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Model_Config

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Abil N George edited this page Sep 1, 2014 · 1 revision

Model Config

  • nnetType : (Mandatory) Type of Network (CNN/RBM/SDA/DNN)

  • train_data : (Mandatory) The working directory containing data configuration and output

  • wdir : (Mandatory) Working Directory.

  • data_spec : (Mandatory) The path of the data sepification relative to model_config.json

  • nnet_spec : (Mandatory) The path of network configuration specification relative to model_config.json

  • output_file : (Mandatory) The path of RBM network output file relative to wdir

  • input_file : The path of PreTrained/FineTuned network input file relative to wdir.(Mandatory for DNN)

  • logger_level : Level of Logger.Valid Values are "INFO","DEBUG" and "ERROR"

  • batch_size : specify the mini batch size while training, default 128

  • n_ins :Dimension of input (Mandatory for all except CNN)

  • n_outs :(Mandatory) Dimension of output (No: of Classes)

  • input_shape: The input shape of a given feature vector.(Mandatory For CNN).Should be an Array.

  • finetune_method : Two methods are supported

  1. C: Constant learning rate.
  2. E: Exponential decay.
  • finetune_rate : Configuration of learning method.Contains a json object with following params
param description default value learning method
learning_rate 0.08 C
epoch_num 10 C
start_rate 0.08 E
scale_by 0.5 E
min_derror_decay_start 0.05 E
min_derror_stop 0.05 E
min_epoch_decay_start 15 E
  • finetune_momentum : The momentum factor while finetuning
  • export_path : path (realative to wdir) for writting (bottleneck) features.
  • processes : Process should be run by program.Contains a json object with following params
  • pretraining : whether Pre-Training is needed.(invalid for DNN and CNN).(Default value = false)
  • finetuning : whether Fine Tuning is needed.(Default value = false)
  • testing : whether Fine Tuning is needed.(Default value = false)
  • export_data : whether extracted features should written to file.If true,export_path is required.(Default value = false).

######Specific to DBN(RBM)######

  • gbrbm_learning_rate : Pretraining learning rate for gbrbm layer.(Default Value = 0.005)

  • pretraining_learning_rate : Pretraining learning rate for all layers except gbrbm layer.(Default Value = 0.08)

  • pretraining_epochs :No of Pretraining epochs(Default Value = 10)

  • initial_pretrain_momentum :The initial momentum factor while pre-training (Default Value = 0.5)

  • final_pretrain_momentum :The final momentum factor while pre-training (Default Value = 0.9)

  • initial_pretrain_momentum_epoch : No: of epochs with the initial momentum factor before switching to final momentum factor.(Default Value = 5)

  • keep_layer_num: From which layer Pre-Trainig Should Start.(Default Value = 0).If non-Zero layer is intilaized with weights from input_file

######Specific to SDA######

  • pretrain_lr :learning rate to be used during pre-training (Default Value = 0.08).

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