ValueError: Importing a SavedModel with tf.saved_model.load requires a 'tags=' argument

Question

I'm following tutorial Hub with Keras with tensorflow 1.14. I didn't pip install tf-nightly-gpu as it does not seem to be available for MacOS. All went ok though until the keras.Sequential() :

import tensorflow_hub as hub
from tensorflow.keras import layers
classifier_url ="https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/3"
IMAGE_SHAPE = (224, 224)
classifier = tf.keras.Sequential([
     hub.KerasLayer(classifier_url, input_shape=IMAGE_SHAPE+(3,))
])

Last line gives following error:

ValueError: Importing a SavedModel with tf.saved_model.load requires a 'tags=' argument if there is more than one MetaGraph. Got 'tags=None', but there are 2 MetaGraphs in the SavedModel with tag sets [[], ['train']]. Pass a 'tags=' argument to load this SavedModel.

Is it related to the tensorflow version or is it related to something else?

MacOS High Sierra 10.13.1 python 3.6.8 tensorflow 1.14.0

Answer 1

This worked for me in TF 1.14.0:

# Image information
HEIGHT = 224
WIDTH = 224
CHANNELS = 3
IMAGE_SHAPE = (HEIGHT, WIDTH)

feature_extractor_url = "https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/3" #@param {type:"string"}

module = hub.Module(feature_extractor_url, tags=['train'])
feature_extractor_layer = hub.KerasLayer(module, 
                                         input_shape=(HEIGHT, WIDTH, CHANNELS))