public static void main(String[] args) throws Exception {
// batches of 10, 60000 examples total
DataSetIterator iter = new MnistDataSetIterator(10, 300);
DBN dbn = SerializationUtils.readObject(new File(args[0]));
for (int i = 0; i < dbn.getnLayers(); i++) dbn.getLayers()[i].setRenderEpochs(10);
DeepAutoEncoder encoder = new DeepAutoEncoder(dbn);
encoder.setRoundCodeLayerInput(true);
encoder.setNormalizeCodeLayerOutput(false);
encoder.setOutputLayerLossFunction(OutputLayer.LossFunction.RMSE_XENT);
encoder.setOutputLayerActivation(Activations.sigmoid());
encoder.setVisibleUnit(RBM.VisibleUnit.GAUSSIAN);
encoder.setHiddenUnit(RBM.HiddenUnit.BINARY);
while (iter.hasNext()) {
DataSet next = iter.next();
if (next == null) break;
log.info("Training on " + next.numExamples());
log.info("Coding layer is " + encoder.encode(next.getFirst()));
encoder.finetune(next.getFirst(), 1e-1, 1000);
NeuralNetPlotter plotter = new NeuralNetPlotter();
encoder.getDecoder().feedForward(encoder.encodeWithScaling(next.getFirst()));
String[] layers = new String[encoder.getDecoder().getnLayers()];
DoubleMatrix[] weights = new DoubleMatrix[layers.length];
for (int i = 0; i < encoder.getDecoder().getnLayers(); i++) {
layers[i] = "" + i;
weights[i] = encoder.getDecoder().getLayers()[i].getW();
}
plotter.histogram(layers, weights);
FilterRenderer f = new FilterRenderer();
f.renderFilters(
encoder.getDecoder().getOutputLayer().getW(),
"outputlayer.png",
28,
28,
next.numExamples());
DeepAutoEncoderDataSetReconstructionRender render =
new DeepAutoEncoderDataSetReconstructionRender(
next.iterator(next.numExamples()), encoder);
render.draw();
}
SerializationUtils.saveObject(encoder, new File("deepautoencoder.ser"));
iter.reset();
DeepAutoEncoderDataSetReconstructionRender render =
new DeepAutoEncoderDataSetReconstructionRender(iter, encoder);
render.draw();
}
