from tqdm import tqdm
from keras.preprocessing.image import ImageDataGenerator
import os
from keras.applications.xception import Xception
import pickle
import gzip
maxlen = 192171
img_nums = {
i: len(os.listdir('assets/train_224/' + str(i) + '/'))
for i in range(1, 129)
}
train_data_gen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_data_gen.flow_from_directory(
directory='assets/train_224/',
target_size=(224, 224),
batch_size=32,
class_mode='categorical',
shuffle=False)
base_model = Xception(weights='imagenet',
include_top=False,
input_shape=(224, 224, 3))
for b in tqdm(range(maxlen // 32)):
x = train_generator.next()
z = base_model.predict_on_batch(x[0])
with gzip.open('assets/bn_xception_train_224/' + str(b) + '.p',
'wb',
compresslevel=6) as f:
pickle.dump((z, x[1]), f)
