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

Data Preparation
1 torch.manual_seed(13)
2
3 # Builds tensors from Numpy arrays
4 x_train_tensor = torch.as_tensor(X_train).float()
5 y_train_tensor = torch.as_tensor(y_train.reshape(-1, 1)).float()
6
7 x_val_tensor = torch.as_tensor(X_val).float()
8 y_val_tensor = torch.as_tensor(y_val.reshape(-1, 1)).float()
9
10 # Builds dataset containing ALL data points
11 train_dataset = TensorDataset(x_train_tensor, y_train_tensor)
12 val_dataset = TensorDataset(x_val_tensor, y_val_tensor)
13
14 # Builds a loader of each set
15 train_loader = DataLoader(
16 dataset=train_dataset,
17 batch_size=16,
18 shuffle=True
19 )
20 val_loader = DataLoader(dataset=val_dataset, batch_size=16)
Model Configuration
1 # Sets learning rate - this is "eta" ~ the "n"-like Greek letter
2 lr = 0.1
3
4 torch.manual_seed(42)
5 model = nn.Sequential()
6 model.add_module('linear', nn.Linear(2, 1))
7
8 # Defines an SGD optimizer to update the parameters
9 optimizer = optim.SGD(model.parameters(), lr=lr)
10
11 # Defines a BCE loss function
12 loss_fn = nn.BCEWithLogitsLoss()
260 | Chapter 3: A Simple Classification Problem