pid = os.getpid()
print('PID: {}'.format(pid))
f = open('pid_' + str(test_split), 'wb')
f.write(str(pid) + '\n')
f.close()
# Load model
model = SupervisedModel('experiment', './', learning_rate=1e-2)
#util.load_checkpoint(model, "./checkpoints_5/experiment-07m-20d-16h-24m-52s.pkl")
monitor = util.Monitor(model,
checkpoint_directory=checkpoint_dir,
save_steps=1000)
# Add dropout to fully-connected layer
model.fc4.dropout = 0.5
model._compile()
# Loading CK+ dataset
print('Loading Data')
#supervised_data_loader = SupervisedDataLoaderCrossVal(
# data_paths.ck_plus_data_path)
#train_data_container = supervised_data_loader.load('train', train_split)
#test_data_container = supervised_data_loader.load('test', train_split)
train_folds, val_fold, _ = data_fold_loader.load_fold_assignment(
test_fold=test_split)
X_train, y_train = data_fold_loader.load_folds(data_paths.ck_plus_data_path,
train_folds)
X_val, y_val = data_fold_loader.load_folds(data_paths.ck_plus_data_path,
[val_fold])
X_test, y_test = data_fold_loader.load_folds(data_paths.ck_plus_data_path,
[test_split])
pid = os.getpid()
print('PID: {}'.format(pid))
f = open('pid_'+str(train_split), 'wb')
f.write(str(pid)+'\n')
f.close()
# Load model
model = SupervisedModel('experiment', './', learning_rate=1e-2)
monitor = util.Monitor(model,
checkpoint_directory='checkpoints_'+str(train_split),
save_steps=1000)
# Add dropout flag to fully-connected layer
model.fc4.dropout = 0.5
model._compile()
# Loading TFD dataset
print('Loading Data')
supervised_data_loader = SupervisedDataLoader(
os.path.join(data_paths.tfd_data_path, 'npy_files/TFD_96/split_'+str(train_split)))
train_data_container = supervised_data_loader.load(0)
val_data_container = supervised_data_loader.load(1)
test_data_container = supervised_data_loader.load(2)
X_train = train_data_container.X
y_train = train_data_container.y
X_val = val_data_container.X
y_val = val_data_container.y
X_test = test_data_container.X
y_test = test_data_container.y
