public void saveModel() {
try {
File tmpFile = File.createTempFile("model", null);
ModelSerializer.writeModel(net.getNN(), tmpFile, true);
} catch (IOException ioe) {
}
}
public void loadModel(File file) {
try {
ModelSerializer.restoreMultiLayerNetwork(file);
} catch (IOException ioe) {
}
}
public static void main(String[] args) throws Exception {
int height = 28;
int width = 28;
int channels = 1;
// recordReader.getLabels()
// In this version Labels are always in order
// So this is no longer needed
// List<Integer> labelList = Arrays.asList(2,3,7,1,6,4,0,5,8,9);
List<Integer> labelList = Arrays.asList(0, 1, 2, 3, 4, 5, 6, 7, 8, 9);
// pop up file chooser
String filechose = fileChose().toString();
// LOAD NEURAL NETWORK
// Where to save model
File locationToSave = new File("trained_mnist_model.zip");
// Check for presence of saved model
if (locationToSave.exists()) {
System.out.println("\n######Saved Model Found######\n");
} else {
System.out.println("\n\n#######File not found!#######");
System.out.println("This example depends on running ");
System.out.println("MnistImagePipelineExampleSave");
System.out.println("Run that Example First");
System.out.println("#############################\n\n");
System.exit(0);
}
MultiLayerNetwork model = ModelSerializer.restoreMultiLayerNetwork(locationToSave);
log.info("*********TEST YOUR IMAGE AGAINST SAVED NETWORK********");
// FileChose is a string we will need a file
File file = new File(filechose);
// Use NativeImageLoader to convert to numerical matrix
NativeImageLoader loader = new NativeImageLoader(height, width, channels);
// Get the image into an INDarray
INDArray image = loader.asMatrix(file);
// 0-255
// 0-1
DataNormalization scaler = new ImagePreProcessingScaler(0, 1);
scaler.transform(image);
// Pass through to neural Net
INDArray output = model.output(image);
log.info("## The FILE CHOSEN WAS " + filechose);
log.info("## The Neural Nets Pediction ##");
log.info("## list of probabilities per label ##");
// log.info("## List of Labels in Order## ");
// In new versions labels are always in order
log.info(output.toString());
log.info(labelList.toString());
}