Learning Data Mining with Python

Classifying Objects in Images Using Deep Learning
Next we set the size of the convolution layers. There are no strict rules here, but I
found the following values to be good starting points;
conv1_num_filters=32,
conv1_filter_size=(3, 3),
conv2_num_filters=64,
conv2_filter_size=(2, 2),
conv3_num_filters=128,
conv3_filter_size=(2, 2),
The filter_size parameter dictates the size of the window of the image that the
convolution layer looks at. In addition, we set the size of the pooling layers:
pool1_ds=(2,2),
pool2_ds=(2,2),
pool3_ds=(2,2),
We then set the size of the two hidden dense layers (the third-last and second-last
layers) and also the size of the output layer, which is just the number of classes in
our dataset;
hidden4_num_units=500,
hidden5_num_units=500,
output_num_units=10,
We also set a nonlinearity for the final layer, again using softmax;
output_nonlinearity=softmax,
We also set the learning rate and momentum. As a rule of thumb, as the number of
samples increase, the learning rate should decrease:
update_learning_rate=0.01,
update_momentum=0.9,
We set regression to be True, as we did before, and set the number of training
epochs to be low as this network will take a long time to run. After a successful run,
increasing the number of epochs will result in a much better model, but you may
need to wait for a day or two (or more!) for it to train:
regression=True,
max_epochs=3,
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