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 npnp.random.seed(1000)import osimport globimport cv2import datetimeimport pandas as pdimport timeimport warningswarnings.filterwarnings("ignore")from sklearn.model_selection import KFoldfrom sklearn.metrics import cohen_kappa_scorefrom keras.models import Sequential,Modelfrom keras.layers.core import Dense, Dropout, Flattenfrom keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2Dfrom keras.layers ...
