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Model_Config.md

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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)

  • random_seed : Random Seed used for initialization of weights.

  • 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_params : Configuration of finetune learning method.Contains a json object with following params:

  • momentum : The momentum factor while finetuning
  • method : Two methods are supported
  1. C: Constant learning rate(DEFAULT): run epoch_num iterations with learning_rate unchanged
  2. E: Exponential decay: we start with the learning rate of start_rate; if the validation error reduction between two epochs is less than min_derror_decay_start, the learning rate is scaled by scale_by during each of the remaining epoch. The whole traing terminates when the validation error reduction between two epochs falls below min_derror_stop. min_epoch_decay_star is the minimum epoch number after which scaling can only be performed.

Default value of other paramters

Param 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

These parameters are used by Constant learning rate or Exponential decay

  • pretrain_params: Configuration of pretraining method.Contains a json object with following params
Param Default value nnet Type Description
gbrbm_learning_rate 0.005 DBN Pretraining learning rate for gbrbm layer.
learning_rate 0.08 SDA,DBN Pretraining learning rate (DBN: for all layers except gbrbm layer)
epochs 15 DBN No of Pretraining epochs
initial_momentum 0.5 DBN The initial momentum factor while pre-training
final_momentum 0.9 DBN The final momentum factor while pre-training
initial_momentum_epoch 5 DBN No: of epochs with the initial momentum factor before switching to final momentum factor
keep_layer_num 0 SDA,DBN From which layer Pre-Trainig Should Start.If non-Zero layer is intilaized with weights from input_file
  • 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).

Also See: