Невозможно использовать данный сеанс для оценки тензора: график тензора отличается от графика сеанса

Question

Я просто не знаю, в чем проблема ... Ранее я пробовал InteractiveSession () и проходил явный сеанс, но эта ошибка просто не решается ... Я новичок в tenorflow ..., пожалуйста, помогите.

cost=-tf.reduce_sum(y*tf.log(y_))
train_step=tf.train.AdamOptimizer(LEARNING_RATE).minimize(cost)
correct_pred=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, 'float'))
predict=tf.argmax(y,1)

А вот мой сеанс

train_accuracies = []
validation_accuracies = []
x_range = []

num_examples=train_images.shape[0]
init=tf.global_variables_initializer()
minibatches=random_mini_batches(train_images,train_labels,
                            mini_batch_size = BATCH_SIZE)
display_step=1
init = tf.initialize_all_variables()
with tf.Session().as_default() as sess:
sess.run(init)
for epoch in range(TRAINING_ITERATIONS):
    for minibatch in minibatches:
        (minibatch_X,minibatch_Y)=minibatch
        if epoch%display_step == 0 or (epoch+1) == TRAINING_ITERATIONS:

            train_accuracy = accuracy.eval(session=sess,feed_dict={x:minibatch_X, 
                                                      y: minibatch_Y, 
                                                      keep_prob: 1.0})       
        if(VALIDATION_SIZE):
            validation_accuracy = accuracy.eval(session=sess,feed_dict={ x: validation_images[0:BATCH_SIZE], 
                                                            y: validation_labels[0:BATCH_SIZE], 
                                                            keep_prob: 1.0})                                  
            print('training_accuracy / validation_accuracy => %.2f / %.2f for step %d'%(train_accuracy, validation_accuracy, epoch))

            validation_accuracies.append(validation_accuracy)

        else:
             print('training_accuracy => %.4f for step %d'%(train_accuracy, epoch))
        train_accuracies.append(train_accuracy)
        x_range.append(epoch)

        # increase display_step
        if epoch%(display_step*10) == 0 and epoch:
            display_step *= 10
    # train on batch
    sess.run(train_step, feed_dict={x: minibatch_X, y:minibatch_Y, keep_prob: DROPOUT})

И генерируется следующая ошибка

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-63-910bbc0840b2> in <module>
      18                 train_accuracy = accuracy.eval(session=sess,feed_dict={x:minibatch_X, 
      19                                                           y: minibatch_Y,
 ---> 20                                                           keep_prob: 1.0})       
      21             if(VALIDATION_SIZE):
      22                 validation_accuracy = accuracy.eval(session=sess,feed_dict={ x: 
      validation_images[0:BATCH_SIZE], 

      /opt/conda/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py in eval(self, 
     feed_dict, session)
      788 
      789     """
  --> 790     return _eval_using_default_session(self, feed_dict, self.graph, session)
      791 
      792   def experimental_ref(self):

      /opt/conda/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py in 
     _eval_using_default_session(tensors, feed_dict, graph, session)
      5307   else:
      5308     if session.graph is not graph:
   -> 5309       raise ValueError("Cannot use the given session to evaluate tensor: "
      5310                        "the tensor's graph is different from the session's "
      5311                        "graph.")

      ValueError: Cannot use the given session to evaluate tensor: the tensor's graph is different 
      from the session's graph.

Подскажите, пожалуйста, как работать с двумя сеансами и как ее устранить. вопрос. И главная проблема в том, что я попытался передать сеанс как eval (session = sess), но он не работает. Это говорит о том, что используемый мной вычислительный граф отличается от графа тензора точности

Answer 1

Я воссоздал ошибку, вызванную возможными способами, а также предоставил исправление.

Предоставил больше комментариев в коде, чтобы быть более понятным об ошибке и ее исправлении.

Примечание - Я использовал один и тот же код с небольшими изменениями, чтобы воссоздать возможность возникновения ошибки и исправить ее.

Лучший код исправления присутствует в конце этого ответа.

Код ошибки 1 - Ошибка сеанса по умолчанию и использования переменной, созданной в другом графике

%tensorflow_version 1.x
import tensorflow as tf

g = tf.Graph()
with g.as_default():
  x = tf.constant(1.0)  # x is created in graph g

with tf.Session().as_default() as sess:
  y = tf.constant(2.0) # y is created in TensorFlow's default graph!!!
  print(y.eval(session=sess)) # y was created in TF's default graph, and is evaluated in
                  # default session, so everything is ok.  
  print(x.eval(session=sess)) # x was created in graph g and it is evaluated in session s
                  # which is tied to graph g, but it is evaluated in
                  # session s which is tied to graph g => ERROR

Выход -

2.0
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-5-f35cb204cf59> in <module>()
     10   print(y.eval(session=sess)) # y was created in TF's default graph, and is evaluated in
     11                   # default session, so everything is ok.
---> 12   print(x.eval(session=sess)) # x was created in graph g and it is evaluated in session s
     13                   # which is tied to graph g, but it is evaluated in
     14                   # session s which is tied to graph g => ERROR

1 frames
/tensorflow-1.15.2/python3.6/tensorflow_core/python/framework/ops.py in _eval_using_default_session(tensors, feed_dict, graph, session)
   5402   else:
   5403     if session.graph is not graph:
-> 5404       raise ValueError("Cannot use the given session to evaluate tensor: "
   5405                        "the tensor's graph is different from the session's "
   5406                        "graph.")

