def build_resnet50(num_classes, input_shape=(150, 150, 3)):
    return ResNet(
        num_classes=num_classes,
        block_counts=[3, 4, 6, 3],  # From the table for ResNet-152
        blocktype=BottleneckBlock,  # Use bottleneck blocks
        initial_filters=64,
    )


resnet50 = build_resnet50(num_classes=len(class_names))
dummy_input = tf.keras.Input(shape=(150, 150, 3))
resnet50(dummy_input)  # Build the model
resnet50.summary()

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