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from tensorflow.keras.applications import VGG16
from tensorflow.keras import layers, models
from tensorflow.keras.datasets import cifar10
import tensorflow as tf
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
X_train = tf.image.resize(X_train, (96,96)) / 255.0
X_test = tf.image.resize(X_test, (96,96)) / 255.0
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(96,96,3))
base_model.trainable = False # Freeze pre-trained layers
model = models.Sequential([
    base_model,
    layers.Flatten(),
    layers.Dense(128, activation='relu'),
    layers.Dropout(0.3),
    layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(X_train[:5000], y_train[:5000], epochs=5,
validation_data=(X_test[:1000], y_test[:1000]))
test_loss, test_acc = model.evaluate(X_test[:1000], y_test[:1000])
print(f"Test Accuracy: {test_acc:.4f}")