def predict(dataset): print 'STEP 4 start...' train_rnnrbm(dataset=dataset, hidden_layers_sizes=params['STEP4']['hidden_layers_size'], hidden_recurrent=params['STEP4']['hidden_recurrent'], pretrain_lr=params['STEP4']['pretrain']['learning_rate'], pretrain_batch_size=params['STEP4']['pretrain']['batch_size'], pretrain_epochs=params['STEP4']['pretrain']['epochs'], finetune_lr=params['STEP4']['finetune']['learning_rate'], finetune_batch_size=params['STEP4']['finetune']['batch_size'], finetune_epochs=params['STEP4']['finetune']['epochs'])
def predict(dataset): print 'STEP 4 start...' train_rnnrbm(dataset=dataset, hidden_layers_sizes=params['STEP4']['hidden_layers_size'], hidden_recurrent = params['STEP4']['hidden_recurrent'], pretrain_lr=params['STEP4']['pretrain']['learning_rate'], pretrain_batch_size=params['STEP4']['pretrain']['batch_size'], pretrain_epochs=params['STEP4']['pretrain']['epochs'], finetune_lr=params['STEP4']['finetune']['learning_rate'], finetune_batch_size=params['STEP4']['finetune']['batch_size'], finetune_epochs=params['STEP4']['finetune']['epochs'] )
'learning_rate']: for epochs_pretrain in params['STEP4']['pretrain'][ 'epochs']: for batch_size_finetune in params['STEP4'][ 'finetune']['batch_size']: for learning_rate_finetune in params['STEP4'][ 'finetune']['learning_rate']: for epochs_finetune in params['STEP4'][ 'finetune']['epochs']: result = train_rnnrbm( dataset=dataset, hidden_layers_sizes= hidden_layers_sizes, hidden_recurrent=hidden_recurrent, pretrain_lr=learning_rate_pretrain, pretrain_batch_size= batch_size_pretrain, pretrain_epochs=epochs_pretrain, finetune_lr=learning_rate_finetune, finetune_batch_size= batch_size_finetune, finetune_epochs=epochs_finetune) i += 1 print '%d / %d is done...' % (i, all_size) out = open(model_dirs['STEP4_logs'], 'a') out.write( '%f,%s,%s,%s,%d,%f,%d,%d,%f,%d\n' % (result, brandcode, str(hidden_layers_sizes),