cat-vs-noncat-classification-logistic-regression

The model comprises of :

1. A training set of m_train images labeled as cat (y=1) or non-cat (y=0)
2. A test set of m_test images labeled as cat or non-cat
3. Each image is of shape (num_px, num_px, 3) where 3 is for the 3 channels (RGB). Thus, each image is square (height = num_px) and (width = num_px).
4. num_px = 64, m_train = 209 (Number of training examples) and m_test = 50 (Number of test examples).

Result :

Training accuracy is close to 100% and test accuracy is 74%.