############# GET MODEL PARAMETERS ################# seth = GetValues() prep, folds, save_model, model_type,\ model,feature, dropout, act1, act2,\ act3, input_neurons, epochs, batchsize,\ num_classes, agg_num, hop, loss, optimizer,\ dataset=seth.get_parameters(dataset='dcase_2016') import config as cfg ############# EXTRACT FEATURES ##################### extract = False if extract: aud_audio.extract(feature, cfg.wav_dev_fd, cfg.dev_fd + '/' + feature, 'parameters.yaml', dataset=dataset) aud_audio.extract(feature, cfg.wav_eva_fd, cfg.eva_fd + '/' + feature, 'parameters.yaml', dataset=dataset) ############# LOAD DATA ########################### tr_X, tr_y = seth.get_train_data() dimx = tr_X.shape[-2] dimy = tr_X.shape[-1] tr_X = aud_utils.mat_3d_to_nd(model, tr_X) miz = aud_model.Functional_Model(input_neurons=input_neurons, dropout=dropout,
input_neurons=400 # Number of Neurons epochs=10 # Number of Epochs batchsize=128 # Batch Size num_classes=15 # Number of classes filter_length=3 # Size of Filter nb_filter=100 # Number of Filters #Parameters that are passed to the features. agg_num=10 # Agg Number(Integer) Number of frames hop=10 # Hop Length(Integer) dataset = 'dcase_2016' extract = 0 ## EXTRACT FEATURES if extract: aud_audio.extract(feature, wav_dev_fd, dev_fd+'/'+feature,'example.yaml',dataset=dataset) aud_audio.extract(feature, wav_eva_fd, eva_fd+'/'+feature,'example.yaml',dataset=dataset) def GetAllData(fe_fd, csv_file, agg_num, hop): """ Input: Features folder(String), CSV file(String), agg_num(Integer), hop(Integer). Output: Loaded features(Numpy Array) and labels(Numpy Array). Loads all the features saved as pickle files. """ # read csv with open( csv_file, 'rb') as f: reader = csv.reader(f) lis = list(reader) # init list X3d_all = []
print "Epochs", epochs print "Batchsize", batchsize print "Number of filters", nb_filter ## UNPACK THE DATASET ACCORDING TO KERAS_AUD # [NEEDED AT INITIAL STAGE] path = 'E:/akshita_workspace/chime_home' # [NEEDED AT INITIAL STAGE] #aud_utils.unpack_chime_2k16(path,wav_dev_fd,wav_eva_fd,meta_train_csv,meta_test_csv,label_csv) ## EXTRACT FEATURES aud_audio.extract(feature, wav_dev_fd, dev_fd + '/' + feature, 'defaults.yaml', dataset='chime_2016') aud_audio.extract(feature, wav_eva_fd, eva_fd + '/' + feature, 'defaults.yaml', dataset='chime_2016') def GetAllData(fe_fd, csv_file, agg_num, hop): """ Input: Features folder(String), CSV file(String), agg_num(Integer), hop(Integer). Output: Loaded features(Numpy Array) and labels(Numpy Array). Loads all the features saved as pickle files. """