Hello,
I was going through the lesson 6 code but unable to understand the SGD code for classification.I am confused about how y_hat is calculated.
Can anyone explain what following three lines of code do?
p = (-lin(a,b,x)).exp()
y_hat = 1/(1+p)
loss = nll(y_hat,y)
Complete cod is show in the picture.
We’re doing logistic regression here: that is, we’re calculating
yhat = sigmoid(lin(a, b, x))
You can write the sigmoid function as
def sigmoid(x):
return 1/(1 + (-x).exp())
so that’s what’s happening in yhat = 1/(1+p).
The loss is then the “negative log likelihood”, aka the cross entropy.
Thank you 