Hello @ptrblck
I got following error when i plot train and validation acc. Could yu please help me to solve this error?
Thank you
#save the losses for further visualization
losses = {‘train’:, ‘validation’:}
accuracies = {‘train’:, ‘validation’:}
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
since = time.time()
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
for epoch in range(num_epochs):
print(f'Epoch {epoch}/{num_epochs - 1}')
print('-' * 10)
# Each epoch has a training and validation phase
for phase in ['train', 'validation']:
if phase == 'train':
model.train() # Set model to training mode
else:
model.eval() # Set model to evaluate mode
running_loss = 0.0
running_corrects = 0.0
# Iterate over data.
for inputs, labels in dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
# statistics
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
if phase == 'train':
scheduler.step()
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects.double() / dataset_sizes[phase]
losses[phase].append(epoch_loss)
accuracies[phase].append(epoch_acc)
print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')
# deep copy the model
if phase == 'validation' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
print()
time_elapsed = time.time() - since
print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')
print(f'Best val Acc: {best_acc:4f}')
# load best model weights
model.load_state_dict(best_model_wts)
return model
num_epochs=12
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
t = f.suptitle(‘Performance’, fontsize=12)
f.subplots_adjust(top=0.85, wspace=0.3)
epoch_list = list(range(1,num_epochs*2+1))
ax1.plot(epoch_list, accuracies[‘train’], label=‘Train Accuracy’)
ax1.plot(epoch_list, accuracies[‘validation’], label=‘Validation Accuracy’)
ax1.set_xticks(np.arange(0, num_epochs*2+1, 5))
ax1.set_ylabel(‘Accuracy Value’)
ax1.set_xlabel(‘Epoch’)
ax1.set_title(‘Accuracy’)
l1 = ax1.legend(loc=“best”)
ax2.plot(epoch_list, losses[‘train’], label=‘Train Loss’)
ax2.plot(epoch_list, losses[‘validation’], label=‘Validation Loss’)
ax2.set_xticks(np.arange(0, epochs*2+1, 5))
ax2.set_ylabel(‘Loss Value’)
ax2.set_xlabel(‘Epoch’)
ax2.set_title(‘Loss’)
l2 = ax2.legend(loc=“best”)