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

all_predictions = np.apply_along_axis(
func1d=lambda x: bs + ws * x,
axis=1,
arr=x_train,
)
all_predictions.shape
Output
(80, 101, 101)
Cool! We got 80 matrices of shape (101, 101), one matrix for each data point, each
matrix containing a grid of predictions.
The errors are the difference between the predictions and the labels, but we
cannot perform this operation right away—we need to work a bit on our labels (y),
so they have the proper shape for it (broadcasting is good, but not that good):
all_labels = y_train.reshape(-1, 1, 1)
all_labels.shape
Output
(80, 1, 1)
Our labels turned out to be 80 matrices of shape (1, 1)—the most boring kind of
matrix—but that is enough for broadcasting to work its magic. We can compute the
errors now:
all_errors = (all_predictions - all_labels)
all_errors.shape
Output
(80, 101, 101)
Each prediction has its own error, so we get 80 matrices of shape (101, 101), again,
34 | Chapter 0: Visualizing Gradient Descent