Does this make any sense at all given the size of my images 224x224 and 4 number of classes?
for param in model.parameters():
    param.requires_grad = True
    
model.classifier = nn.Sequential(nn.Linear(25088, 64),
                                 nn.ReLU(),
                                 #nn.Dropout(0.2),
                                 nn.Linear(64, 4),
                                 nn.LogSoftmax(dim=1))
criterion = nn.NLLLoss()
optimizer = optim.Adam(model.classifier.parameters(), lr=0.0001)
model.to(device)

I am trying to replicate last layer of this ResNet50:
for param in model.parameters():
    param.requires_grad = False
    
model.fc = nn.Sequential(nn.Linear(2048, 512),
                                 nn.ReLU(),
                                 nn.Linear(512, 4),
                                 nn.LogSoftmax(dim=1))
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.fc.parameters(), lr=0.0001)
model.to(device)

I am kind of lost here…