Tensorflow 오류 : InvalidArgumentError : dtype float 및 shape [? : 784]로 자리 표시 자 텐서 '자리 표시 자'에 값을 입력해야합니다.

Question

우분투 16.04에서 tensorflow 버전 1.3.0을 실행 중입니다. 주요 의도는 tensorboard에 그래프를 시각화하는 것입니다. 코드를 실행하는 동안 코드가 처음 실행될 때 모든 것이 완벽하게 잘된 것처럼 보입니다. 나는이 오류가 그러나 나는 2 시간 동안 코드를 실행했을 때 그 이후 : 코드 여기

InvalidArgumentError Traceback (most recent call last) 
<ipython-input-26-149c9b9d8878> in <module>() 
11    sess.run(optimizer, feed_dict={x: batch_xs, y: 
batch_ys}) 
12    avg_cost += sess.run(cost_function, feed_dict={x: 
batch_xs, y: batch_ys})/total_batch 
---> 13    summary_str = sess.run(merged_summary_op, 
feed_dict={x: batch_xs, y: batch_ys}) 
14    summary_writer.add_summary(summary_str, 
iteration*total_batch + i) 
15   if iteration % display_step == 0: 

/home/niraj/anaconda2/lib/python2.7/site- 
packages/tensorflow/python/client/session.pyc in run(self, fetches, 
feed_dict, options, run_metadata) 
893  try: 
894  result = self._run(None, fetches, feed_dict, options_ptr, 
--> 895       run_metadata_ptr) 
896  if run_metadata: 
897   proto_data = tf_session.TF_GetBuffer(run_metadata_ptr) 

InvalidArgumentError      Traceback (most recent call 
last) 
<ipython-input-26-149c9b9d8878> in <module>() 
11    sess.run(optimizer, feed_dict={x: batch_xs, y: 
batch_ys}) 
12    avg_cost += sess.run(cost_function, feed_dict={x: 
batch_xs, y: batch_ys})/total_batch 
---> 13    summary_str = sess.run(merged_summary_op, 
feed_dict={x: batch_xs, y: batch_ys}) 
14    summary_writer.add_summary(summary_str, 
iteration*total_batch + i) 
15   if iteration % display_step == 0: 

/home/niraj/anaconda2/lib/python2.7/site- 
packages/tensorflow/python/client/session.pyc in run(self, fetches, 
feed_dict, options, run_metadata) 
893  try: 
894  result = self._run(None, fetches, feed_dict, options_ptr, 
--> 895       run_metadata_ptr) 
896  if run_metadata: 
897   proto_data = tf_session.TF_GetBuffer(run_metadata_ptr) 

/home/niraj/anaconda2/lib/python2.7/site- 
packages/tensorflow/python/client/session.pyc in _run(self, handle, 
fetches, feed_dict, options, run_metadata) 
1122  if final_fetches or final_targets or (handle and 
feed_dict_tensor): 
1123  results = self._do_run(handle, final_targets, 
final_fetches, 
-> 1124        feed_dict_tensor, options, 
run_metadata) 
1125  else: 
1126  results = [] 

/home/niraj/anaconda2/lib/python2.7/site- 
packages/tensorflow/python/client/session.pyc in _do_run(self, handle, 
target_list, fetch_list, feed_dict, options, run_metadata) 
1319  if handle is None: 
1320  return self._do_call(_run_fn, self._session, feeds, 
fetches, targets, 
-> 1321       options, run_metadata) 
1322  else: 
1323  return self._do_call(_prun_fn, self._session, handle, 
feeds, fetches) 

/home/niraj/anaconda2/lib/python2.7/site- 
packages/tensorflow/python/client/session.pyc in _do_call(self, fn, 
*args) 
1338   except KeyError: 
1339   pass 
-> 1340  raise type(e)(node_def, op, message) 
1341 
1342 def _extend_graph(self): 

됩니다 :

import tensorflow as tf 
from tensorflow.examples.tutorials.mnist import input_data 
mnist = input_data.read_data_sets("/home/niraj/Documents/artificial 
intelligence/projects/tensorboard", one_hot=True) 

learning_rate = 0.01 
training_iteration = 200 
batch_size = 100 
display_step = 2 

# TF graph input 
x = tf.placeholder('float32', [None, 784]) # mnist data image of shape 
28*28=784 
y = tf.placeholder('float32',[None, 10]) # 0-9 digits recognition => 
10 classes 

W = tf.Variable(tf.zeros([784, 10])) 
b = tf.Variable(tf.zeros([10])) 

with tf.name_scope("Wx_b") as scope: 
    model = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax 

w_h = tf.summary.histogram("weights", W) 
b_h = tf.summary.histogram("biases", b) 

with tf.name_scope("cost_function") as scope: 
    cost_function = -tf.reduce_sum(y*tf.log(model)) 
tf.summary.scalar("cost_function", cost_function) 

with tf.name_scope("train") as scope: 
    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function) 

init = tf.global_variables_initializer() 
merged_summary_op = tf.summary.merge_all() 
with tf.Session() as sess: 
    sess.run(init) 
summary_writer = tf.summary.FileWriter('/home/niraj/Documents/artificial intelligence/projects/tensorboard', graph=sess.graph) 
for iteration in range(training_iteration): 
    avg_cost = 0 
    total_batch = int(mnist.train.num_examples/batch_size) 
    for i in range(total_batch): 
     batch_xs, batch_ys = mnist.train.next_batch(batch_size) 
     sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys}) 
     avg_cost += sess.run(cost_function, feed_dict={x: batch_xs, y: batch_ys})/total_batch 
     summary_str = sess.run(merged_summary_op, feed_dict={x: batch_xs, y: batch_ys})  
     summary_writer.add_summary(summary_str, iteration*total_batch + i) 
    if iteration % display_step == 0: 
     print "Iteration:", '%04d' % (iteration + 1), "cost=", "{:.9f}".format(avg_cost) 


print "Tuning completed!" 
predictions = tf.equal(tf.argmax(model, 1), tf.argmax(y, 1)) 
accuracy = tf.reduce_mean(tf.cast(predictions, "float")) 
print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}) 

하는 당신에게 상기시켜 여기

InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [?,784] 
[[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[?,784], _device="/job:localhost/replica:0/task:0/cpu:0"]()]] 

는 역 추적입니다 이 코드는 처음 실행했을 때 완벽하게 작동합니다. 두 번째 실행에서는 오류가 발생했습니다. 그러나 노트북과 jupyter 터미널을 닫은 다음 다시 열고 다시 실행하면 ru가됩니다. n은 오류없이 실행되고 두 번째 실행에서는 위의 오류가 발생합니다.

Answer 1

동일한 문제가있어 요약 작업을 제거 할 때 오류가 발생하지 않는다는 사실을 발견했습니다. 나는 그것이 요약 작업을 얻을 수있는 방법을 찾을 수 있을지 ...이 업데이트됩니다

UPDATE :

내가 여기 제안에 따라이 고정 : Error with feed values for placeholders when running the merged summary op

내가 tf.summary.merge([summary_var1, summary_var2])로 tf.summary.merge_all 교체

쉽게 해결할 수있는 방법은 반복 학습을 다시 시작하기 전에 루프가 끝날 때 tf.reset_default_graph()으로 전화하는 것입니다.