@@ -0,0 +1,1509 @@
+package com.infinity.bumblebee.training;%0A%0Aimport static com.infinity.bumblebee.training.net.NetworkDSL.usingTrainingData;%0A%0Aimport com.infinity.bumblebee.network.NeuralNet;%0A%0Apublic class LidarTrainer %7B%0A%0A%09public static void main(String%5B%5D args) %7B%0A%09%09NeuralNet network = %0A%09%09%09%09usingTrainingData(%22/Users/jeffreyrichley/Documents/workspace/raven/data/las_s23_1503_10.2011.fema-normalized.csv%22)%0A//%09%09%09%09usingTrainingData(%22/Users/jeffreyrichley/Documents/workspace/raven/data/las_s23_1503_30.2011.fema-normalized.csv%22)%0A//%09%09%09%09usingTrainingData(%22/Volumes/TANK/lidar/data/hampton/training-files/las_s23_1503_30.2011.fema-normalized.csv%22)%0A//%09%09%09%09usingTrainingData(%22/Volumes/TANK/lidar/data/hampton/all-training-samples.csv%22)  %09// (required) gives the file with training data%0A                .havingLayers(8100, 4000, 4)         %09%09%09%09// (required) gives the shape of the neural network%0A                .atMostIterations(1000)        %09%09%09%09// (optional) sets the maximum number of iterations to train (default: 100)%0A                .withLearningRate(0.3)         %09%09%09%09// (optional) sets the learning rate (default: 0.3)%0A//                .savingProgress(%22/Volumes/TANK/lidar/training%22)%0A                .savingProgress(%22/Users/jeffreyrichley/Documents/workspace/raven/data%22)%0A//                .savingWhenComplete(%22/Volumes/TANK/lidar/training%22)%0A                .savingWhenComplete(%22/Users/jeffreyrichley/Documents/workspace/raven/data%22)%0A                .train(true);                      %09%09%09%09// executes the training mechanism%0A%09%7D%0A%0A%7D%0A

clean up