[英]How to compute the uncertainty of a Monte Carlo Dropout neural network with PyTorch?
I am trying to implement Bayesian CNN using Mc Dropout on Pytorch, the main idea is that by applying dropout at test time and running over many forward passes, you get predictions from a variety of different models.我正在尝试在 Pytorch 上使用 Mc Dropout 实现贝叶斯 CNN,主要思想是通过在测试时应用 dropout 并运行多次前向传递,您可以获得来自各种不同模型的预测。 I need to obtain the uncertainty, does anyone have an idea of how I can do it Please
我需要获得不确定性,有没有人知道我该怎么做,请
This is how I defined my CNN '''这就是我如何定义我的 CNN '''
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
self.dropout = nn.Dropout(p=0.3)
nn.init.xavier_uniform_(self.conv1.weight)
nn.init.constant_(self.conv1.bias, 0.0)
nn.init.xavier_uniform_(self.conv2.weight)
nn.init.constant_(self.conv2.bias, 0.0)
nn.init.xavier_uniform_(self.fc1.weight)
nn.init.constant_(self.fc1.bias, 0.0)
nn.init.xavier_uniform_(self.fc2.weight)
nn.init.constant_(self.fc2.bias, 0.0)
nn.init.xavier_uniform_(self.fc3.weight)
nn.init.constant_(self.fc3.bias, 0.0)
def forward(self, x):
x = self.pool(F.relu(self.dropout(self.conv1(x)))) # recommended to add the relu
x = self.pool(F.relu(self.dropout(self.conv2(x)))) # recommended to add the relu
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(self.dropout(x)))
x = self.fc3(self.dropout(x)) # no activation function needed for the last layer
return x
model = Net().to(device)
train_accuracies=np.zeros(num_epochs)
test_accuracies=np.zeros(num_epochs)
dataiter = iter(trainloader)
images, labels = dataiter.next()
#initializing variables
loss_acc = []
class_acc_mcdo = []
start_train = True
#Defining the Loss Function and Optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
def train():
loss_vals = []
acc_vals = []
for epoch in range(num_epochs): # loop over the dataset multiple times
n_correct = 0 # initialize number of correct predictions
acc = 0 # initialize accuracy of each epoch
somme = 0 # initialize somme of losses of each epoch
epoch_loss = []
for i, (images, labels) in enumerate(trainloader):
# origin shape: [4, 3, 32, 32] = 4, 3, 1024
# input_layer: 3 input channels, 6 output channels, 5 kernel size
images = images.to(device)
labels = labels.to(device)
# Forward pass
outputs = model.train()(images)
loss = criterion(outputs, labels)
# Backward and optimize
optimizer.zero_grad() # zero the parameter gradients
loss.backward()
epoch_loss.append(loss.item()) # add the loss to epoch_loss list
optimizer.step()
# max returns (value ,index)
_, predicted = torch.max(outputs, 1)
n_correct += (predicted == labels).sum().item()
# print statistics
if (i + 1) % 2000 == 0:
print(f'Epoch [{epoch + 1}/{num_epochs}], Step [{i + 1}/{n_total_steps}], Loss:
{loss.item():.4f}')
somme = (sum(epoch_loss)) / len(epoch_loss)
loss_vals.append(somme) # add the epoch's loss to loss_vals
print("Loss = {}".format(somme))
acc = 100 * n_correct / len(trainset)
acc_vals.append(acc) # add the epoch's Accuracy to acc_vals
print("Accuracy = {}".format(acc))
# SAVE
PATH = './cnn.pth'
torch.save(model.state_dict(), PATH)
loss_acc.append(loss_vals)
loss_acc.append(acc_vals)
return loss_acc
And here is the code of the mc dropout这是 mc dropout 的代码
''' '''
def enable_dropout(model):
""" Function to enable the dropout layers during test-time """
for m in model.modules():
if m.__class__.__name__.startswith('Dropout'):
m.train()
def test():
# set non-dropout layers to eval mode
model.eval()
# set dropout layers to train mode
enable_dropout(model)
test_loss = 0
correct = 0
n_samples = 0
n_class_correct = [0 for i in range(10)]
n_class_samples = [0 for i in range(10)]
T = 100
for images, labels in testloader:
images = images.to(device)
labels = labels.to(device)
with torch.no_grad():
output_list = []
# getting outputs for T forward passes
for i in range(T):
output_list.append(torch.unsqueeze(model(images), 0))
# calculating mean
output_mean = torch.cat(output_list, 0).mean(0)
test_loss += F.nll_loss(F.log_softmax(output_mean, dim=1), labels,
reduction='sum').data # sum up batch loss
_, predicted = torch.max(output_mean, 1) # get the index of the max log-probability
correct += (predicted == labels).sum().item() # sum up correct predictions
n_samples += labels.size(0)
for i in range(batch_size):
label = labels[i]
predi = predicted[i]
if (label == predi):
n_class_correct[label] += 1
n_class_samples[label] += 1
test_loss /= len(testloader.dataset)
# PRINT TO HTML PAGE
print('\n Average loss: {:.4f}, Accuracy: ({:.3f}%)\n'.format(
test_loss,
100. * correct / n_samples))
# Accuracy for each class
acc_classes = []
for i in range(10):
acc = 100.0 * n_class_correct[i] / n_class_samples[i]
print(f'Accuracy of {classes[i]}: {acc} %')
acc_classes.append(acc)
class_acc_mcdo.extend(acc_classes)
print('Finished Testing')
You can compute the statistics, such as the sample mean or the sample variance, of different stochastic forward passes at test time (ie with the test or validation data), when the dropout is enabled.当启用 dropout 时,您可以在测试时(即使用测试或验证数据)计算不同随机前向传递的统计数据,例如样本均值或样本方差。 These statistics can be used to represent uncertainty.
这些统计数据可用于表示不确定性。 For example, you can compute the entropy, which is a measure of uncertainty, from the sample mean.
例如,您可以根据样本均值计算熵,它是不确定性的度量。
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