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Improved example

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 space it's very easy to do face recognition with some kind of k-nearest
neighbor classifier. neighbor classifier.
To keep this example as simple as possible we won't do face recognition. 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 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 from 8D vectors to 2D vectors such that vectors with the same class labels
@ -65,15 +64,20 @@ int main() try
// vectors. // vectors.
using net_type = loss_metric<fc<2,input<matrix<double,0,1>>>>; using net_type = loss_metric<fc<2,input<matrix<double,0,1>>>>;
net_type net; net_type net;
// Now setup the trainer and train the network using our data.
dnn_trainer<net_type> trainer(net); dnn_trainer<net_type> trainer(net);
trainer.set_learning_rate(0.1); 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. // Run all the samples through the network to get their 2D vector embeddings.