the datasets then start the training for both, age as well as gender estimation
# with the default amount of epochs with the value 312 and an early stopping callback with the patience of 4.
# You can change the amount of epochs by typing "epochs = X" into the Trainer constructor.
# If you want to train a model by loading the weights of an allready trained model,
# use the load_model method from the OurModel class.
# After the training is done, the models will be saved and evulation files (.csv) will be created.
# Use the parsers to interpret the .csv files
dataset = Dataset()
dataset.createFolder("../data/classification")
dataset.createClassificationFolders("../data/classification/age/Train")
dataset.createClassificationFolders("../data/classification/age/Valid")
dataset.createClassificationFolders("../data/classification/age/Test")
#LAP
dataset.downloadAndUnzip(
"http://158.109.8.102/AppaRealAge/appa-real-release.zip",
"../data/appa-real-release.zip", "../data/appa-real-release")
dataset.readAndPrintDataImagesLAP("../data/appa-real-release", "train",
"../data/classification/age/Train")
dataset.readAndPrintDataImagesLAP("../data/appa-real-release", "valid",
"../data/classification/age/Valid")
dataset.readAndPrintDataImagesLAP("../data/appa-real-release", "test",
"../data/classification/age/Test")
#FGNET
dataset.downloadAndUnzip("http://yanweifu.github.io/FG_NET_data/FGNET.zip",
"../data/FGNET.zip", "../data/FGNET")
dataset.readAndPrintDataImagesFGNET("../data/FGNET",
"../data/classification/age")
#IMDB
dataset.downloadAndUnpack(
"https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/static/imdb"
"_crop.tar", "../data/imdb_crop.tar", "../data/imdb_crop")
dataset.readAndPrintDataImagesIMDB('../data/imdb_metadata.csv',
"../data/imdb_crop",
"../data/classification")
# Start training for age estimation
AgeModel = OurModel(0)
AgeModel = Trainer(AgeModel.model,
filepath_age + "Train",
filepath_age + "Valid",
filepath_age + "Test",
identifier=0)
AgeModel.train()
# Start training for gender estimation
GenderModel = OurModel(1)
GenderModel = Trainer(GenderModel.model,
filepath_gender + "Train",
filepath_gender + "Valid",
filepath_gender + "Test",
identifier=1)
GenderModel.train()
def test_model_layers_allTrainable(self):
with self.test_session():
GenderModel = OurModel(1)
trainable = True
for layer in GenderModel.model.layers:
if layer.trainable != True:
trainable = False
AgeModel = OurModel(0)
for layer in AgeModel.model.layers:
if layer.trainable != True:
trainable = False
self.assertEqual(trainable, True)
def test_Agemodel_layerLength(self):
with self.test_session():
AgeModel = OurModel(0)
length = 0
for layer in AgeModel.model.layers:
length = length + 1
self.assertEqual(length, 24)
def test_Gendermodel_layerLength(self):
with self.test_session():
GenderModel = OurModel(1)
GenderModel.model.summary()
length = 0
for layer in GenderModel.model.layers:
length = length + 1
self.assertEqual(length, 24)
def test_model_get_saved(self):
with self.test_session():
model = OurModel(0)
trainer = Trainer(model.model,
filepath + "Train",
filepath + "Valid",
filepath + "Test",
identifier=0,
epochs=1,
save_model=True)
trainer.train()
self.assertEqual(True, os.path.exists(trainer.saved_model_path))
def test_model_get_saved_and_loaded_correctly(self):
with self.test_session():
model = OurModel(0)
trainer = Trainer(model.model,
filepath + "Valid",
filepath + "Valid",
filepath + "Valid",
identifier=0,
epochs=1,
save_model=True)
trainer.train()
model_load = OurModel(0)
model_load.load_model(trainer.saved_model_path, 0)
a = trainer.model.get_weights()
b = model_load.model.get_weights()
equal = True
while len(a) != 0:
c = a.pop()
d = b.pop()
if (c != d).any():
equal = False
self.assertEqual(True, equal)
def test_one_training_step(self):
with self.test_session():
model = OurModel(0)
a = model.model.get_weights()
trainer = Trainer(model.model,
filepath + "Train",
filepath + "Valid",
filepath + "Test",
identifier=0,
epochs=1,
save_model=False)
trainer = trainer.train()
b = trainer.get_weights()
same = True
while len(a) != 0:
c = a.pop()
d = b.pop()
if (c != d).any():
same = False
self.assertEqual(False, same)