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#Initializing hyperparameters
num_epochs = 8
num_classes = 10
batch_size = 100
learning_rate = 0.001

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#Loading dataset
transform = transforms.Compose([transforms.ToTensor(),
                                transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))])

train_dataset = datasets.FashionMNIST(root='./data', 
                            train=True, 
                            download=True,
                            transform=transform)

test_dataset = datasets.FashionMNIST(root='./data', 

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#Loading dataset into dataloader
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)

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#Defining the network          
class CNNModel(nn.Module):

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    def __init__(self):
        super(CNNModel, self).__init__()
        
        #Convolution 1
        self.cnn1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2)
        self.relu1 = nn.ReLU()
        
        #Max pool 1
        self.maxpool1 = nn.MaxPool2d(kernel_size=2)
        

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    def forward(self, x):
        #Convolution 1
        out = self.cnn1(x)
        out = self.relu1(out)
        
        #Max pool 1
        out = self.maxpool1(out)
        
        #Convolution 2
        out = self.cnn2(out)

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#Create instance of model
model = CNNModel()

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#Create instance of loss
criterion = nn.CrossEntropyLoss()

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#Create instance of optimizer (Adam)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

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#Train the model
iter = 0
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = Variable(images)
        labels = Variable(labels)
        
        #Clear the gradients
        optimizer.zero_grad()