www.allitebooks.com

This will unzip only one Coval.otf file (there are lots of files in this zip folder
that we don't need).
While still in the virtual machine, you can run the program with the following
command:
Chapter 11
python3 chapter11script.py
The program will run through as it would in the IPython Notebook and the results
will print to the command line.
The results should be the same as before, but the actual training and testing of the
neural network will be much faster. Note that it won't be that much faster in the
other aspects of the program—we didn't write the CAPTCHA dataset creation to
use a GPU, so we will not obtain a speedup there.
You may wish to shut down the Amazon virtual machine to
save some money; we will be using it at the end of this chapter
to run our main experiment, but will be developing the code on
your main computer first.
Application
Back on your main computer now, open the first IPython Notebook we created
in this chapter—the one that we loaded the CIFAR dataset with. In this major
experiment, we will take the CIFAR dataset, create a deep convolution neural
network, and then run it on our GPU-based virtual machine.
Getting the data
To start with, we will take our CIFAR images and create a dataset with them.
Unlike previously, we are going to preserve the pixel structure—that is,. in rows
and columns. First, load all the batches into a list:
import numpy as np
batches = []
for i in range(1, 6):
batch_filename = os.path.join(data_folder, "data_batch_{}".
format(i))
batches.append(unpickle(batch1_filename))
break
[ 263 ]