acc /= times
return acc, loss
def inference(model, X): # Test Process
model.eval()
pred_ = model(torch.from_numpy(X).to(device))
return pred_.cpu().data.numpy()
if __name__ == '__main__':
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if not os.path.exists(args.train_dir):
os.mkdir(args.train_dir)
if args.is_train:
X_train, X_test, y_train, y_test = load_cifar_4d(args.data_dir)
X_val, y_val = X_train[40000:], y_train[40000:]
X_train, y_train = X_train[:40000], y_train[:40000]
cnn_model = Model(drop_rate=args.drop_rate)
cnn_model.to(device)
print(cnn_model)
optimizer = optim.Adam(cnn_model.parameters(), lr=args.learning_rate)
# model_path = os.path.join(args.train_dir, 'checkpoint_%d.pth.tar' % args.inference_version)
# if os.path.exists(model_path):
# cnn_model = torch.load(model_path)
pre_losses = [1e18] * 3
best_val_acc = 0.0
for epoch in range(1, args.num_epochs + 1):
start_time = time.time()
return acc, loss, acc_list, loss_list
def inference(model, sess, X): # Test Process
return sess.run([model.pred_val], {model.x_: X})[0]
config = tf.ConfigProto(log_device_placement=True)
os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.device
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
if not os.path.exists(FLAGS.train_dir):
os.mkdir(FLAGS.train_dir)
if FLAGS.is_train:
X_train, X_test, y_train, y_test = load_cifar_4d(FLAGS.data_dir)
X_val, y_val = X_train[40000:], y_train[40000:]
X_train, y_train = X_train[:40000], y_train[:40000]
cnn_model = Model(dropout=FLAGS.drop_rate, batch_norm=FLAGS.is_BN)
'''
if tf.train.get_checkpoint_state(FLAGS.train_dir):
cnn_model.saver.restore(sess, tf.train.latest_checkpoint(FLAGS.train_dir))
else:
'''
tf.global_variables_initializer().run()
pre_losses = [1e18] * 3
best_val_acc = 0.0
print("training....")
train_loss_list = []
train_acc_list = []