Tensor is not an element of this graph

Question

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)         tf.global_variables_initializer().run(session=sess)     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__':     Save = './save'     M, W, V = LoadPredictor(Save)     Sess = SessionOpener(Save)     word = M.sample(Sess, W, V, 1, str(123), 2, 1, 4)     Sess.close() 

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             else:                 raise Exception("model type not supported: {}".format(args.model))              cells = []             for _ in range(args.num_layers):                 cell = cell_fn(args.rnn_size)                 cells.append(cell)              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])                 variable_summaries(softmax_w)                 softmax_b = tf.get_variable("softmax_b", [args.vocab_size])                 variable_summaries(softmax_b)                 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])],                     args.vocab_size)             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),                 args.grad_clip)             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)))          ret = ''         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)                     else:                         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     allow_operation=False)   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. 

Answer 1

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)