x_train_private_gen = batch_generator(settings,
x_train_private,
private=True)
x_val_public_gen = batch_generator(settings, x_val_public)
x_val_private_gen = batch_generator(settings, x_val_private, private=True)
train_steps_per_epoch = np.floor(x_train_private.shape[0] /
settings['batch_size']).astype(np.int32)
val_steps_per_epoch = np.floor(x_val_private.shape[0] /
settings['batch_size']).astype(np.int32)
for iteration in range(3):
folder = 'results/propAdd_blind_dataset3/{}'.format(iteration)
obfuscator, obfuscator_part = get_model.load_model(settings)
if not os.path.exists(folder):
os.makedirs(folder)
plot_model(obfuscator, to_file=folder + '\model.png', show_shapes=True)
obfuscator.compile(optimizer=Adam(lr=settings['learning_rate']),
loss=settings['loss'])
obfuscator_part.compile(optimizer=Adam(lr=settings['learning_rate']),
loss=['mae', settings['loss']])
#create metrics dictionary
metrics = dict()
metrics['train_public_loss'] = np.zeros(settings['epochs'])
metrics['train_private_loss'] = np.zeros(settings['epochs'])
metrics['val_public_loss'] = np.zeros(settings['epochs'])
metrics['val_private_loss'] = np.zeros(settings['epochs'])
metrics['train_obj_loss'] = np.zeros(settings['epochs'])
depth = [3]
initial_filters = [8]
extra_block = [True, False, 'skip']
num_models = len(batchnorm) * len(same_batch) * len(depth) * len(
initial_filters) * len(extra_block)
train_steps_per_epoch = np.floor(x_train_private.shape[0] /
settings['batch_size']).astype(np.int32)
val_steps_per_epoch = np.floor(x_val_private.shape[0] /
settings['batch_size']).astype(np.int32)
for iteration in range(3):
folder = 'results/dataset3/{}'.format(iteration)
obfuscator = get_model.load_model(settings)
if not os.path.exists(folder):
os.makedirs(folder)
plot_model(obfuscator, to_file=folder + '\model.png', show_shapes=True)
obfuscator.compile(optimizer=Adam(lr=settings['learning_rate']),
loss='mean_absolute_error')
#create metrics dictionary
metrics = dict()
metrics['train_public_loss'] = np.zeros(settings['epochs'])
metrics['train_private_loss'] = np.zeros(settings['epochs'])
metrics['val_public_loss'] = np.zeros(settings['epochs'])
metrics['val_private_loss'] = np.zeros(settings['epochs'])
metrics['train_obj_loss'] = np.zeros(settings['epochs'])
metrics['val_obj_loss'] = np.zeros(settings['epochs'])
settings['batch_size']).astype(
np.int32)
val_steps_per_epoch = np.floor(x_val_private.shape[0] /
settings['batch_size']).astype(
np.int32)
#determine loss weights per loss
obf_loss_weight = K.variable(settings['loss_weight_obf'])
att_loss_weight = K.variable(1 - settings['loss_weight_obf'])
folder = 'results/direct_approach3_dataset1/weight_{}_number_{}'.format(
settings['loss_weight_obf'], number)
if not os.path.exists(folder):
os.makedirs(folder)
obfuscator, comb = get_model.load_model(settings)
plot_model(obfuscator,
to_file=folder + '\obfuscator.png',
show_shapes=True)
obfuscator.compile(
optimizer=Adam(lr=settings['learning_rate_obf']),
loss='mean_absolute_error')
comb.compile(optimizer=Adam(lr=settings['learning_rate_att']),
loss=['mean_absolute_error', 'binary_crossentropy'],
loss_weights=[obf_loss_weight, att_loss_weight])
#create metrics dictionary
metrics = dict()
metrics['train_public_loss'] = np.zeros(settings['epochs'])
metrics['train_private_loss'] = np.zeros(settings['epochs'])
metrics['val_public_loss'] = np.zeros(settings['epochs'])