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Chapter 11
Finally, we set the verbosity as equal to 1, which will give us a printout of the results
of each epoch. This allows us to know the progress of the model and also that it is
still running. Another feature is that it tells us the time it takes for each epoch to run.
This is pretty consistent, so you can compute the time left in training by multiplying
this value by the number of remaining epochs, giving a good estimate on how long
you need to wait for the training to complete:
verbose=1)
Putting it all together
Now that we have our network, we can train it with our training dataset:
nnet.fit(X_train, y_train)
This will take quite a while to run, even with the reduced dataset size and the
reduced number of epochs. Once the code completes, you can test it as we did before:
from sklearn.metrics import f1_score
y_pred = nnet.predict(X_test)
print(f1_score(y_test.argmax(axis=1), y_pred.argmax(axis=1)))
The results will be terrible—as they should be! We haven't trained the network very
much—only for a few iterations and only on one fifth of the data.
First, go back and remove the break line we put in when creating the dataset
(it is in the batches loop). This will allow the code to train on all of the samples,
not just some of them.
Next, change the number of epochs to 100 in the neural network definition.
Now, we upload the script to our virtual machine. As with before, click on
File | Download as, Python, and save the script somewhere on your computer.
Launch and connect to the virtual machine and upload the script as you did earlier
(I called my script chapter11cifar.py—if you named yours differently, just update
the following code).
The next thing we need is for the dataset to be on the virtual machine. The easiest
way to do this is to go to the virtual machine and type:
wget http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
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