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

Model Configuration
1 torch.manual_seed(21)
2 model = nn.Sequential()
3 model.add_module('conv1d', nn.Conv1d(in_channels=2,
4 out_channels=1,
5 kernel_size=2))
6 model.add_module('relu', nn.ReLU())
7 model.add_module('flatten', nn.Flatten())
8 model.add_module('output', nn.Linear(3, 1))
9
10 loss = nn.BCEWithLogitsLoss()
11 optimizer = optim.Adam(model.parameters(), lr=0.01)
Model Training
1 sbs_conv1 = StepByStep(model, loss, optimizer)
2 sbs_conv1.set_loaders(train_loader, test_loader)
3 sbs_conv1.train(100)
fig = sbs_conv1.plot_losses()
Figure 8.33 - Losses—the edge model
StepByStep.loader_apply(test_loader, sbs_conv1.correct)
1D Convolutions | 677