def get_model():
if mode == '':
# a 25 layers deep VGG-style network with batchnorm
k = 32
model = VGG(input_shape=x_train.shape[1:],
nbstages=4,
nblayers=[6] * 4,
nbfilters=[1 * k, 2 * k, 4 * k, 8 * k],
nbclasses=y_train.shape[1],
use_bias=False,
batchnorm_training=False,
kernel_initializer='he_uniform')
elif mode == 'fast':
k = 16
# a 13 layers deep VGG-style network with batchnorm
model = VGG(input_shape=x_train.shape[1:],
nbstages=4,
nblayers=[3] * 4,
nbfilters=[1 * k, 2 * k, 4 * k, 8 * k],
nbclasses=y_train.shape[1],
use_bias=False,
batchnorm_training=False,
kernel_initializer='he_uniform')
weights_location = 'model_initial_weights/cifar10_initial_weights' + mode + '.h5'
if 'cifar10_initial_weights' + mode + '.h5' not in os.listdir(
'model_initial_weights'):
model.save_weights(weights_location)
else:
model.load_weights(weights_location)
return model
def _load_custom(self, model_id):
# return the model if it's already been constructed
if model_id in self.custom_models:
return self.custom_models[model_id]
model_dir = os.path.join(self.custom_models_dir,
'model_{}'.format(model_id))
print(model_dir)
if not os.path.exists(model_dir):
raise OSError(
"Directory for custom model {} does not exist!".format(
model_id))
config_path = os.path.join(model_dir,
'config_{}.json'.format(model_id))
weights_path = os.path.join(model_dir,
'weights_{}.h5'.format(model_id))
with open(config_path) as f:
config = json.load(f)
# print('Hyperparameters: ', config['hparams'], type(config['hparams']))
# print('Train Time: {}'.format(config['train_time']))
# train_acc, test_acc = config['train_acc'], config['test_acc']
# print('Train Accuracy : {} | Test Accuracy {}'.format(train_acc, test_acc))
hparams = argparse.Namespace(**config['hparams'])
model = VGG(hparams)
model.load_weights(weights_path)
self.custom_models[model_id] = model
return model
def construct_model(config_path, weights_path):
# Recover JSON contents
with open(config_path) as config_file:
config = json.load(config_file)
print('Hyperparameters: ', config['hparams'], type(config['hparams']))
print('Train Time: {}'.format(config['train_time']))
train_acc, test_acc = config['train_acc'], config['test_acc']
print('Train Accuracy : {} | Test Accuracy {}'.format(
train_acc, test_acc))
args = Namespace(**config['hparams'])
model = VGG(args)
model.load_weights(weights_path)
return model
def get_model():
k = 32
model = VGG(
input_shape=x_train.shape[1:],
nbstages=5,
nblayers=[2] * 5,
nbfilters=[1 * k, 2 * k, 4 * k, 8 * k, 16 * k],
nbclasses=y_train.shape[1],
use_bias=False,
batchnorm_training=False, #use_batchnorm = False,
kernel_initializer='he_uniform',
batchnorm_momentum=0.9
) ### because training sometimes stops after very few epochs (~15)
weights_location = 'model_initial_weights/tinyImagenet_initial_weights_batchnorm.h5'
if 'tinyImagenet_initial_weights_batchnorm.h5' not in os.listdir(
'model_initial_weights'):
model.save_weights(weights_location)
else:
model.load_weights(weights_location)
return model