Tensor is not an element of this graph

Try first:

import tensorflow as tfgraph = tf.get_default_graph()

Then, when you need to use predict:

with graph.as_default():     y = model.predict(X)

When you create a Model, the session hasn't been restored yet. All placeholders, variables and ops that are defined in Model.__init__ are placed in a new graph, which makes itself a default graph inside with block. This is the key line:

with tf.Graph().as_default():  ...

This means that this instance of tf.Graph() equals to tf.get_default_graph() instance inside with block, but not before or after it. From this moment on, there exist two different graphs.

When you later create a session and restore a graph into it, you can't access the previous instance of tf.Graph() in that session. Here's a short example:

with tf.Graph().as_default() as graph:  var = tf.get_variable("var", shape=[3], initializer=tf.zeros_initializer)# This workswith tf.Session(graph=graph) as sess:  sess.run(tf.global_variables_initializer())  print(sess.run(var))  # ok because `sess.graph == graph`# This failssaver = tf.train.import_meta_graph('/tmp/model.ckpt.meta')with tf.Session() as sess:  saver.restore(sess, "/tmp/model.ckpt")  print(sess.run(var))   # var is from `graph`, not `sess.graph`!

The best way to deal with this is give names to all nodes, e.g. 'input', 'target', etc, save the model and then look up the nodes in the restored graph by name, something like this:

saver = tf.train.import_meta_graph('/tmp/model.ckpt.meta')with tf.Session() as sess:  saver.restore(sess, "/tmp/model.ckpt")        input_data = sess.graph.get_tensor_by_name('input')  target = sess.graph.get_tensor_by_name('target')

This method guarantees that all nodes will be from the graph in session.

If you are calling the python function that calls Tensorflow from an external module, make sure that you the model isn't being loaded as a global variable or else it may not be loaded in time for usage. This happened to me calling a Tensorflow model from the Flask server.