www.allitebooks.com

Chapter 11
Next, we define how the network will train. The nolearn package doesn't
have the exact same training mechanism as we used in Chapter 8, Beating CAPTCHAs
with Neural Networks, as it doesn't have a way to decay weights. However, it does
have momentum, which we will use, along with a high learning rate and low
momentum value:
update=updates.momentum,
update_learning_rate=0.9,
update_momentum=0.1,
Next, we define the problem as a regression problem. This may seem odd, as we
are performing a classification task. However, the outputs are real-valued, and
optimizing them as a regression problem appears to do much better in training
than trying to optimize on classification:
regression=True,
Finally, we set the maximum number of epochs for training at 1,000, which is a good
fit between good training and not taking a long time to train (for this dataset; other
datasets may require more or less training):
max_epochs=1000,
We can now close off the parenthesis for the neural network constructor;
)
Next, we train the network on our training dataset:
net1.fit(X_train, y_train)
Now we can evaluate the trained network. To do this, we get the output of our
network and, as with the Iris example, we need to perform an argmax to get the
actual classification by choosing the highest activation:
y_pred = net1.predict(X_test)
y_pred = y_pred.argmax(axis=1)
assert len(y_pred) == len(X_test)
if len(y_test.shape) > 1:
y_test = y_test.argmax(axis=1)
print(f1_score(y_test, y_pred))
The results are equally impressive—another perfect score on my machine. However,
your results may vary as the nolearn package has some randomness that can't be
directly controlled at this stage.
[ 257 ]