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

Output
array([[-2. , -1.94, -1.88, ..., 3.88, 3.94, 4. ],
[-2. , -1.94, -1.88, ..., 3.88, 3.94, 4. ],
[-2. , -1.94, -1.88, ..., 3.88, 3.94, 4. ],
...,
[-2. , -1.94, -1.88, ..., 3.88, 3.94, 4. ],
[-2. , -1.94, -1.88, ..., 3.88, 3.94, 4. ],
[-2. , -1.94, -1.88, ..., 3.88, 3.94, 4. ]])
Sure, we’re somewhat cheating here, since we know the true values of b and w, so
we can choose the perfect ranges for the parameters. But it is for educational
purposes only :-)
Next, we could use those values to compute the corresponding predictions, errors,
and losses. Let’s start by taking a single data point from the training set and
computing the predictions for every combination in our grid:
dummy_x = x_train[0]
dummy_yhat = bs + ws * dummy_x
dummy_yhat.shape
Output
(101, 101)
Thanks to its broadcasting capabilities, Numpy is able to understand we want to
multiply the same x value by every entry in the ws matrix. This operation resulted
in a grid of predictions for that single data point. Now we need to do this for every
one of our 80 data points in the training set.
We can use Numpy's apply_along_axis() to accomplish this:
Look ma, no loops! Step 2 - Compute the Loss | 33