
train_x, train_y
test_x, test_y

x = dataload(train_x, bs)
y = dataload(train_y, bs)
#train every bs data

for epoch in epochs:
model.train()
model(x)
backward
optimize
print

#evaluation:
x = dataload(test_x, bs)
y = dataload(test_y, bs)

for epoch in epochs:
model.eval()
model(x)
print

class model(nn.Torch):
	
	def __init__():


	def forward():
		w1 = 
		b = 
		w2 = 


pred = model(x)





//
import gzip

with gzip.open((PATH / FILENAME).as_posix(), "rb") as f:
        ((x_train, y_train), (x_valid, y_valid), _) = pickle.load(f, encoding="latin-1")

x_train, y_train, x_valid, y_valid = map(torch.tensor, (x_train, y_train, x_valid, y_valid))



///
train_ds = TensorDataset(x_train, y_train)
train_dl = DataLoader(train_ds, batch_size=bs)

///

transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(dataset = trainset, batch_size=4, shuffle=True, num_workers=2)  # what's num_workers?
testloader = torch.utils.data.DataLoader(dataset = testset, batch_size=4, shuffle=False, num_workers=2)	  # 

///
	
train_dataset = torchivision.datasets.MNIST(root = "../data/MNIST", train = True, download =True, transform = transforms.ToTensor())
test_dataset = torchivision.datasets.MNIST(root = "../data/MNIST", train = False, transform = transforms.ToTensor())
train_loader = torch.utils.data.DataLoader(dataset = train_dataset, batch_size = BATCH_SIZE, shuffle = True)
test_loader = torch.utils.data.DataLoader(dataset = test_dataset, batch_size = BATCH_SIZE, shuffle = False)

///



output = model(image)
test_loss += criterion(output, label).item()
prediction = output.max(1, keepdim = True)[1






for xb,yb in train_dl:
    pred = model(xb)



for epoch in range(2):
    for i, (image, labels) in enumerate(trainloader):
        optimizer.zero_grad()
        outputs = model(image)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()




model.train()
for Epoch in range(1, EPOCHS+1):
	for batch_idx, (image, label) in enumerate(train_loader):
		image = image.to(DEVICE)
		label = label.to(DEVICE)
		optimizer.zero_grad()
		output = model(image)
		loss = criterion(output, label)
		loss.backward()
		optimizer.step()





x_train, x_train.shape, y_train.min(), y_train.max()
print(x_train, y_train)
print(x_train.shape)
print(y_train.min(), y_train.max())