Classification problems, such as logistic regression or multinomial
logistic regression, optimize a cross-entropy loss.
Normally, the cross-entropy layer follows the softmax layer,
which produces probability distribution.
From the Udacity’s deep learning class, the softmax of y_i is simply the exponential divided by the sum of exponential of the whole Y vector:
I attempt to solve this problem 6 in this notebook. The question is to train a simple model on this data using 50, 100, 1000 and 5000 training samples by using the LogisticRegression model from sklearn.linear_model.