First part of CNN tutorial on KMNIST data set

CNN_prep.py

import tensorflow as tf
from tensorflow.python import keras
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense, Flatten, Conv2D, Dropout
from tensorflow.python.keras.layers import MaxPooling2D, BatchNormalization
from keras.utils.vis_utils import model_to_dot
from keras.utils import plot_model
from tensorflow.python.keras.callbacks import ModelCheckpoint
from tensorflow.keras.callbacks import EarlyStopping
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, roc_auc_score, accuracy_score
from sklearn.metrics import confusion_matrix
from tensorflow.keras.callbacks import Callback

IMG_ROWS = kmnist_train_image.shape[1]
IMG_COLS = kmnist_train_image.shape[2]
NUM_CLASS = len(np.unique(kmnist_train_label))

def preprocessing_kmnist(img_array, lab_array):
    """
    Function to preprocess the predictors array and label array to suitable format
    @Args:
      img_array: the numpy array of the image
      lab_array: the numpy array of the label
      
    Return:
      predictor array with channel dimension, and categorical encoding label in numpy array
    """
    y_ = keras.utils.to_categorical(lab_array, NUM_CLASS)  
    num_img = img_array.shape[0]
    x_ = img_array.reshape(num_img, IMG_ROWS, IMG_COLS, 1)  # we know that this is grayscale
    x_ = x_ / 255.
    return x_, y_


X, y = preprocessing_kmnist(kmnist_train_image, kmnist_train_label)
X_test, y_test = preprocessing_kmnist(kmnist_test_image, kmnist_test_label)
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=SEED) 
print('Training data shape: ', X_train.shape)
print('Validation data shape: ', X_val.shape)
print('Testing data shape: ', X_test.shape)

# Training data shape:  (48000, 28, 28, 1)
# Validation data shape:  (12000, 28, 28, 1)
# Testing data shape:  (10000, 28, 28, 1)