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)
There are 80 data points (N = 80) in our training set. We have two features, x 1 and
x 2 , and the labels (y) are either zero (red) or one (blue). We have a dataset; now we
need a…
Model
Given a classification problem, one of the more straightforward models is the
logistic regression. But, instead of simply presenting it and using it right away, I am
going to build up to it. The rationale behind this approach is twofold: First, it will
make clear why this algorithm is called logistic regression if it is used for
classification; second, you’ll get a clear understanding of what a logit is.
Well, since it is called logistic regression, I would say that linear regression is a
good starting point. What would a linear regression model with two features look
like?
210 | Chapter 3: A Simple Classification Problem