Working with CIFAR_10 Grayscale

app/cnn/MkCCNApp.kl

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+//************************************************************************************************//
+//                                                                                                //
+//  Code part of the project MLKL                                                                 //
+//                                                                                                //
+//  couet.julien@gmail.com                                                                        //
+//                                                                                                //
+//************************************************************************************************//
+
+require MLKL; 
+
+
+operator entry() {
+
+  String path_config = "C:/Users/Julien/Documents/Dev/MLKL/app/cnn/cnn_config.mlkl";
+  String path_image = 'C:/Users/Julien/Documents/Dev/MLKL/resources/mnist/4.bmp';
+
+  // Load the configuration file and set the network layers
+  MkCNNConfig config;
+  MkCNNLayer layers[];
+  if(!config.config(path_config, layers))
+    return;
+
+  // Create the network
+  MkCNNNetwork nn(config, layers);
+
+  // Set the initiale learning rate
+  nn.optimizer().learningRate(nn.optimizer().learningRate()* sqrt(config.batch_size));
+  nn.display();
+
+  // Declare the enumeration, here we can set the larning rate decay
+  // However, it should be set from the file, see Convnet for this
+  MkCNNEnumEpoch epoch_enum(); 
+
+  // Load the training data
+  MkCNNData data = LoadTrainingCIFAR(config, true, -1.0, -1.0);
+  //data.loadTrainingMNIST(config); 
+
+  // Train the network
+  // The network (the layers' weights) is tested and saved at each epch
+  nn.train(config, data, epoch_enum);
+
+
+  // Finally test it and save it
+  Float64 image_data[][] = LoadValidationCIFAR(config, true, -1.0, -1.0);
+  report(nn.predictRescale(image_data));
+}
+

app/cnn/cnn_config.mlkl

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+### MLKL network configuration  
+
+
+##############################################################################
+#                
+# 1. Network parameters
+# tool*            : "train" or "predict" 
+# gpu              : GPU (0, 1)       
+# nbEpoch          : Number of epochs
+# batchSize        : Batch size
+# optimizer        : Optimisation (0 : GD, 1 : GDLM)    
+# lossFunction     : Loss funtion (0 : MSE, 1 : CE)     
+#
+# 2. Layer definitions 
+# layersDefsPath   : Path to the layers definition file
+# layersParamsPath : Path to the layers parameters file
+#
+# 3. Images and labels
+# trainImagesPath  : Path of the train images, use for training
+# trainLabelsPath  : Path of the train labels, use for training
+# testImagesPath*  : Path of the test images, use for training
+# testLabelsPath*  : Path of the test labels, use for training
+#
+# 4. Network saving and loading
+# save             : Save the netwok
+# autoSaving*      : Automatically save the network at each epoch (0, 1)
+# saveDirPath      : Path of the network output folder
+# saveFileName*    : Name of the network output file
+# loadFilePath*    : Path of the network file to load
+#
+##############################################################################
+
+
+# 1. Network parameters
+tool=train
+gpu=0
+nbEpoch=5
+batchSize=10
+optimizer=3
+lossFunction=0
+
+
+# 2. Layer definitions 
+layersDefsPath=C:/Users/Julien/Documents/Dev/MLKL/app/cnn/cnn_layers_def.mlkl
+layersParamsPath=C:/Users/Julien/Documents/Dev/MLKL/app/cnn/cnn_layers_params.mlkl
+
+
+# 3. Images and labels
+# For CIFAR, the same file contains both the image and labels
+trainImagesPath=C:/Users/Julien/Documents/Dev/MLKL/resources/cifar-10/data_batch_1.bin
+testImagesPath=C:/Users/Julien/Documents/Dev/MLKL/resources/cifar-10/test_batch.bin
+
+# For MNIST
+#trainImagesPath=C:/Users/Julien/Documents/Dev/MLKL/resources/mnist/train-images.idx3-ubyte
+#testImagesPath=C:/Users/Julien/Documents/Dev/MLKL/resources/mnist/t10k-images.idx3-ubyte
+#trainLabelsPath=C:/Users/Julien/Documents/Dev/MLKL/resources/mnist/train-labels.idx1-ubyte
+#testLabelsPath=C:/Users/Julien/Documents/Dev/MLKL/resources/mnist/t10k-labels.idx1-ubyte
+
+
+# 4. Network saving and loading
+save=1
+autoSaving=1
+saveDirPath=C:/Users/Julien/Documents/Dev/MLKL/resources/
+saveFileName=cnn_network.mlkl
+#loadFilePath=C:/Users/Julien/Documents/Dev/MLKL/resources/2015-07-04_18-25-59/res.mlkl

app/cnn/cnn_layers_def.mlkl

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+### MLKL layers definition  
+
+
+##############################################################################
+#
+# [name]      : Name of the layer           
+# type        : Type (data, conv, pool, fc)           
+# neuron      : Neuron (tanh, relu, sigm, id)         
+# filters     : number of filters               
+# filterSize  : Filter size  
+# inChannels  : Number of channels inputs    
+# outChannels : Number of channels outputs                   
+# initW       : Initiliaze the weights with a normal dist of mean initW
+# initB       : Add bias initB
+#
+##############################################################################
+
+[conv1]
+type=conv
+neuron=relu
+filters=32
+filterSize=5
+inChannels=3
+outChannels=6
+#initW=0.0
+#initB=0.0
+
+[pool1]
+type=pool
+pool=avg
+neuron=relu
+filters=28
+inChannels=6
+poolingSize=2
+#initW=0.0
+#initB=0.0
+
+[conv2]
+type=conv
+neuron=relu
+filters=14
+filterSize=5
+inChannels=6
+outChannels=16
+#initW=0.0
+#initB=0.0
+
+[pool2]
+type=pool
+pool=avg
+neuron=relu
+filters=10
+inChannels=16
+poolingSize=2
+#initW=0.0
+#initB=0.0
+
+[conv3]
+type=conv
+neuron=relu
+filters=5
+filterSize=5
+inChannels=16
+outChannels=120
+#initW=0.0
+#initB=0.0
+
+[fc3]
+type=fc
+neuron=relu
+inChannels=120
+outChannels=10
+#initW=0.0
+#initB=0.0
+

