Vortana Say vsay01

## seed_lab_index.html
<html>
  <head>
    <title>Title</title>
    <body>
      <center>
        <h2>Welcome to my new website</h2>
        <iframe id = "new" src="http://www.cnn.com" style="opacity:0.0;position:absolute;top:195px;left:10px;width:1000px;height:200px">
        </iframe>
      </center>
    </body>

## eval.py
trained_model.eval()

## load_model.py
# load the saved checkpoint
model, optimizer, start_epoch, valid_loss_min = load_ckp(ckp_path, model, optimizer)

## list2.py
%ls ./checkpoint/

## list.sh
%ls ./best_model/

## checkpoint.py
checkpoint = {
            'epoch': epoch + 1,
            'valid_loss_min': valid_loss,
            'state_dict': model.state_dict(),
            'optimizer': optimizer.state_dict(),
        }

## continue_training.py
trained_model = train(start_epoch, 6, valid_loss_min, loaders, model, optimizer, criterion, use_cuda, "./checkpoint/current_checkpoint.pt", "./best_model/best_model.pt")

## print_saved_data.py
print("model = ", model)
print("optimizer = ", optimizer)
print("start_epoch = ", start_epoch)
print("valid_loss_min = ", valid_loss_min)
print("valid_loss_min = {:.6f}".format(valid_loss_min))

## load_saved_checkpoint_param.py
# define optimzer
optimizer = optim.Adam(model.parameters(), lr=0.001)

# define checkpoint saved path
ckp_path = "./checkpoint/current_checkpoint.pt"

## load_model_instance.py
model = FashionClassifier()

# move model to GPU if CUDA is available
if use_cuda:
    model = model.cuda()

print(model)
	<html>
	<head>
	<title>Title</title>
	<body>
	<center>
	<h2>Welcome to my new website</h2>
	<iframe id = "new" src="http://www.cnn.com" style="opacity:0.0;position:absolute;top:195px;left:10px;width:1000px;height:200px">
	</iframe>
	</center>
	</body>
	# load the saved checkpoint
	model, optimizer, start_epoch, valid_loss_min = load_ckp(ckp_path, model, optimizer)
	checkpoint = {
	'epoch': epoch + 1,
	'valid_loss_min': valid_loss,
	'state_dict': model.state_dict(),
	'optimizer': optimizer.state_dict(),
	}
	print("model = ", model)
	print("optimizer = ", optimizer)
	print("start_epoch = ", start_epoch)
	print("valid_loss_min = ", valid_loss_min)
	print("valid_loss_min = {:.6f}".format(valid_loss_min))
	# define optimzer
	optimizer = optim.Adam(model.parameters(), lr=0.001)

	# define checkpoint saved path
	ckp_path = "./checkpoint/current_checkpoint.pt"
	model = FashionClassifier()

	# move model to GPU if CUDA is available
	if use_cuda:
	model = model.cuda()

	print(model)