如何解决如何使用 Tensorboard 绘制损失和准确度
我有三个数据集(训练、测试和验证)。我结合训练数据集和测试数据集来做 k 折交叉验证。我没有使用验证数据集。我是上一个问题的张量板的新手,我能够在每个时期的训练期间进行绘图损失准确性。我如何绘制损失和准确性的图,以便在每个时期进行测试。因为我想看看每个时期的表现。我应该为 set 使用验证集吗?如果是,如何使用?
# Prepare dataset by concatenating Train/Test part; we split later.
training_set = CustomDataset('one_hot_train_data.txt','train_3states_target.txt') #training_set = CustomDataset_3('one_hot_train_data.txt','train_5_target.txt')
training_generator = torch.utils.data.DataLoader(training_set,**params)
val_set = CustomDataset('one_hot_val_data.txt','val_3states_target.txt')
test_set = CustomDataset('one_hot_test_data.txt','test_3states_target.txt')
testloader_ = torch.utils.data.DataLoader(test_set,**params)
dataset = ConcatDataset([training_set,test_set])
kfold = KFold(n_splits=k_folds,shuffle=True)
# Start print
print('--------------------------------')
# K-fold Cross Validation model evaluation
for fold,(train_ids,test_ids) in enumerate(kfold.split(dataset)):
# Print
print(f'FOLD {fold}')
print('--------------------------------')
# Sample elements randomly from a given list of ids,no replacement.
train_subsampler = torch.utils.data.SubsetRandomSampler(train_ids)
test_subsampler = torch.utils.data.SubsetRandomSampler(test_ids)
# Define data loaders for training and testing data in this fold
trainloader = torch.utils.data.DataLoader(
dataset,**params,sampler=train_subsampler)
testloader = torch.utils.data.DataLoader(
dataset,sampler=test_subsampler)
# Init the neural network
model = PPS()
model.to(device)
# Initialize optimizer
optimizer = optim.SGD(model.parameters(),lr=LEARNING_RATE)
# Run the training loop for defined number of epochs
for epoch in range(0,N_EPOCHES):
# Print epoch
print(f'Starting epoch {epoch + 1}')
# Set current loss value
running_loss = 0.0
epoch_loss = 0.0
a = []
# Iterate over the DataLoader for training data
for i,data in enumerate(trainloader,0):
inputs,targets = data
inputs = inputs.unsqueeze(-1)
#inputs = inputs.to(device)
targets = targets.to(device)
inputs = inputs.to(device)
# print(inputs.shape,targets.shape)
# Zero the gradients
optimizer.zero_grad()
# Perform forward pass
loss,outputs = model(inputs,targets)
outputs = outputs.to(device)
# Perform backward pass
loss.backward()
# Perform optimization
optimizer.step()
# print statistics
running_loss += loss.item()
epoch_loss += loss
a.append(torch.sum(outputs == targets))
# print(outputs.shape,outputs.shape[0])
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d,%5d] loss: %.3f' %
(epoch + 1,i + 1,running_loss / 2000),"acc",torch.sum(outputs == targets) / float(outputs.shape[0]))
running_loss = 0.0
# sum_acc += (outputs == stat_batch.argmax(1)).float().sum()
print("epoch",epoch + 1,sum(a) / len(train_subsampler),"loss",epoch_loss / len(trainloader))
accuracy = 100 * sum(a) / len(training_set)
avg_loss = sum(a) / len(training_set)
writer.add_scalar('train/loss',avg_loss.item(),epoch)
writer.add_scalar('accuracy/loss',accuracy,epoch)
state = {'epoch': epoch + 1,'state_dict': model.state_dict(),'optimizer': optimizer.state_dict() }
torch.save(state,path + name_file + "model_epoch_i_" + str(epoch) + str(fold)+".cnn")
#torch.save(model.state_dict(),path + name_file + "model_epoch_i_" + str(epoch) + ".cnn")
# Print about testing
print('Starting testing')
# Evaluation for this fold
correct,total = 0,0
with torch.no_grad():
# Iterate over the test data and generate predictions
for i,data in enumerate(testloader,0):
# Get inputs
inputs,targets = data
#targets = targets.to(device)
inputs = inputs.unsqueeze(-1)
inputs = inputs.to(device)
# Generate outputs
loss,targets)
outputs.to(device)
print("out",outputs.shape)
print("target",targets.shape)
print("targetsize",targets.size(0))
print("sum",(outputs == targets).sum().item())
#print("sum",torch.sum(outputs == targets))
# Set total and correct
# _,predicted = torch.max(outputs.data,1)
total += targets.size(0)
correct += (outputs == targets).sum().item()
#correct += torch.sum(outputs == targets)
# Print accuracy
print('Accuracy for fold %d: %d %%' % (fold,float( 100.0 * float(correct / total))))
print('--------------------------------')
results[fold] = 100.0 * float(correct / total)
# Print fold results
print(f'K-FOLD CROSS VALIDATION RESULTS FOR {k_folds} FOLDS')
print('--------------------------------')
sum = 0.0
for key,value in results.items():
print(f'Fold {key}: {value} %')
sum += value
print(f'Average: {float(sum / len(results.items()))} %')
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