ValueError: Cannot use the given session to evaluate tensor: the tensor's graph is different from the session's graph.

Код ошибки 2 - Ошибка с сеансом графика по умолчанию и использованием переменной, созданной в графике по умолчанию

%tensorflow_version 1.x
import tensorflow as tf

g = tf.Graph()
with g.as_default():
  x = tf.constant(1.0)  # x is created in graph g

with tf.Session(graph=g).as_default() as sess:
  print(x.eval(session=sess)) # x was created in graph g and it is evaluated in session s
                         # which is tied to graph g, so everything is ok.
  y = tf.constant(2.0) # y is created in TensorFlow's default graph!!!
  print(y.eval()) # y was created in TF's default graph, but it is evaluated in
                  # session s which is tied to graph g => ERROR

Вывод -

1.0
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-15-6b8b687c5178> in <module>()
     10                          # which is tied to graph g, so everything is ok.
     11   y = tf.constant(2.0) # y is created in TensorFlow's default graph!!!
---> 12   print(y.eval()) # y was created in TF's default graph, but it is evaluated in
     13                   # session s which is tied to graph g => ERROR

1 frames
/tensorflow-1.15.2/python3.6/tensorflow_core/python/framework/ops.py in _eval_using_default_session(tensors, feed_dict, graph, session)
   5396                        "`eval(session=sess)`")
   5397     if session.graph is not graph:
-> 5398       raise ValueError("Cannot use the default session to evaluate tensor: "
   5399                        "the tensor's graph is different from the session's "
   5400                        "graph. Pass an explicit session to "

ValueError: Cannot use the default session to evaluate tensor: the tensor's graph is different from the session's graph. Pass an explicit session to `eval(session=sess)`.

Код ошибки 3 - Как предлагается в Код ошибки 2 - выходной сигнал, чтобы передать явный сеанс в eval(session=sess). Давайте попробуем это.

%tensorflow_version 1.x
import tensorflow as tf

g = tf.Graph()
with g.as_default():
  x = tf.constant(1.0)  # x is created in graph g

with tf.Session(graph=g).as_default() as sess:
  print(x.eval(session=sess)) # x was created in graph g and it is evaluated in session s
                         # which is tied to graph g, so everything is ok.
  y = tf.constant(2.0) # y is created in TensorFlow's default graph!!!
  print(y.eval(session=sess)) # y was created in TF's default graph, but it is evaluated in
                  # session s which is tied to graph g => ERROR

Вывод -

1.0
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-16-83809aa4e485> in <module>()
     10                          # which is tied to graph g, so everything is ok.
     11   y = tf.constant(2.0) # y is created in TensorFlow's default graph!!!
---> 12   print(y.eval(session=sess)) # y was created in TF's default graph, but it is evaluated in
     13                   # session s which is tied to graph g => ERROR

1 frames
/tensorflow-1.15.2/python3.6/tensorflow_core/python/framework/ops.py in _eval_using_default_session(tensors, feed_dict, graph, session)
   5402   else:
   5403     if session.graph is not graph:
-> 5404       raise ValueError("Cannot use the given session to evaluate tensor: "
   5405                        "the tensor's graph is different from the session's "
   5406                        "graph.")

ValueError: Cannot use the given session to evaluate tensor: the tensor's graph is different from the session's graph.

Fix 1 - Исправить сессией по умолчанию и переменной, не назначенной ни одному графику

%tensorflow_version 1.x
import tensorflow as tf

x = tf.constant(1.0)  # x is in not assigned to any graph

with tf.Session().as_default() as sess:
  y = tf.constant(2.0) # y is created in TensorFlow's default graph!!!
  print(y.eval(session=sess)) # y was created in TF's default graph, and is evaluated in
                  # default session, so everything is ok.  
  print(x.eval(session=sess)) # x not assigned to any graph, and is evaluated in
                  # default session, so everything is ok.  

Вывод -

2.0
1.0

Исправление 2 - Лучшее исправление - это четкое разделение фазы построения и фазы выполнения.

import tensorflow as tf

g = tf.Graph()
with g.as_default():
  x = tf.constant(1.0)  # x is created in graph g
  y = tf.constant(2.0) # y is created in graph g

with tf.Session(graph=g).as_default() as sess:
  print(x.eval()) # x was created in graph g and it is evaluated in session s
                         # which is tied to graph g, so everything is ok.
  print(y.eval()) # y was created in graph g and it is evaluated in session s
                         # which is tied to graph g, so everything is ok.

Выход -

1.0
2.0