import tensorflow as tf

# Define a simple CNN model
model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(10, activation='softmax')
])

# Define the optimizer
optimizer = tf.keras.optimizers.Adam()

# Compile the model with the optimizer and loss function
model.compile(optimizer=optimizer,
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Train the model on the dataset
model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels))