cnn_mnist.py: For the Convolutional Neural Network (CNN) model.

Author: asRot0

machine_learning/neural_networks/cnn_mnist.py

@@ -19,11 +19,11 @@
 from tensorflow.keras.utils import to_categorical
 
 # Load and preprocess the MNIST data
-(X_train, y_train), (X_test, y_test) = mnist.load_data()
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
 
 # Normalize the pixel values (0 to 1)
-X_train = X_train.reshape(-1, 28, 28, 1).astype("float32") / 255
-X_test = X_test.reshape(-1, 28, 28, 1).astype("float32") / 255
+x_train = x_train.reshape(-1, 28, 28, 1).astype("float32") / 255
+x_test = x_test.reshape(-1, 28, 28, 1).astype("float32") / 255
 
 # Convert labels to one-hot encoding
 y_train = to_categorical(y_train, 10)
@@ -64,9 +64,9 @@
 
 # Train the model
 history = model.fit(
-    X_train, y_train, epochs=5, batch_size=32, validation_data=(X_test, y_test)
+    x_train, y_train, epochs=5, batch_size=32, validation_data=(x_test, y_test)
 )
 
 # Evaluate the model on test data
-test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)
+test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
 print(f"\nTest accuracy: {test_acc}")