Hands-On Transfer Learning with Python

Building datasets from base features

The first step is to load our base features and create our train, validation and test datasets. For this, we will need to load our base features and labels from disk:

features = joblib.load('base_features.pkl') 
labels = joblib.load('dataset_labels.pkl') 
data = np.array(list(zip(features, labels))) 
features.shape, labels.shape ((30500, 64, 64, 3), (30500,)) 

We will now randomly shuffle our data and create our train, validation, and test datasets:

np.random.shuffle(data) 
train, validate, test = np.split(data, [int(.6*len(data)),int(.8*len(data))]) 
train.shape, validate.shape, test.shape ((18300, 2), (6100, 2), (6100, 2)) 

Finally, we can also check the per class distribution in each of these datasets using ...