import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# Load CIFAR-10 dataset
(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()

# Normalize pixel values between 0 and 1
x_train = x_train.astype("float32") / 255.0
x_test = x_test.astype("float32") / 255.0

# Define the CNN architecture
model = keras.Sequential([
    layers.Conv2D(32, (3, 3), activation="relu", input_shape=(32, 32, 3)),
    layers.MaxPooling2D(pool_size=(2, 2)),
    layers.Conv2D(64, (3, 3), activation="relu"),
    layers.MaxPooling2D(pool_size=(2, 2)),
    layers.Flatten(),
    layers.Dense(64, activation="relu"),
    layers.Dense(10),
])

# Compile the model
model.compile(optimizer="adam",
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=["accuracy"])

# Train the model
model.fit(x_train, y_train, epochs=10, batch_size=64, validation_split=0.1)

# Evaluate the model on the test dataset
test_loss, test_acc = model.evaluate(x_test, y_test)
print("Test Loss:", test_loss)
print("Test Accuracy:", test_acc)