Python implementation of the training process

The following Python code block shows an end-to-end implementation of the training process. It consists of all of the functional blocks that were discussed in the preceding sections. Let's start by calling all of the Python packages that are required, as follows:

import numpy as np
np.random.seed(1000)

import os
import glob
import cv2
import datetime
import pandas as pd
import time
import warnings
warnings.filterwarnings("ignore")
from sklearn.model_selection import KFold
from sklearn.metrics import cohen_kappa_score
from keras.models import Sequential,Model
from keras.layers.core import Dense, Dropout, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras.layers import GlobalMaxPooling2D,GlobalAveragePooling2D
from keras.optimizers import SGD
from keras.callbacks import EarlyStopping
from...