def train_vgg16bn():
print('training vgg16bn')
model = create_vgg16bn()
try:
load_best_weights(model)
except:
print('Failed to load weigths')
train(model, max_num=1)
def train_inception_v3():
print('training inception_v3')
model = create_inceptionv3()
try:
load_best_weights(model)
except:
print('Failed to load weigths')
train(model)
def train_dense121():
print('training densenet 121')
model = create_dense121()
try:
load_best_weights(model)
except:
print('Failed to load weigths')
train(model)
def train_dense201():
print('training densenet 201')
model = create_dense201()
try:
load_best_weights(model)
except:
print('Failed to load weigths')
train(model, max_num=3)
def train_res152():
print('training resnet 152')
model = create_res152()
try:
load_best_weights(model)
except:
print('Failed to load weigths')
train(model, max_num=3)
def train_res50():
print('training resnet 50')
model = create_res50()
try:
load_best_weights(model)
except:
print('Failed to load weigths')
train(model)
def create_model(arch, fine_tune):
print('Training {}...'.format(arch))
model = models.create_model(arch, fine_tune=fine_tune)
try:
load_best_weights(model)
except:
print('Failed to load weights')
if not hasattr(model, 'max_num'):
model.max_num = 2
return model
def train_net(model_name):
print('Training {}...'.format(model_name))
model = create_model(model_name)
try:
load_best_weights(model)
except:
print('Failed to load weigths')
if not hasattr(model, 'max_num'):
model.max_num = 1
train(model)
def train_net(model_name, freeze=False, num_epochs=epochs):
print('Training {}...'.format(model_name))
model = create_model(model_name)
try:
load_best_weights(model)
except:
print('Failed to load weigths')
if not hasattr(model, 'max_num'):
model.max_num = 2
train(model, freeze=freeze, num_epochs=num_epochs)