Hello,
I am training the model written below, but the Cross Entropy Loss is not decreasing (it oscillates close to the initial value), even increasing the learning rate.
I have already searched for related topics in the forum, but no one is solving my problem.
The model seems pretty straightforward and I cannot detect any mistakes by myself.
If someone can spot something unusual, it would be very helpful.
class Classifier(nn.Module):
    def __init__(self):
        super(Classifier, self).__init__()
        #1st
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, 3, padding=1),
            nn.Conv2d(64, 64, 3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Dropout(p=0.25)
        )
        #2nd
        self.conv2 = nn.Sequential(
            nn.Conv2d(64, 64, 3, padding=1),
            nn.Conv2d(64, 64, 3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Dropout(p=0.25)
        )
        #3rd
        self.conv3 = nn.Sequential(
            nn.Conv2d(64, 64, 3, padding=1),
            nn.Conv2d(64, 64, 3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Dropout(p=0.25)
        )
        #4th
        self.fc1 = nn.Sequential(
            nn.Linear(64*8*8, 128),
            nn.Dropout(p=0.5)
        )
        #5th
        self.fc2 = nn.Sequential(
            nn.Linear(128, 128),
            nn.Dropout(p=0.5)
        )
        #6th
        self.fc3 = nn.Sequential(
            nn.Linear(128, 22),
        )
    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x= x.view(x.size(0), -1)
        x = self.fc1(x)
        x = self.fc2(x)
        x = self.fc3(x)
        return x