I’m trying to make a model predict the race of a 75x75 image’s ethnicity, but when ever I train the model, the accuracy always stays completely still at 42.5%. I didn’t realize why until I actually ran it on some of photos. It turned out, that no matter what the photo was, it would always predict ‘white’. I’m not entirely sure why though.
I copied the code over from the Quickstart tutorial, and in that dataset or the standard MNIST data, it worked fine. I changed the dataset to the UTKFace, and then it started only predicting one label, all the time.
Here’s my code:
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder
from torchvision.transforms import ToTensor
import torch.nn.functional as F

training_data = ImageFolder(
    root = "data_training/",
    transform = ToTensor(),
)

testing_data = ImageFolder(
    root = "data_testing/",
    transform = ToTensor()
)

training_dataloader = DataLoader(training_data, batch_size=64, shuffle=True)
test_dataloader = DataLoader(testing_data, batch_size=64, shuffle=True)

class NeuralNetwork(nn.Module):
    def __init__(self):
        super(NeuralNetwork, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        
        self.fc1 = nn.Linear(1296, 1024)
        self.fc2 = nn.Linear(1024, 1024)
        self.fc3 = nn.Linear(1024, 512)
        self.fc4 = nn.Linear(512, 84)
        self.fc5 = nn.Linear(84, 5)
        
    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        
        
        x = x.view(x.size(0), -1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = F.relu(self.fc3(x))
        x = F.relu(self.fc4(x))
        
        x = self.fc5(x)
        return x
    
model = NeuralNetwork().to("cpu")
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)

def train(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)
    for batch, (X, y) in enumerate(dataloader):
        X, y = X.to("cpu"), y.to("cpu")
        # Compute prediction error
        pred = model(X)
        loss = loss_fn(pred, y)

        # Backpropagation
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if batch % 100 == 0:
            loss, current = loss.item(), batch * len(X)
            print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")

def tests(dataloader, model):
    size = len(dataloader.dataset)
    model.eval()
    test_loss, correct = 0, 0
    with torch.no_grad():
        for X, y in dataloader:
            X, y = X.to("cpu"), y.to("cpu")
            pred = model(X)
            test_loss += loss_fn(pred, y).item()
            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
    test_loss /= size
    correct /= size
    print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")

epochs = 10
for t in range(epochs):
    print(f"Epoch {t+1}\n-------------------------------")
    train(training_dataloader, model, loss_fn, optimizer)
    tests(test_dataloader, model)

torch.save(model.state_dict(), "model.pth")

No matter how many epochs I set it to, or how many layers I add in to try to get it to overfit, it always just seems to guess the same thing over and over again, with no signs of improvement.
I separated the UTKFace dataset into folders based on the ethnicity category of the name. There are 23705 images in the training data and 10134 in the testing.