Le tenseur n'est pas un élément de ce graphique
Je reçois cette erreur
'ValueError: Tensor Tensor ("Placeholder: 0", forme = (1, 1), dtype = int32) n'est pas un élément de ce graphique.
Le code fonctionne parfaitement sans with tf.Graph(). as_default():. Cependant, je dois appeler M.sample(...) plusieurs fois et chaque fois que la mémoire ne sera pas libre après session.close(). Il y a probablement une fuite de mémoire, mais vous ne savez pas où elle se trouve.
Je souhaite restaurer un réseau de neurones pré-entraîné, le définir en tant que graphique par défaut et le tester plusieurs fois (comme 10000) par-dessus le graphique par défaut sans le rendre plus grand à chaque fois.
Le code est:
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 __== '__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()
Et le modèle est:
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
et le résultat est:
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.
Lorsque vous créez une Model, la session n'a pas encore été restaurée. Tous les espaces réservés, variables et opérations définis dans Model.__init__ sont placés dans un new graph, ce qui en fait un graphe par défaut dans le bloc with. Ceci est la ligne clé:
with tf.Graph().as_default():
...
Cela signifie que cette instance de tf.Graph() équivaut à tf.get_default_graph() instance dans le bloc with, mais pas avant ni après . A partir de ce moment, il existe deux graphes différents.
Lorsque vous créez ultérieurement une session et que vous y restaurez un graphique, vous ne pouvez pas accéder à l'instance précédente de tf.Graph() dans cette session. Voici un court exemple:
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`!
La meilleure façon de gérer cela consiste à donner des noms à tous les nœuds, par exemple. 'input', 'target', etc., enregistrez le modèle, puis recherchez les nœuds dans le graphique restore par nom, comme suit:
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')
Cette méthode garantit que tous les nœuds seront issus du graphique en session.
Essayez de commencer avec:
import tensorflow as tf
global graph,model
graph = tf.get_default_graph()
Quand vous avez besoin d'utiliser Pred:
with graph.as_default():
y = model.predict(X)
Inside def LoadPredictor(save):
Juste après le chargement du modèle, ajoutez model._make_predict_function()
Donc, la fonction devient:
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