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

model = nn.Sequential()
model.add_module('hidden', nn.Linear(2, 10))
model.add_module('activation', nn.ReLU())
model.add_module('output', nn.Linear(10, 1))
model.add_module('sigmoid', nn.Sigmoid())
loss_fn = nn.BCELoss()
The model above increases dimensionality from two dimensions (two
features) to ten dimensions and then uses those ten dimensions to compute
logits. But it only works if there is an activation function between the
layers.
I suppose you may have two questions right now: "Why is that?" and "What
actually is an activation function?" Fair enough. But these are topics for the
next chapter.
Classification Threshold
This section is optional. In it, I will dive deeper into using different
thresholds for classification and how this affects the confusion
matrix. I will explain the most common classification metrics: true
and false positive rates, precision and recall, and accuracy.
Finally, I will show you how these metrics can be combined to
build ROC and Precision-Recall curves.
If you are already comfortable with these concepts, feel free to
skip this section.
So far, we’ve been using the trivial threshold of 50% to classify our data points,
given the probabilities predicted by our model. Let’s dive a bit deeper into this and
see the effects of choosing different thresholds. We’ll be working on the data
points in the validation set. There are only 20 data points in it, so we can easily
keep track of all of them.
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