Improve style in Keras3 test script

attachments/mnistKeras3.py

 import tensorflow as tf
 import keras
 from keras.models import Sequential
-from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
+from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Input
 from keras.utils import to_categorical
 from keras.datasets import mnist
 
@@ -9,35 +9,38 @@ print('=========================')
 print('TensorFlow Version', tf.__version__)
 print('Keras Version     ', keras.__version__)
 print('=========================')
+print(tf.config.list_physical_devices())
+print('=========================')
 
-(XTrain, yTrain), (XTest, yTest) = mnist.load_data()
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
 
-XTrain = XTrain.reshape(60000, 28, 28, 1)
-XTest  = XTest.reshape(10000, 28, 28, 1)
-XTrain = XTrain / 255
-XTest  = XTest / 255
+X_train = X_train.reshape(60000, 28, 28, 1)
+X_test  = X_test.reshape(10000, 28, 28, 1)
+X_train = X_train / 255
+X_test  = X_test / 255
 
-YTrain = to_categorical(yTrain, 10)
-YTest  = to_categorical(yTest, 10)
+y_train = to_categorical(y_train, 10)
+y_test  = to_categorical(y_test, 10)
 
-myANN = Sequential()
-myANN.add(Conv2D(9, kernel_size=(3, 3), activation='relu', kernel_initializer='he_uniform', input_shape=(28, 28, 1)))
-myANN.add(MaxPooling2D(2))
-myANN.add(Flatten())
-myANN.add(Dense(20, activation='tanh'))
-myANN.add(Dense(10, activation='softmax'))
-myANN.compile(loss='categorical_crossentropy', optimizer='nadam', metrics=['accuracy'])
-myANN.fit(XTrain, YTrain, batch_size=500, epochs=5, verbose=True)
+model = Sequential()
+myANN.add(Input((28, 28, 1)))
+model.add(Conv2D(9, kernel_size=(3, 3), activation='relu', kernel_initializer='he_uniform'))
+model.add(MaxPooling2D(2))
+model.add(Flatten())
+model.add(Dense(20, activation='tanh'))
+model.add(Dense(10, activation='softmax'))
+model.compile(loss='categorical_crossentropy', optimizer='nadam', metrics=['accuracy'])
+model.fit(X_train, y_train, batch_size=500, epochs=5, verbose=True)
 
-mse, acc = myANN.evaluate(XTest, YTest, verbose=False)
+mse, acc = model.evaluate(X_test, y_test, verbose=False)
 print('Test loss:', mse, 'Test accuracy:', acc)
 
 import matplotlib.pyplot as plt
 fig = plt.figure()
 for i in range(9):
     ax = fig.add_subplot(3, 3, i + 1)
-    ax.imshow(XTrain[i].squeeze(), cmap='gray', interpolation='none')
-    ax.set_title(yTrain[i])
+    ax.imshow(X_train[i].squeeze(), cmap='gray', interpolation='none')
+    ax.set_title(y_train[i])
 plt.tight_layout()
 plt.show()