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Chapter 11
First we create the layers of our neural network:
from lasagne import layers
layers=[
('input', layers.InputLayer),
('conv1', layers.Conv2DLayer),
('pool1', layers.MaxPool2DLayer),
('conv2', layers.Conv2DLayer),
('pool2', layers.MaxPool2DLayer),
('conv3', layers.Conv2DLayer),
('pool3', layers.MaxPool2DLayer),
('hidden4', layers.DenseLayer),
('hidden5', layers.DenseLayer),
('output', layers.DenseLayer),
]
We use dense layers for the last three layers, but before that we use convolution
layers combined with pooling layers. We have three sets of these. In addition, we
start (as we must) with an input layer. This gives us a total of 10 layers. As before,
the size of the first and last layers is easy to work out from the dataset, although our
input size will have the same shape as the dataset rather than just the same number
of nodes/inputs.
Start building our neural network (remember to not close the parentheses):
from nolearn.lasagne import NeuralNet
nnet = NeuralNet(layers=layers,
Add the input shape. The shape here resembles the shape of the dataset (three values
per pixel and a 32 by 32 pixel image). The first value, None, is the default batch size
used by nolearn—it will train on this number of samples at once, decreasing the
running time of the algorithm. Setting it to None removes this hard-coded value,
giving us more flexibility in running our algorithm:
input_shape=(None, 3, 32, 32),
To change the batch size, you will need to create a BatchIterator
instance. Those who are interested in this parameter can view the
source of the file at https://github.com/dnouri/nolearn/
blob/master/nolearn/lasagne.py, track the batch_iterator_
train and batch_iterator_test parameters, and see how they
are set in the NeuralNet class in this file.
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