I am using a dataset from kaggle to train a feed forward neural-neteork with no convolutional layers. I wanted to try it this was as a learning exercise with Pytorch without Transfer Learning and Convolutional Layers. Here is the code with its output.
Network Architecture
class Classifier(nn.Module):
def __init__(self):
super().__init__()
self.h0 = nn.Linear(99*99*3,1024)
self.h1 = nn.Linear(1024,512)
self.h2 = nn.Linear(512,256)
self.h3 = nn.Linear(256,128)
self.h4 = nn.Linear(128,1)
self.dropout = nn.Dropout(p=0.2)
def forward(self,x):
x = x.view(x.shape[0],-1)
x = torch.tanh(self.dropout(self.h0(x)))
x = torch.tanh(self.dropout(self.h1(x)))
x = torch.tanh(self.dropout(self.h2(x)))
x = torch.tanh(self.dropout(self.h3(x)))
x = torch.sigmoid(self.h4(x))
return x
Paramerters
model = Classifier()
model.to(device)
criterion = nn.BCELoss()
optimizer = optim.SGD(model.parameters(),lr=0.00000001,momentum=0.9)
images,labels = next(iter(trainloader))
images,labels = make_gpu(images,labels)
print("Labels:{} ".format(labels.shape),"Images:{} ".format(images.shape))
probs = model(images)
print("Probabilities:{}".format(probs.shape))
loss = criterion(probs,labels)
loss.backward()
optimizer.step()
print(loss.item())
print(len(trainloader))
print(len(testloader))
Training and Validation
training_losses, testing_losses, test_acc, train_acc = [],[],[],[]
epochs = 10
for e in range(epochs):
running_loss = 0
tr_acc = 0
for images,labels in trainloader:
optimizer.zero_grad()
images,labels = make_gpu(images,labels)
probs = model(images)
loss = criterion(probs,labels)
loss.backward()
optimizer.step()
running_loss+=loss.item()
probs = torch.round(probs)
equals = probs == labels.view(*probs.shape)
tr_acc += torch.mean(equals.type(torch.cuda.FloatTensor))
else:
testing_loss = 0
acc = 0
with torch.no_grad():
model.eval()
for images,labels in testloader:
images,labels = make_gpu(images,labels)
probs = model(images)
loss = criterion(probs,labels)
testing_loss+=loss
probs = torch.round(probs)
equals = probs == labels.view(*probs.shape)
acc += torch.mean(equals.type(torch.cuda.FloatTensor))
model.train()
training_losses.append(running_loss/len(trainloader))
testing_losses.append(testing_loss/len(testloader))
test_acc.append(acc/len(testloader))
train_acc.append(tr_acc/len(trainloader))
print("Epoch: {}/{}.. ".format(e+1, epochs),
"Training Loss: {:.3f}.. ".format(training_losses[-1]),
"Test Loss: {:.3f}.. ".format(testing_losses[-1]),
"Test Accuracy: {:.3f}..".format(test_acc[-1]),
"Train Accuracy: {:.3f}".format(train_acc[-1]))
Output
Epoch: 1/10.. Training Loss: 0.694.. Test Loss: 0.694.. Test Accuracy: 0.505.. Train Accuracy: 0.503
Epoch: 2/10.. Training Loss: 0.694.. Test Loss: 0.694.. Test Accuracy: 0.505.. Train Accuracy: 0.502
Epoch: 3/10.. Training Loss: 0.694.. Test Loss: 0.694.. Test Accuracy: 0.505.. Train Accuracy: 0.501
Epoch: 4/10.. Training Loss: 0.694.. Test Loss: 0.694.. Test Accuracy: 0.505.. Train Accuracy: 0.503
Epoch: 5/10.. Training Loss: 0.694.. Test Loss: 0.694.. Test Accuracy: 0.505.. Train Accuracy: 0.503
Epoch: 6/10.. Training Loss: 0.695.. Test Loss: 0.694.. Test Accuracy: 0.505.. Train Accuracy: 0.497
Epoch: 7/10.. Training Loss: 0.695.. Test Loss: 0.694.. Test Accuracy: 0.505.. Train Accuracy: 0.499
Epoch: 8/10.. Training Loss: 0.695.. Test Loss: 0.694.. Test Accuracy: 0.505.. Train Accuracy: 0.499
Epoch: 9/10.. Training Loss: 0.695.. Test Loss: 0.694.. Test Accuracy: 0.505.. Train Accuracy: 0.499
Epoch: 10/10.. Training Loss: 0.695.. Test Loss: 0.694.. Test Accuracy: 0.505.. Train Accuracy: 0.501
As you can see from the code the classifier performs worse the more it trains. Can someone please tell me why this is happening and how I can improve the model?
I have tried using SGD and Adam as the optimisers, both give a similar result. I have also tried learning rates 0.01 - 0.00000001 to no avail. Please help!
