Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

of the output according to the formula below (d stands for dilation size):
Equation 8.16 - Resulting shape with dilation
If any of the resulting dimensions are not an integer, they must be rounded down.
Data Preparation
The data preparation step is very much like that of the previous models except for
the fact that we need to permute the dimensions to comply with 1D convolutions'
"sequence-last" (NFL) shape:
Data Preparation
1 train_data = TensorDataset(
2 torch.as_tensor(points).float().permute(0, 2, 1),
3 torch.as_tensor(directions).view(-1, 1).float()
4 )
5 test_data = TensorDataset(
6 torch.as_tensor(test_points).float().permute(0, 2, 1),
7 torch.as_tensor(test_directions).view(-1, 1).float()
8 )
9 train_loader = DataLoader(
10 train_data, batch_size=16, shuffle=True
11 )
12 test_loader = DataLoader(test_data, batch_size=16)
Model Configuration & Training
The model is quite simple: a single nn.Conv1d layer followed by an activation
function (ReLU), a flattening layer (which is only squeezing the channel dimension
out), and a linear layer to combine the outputs (three values for each sequence, as
shown in Figure 8.31) into logits for our binary classification.
676 | Chapter 8: Sequences