Learning Data Mining with Python

Classifying Objects in Images Using Deep Learning
These include the following:
• Network-in-network layers: These are small neural networks that are easier
to interpret than traditional neural network layers.
• Dropout layers: These randomly drop units during training, preventing
overfitting, which is a major problem in neural networks.
• Noise layers: These introduce noise into the neurons; again, addressing the
overfitting problem.
In this chapter, we will use convolution layers (layers that are organized to mimic
the way in which human vision works). They use small collections of connected
neurons that analyze only a segment of the input values (in this case, an image).
This allows the network to deal with standard alterations such as dealing with
translations of images. In the case of vision-based experiments, an example of an
alteration dealt with by convolution layers is translating the image.
In contrast, a traditional neural network is often heavily connected—all neurons
from one layer connect to all neurons in the next layer.
Convolutional networks are implemented in the lasagne.layers.Conv1DLayer
and lasagne.layers.Conv2DLayer classes.
At the time of writing, Lasagne hasn't had a formal release and is not
on pip. You can install it from github. In a new folder, download the
source code repository using the following:
git clone https://github.com/Lasagne/Lasagne.git
From within the created Lasagne folder, you can then install the library
using the following:
sudo python3 setup.py install
See http://lasagne.readthedocs.org/en/latest/user/
installation.html for installation instructions.
Neural networks use convolutional layers (generally, just Convolutional Neural
Networks) and also the pooling layers, which take the maximum output for a
certain region. This reduces noise caused by small variations in the image, and
reduces (or down-samples) the amount of information. This has the added benefit
of reducing the amount of work needed to be done in later layers.
Lasagne also implements these pooling layers—for example in the lasagne.layers.
MaxPool2DLayer class. Together with the convolution layers, we have all the tools
needed to build a convolution neural network.
[ 250 ]