app/cnn/cnn_layers_def_64.mlkl

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+### MLKL layers definition  
+
+
+##############################################################################
+#
+# [name]      : Name of the layer           
+# type        : Type (data, conv, pool, fc)           
+# neuron      : Neuron (tanh, relu, sigm, id)         
+# filters     : number of filters               
+# filterSize  : Filter size  
+# inChannels  : Number of channels inputs    
+# outChannels : Number of channels outputs                   
+# initW       : Initiliaze the weights with a normal dist of mean initW
+# initB       : Add bias initB
+#
+##############################################################################
+
+[conv1]
+type=conv
+neuron=relu
+filters=64
+filterSize=5
+inChannels=3
+outChannels=6
+#initW=0.0
+#initB=0.0
+
+[pool1]
+type=pool
+pool=avg
+neuron=relu
+filters=60
+inChannels=6
+poolingSize=2
+#initW=0.0
+#initB=0.0
+
+[conv2]
+type=conv
+neuron=relu
+filters=30
+filterSize=5
+inChannels=6
+outChannels=16
+#initW=0.0
+#initB=0.0
+
+[pool2]
+type=pool
+pool=avg
+neuron=relu
+filters=26
+inChannels=16
+poolingSize=2
+#initW=0.0
+#initB=0.0
+
+[conv3]
+type=conv
+neuron=relu
+filters=13
+filterSize=5
+inChannels=16
+outChannels=120
+#initW=0.0
+#initB=0.0
+
+[fc3]
+type=fc
+neuron=relu
+inChannels=9720
+outChannels=10
+#initW=0.0
+#initB=0.0
+

app/cnn/cnn_layers_params.mlkl

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+### MLKL layers parameters  
+
+
+##############################################################################
+#
+# [name] : Name of the layer           
+# epsW   : Type (data, conv, pool, fc)      
+# epsB   : Neuron (relu, relu, exp)         
+# momW   :                
+# momB   : Windowing size  
+# wc     : Number of inputs    
+#
+##############################################################################
+
+[conv1]
+epsW=1
+epsB=1
+momW=1
+momB=1
+wc=1
+
+[conv2]
+epsW=2
+epsB=2
+momW=2
+momB=2
+wc=2
+
+[conv3]
+epsW=3
+epsB=3
+momW=3
+momB=3
+wc=3
+
+[fc3]
+epsW=4
+epsB=4
+momW=4
+momB=4
+wc=4

app/cnn/sample/MkCCNSample_DAE.kl

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+//************************************************************************************************//
+//                                                                                                //
+//  Code part of the project MLKL                                                                 //
+//                                                                                                //
+//  couet.julien@gmail.com                                                                        //
+//                                                                                                //
+//************************************************************************************************//
+
+require MLKL; 
+
+function MkCNNConfig ConfigMLP() {
+  MkCNNConfig config;
+
+  config.save=0;
+  config.gpu=0;
+  config.epoch = 20;
+  config.batch_size = 10;
+  config.optimizer = MK_OPTIMIZER_GD;
+  config.loss_function = MK_LOSS_MSE;
+
+  config.train_images_path = "C:/Users/Julien/Documents/Dev/MLKL/resources/mnist/train-images.idx3-ubyte";
+  config.test_images_path = "C:/Users/Julien/Documents/Dev/MLKL/resources/mnist/t10k-images.idx3-ubyte";
+  config.train_labels_path = "C:/Users/Julien/Documents/Dev/MLKL/resources/mnist/train-labels.idx1-ubyte";
+  config.test_labels_path = "C:/Users/Julien/Documents/Dev/MLKL/resources/mnist/t10k-labels.idx1-ubyte";
+
+  return config;
+}
+
+function MkCNNLayer[] ConstructMLP(
+  Index nb_input_units,
+  Index nb_hidden_units,
+  Index nb_output_units) 
+{
+  MkCNNLayer layers[];
+  //layers.push(MkCNNLayerConvolutional(MK_NEURON_TANH,  32,  5,  5,  16, 120));
+  layers.push(MkCNNLayerFully(MK_NEURON_TANH, nb_input_units, nb_hidden_units));
+  layers.push(MkCNNLayerFully(MK_NEURON_TANH, nb_hidden_units, nb_output_units));
+  return layers;
+}
+
+operator entry() {
+
+  Index nb_hidden_neuron = 200;
+  MkCNNConfig config = ConfigMLP();
+  MkCNNLayer layers[] = ConstructMLP(32*32, nb_hidden_neuron, 10);
+  MkCNNNetwork nn(config.loss_function, config.optimizer, layers);
+
+  nn.display();
+  // load MNIST dataset
+  MkCNNData data;
+  data.loadTrainingMNIST(config);
+
+  nn.optimizer().learningRate(0.001);
+
+  // Declare the enumeration, here we can set the larning rate decay
+  MkCNNEnumEpoch epoch_enum(); 
+
+  nn.train(data, config, epoch_enum);
+}