Now our baseline is 94.50% on the training set, 94.63% on validation, and 94.41% on the test. A second improvement is very simple. We decide to randomly drop with the dropout probability some of the values propagated inside our internal dense network of hidden layers. In machine learning, this is a well-known form of regularization. Surprisingly enough, this idea of randomly dropping a few values can improve our performance:
from __future__ import print_functionimport numpy as npfrom keras.datasets import mnistfrom keras.models import Sequentialfrom keras.layers.core import Dense, Dropout, Activationfrom keras.optimizers import SGDfrom keras.utils import np_utilsnp.random.seed(1671) ...
