Tensor is not an element of this graph

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I’m getting this error

‘ValueError: Tensor Tensor(“Placeholder:0”, shape=(1, 1), dtype=int32)
is not an element of this graph.’

The code is running perfectly fine without with tf.Graph(). as_default():. However I need to call M.sample(...) multiple times and each time the memory won’t be free after session.close(). Probably there is a memory leak but not sure where is it.

I want to restore a pre-trained neural network, set it as default graph, and testing it multiple times (like 10000) over the default graph without making it larger each time.

The code is:

def SessionOpener(save):
    grph = tf.get_default_graph()
    sess = tf.Session(graph=grph)
    ckpt = tf.train.get_checkpoint_state(save)
    saver = tf.train.import_meta_graph('./predictor/save/model.ckpt.meta')
    if ckpt and ckpt.model_checkpoint_path:
        saver.restore(sess, ckpt.model_checkpoint_path)
    return sess

def LoadPredictor(save):
    with open(os.path.join(save, 'config.pkl'), 'rb') as f:
        saved_args = cPickle.load(f)
    with open(os.path.join(save, 'words_vocab.pkl'), 'rb') as f:
        words, vocab = cPickle.load(f)
    model = Model(saved_args, True)
    return model, words, vocab

if __name__ == '__main__':
    M, W, V = LoadPredictor(Save)
    Sess = SessionOpener(Save)
    word = M.sample(Sess, W, V, 1, str(123), 2, 1, 4)

And the model is:

class Model():
    def __init__(self, args, infer=False):
        with tf.Graph().as_default():
            self.args = args
            if infer:
                args.batch_size = 1
                args.seq_length = 1

            if args.model == 'rnn':
                cell_fn = rnn.BasicRNNCell
            elif args.model == 'gru':
                cell_fn = rnn.GRUCell
            elif args.model == 'lstm':
                cell_fn = rnn.BasicLSTMCell
                raise Exception("model type not supported: {}".format(args.model))

            cells = []
            for _ in range(args.num_layers):
                cell = cell_fn(args.rnn_size)

            self.cell = cell = rnn.MultiRNNCell(cells)

            self.input_data = tf.placeholder(tf.int32, [args.batch_size, args.seq_length])
            self.targets = tf.placeholder(tf.int32, [args.batch_size, args.seq_length])
            self.initial_state = cell.zero_state(args.batch_size, tf.float32)
            self.batch_pointer = tf.Variable(0, name="batch_pointer", trainable=False, dtype=tf.int32)
            self.inc_batch_pointer_op = tf.assign(self.batch_pointer, self.batch_pointer + 1)
            self.epoch_pointer = tf.Variable(0, name="epoch_pointer", trainable=False)
            self.batch_time = tf.Variable(0.0, name="batch_time", trainable=False)
            tf.summary.scalar("time_batch", self.batch_time)

            def variable_summaries(var):
            """Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
                with tf.name_scope('summaries'):
                    mean = tf.reduce_mean(var)
                    tf.summary.scalar('mean', mean)
                    tf.summary.scalar('max', tf.reduce_max(var))
                    tf.summary.scalar('min', tf.reduce_min(var))

            with tf.variable_scope('rnnlm'):
                softmax_w = tf.get_variable("softmax_w", [args.rnn_size, args.vocab_size])
                softmax_b = tf.get_variable("softmax_b", [args.vocab_size])
                with tf.device("/cpu:0"):
                    embedding = tf.get_variable("embedding", [args.vocab_size, args.rnn_size])
                    inputs = tf.split(tf.nn.embedding_lookup(embedding, self.input_data), args.seq_length, 1)
                    inputs = [tf.squeeze(input_, [1]) for input_ in inputs]

            def loop(prev, _):
                prev = tf.matmul(prev, softmax_w) + softmax_b
                prev_symbol = tf.stop_gradient(tf.argmax(prev, 1))
                return tf.nn.embedding_lookup(embedding, prev_symbol)

