def evaluate(self, split="test"):
"""
Get the loss and accuracy on the test/train dataset
"""
self.model.eval()
num_examples = 0
num_correct = 0
loss = 0
for _, batch in enumerate(
self.test_loader if split is "test" else self.train_loader):
if self.cuda:
input_data, target_data = Variable(batch[0]).cuda(), Variable(
batch[1]).cuda()
else:
input_data, target_data = Variable(batch[0]), Variable(
batch[1])
output_data = self.model(input_data)
num_examples += input_data.shape[0]
loss += float(
compute_loss(self.model,
output_data,
target_data,
is_normalize=False))
predicted_labels = predict_labels(output_data)
num_correct += torch.sum(
predicted_labels == target_data).cpu().item()
self.model.train()
return loss / float(num_examples), float(num_correct) / float(
num_examples)
def get_accuracy(self, split='test'):
'''
Get the accuracy on the test/train dataset
'''
self.model.eval()
num_examples = 0
num_correct = 0
for batch_idx, batch in enumerate(
self.test_loader if split is 'test' else self.train_loader):
if self.cuda:
input_data, target_data = Variable(batch[0]).cuda(), Variable(
batch[1]).cuda()
else:
input_data, target_data = Variable(batch[0]), Variable(
batch[1])
num_examples += input_data.shape[0]
predicted_labels = predict_labels(self.model, input_data)
num_correct += torch.sum(
predicted_labels == target_data).cpu().item()
self.model.train()
return float(num_correct) / float(num_examples)
def test_predict_labels():
'''
Test the label prediction logic on a dummy net
'''
test_net = TestModel()
x = torch.FloatTensor([+1.4, -1.4, -0.7, 2.3, 0.3]).reshape(1, -1)
labels = predict_labels(test_net, x)
assert labels.item() == 4