问题
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.
回答1:
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:
sess.run(tf.global_variables_initializer())
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.
回答2:
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)
回答3:
Inside
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)
model._make_predict_function()
return model, words, vocab
回答4:
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.
来源：https://stackoverflow.com/questions/47115946/tensor-is-not-an-element-of-this-graph