A binary classification problem with Batch Size = 10. Trying to use torch.nn.BCEWithLogitsLoss().
~\Anaconda3\envs\notebook\lib\site-packages\torch\nn\functional.py in binary_cross_entropy_with_logits(input, target, weight, size_average, reduce, reduction, pos_weight)
2578
2579 if not (target.size() == input.size()):
-> 2580 raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size()))
2581
2582 return torch.binary_cross_entropy_with_logits(input, target, weight, pos_weight, reduction_enum)
ValueError: Target size (torch.Size([1, 10])) must be the same as input size (torch.Size([10, 2]))
Here is my training code:
def train(epochs):
print('Starting training..')
for e in range(0, epochs):
exp_lr_scheduler.step()
print('='*20)
print(f'Starting epoch {e + 1}/{epochs}')
print('='*20)
train_loss = 0.
val_loss = 0.
resnet18.train() # set model to training phase
for train_step, (images, labels) in enumerate(dl_train):
optimizer.zero_grad()
outputs = resnet18(images)
outputs = outputs.float()
loss = loss_fn(outputs, labels.unsqueeze(0))
loss.backward()
optimizer.step()
train_loss += loss.item()
if train_step % 20 == 0:
print('Evaluating at step', train_step)
accuracy = 0
resnet18.eval() # set model to eval phase
for val_step, (images, labels) in enumerate(dl_val):
outputs = resnet18(images)
outputs = outputs.float()
loss = loss_fn(outputs, labels.unsqueeze(0))
val_loss += loss.item()
_, preds = torch.max(outputs, 1)
accuracy += sum((preds == labels).numpy())
val_loss /= (val_step + 1)
accuracy = accuracy/len(val_dataset)
print(f'Validation Loss: {val_loss:.4f}, Accuracy: {accuracy:.4f}')
show_preds()
resnet18.train() #set model to training phase
if accuracy >= 0.95:
print('Performance condition satisfied, stopping..')
return
train_loss /= (train_step + 1)
print(f'Training Loss: {train_loss:.4f}')
print('Training complete..')**
train(epochs=30)
