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unstablecnn edit
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‎config/CNN/NP/data_spec.json‎

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{
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"validation": {
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"base_path" : "data/NPFILE",
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"filename" : "val.dat",
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"partition" : 200,
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"random" : true,
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"random_seed" : 123,
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"dim_shuffle" : [0,3,2,1],
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"batch_size": 256,
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"keep_flatten" : false,
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"reader_type" : "NP"
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},
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"training" : {
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"base_path" : "data/NPFILE",
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"filename" : "train.dat",
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"partition" : 200,
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"random" : true,
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"random_seed" : 123,
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"dim_shuffle" : [0,3,2,1],
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"batch_size": 256,
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"keep_flatten" : false,
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"reader_type" : "NP"
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},
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"testing" : {
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"base_path" : "data/NPFILE",
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"filename" : "train.dat",
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"partition" : 200,
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"random" : true,
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"random_seed" : 123,
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"dim_shuffle" : [0,3,2,1],
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"batch_size": 256,
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"keep_flatten" : false,
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"reader_type" : "NP"
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}
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}

‎config/CNN/NP/model_conf.json‎

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{
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"comment" : "nnetType :: (Mandatory) specify Type of Network (CNN,RBM) ",
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"nnetType" : "CNN",
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"comment" : "train_data :: (Mandatory) specify the working directory containing data configuration and output ",
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"wdir" : "wdir",
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"comment" : "valid_data (Mandatory) specify the path of the validation data relative to the working directory",
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"data_spec" : "config/CNN/NP/data_spec.json",
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"comment" : "hidden_nnet_spec :: (Mandatory) specify the path of hidden network configuration specification relative to working directory",
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"nnet_spec" : "config/CNN/NP/nnet_spec.json",
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"comment" : "hidden_output_file :: (Mandatory) specify the path of convolution network output file relative to working directory",
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"hidden_output_file" : "hidden_out.model",
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"comment" : "conv_output_file :: (Mandatory) specify the path of convolution network output file relative to working directory",
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"conv_output_file" : "conv_out.model",
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"comment" : "input_shape :: (Mandatory) specify the input shape of a given feature vector" ,
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"input_shape" : [3,28,28],
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"comment" : "batch_size :: specify the mini batch size while training, default 256",
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"batch_size" : 256,
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"comment" : "momentum :: Specify the momentum factor while training default 0.5",
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"momentum" : 0.5,
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"comment" : "n_outs :: Specify the number of outputs",
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"n_outs" : 8,
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"comment" : "l_rate_method :: Two method l_rate method are supported C: Constant learning rate and E : Exponential decay",
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"l_rate_method" : "C",
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"comment" : "l_rate :: learning rate configuration",
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"l_rate" : {
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"learning_rate" : 0.08,
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"epoch_num" : 0,
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"start_rate" : 0.08,
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"scale_by" : 0.5,
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"min_derror_decay_start" : 0.05,
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"min_derror_stop" : 0.05,
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"min_epoch_decay_start" : 15,
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"init_error" :100
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},
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"export_path" : "data/export"
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}

‎config/CNN/NP/nnet_spec.json‎

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{
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"comment" : "layers :: convolution layer configuration",
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"cnn": {
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"layers": [
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{
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"convmat_dim" : [5,5],
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"num_filters" : 100,
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"poolsize" : [2,2],
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"flatten" : false,
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"update" : true
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},
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{
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"convmat_dim" : [4,4],
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"num_filters" : 200,
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"poolsize" : [2,2],
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"flatten" : true,
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"update" : true
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}
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],
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"comment" : "activation :: sigmoid or tanh",
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"activation" : "tanh",
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24+
"comment" : "use_fast :: To use pylearn2 library for faster computation or not",
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"use_fast" : false
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},
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"comment" : "layers :: hidden layer configuration",
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"mlp" : {
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"layers": [500,200],
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"comment" : "activation :: sigmoid or tanh",
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"activation" : "tanh"
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}
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}
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‎config/CNN/T/data_spec.json‎

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{
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"validation": {
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"base_path" : "data/TFILE",
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"filename" : "val1.dat",
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"partition" : 0.002,
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"random" : true,
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"random_seed" : 123,
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"input_shape" : [1,10,5],
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"dim_shuffle" : [0,1,2,3],
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"batch_size": 8,
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"keep_flatten" : false,
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"reader_type" : "T1"
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},
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"training" : {
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"base_path" : "data/TFILE",
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"filename" : "train1.dat",
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"partition" : 0.002,
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"random" : true,
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"random_seed" : 123,
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"input_shape" : [1,10,5],
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"dim_shuffle" : [0,1,2,3],
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"batch_size": 8,
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"keep_flatten" : false,
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"reader_type" : "T1"
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},
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"testing" : {
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"base_path" : "data/TFILE",
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"filename" : "train1.dat",
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"partition" : 0.002,
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"random" : true,
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"random_seed" : 123,
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"input_shape" : [1,10,5],
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"dim_shuffle" : [0,1,2,3],
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"batch_size": 8,
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"keep_flatten" : false,
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"reader_type" : "T1"
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}
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}

‎config/CNN/T/mlp_spec.json‎

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{
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"comment" : "layers :: hidden layer configuration",
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"layers": [100,200],
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"comment" : "activation :: sigmoid or tanh",
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"activation" : "tanh"
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}

