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

The choice of which class is positive and which class is negative does
not affect model performance. If we reverse the mapping, making
red the positive class, the only difference would be that the model
would predict the probability of a point being red. But, since both
probabilities have to add up to one, we could easily convert
between them, so the models are equivalent.
Instead of defining a model first and then generating synthetic data for it, we’ll do it
the other way around.
Data Generation
Let’s make the data a bit more interesting by using two features (x 1 and x 2 ) this
time. We’ll use Scikit-Learn’s make_moons() to generate a toy dataset with 100
data points. We will also add some Gaussian noise and set a random seed to ensure
reproducibility.
Data Generation
1 X, y = make_moons(n_samples=100, noise=0.3, random_state=0)
Then, we’ll perform the train-validation split using Scikit-Learn’s
train_test_split() for convenience (we’ll get back to splitting indices later):
Train-validation Split
1 X_train, X_val, y_train, y_val = train_test_split(
2 X,
3 y,
4 test_size=.2,
5 random_state=13
6 )
Remember, the split should always be the first thing you do—no
pre-processing, no transformations, nothing happens before the
split.
208 | Chapter 3: A Simple Classification Problem