pytorch_example.pytorch_cnn()

Complexity

Conditions

7

Size

Total Lines	57
Code Lines	38

Duplication

Lines	0
Ratio	0 %

Importance

Changes

0

import os
def pytorch_cnn(params):
    linear0 = params["linear.0"]
    linear1 = params["linear.1"]

    layers = []

    in_features = 28 * 28

    layers.append(nn.Linear(in_features, linear0))
    layers.append(nn.ReLU())
    layers.append(nn.Dropout(0.2))

    layers.append(nn.Linear(linear0, linear1))
    layers.append(nn.ReLU())
    layers.append(nn.Dropout(0.2))

    layers.append(nn.Linear(linear1, CLASSES))
    layers.append(nn.LogSoftmax(dim=1))

    model = nn.Sequential(*layers)

    # model = create_model(params).to(DEVICE)
    optimizer = getattr(optim, "Adam")(model.parameters(), lr=0.01)

    # Training of the model.
    for epoch in range(EPOCHS):
        model.train()
        for batch_idx, (data, target) in enumerate(train_loader):
            # Limiting training data for faster epochs.
            if batch_idx * BATCHSIZE >= N_TRAIN_EXAMPLES:
                break

            data, target = data.view(data.size(0), -1).to(DEVICE), target.to(DEVICE)

            optimizer.zero_grad()
            output = model(data)
            loss = F.nll_loss(output, target)
            loss.backward()
            optimizer.step()

        # Validation of the model.
        model.eval()
        correct = 0
        with torch.no_grad():
            for batch_idx, (data, target) in enumerate(valid_loader):
                # Limiting validation data.
                if batch_idx * BATCHSIZE >= N_VALID_EXAMPLES:
                    break
                data, target = data.view(data.size(0), -1).to(DEVICE), target.to(DEVICE)
                output = model(data)
                # Get the index of the max log-probability.
                pred = output.argmax(dim=1, keepdim=True)
                correct += pred.eq(target.view_as(pred)).sum().item()

        accuracy = correct / min(len(valid_loader.dataset), N_VALID_EXAMPLES)

    return accuracy

The variable accuracy does not seem to be defined in case the for loop on line 66 is not entered. Are you sure this can never be the case?