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,