Learning Data Mining with Python

Classifying Objects in Images Using Deep Learning
This will download the dataset. Once that has downloaded, you can extract the data
to the Data folder by first creating that folder and then unzipping the data there:
mkdir Data
tar -zxf cifar-10-python.tar.gz -C Data
Finally, we can run our example with the following:
python3 chapter11cifar.py
The first thing you'll notice is a drastic speedup. On my home computer, each epoch
took over 100 seconds to run. On the GPU-enabled virtual machine, each epoch takes
just 16 seconds! If we tried running 100 epochs on my computer, it would take nearly
three hours, compared to just 26 minutes on the virtual machine.
This drastic speedup makes trailing different models much faster. Often with trialing
machine learning algorithms, the computational complexity of a single algorithm
doesn't matter too much. An algorithm might take a few seconds, minutes, or hours
to run. If you are only running one model, it is unlikely that this training time will
matter too much—especially as prediction with most machine learning algorithms is
quite quick, and that is where a machine learning model is mostly used.
However, when you have many parameters to run, you will suddenly need to train
thousands of models with slightly different parameters—suddenly, these speed
increases matter much more.
After 100 epochs of training, taking a whole 26 minutes, you will get a printout
of the final result:
0.8497
Not too bad! We can increase the number of epochs of training to improve this
further or we might try changing the parameters instead; perhaps, more hidden
nodes, more convolution layers, or an additional dense layer. There are other types
of layers in Lasagne that could be tried too; although generally, convolution layers
are better for vision.
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