def CNN_classification(test_id, valid_id): input_size = 100 def LoadData(fileList_NF, fileList_F): data = [] labels = [] for im_path in fileList_NF: img = numpy.asarray( image.load_img(im_path, target_size=(input_size, input_size))) data.append(img) labels.append('0') for im_path in fileList_F: img = numpy.asarray(image.load_img(im_path, target_size=(input_size, input_size)), dtype='float64') data.append(img) labels.append('1') return numpy.asarray(data), numpy.asarray(labels) fileList_NF = sorted(glob.glob('medfilt5_label_0/*.png')) fileList_F = sorted(glob.glob('medfilt5_label_1/*.png')) testfileList_NF = filter( lambda x: x.split('/')[-1].split('E')[0] == 'U' + str(test_id), fileList_NF) testfileList_F = filter( lambda x: x.split('/')[-1].split('E')[0] == 'U' + str(test_id), fileList_F) validfileList_NF = filter( lambda x: x.split('/')[-1].split('E')[0] == 'U' + str(valid_id), fileList_NF) validfileList_F = filter( lambda x: x.split('/')[-1].split('E')[0] == 'U' + str(valid_id), fileList_F) trainfileList_NF = filter( lambda x: x.split('/')[-1].split('E')[0] != 'U' + str(test_id) and x. split('/')[-1].split('E')[0] != 'U' + str(valid_id), fileList_NF) trainfileList_F = filter( lambda x: x.split('/')[-1].split('E')[0] != 'U' + str(test_id) and x. split('/')[-1].split('E')[0] != 'U' + str(valid_id), fileList_F) train_data, train_labels = LoadData(trainfileList_NF, trainfileList_F) test_data, test_labels = LoadData(testfileList_NF, testfileList_F) valid_data, valid_labels = LoadData(validfileList_NF, validfileList_F) print train_data.shape, test_data.shape, valid_data.shape clf.CNNclassifier(train_data, train_labels, valid_data, valid_labels, input_size)