Inference at testing time

The following code can be used to carry out inference on the unlabeled test data:

import keras
import numpy as np
import pandas as pd
import cv2
import os
import time
from sklearn.externals import joblib
import argparse

# Read the Image and resize to the suitable dimension size
def get_im_cv2(path,dim=224):
    img = cv2.imread(path)
    resized = cv2.resize(img, (dim,dim), cv2.INTER_LINEAR)
    return resized

# Pre Process the Images based on the ImageNet pre-trained model Image transformation
def pre_process(img):
    img[:,:,0] = img[:,:,0] - 103.939
    img[:,:,1] = img[:,:,0] - 116.779
    img[:,:,2] = img[:,:,0] - 123.68
    return img

   
# Function to build test input data
def read_data_test(path,dim):
    test_X = [] 
    test_files = []
    file_list = os.listdir(path) 
    for f in file_list:
        img = get_im_cv2(path + '/' + f)
        img = pre_process...