Using a Keras model inside a TF estimator

Question

I want to use one of the pre-built keras\' models (vgg, inception, resnet, etc) included in tf.keras.application for feature extraction to save me some time tra

Answer 1

You can have only tensors in model_fn. Maybe you can try something like this. This can be considered as a hack. The better part is that this code apart from just providing model_fn, it also stores weights of the loaded model as a checkpoint in . This helps you to get the weights when you call estimator.train(...) or estimator.evaluate(...) from the checkpoint.

def model_fn(features, labels, mode):  

    # Import the pretrained model
    base_model = tf.keras.applications.InceptionV3(
        weights='imagenet', 
        include_top=False,
        input_shape=(200,200,3)
    )    

    # some check
    if not hasattr(m, 'optimizer'):
        raise ValueError(
            'Given keras model has not been compiled yet. '
            'Please compile first '
            'before creating the estimator.')

    # get estimator object from model
    keras_estimator_obj = tf.keras.estimator.model_to_estimator(
        keras_model=base_model,
        model_dir=,
        config=,
    ) 

    # pull model_fn that we need (hack)
    return keras_estimator_obj._model_fn