Loss not Converging for CNN Model
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 don'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

The problem with your network is that you are applying softmax() twice - once at fc4() layer and once more while using nn.CrossEntropyLoss(). 

According to the official documentation, Pytorch takes care of softmax() while applying nn.CrossEntropyLoss().

So in your code, please change this line

x = F.log_softmax(self.fc4(x), dim=1)


to 

x = self.fc4(x)

