Using TFlearner or other methods to do emotion detectiondata comes from https://github.com/muxspace/facial_expressions (images for training data, data contains two files: legend for training labels,...

Using TFlearner or other methods to do emotion detectiondata comes from https://github.com/muxspace/facial_expressions (images for training data, data contains two files: legend for training labels, 500_picts_satz for testing labels, test folder for testing images)
You can useimport data_loader data_loader.load_dataset(img_dir_path, label_file_path, valid_rate=0.1)to read the data which returnstrain_file_paths: training image file path list train_labels: training label array (numpy.ndarray) valid_file_paths: validation image file path list valid_labels: validation label array (numpy.ndarray) test_file_paths: test image file path list test_labels: test label array (numpy.ndarray) label_dict: label dictionary (key: str, value: int)
I find a github which may helphttps://github.com/atulapra/Emotion-detection