from config.configure import Configure conf = Configure() #== universal conf.sampling_rate = 44100 conf.duration = 1 conf.hop_length = 347 conf.fmin = 20 conf.fmax = conf.sampling_rate // 2 conf.n_mels = 128 conf.n_fft = conf.n_mels * 20 conf.samples = conf.sampling_rate * conf.duration conf.num_classes = 7 #== for recognizer conf.rt_process_count = 1 conf.rt_oversamples = 10 #== for trainer conf.batch_size = 32 conf.learning_rate = 0.0001 conf.epochs = 60 conf.verbose = 2 conf.dims = (conf.n_mels, 1 + int(np.floor(conf.samples/conf.hop_length)), 1) conf.rt_chunk_samples = conf.sampling_rate // conf.rt_oversamples conf.mels_onestep_samples = conf.rt_chunk_samples * conf.rt_process_count conf.mels_convert_samples = conf.samples + conf.mels_onestep_samples conf.labels = ['noise', 'finger', 'bell', 'gong', 'scissors', 'knock', 'laughter']
from config.configure import Configure conf = Configure() conf.model_name = 'vgg16.h5' conf.classes = ['no_breads', 'breads'] conf.no_breads_path = './dataset/data/pool/no_breads/*' conf.breads_path = './dataset/data/pool/breads/*' # conf.baked_breads_path = './dataset/data/pool/breads/*' conf.lr = 1e-4 conf.momentum = 0.9 conf.batch_size = 20 conf.epochs = 20 conf.image_size = 224
