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RENAME:Configs
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Lines changed: 87 additions & 44 deletions

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config/CNN/NP/README renamed to config/CNN/NP/README.md

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‎config/CNN/NP/model_conf.json‎

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@@ -23,17 +23,17 @@
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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" : "finetune_momentum :: Specify the momentum factor while training default 0.5",
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"finetune_momentum" : 0.5,
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"comment" : "n_outs :: Specify the number of outputs",
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"n_outs" : 10,
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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" : "finetune_method :: Two method l_rate method are supported C: Constant learning rate and E : Exponential decay",
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"finetune_method" : "C",
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"comment" : "l_rate :: learning rate configuration",
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"l_rate" : {
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"comment" : "finetune_rate :: learning rate configuration",
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"finetune_rate" : {
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"learning_rate" : 0.08,
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"epoch_num" : 0,
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‎config/CNN/T/model_conf.json‎

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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,
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"comment" : "finetune_momentum :: Specify the momentum factor while training default 0.5",
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"finetune_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" : "finetune_method :: Two method l_rate method are supported C: Constant learning rate and E : Exponential decay",
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"finetune_method" : "C",
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"comment" : "l_rate :: learning rate configuration",
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"l_rate" : {
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"comment" : "finetune_rate :: learning rate configuration",
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"finetune_rate" : {
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"learning_rate" : 0.08,
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"epoch_num" : 10,
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‎config/DBN/README.md‎

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==Model Config==
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nnetType: (Mandatory) specify Type of Network (CNN,RBM)
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train_data : (Mandatory) specify the working directory containing data configuration and output
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wdir : wdir
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data_spec:(Mandatory) specify the path of the validation data relative to the working directory
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nnet_spec: (Mandatory) specify the path of RBM/CNN/DNN/SDA network configuration specification relative to working directory
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output_file : (Mandatory) specify the path of RBM network output file relative to working directory
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input_file : specify the path of RBM network inpu file relative to working directory
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batch_size : specify the mini batch size while training, default 128
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n_ins:784
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n_outs:10
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gbrbm_learning_rate: pretraining
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pretraining_learning_rate: pretraining
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pretraining_epochs:
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initial_pretrain_momentum:Specify the momentum factor while training default 0.5
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final_pretrain_momentum:Specify the momentum factor while training default 0.9
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initial_pretrain_momentum_epoch : Specify the momentum factor while training default 5
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finetune_method: Two methods are supported C: Constant learning rate and E : Exponential decay"
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finetune_rate : learning rate configuration"
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>learning_rate: 0.08
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>epoch_num: 10
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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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finetune_momentum : Specify the momentum factor while finetuning"
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finetune_momentum : 0.5
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processes:
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>pretraining":true
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>finetuning":true
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>testing":true
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>export_data":false
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‎config/DBN/model_conf.json‎

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"comment" :"TODO",
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"gbrbm_learning_rate":0.005,
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"learning_rate":0.08,
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"pretraining_learning_rate":0.08,
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"pretraining_epochs":10,
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"comment" : "initial_momentum,final_momentum,initial_momentum_epoch :: Specify the momentum factor while training default 0.5,0.9,5",
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"initial_momentum":0.5,
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"final_momentum":0.9,
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"initial_momentum_epoch":5,
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"comment" : "initial_pretrain_momentum,final_pretrain_momentum,initial_pretrain_momentum_epoch :: Specify the momentum factor while training default 0.5,0.9,5",
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"initial_pretrain_momentum":0.5,
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"final_pretrain_momentum":0.9,
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"initial_pretrain_momentum_epoch":5,
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"comment" : "finetune_method:: Two methods are supported C: Constant learning rate and E : Exponential decay",
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"finetune_method":"C",

config/DBN/README renamed to config/DNN/README.md

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‎config/SDA/README‎

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config/DNN/README renamed to config/SDA/README.md

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‎run/run_CNN.py‎

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@@ -69,8 +69,8 @@ def runCNN(arg):
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if model_config['processes']['finetuning']:
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#learning rate, batch-size and momentum
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lrate = LearningRate.get_instance(model_config['l_rate_method'],model_config['l_rate']);
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momentum = model_config['momentum']
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lrate = LearningRate.get_instance(model_config['finetune_method'],model_config['finetune_rate']);
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momentum = model_config['finetune_momentum']
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train_sets, train_xy, train_x, train_y = read_dataset(data_spec['training'])
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valid_sets, valid_xy, valid_x, valid_y = read_dataset(data_spec['validation'])

‎run/run_DBN.py‎

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@@ -50,9 +50,9 @@ def preTraining(nnetModel,train_sets,train_xy,train_x,train_y,model_config):
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pretrainingEpochs = model_config['pretraining_epochs']
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keep_layer_num=model_config['keep_layer_num']
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initialMomentum = model_config['initial_momentum']
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initMomentumEpochs = model_config['initial_momentum_epoch']
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finalMomentum = model_config['final_momentum']
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initialMomentum = model_config['initial_pretrain_momentum']
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initMomentumEpochs = model_config['initial_pretrain_momentum_epoch']
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finalMomentum = model_config['final_pretrain_momentum']
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logger.info('Pre-training the model ...')
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start_time = time.clock()
@@ -63,7 +63,7 @@ def preTraining(nnetModel,train_sets,train_xy,train_x,train_y,model_config):
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if (nnetModel.rbm_layers[i].is_gbrbm()):
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pretrain_lr = model_config['gbrbm_learning_rate']
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else:
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pretrain_lr = model_config['learning_rate']
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pretrain_lr = model_config['pretraining_learning_rate']
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# go through pretraining epochs
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momentum = initialMomentum
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for epoch in xrange(pretrainingEpochs):

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