mapping_dict = json.loads(open(ID_MAPPING, "r").read()) encodedLabel_to_className = mapping_dict["encodedLabel_to_className"] className_to_categoryID = mapping_dict["className_to_categoryID"] # load submission.csv & reset 0 submission = pd.read_csv("./sample_submission.csv") submission["Category"] = [0] * submission.shape[0] print("[INFO] sample_sumission\n") print(submission.head()) print("[INFO] expect to predict =", submission.shape) ## augmentation aap = AspectAwarePreprocessor(64, 64) iap = ImageToArrayPreprocessor() means = json.loads(open(DATASET_MEAN).read()) mp = MeanPreprocessor(means["R"], means["G"], means["B"]) sdl = SimpleDatasetLoader(preprocessors=[aap, mp, iap], mode="test") # load in images print("[INFO] loading test images....") imagePaths = list(paths.list_images(args["dataset"])) print("[INFO] fetched %d images to test" % len(imagePaths)) data, names = sdl.load(imagePaths, verbose=1e4) testX = data.astype("float") / 255.0 imageIds = [name.split(".")[0] for name in names] ## load in models & predict with tf.device("/cpu:0"): model = load_model(MODEL, custom_objects={"f1_score": f1_score})
from keras.callbacks import ModelCheckpoint import json import matplotlib matplotlib.use("Agg") # parameters BATCH_SIZE = 128 ## initiate all image preprocessors sp = SimplePreprocessor(227, 227) pp = PatchPreprocessor(227, 227) iap = ImageToArrayPreprocessor() # load in RGB mean values of training set trainmeans = json.loads(open("./output/dogs_vs_cats_train_mean.json").read()) mp = MeanPreprocessor(trainmeans["R"], trainmeans["G"], trainmeans["B"]) ## initiate HDF5DataGenerator for trainset, trainvalset # initiate data augmentor for trainingset aug = ImageDataGenerator( rotation_range=20, zoom_range=0.15, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.15, horizontal_flip=True, fill_mode="nearest", ) trainGen = HDF5DatasetGenerator( config.TRAIN_HDF5,