Image Transformation and Batch

```
transform = transforms.Compose([
transforms.Resize((100,100)),
transforms.ToTensor(),
transforms.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225])
])
data_set = datasets.ImageFolder(root="/content/drive/My Drive/models/pokemon/dataset",transform=transform)
train_loader = DataLoader(data_set,batch_size=10,shuffle=True,num_workers=6)
```

Below is my Model

```
class pokimonClassifier(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3,6,3,1)
self.conv2 = nn.Conv2d(6,18,3,1)
self.fc1 = nn.Linear(23*23*18,520)
self.fc2 = nn.Linear(520,400)
self.fc3 = nn.Linear(400,320)
self.fc4 = nn.Linear(320,149)
def forward(self,x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x,2,2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x,2,2)
x = x.view(-1,23*23*18)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
x = F.log_softmax(self.fc4(x), dim=1)
return x
```

Creating Instance of model, Use GPU, Set Criterion and optimizer

Here is firsr set `lr = 0.001`

then later changed to `0.0001`

```
model = pokimonClassifier()
model.to('cuda')
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(),lr = 0.0001)
```

Training Dataset

```
for e in range(epochs):
train_crt = 0
for b,(train_x,train_y) in enumerate(train_loader):
b+=1
train_x, train_y = train_x.to('cuda'), train_y.to('cuda')
# train model
y_preds = model(train_x)
loss = criterion(y_preds,train_y)
# analysis model
predicted = torch.max(y_preds,1)[1]
correct = (predicted == train_y).sum()
train_crt += correct
# print loss and accuracy
if b%50 == 0:
print(f'Epoch {e} batch{b} loss:{loss.item()} ')
# updating weights and bais
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss.append(loss)
train_correct.append(train_crt)
```

My loss value remains between 4 - 3 and its not converging to 0.

I am super new to deep learning and I donâ€™t know much about it.

The dataset I am using is here: https://www.kaggle.com/thedagger/pokemon-generation-one

A help will be much appreciated.

Thank You