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

true_b = 1
true_w = 2
N = 100
# Data Generation
np.random.seed(42)
# We divide w by 10
bad_w = true_w / 10
# And multiply x by 10
bad_x = np.random.rand(N, 1) * 10
# So, the net effect on y is zero - it is still
# the same as before
y = true_b + bad_w * bad_x + (.1 * np.random.randn(N, 1))
Then, I performed the same split as before for both original and bad datasets and
plotted the training sets side-by-side, as you can see below:
# Generates train and validation sets
# It uses the same train_idx and val_idx as before,
# but it applies to bad_x
bad_x_train, y_train = bad_x[train_idx], y[train_idx]
bad_x_val, y_val = bad_x[val_idx], y[val_idx]
Figure 0.13 - Same data, different scales for feature x
Step 4 - Update the Parameters | 49