# How to use dropout at prediction time?

I’m looking to try and use dropout at prediction time to get a distribution of predictions.

I was inspired by this thread:

How I understand it is:

Run 100~ predictions with dropout applied to get 100~ different predictions, then by measuring the variance of the predictions you can get a measure of ‘uncertainty’ for each sample.

Some boilerplate code I’ve been experimenting with using MNIST:

``````class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5, padding=2)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5, padding=2)
self.dropout = nn.Dropout2d(p=0.3)
self.fc1 = nn.Linear(7*7*20, 1000)
self.fc2 = nn.Linear(1000, 10)

def forward(self, x):
# dropout at every activation layer
x = F.relu(F.max_pool2d(self.dropout(self.conv1(x)), 2))
x = F.relu(F.max_pool2d(self.dropout(self.conv2(x)), 2))
#print(x.shape)
x = x.view(-1, 7*7*20)
x = F.relu(self.fc1(self.dropout(x)))
x = F.relu(self.fc2(self.dropout(x)))
return F.log_softmax(x, dim=1)

model = Net().to(device)

...[Train model]...

# Create function to apply to model at eval() time
def apply_dropout(m):
if type(m) == nn.Dropout2d:
m.train()

# Predict the class of X using model
def predict_class(model, X):
model = model.eval()
model.apply(apply_dropout) # apply dropout at pred time (see func above)
outputs = model(Variable(X))
#print(outputs)
_, pred = torch.max(outputs.data, 1)
return pred.numpy()

# Predict T times to get a distribution of predictions
def predict(model, X, T=100):
list_of_preds = []
standard_pred=predict_class(model, X)
y1 = []
y2 = []
for _ in range(T):
_y1 = model(Variable(X))
_y2 = F.softmax(_y1, dim=1)
y1.append(_y1.data.numpy())
y2.append(_y2.data.numpy())
list_of_preds.append(predict_class(model, X)) # predict T times
return standard_pred, np.array(y1), np.array(y2), np.array(list_of_preds)
``````

By calling `.predict()` and passing it `model`, 100 predictions are made with `Dropout2d` applied.

What my empirical testing has shown is examples of data which are ‘bad’ (don’t look good compared to others) have a higher variance compared to ‘good’ examples.

Does anyone know how I would go about doing this with the fastai library?

Could I do it with a wrapper function on the `Learner` class?

I’m currently investigating this now but if anyone has a clue, that’d be amazing.

1 Like

Hi, were you able to make any progress with this? I am trying to implement this method now as well, and would love to see if you had any success.