Hello. I want to transfer learning with pytorch but my error rate is increasing and decreasing abnormally and my accuracy rate is not increasing. Am I doing something wrong? Yield is 19201080 but due to lack of memory there are 2828 and formula 1 car images
note=The ready models and weights I use are not suitable for my dataset. what do you think
import torchvision.models as models
    
model = models.resnet152(pretrained=True) # Önceden eğitilmiş modelleri kullanmak için pretrained=True 

for param in model.parameters(): # Tüm modeli eğitmek için True
    param.requires_grad = False # Parametreleri donduruyoruz

num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 100)
model.fc2=nn.ReLU()
model.fc3=nn.Linear(100,20)
model.fc4=nn.ReLU()
model.fc5=nn.Linear(20,10)
model.fc6=nn.ReLU()
model.fc7=nn.Linear(10,4)

error = nn.CrossEntropyLoss()
optimizer = optim.Adamax(model.parameters(), lr=0.0001)
num_epochs=20
count=0
losses = []
iterasyon=[]

for epoch in range(num_epochs):

    for i,(images,label) in enumerate (train_loader):
        
        out = model(images)
        loss = error(out, label)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        losses.append(loss.item())
        
        count+=1
        
        if count % 200 == 0:
            
            total=0
            correct=0
            correct_hata=0
            
            for image,labels in test_loader:
                
                out=model(images)
                
                pred=torch.max(out.data,1)[1]
                
                total+=len(label)
                
                correct+= (pred==labels).sum()
                correct_hata+=(pred!=labels).sum()
                
            dogruluk=100*correct /float(total)
            hata=100*correct_hata /float(total)
            
            losses.append(loss.data)
            iterasyon.append(count)
            
            
        if count % 200 == 0:
            print('Iteration: {}  Loss: {}  Accuracy: {}% Error: {}%'.format(count, loss.data, dogruluk,hata))
        ```
result:

Iteration: 200  Loss: 1.5698899030685425  Accuracy: 27.848100662231445% Error: 72.15190124511719%
Iteration: 400  Loss: 1.530423879623413  Accuracy: 26.582279205322266% Error: 73.417724609375%
Iteration: 600  Loss: 1.5104633569717407  Accuracy: 26.582279205322266% Error: 73.417724609375%
Iteration: 800  Loss: 1.3361421823501587  Accuracy: 27.848100662231445% Error: 72.15190124511719%
Iteration: 1000  Loss: 1.297659158706665  Accuracy: 20.253164291381836% Error: 79.74683380126953%
Iteration: 1200  Loss: 1.3579907417297363  Accuracy: 27.848100662231445% Error: 72.15190124511719%
Iteration: 1400  Loss: 1.1254953145980835  Accuracy: 25.316455841064453% Error: 74.68354797363281%
Iteration: 1600  Loss: 1.9074203968048096  Accuracy: 25.316455841064453% Error: 74.68354797363281%
Iteration: 1800  Loss: 1.3322985172271729  Accuracy: 27.848100662231445% Error: 72.15190124511719%
Iteration: 2000  Loss: 1.3254060745239258  Accuracy: 25.316455841064453% Error: 74.68354797363281%
Iteration: 2200  Loss: 1.1631817817687988  Accuracy: 20.253164291381836% Error: 79.74683380126953%
Iteration: 2400  Loss: 1.2752320766448975  Accuracy: 20.253164291381836% Error: 79.74683380126953%
Iteration: 2600  Loss: 1.3860232830047607  Accuracy: 27.848100662231445% Error: 72.15190124511719%
Iteration: 2800  Loss: 1.2704166173934937  Accuracy: 20.253164291381836% Error: 79.74683380126953%
Iteration: 3000  Loss: 1.081123948097229  Accuracy: 25.316455841064453% Error: 74.68354797363281%
2.accuracy :


Train Doğruluk:
Got 180 / 200 with accuracy 90.00
Test Doğruluk:
Got 43 / 79 with accuracy 54.43```