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Improved example

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This commit is contained in:
Davis King 2016-12-17 16:46:39 -05:00
parent f28d2f7329
commit f4b3c7ee0f
1 changed files with 11 additions and 7 deletions
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18
examples/dnn_metric_learning_ex.cpp
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@ -14,7 +14,6 @@
space it's very easy to do face recognition with some kind of k-nearest
neighbor classifier.
To keep this example as simple as possible we won't do face recognition.
Instead, we will create a very simple network and use it to learn a mapping
from 8D vectors to 2D vectors such that vectors with the same class labels
@ -65,15 +64,20 @@ int main() try
// vectors.
using net_type = loss_metric<fc<2,input<matrix<double,0,1>>>>;
net_type net;
// Now setup the trainer and train the network using our data.
dnn_trainer<net_type> trainer(net);
trainer.set_learning_rate(0.1);
trainer.set_min_learning_rate(0.001);
trainer.set_mini_batch_size(128);
trainer.be_verbose();
trainer.set_iterations_without_progress_threshold(100);
trainer.train(samples, labels);
// It should be emphasized out that it's really important that each mini-batch contain
// multiple instances of each class of object. This is because the metric learning
// algorithm needs to consider pairs of objects that should be close as well as pairs
// of objects that should be far apart during each training step. Here we just keep
// training on the same small batch so this constraint is trivially satisfied.
while(trainer.get_learning_rate() >= 1e-4)
trainer.train_one_step(samples, labels);
// Wait for training threads to stop
trainer.get_net();
cout << "done training" << endl;
// Run all the samples through the network to get their 2D vector embeddings.