            outputs, last_state = legacy_seq2seq.rnn_decoder(inputs, self.initial_state, cell, loop_function=loop if infer else None, scope="rnnlm")
            output = tf.reshape(tf.concat(outputs, 1), [-1, args.rnn_size])
            self.logits = tf.matmul(output, softmax_w) + softmax_b
            self.probs = tf.nn.softmax(self.logits)
            loss = legacy_seq2seq.sequence_loss_by_example([self.logits],
                    [tf.reshape(self.targets, [-1])],
                    [tf.ones([args.batch_size * args.seq_length])],
            self.cost = tf.reduce_sum(loss) / args.batch_size / args.seq_length
            tf.summary.scalar("cost", self.cost)
            self.final_state = last_state
            self.lr = tf.Variable(0.0, trainable=False)
            tvars = tf.trainable_variables()
            grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, tvars),
            optimizer = tf.train.AdamOptimizer(self.lr)
            self.train_op = optimizer.apply_gradients(zip(grads, tvars))

    def sample(self, sess, words, vocab, num=200, prime="first all", sampling_type=1, pick=0, width=4):
        def weighted_pick(weights):
            t = np.cumsum(weights)
            s = np.sum(weights)
            return(int(np.searchsorted(t, np.random.rand(1)*s)))

        if pick == 1:
            state = sess.run(self.cell.zero_state(1, tf.float32))

            if not len(prime) or prime == ' ':
                prime  = random.choice(list(vocab.keys()))
            for word in prime.split()[:-1]:
                x = np.zeros((1, 1))
                x[0, 0] = vocab.get(word,0)
                feed = {self.input_data: x, self.initial_state:state}
                [state] = sess.run([self.final_state], feed)

            ret = prime
            word = prime.split()[-1]
            for n in range(num):
                x = np.zeros((1, 1))
                x[0, 0] = vocab.get(word, 0)
                feed = {self.input_data: x, self.initial_state:state}
                [probs, state] = sess.run([self.probs, self.final_state], feed)
                p = probs[0]

                if sampling_type == 0:
                    sample = np.argmax(p)
                elif sampling_type == 2:
                    if word == '\n':
                        sample = weighted_pick(p)
                        sample = np.argmax(p)
                else: # sampling_type == 1 default:
                    sample = weighted_pick(p)

                ret = words[sample]
        return ret

and the output is:

Traceback (most recent call last):
  File "/rcg/software/Linux/Ubuntu/16.04/amd64/TOOLS/TENSORFLOW/1.2.1-GPU-PY352/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 942, in _run
  File "/rcg/software/Linux/Ubuntu/16.04/amd64/TOOLS/TENSORFLOW/1.2.1-GPU-PY352/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2584, in as_graph_element
    return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
  File "/rcg/software/Linux/Ubuntu/16.04/amd64/TOOLS/TENSORFLOW/1.2.1-GPU-PY352/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2663, in _as_graph_element_locked
    raise ValueError("Tensor %s is not an element of this graph." % obj)
ValueError: Tensor Tensor("Placeholder:0", shape=(1, 1), dtype=int32) is not an element of this graph.

Try first:

import tensorflow as tf
graph = 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 works
with tf.Session(graph=graph) as sess:
  print(sess.run(var))  # ok because `sess.graph == graph`

# This fails
saver = 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.

Use this line before making models:


This will make a new graph to use in new models.

For me, this issue was resolved by using Keras’ APIs to save and load model. I had more than one models being trained in my code and I had to use the particular model for prediction under a condition.

So I saved the entire model to a HDF5 file after model training

# The '.h5' extension indicates that the model should be saved to HDF5.

and then recreate/reload the saved model at the time of prediction

my_model = tf.keras.models.load_model('my_model.h5')

This helped me get rid of

*Tensor not an element of this graph*


def LoadPredictor(save):

Just after loading the model, add model._make_predict_function()

So the function becomes:

def LoadPredictor(save):
    with open(os.path.join(save, 'config.pkl'), 'rb') as f:
        saved_args = cPickle.load(f)
    with open(os.path.join(save, 'words_vocab.pkl'), 'rb') as f:
        words, vocab = cPickle.load(f)
    model = Model(saved_args, True)
    return model, words, vocab

I had this issue when trying to make a model using another class that uses keras to create a model. I got this issue corrected by doing the following

import nn_classifierclass as cls
from keras import backend
for repeat in range(repeats):
    backend.clear_session() ##NOTICE THIS
    neural_net = cls.Classifier(.....)

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