block1.py

#Calculate class weights so we can account for the class imbalance
from sklearn.utils import class_weight
y_ints = [y.argmax() for y in y_train]
class_weights = class_weight.compute_class_weight('balanced',
                                                 np.unique(y_train),
                                                 y_ints)
feed_class_weights = {0: class_weights[0],
                      1: class_weights[1]}