Reproduces an error when loading multiple shared instances of a Keras model in the same scope

tf_keras_error_repro.py

import tensorflow as tf

'''
This snippet reproduces an error when trying to load multiple shared instances
of a Keras model in TensorFlow, when accessing the same scope multiple times.
Creating multiple instances repeatedly in a single step works.
'''

def load_vgg19_multiple(tensors):
    '''Creates n shared instances of VGG19, in a single run.'''
    
    with tf.variable_scope('VGG19_OK', None, [tensors], reuse=tf.AUTO_REUSE):
        return [
            tf.contrib.keras.applications.VGG19(weights='imagenet', include_top=False, input_tensor=x).layers[-1]
            for x in tensors
        ]

def load_vgg19(x):
    '''Creates a single shared instance of VGG19.'''
    
    with tf.variable_scope('VGG19_FAIL', None, [x], reuse=tf.AUTO_REUSE):
        m = tf.contrib.keras.applications.VGG19(weights='imagenet', include_top=False, input_tensor=x)
        return m.layers[-1]

t = tf.placeholder(tf.float32, [12, 128, 128, 3]) # dummy

vgg_multiple = load_vgg19_multiple([t, t, t, t, t])
print('Multiple OK')

vgg1 = load_vgg19(t)
vgg2 = load_vgg19(t) # fails here: ValueError: You are trying to load a weight file containing 16 layers into a model with 0 layers
print('Single OK')