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Merge branch 'test'
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Lines changed: 93 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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"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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------------
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* `nnetType` : (Mandatory) specify Type of Network (CNN/RBM/SDA/DNN)
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* `train_data` : (Mandatory) specify the working directory containing data configuration and output
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* `wdir` : (Mandatory) Working Directory.
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* `data_spec` : (Mandatory) specify the path of the data sepification relative to `model_config.json`
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* `nnet_spec` : (Mandatory) specify the path of network configuration specification relative to `model_config.json`
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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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> param | description | default value |learning method
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> :-----|:------------|:--------------:|:---------------:
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> `learning_rate` ||0.08 | C
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> `epoch_num` ||10 | C
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> `start_rate` ||0.08 | E
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> `scale_by` ||0.5 | E
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> `min_derror_decay_start` ||0.05 | E
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> `min_derror_stop` ||0.05 | E
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> `min_epoch_decay_start` || 15 | E
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> `init_error` || 100 | E
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* `finetune_momentum` : Specify the momentum factor while finetuning
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* `processes` :
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>> * `pretraining` : default:false
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>> * `finetuning` : default:false
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>> * `testing` : default:false
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>> * `export_data` : default:false
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* `export_path` : path (realative to wdir) for writting (bottleneck) features.
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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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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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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()
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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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