def main():
args = parse_args()
# dataset
X_train, y_train, X_val, y_val, X_test, y_test = load_dataset(flatten=True)
# model layer dimensions
input_dim = X_train.shape[1]
num_classes = 10
# create model
model = Model()
model.add(Dense(input_dim, 100), activation='relu')
model.add(Dense(100, 200), activation='relu')
model.add(Dense(200, 200), activation='relu')
model.add(Dense(200, num_classes))
# train model
model.fit(X_train,
y_train,
val_data=(X_val, y_val),
verbose=True,
epochs=args.epochs,
batch_size=args.batch_size,
lr=args.lr)
# evaluate model
model.eval(X_test, y_test, verbose=True)
def main():
seed = 0
fix_seed(seed)
data = cr.Dataset(
data_paths=cfg.data_paths,
exp_id=cfg.exp_id,
img_shape=cfg.img_shape,
img_crop_size=cfg.img_crop_size,
max_trace=cfg.max_trace_len,
)
x_train, x_test, y_train, y_test = data.split_training_test_data(
test_split=.20, seed=10, for_deep=True)
trainsets = NNDataset(x_train, y_train, DEVICE)
testsets = NNDataset(x_test, y_test, DEVICE)
train_loader = torch.utils.data.DataLoader(trainsets, batch_size=32)
test_loader = torch.utils.data.DataLoader(testsets, batch_size=32)
for model_name in MODEL_LIST:
print(f'\n======== {model_name} ========\n')
if model_name == 'LSTM':
model = Model(t_stage='LSTM',
device=DEVICE,
t_hidden_dim=500,
t_output_dim=500,
use_cnn_for_trace=False)
elif model_name == 'CNN_LSTM':
model = Model(t_stage='LSTM',
device=DEVICE,
t_hidden_dim=500,
t_output_dim=500)
elif model_name == 'OnlyCNN':
model = Model(s_stage='CNN', device=DEVICE, block_num=3)
else:
model = Model(s_stage=model_name,
t_stage='LSTM',
device=DEVICE,
pretrained=PRETRAINED,
block_num=3,
t_hidden_dim=500,
t_output_dim=500)
if MODE == 'train':
score, model = train(model,
model_name,
train_loader,
test_loader,
DEVICE,
log_path=f'{ROOT}/out/{model_name}.txt')
model = model.to('cpu')
torch.save(model.state_dict(),
f'{ROOT}/best_models/{model_name}.pth')
elif MODE == 'fps':
model.eval()
inputs = (torch.rand(1, 1,
500).to(DEVICE), torch.rand(1, 1, 80,
80).to(DEVICE))
t0 = time.time()
for i in range(100):
model(inputs)
with open(f'{ROOT}/out/speed.txt', 'a') as f:
f.write(f'{model_name}: {100 / (time.time() - t0):.04f} fps\n')
else:
raise ValueError