‎config/CNN/T/model_conf.json‎

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{
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"comment" : "nnetType :: (Mandatory) specify Type of Network (CNN,RBM) ",
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"nnetType" : "CNN",
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"comment" : "train_data :: (Mandatory) specify the working directory containing data configuration and output ",
6+
"wdir" : "wdir",
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8+
"comment" : "valid_data (Mandatory) specify the path of the validation data relative to the working directory",
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"data_spec" : "config/CNN/T/data_spec.json",
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"comment" : "nnet_spec :: (Mandatory) specify the path of convolution network configuration specification relative to working directory",
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"nnet_spec" : "config/CNN/T/nnet_spec.json",
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"comment" : "hidden_output_file :: (Mandatory) specify the path of convolution network output file relative to working directory",
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"hidden_output_file" : "hidden_out.model",
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"comment" : "conv_output_file :: (Mandatory) specify the path of convolution network output file relative to working directory",
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"conv_output_file" : "conv_out.model",
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"comment" : "input_shape :: (Mandatory) specify the input shape of a given feature vector" ,
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"input_shape" : [1,10,5],
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"comment" : "batch_size :: specify the mini batch size while training, default 256",
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"batch_size" : 8,
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"comment" : "momentum :: Specify the momentum factor while training default 0.5",
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"momentum" : 0.5,
28+
29+
"comment" : "n_outs :: Specify the number of outputs",
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"n_outs" : 8,
31+
32+
"comment" : "l_rate_method :: Two method l_rate method are supported C: Constant learning rate and E : Exponential decay",
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"l_rate_method" : "C",
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35+
"comment" : "l_rate :: learning rate configuration",
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"l_rate" : {
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"learning_rate" : 0.08,
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"epoch_num" : 10,
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40+
"start_rate" : 0.08,
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"scale_by" : 0.5,
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"min_derror_decay_start" : 0.05,
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"min_derror_stop" : 0.05,
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"min_epoch_decay_start" : 15,
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"init_error" :100
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},
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"export_path" : "data/export"
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}

‎config/CNN/T/nnet_spec.json‎

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{
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"comment" : "layers :: convolution layer configuration",
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"cnn" : {
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"layers": [
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{
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"convmat_dim" : [3,2],
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"num_filters" : 100,
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"poolsize" : [2,2],
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"flatten" : true,
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"update" : true
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}
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],
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"comment" : "activation :: sigmoid or tanh",
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"activation" : "tanh",
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"comment" : "use_fast :: To use pylearn2 library for faster computation or not",
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"use_fast" : false
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},
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"comment" : "layers :: hidden layer configuration",
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"mlp" : {
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"layers": [100,200],
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"comment" : "activation :: sigmoid or tanh",
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"activation" : "tanh"
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}
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}

‎run/run_CNN.py‎

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@@ -43,10 +43,9 @@ def runCNN(arg):
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else :
4444
model_configs = load_model(arg,'CNN')
4545

46-
conv_configs,conv_layer_configs = load_conv_spec(model_configs['conv_nnet_spec'],model_configs['batch_size'],
46+
conv_configs,conv_layer_configs,mlp_configs = load_conv_spec(model_configs['nnet_spec'],model_configs['batch_size'],
4747
model_configs['input_shape'])
4848

49-
mlp_configs = load_mlp_spec(model_configs['hidden_nnet_spec']);
5049
data_spec = load_data_spec(model_configs['data_spec']);
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5251
numpy_rng = numpy.random.RandomState(89677)

‎utils/load_conf.py‎

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@@ -109,7 +109,7 @@ def load_mlp_spec(input_file):
109109
logger.info("Loading mlp properties from %s ...",input_file)
110110
return load_json(input_file);
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112-
112+
113113
#############################################################################
114114
#CNN
115115
#############################################################################
@@ -141,9 +141,12 @@ def initModelCNN(data):
141141
def load_conv_spec(input_file,batch_size,input_shape):
142142
logger.info("Loading convnet properties from %s ...",input_file)
143143
data = load_json(input_file)
144-
145-
layer_configs=data.pop('layers');
146-
conv_configs = data;
144+
if not data.has_key('cnn'):
145+
logger.critical("CNN configuration is not present in " + str(input_file))
146+
exit(1)
147+
cnn_data = data['cnn'];
148+
layer_configs=cnn_data.pop('layers');
149+
conv_configs = cnn_data;
147150
if len(layer_configs)==0:
148151
print "Error: No convnet configuration avaialable.."
149152
exit(1)
@@ -159,7 +162,7 @@ def load_conv_spec(input_file,batch_size,input_shape):
159162

160163
current_map_number = layer_configs[layer_index]['num_filters']
161164
layer_configs[layer_index]['filter_shape']=[current_map_number,prev_map_number];
162-
layer_configs[layer_index]['filter_shape'].extend(layer_configs[layer_index]['convmat_dim']);
165+
layer_configs[layer_index]['filter_shape'].extend(layer_configs[layer_index][' ']);
163166

164167
layer_configs[layer_index]['output_shape'] = [batch_size,current_map_number];
165168
if not len(layer_configs[layer_index]['input_shape'][2:]) == len(layer_configs[layer_index]['convmat_dim']):
@@ -172,7 +175,11 @@ def load_conv_spec(input_file,batch_size,input_shape):
172175
input_shape.append(outdim);
173176

174177
prev_map_number = current_map_number
175-
return (conv_configs,layer_configs)
178+
if not data.has_key('cnn'):
179+
logger.critical("mlp configuration is not present in " + str(input_file))
180+
exit(1)
181+
mlp_configs = data['mlp'];
182+
return (conv_configs,layer_configs,mlp_configs)
176183

177184
#############################################################################
178185
#DBN/RBM

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