from tensorflow.keras.applications import ResNet50
from tensorflow.keras import layers, models
from tensorflow.keras.optimizers import Adam

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(32, 32, 3))
x = base_model.output
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dense(1024, activation='relu')(x)
x = layers.Dense(10, activation='softmax')(x)

model = models.Model(inputs=base_model.input, outputs=x)

for layer in base_model.layers:
    layer.trainable = False

model.compile(optimizer=Adam(), loss='sparse_categorical_crossentropy', metrics=['accuracy'])

model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))

for layer in base_model.layers[-10:]:
    layer.trainable = True

model.compile(optimizer=Adam(learning_rate=1e-5), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))

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