DeepLearning.py

import tensorflow as tf
from tensorflow import keras

# Load the 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
x_test = x_test.astype('float32') / 255

# Define the CNN architecture
model = keras.Sequential([
    keras.layers.Conv2D(32, (3, 3), padding='same',
                        activation='relu', input_shape=x_train.shape[1:]),
    keras.layers.MaxPooling2D((2, 2)),
    keras.layers.Conv2D(64, (3, 3), padding='same', activation='relu'),
    keras.layers.MaxPooling2D((2, 2)),
    keras.layers.Flatten(),
    keras.layers.Dense(64, activation='relu'),
    keras.layers.Dense(10)
])

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

# Train the model on the training data
model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))

# Evaluate the model on the test data
test_loss, test_acc = model.evaluate(x_test, y_test)
print(f'Test accuracy: {test_acc}')