Question

>   def train(self,iterations=1000,save_dir="saved_models"):
>       #Removing previous save directory if there is one
>       if os.path.exists(save_dir):
>           shutil.rmtree(save_dir)
>       #Make new save directory
>       os.mkdir(save_dir)
>       #Just a tf thing, to merge all summaries into one
>       merged = tf.summary.merge_all()
>       #Using adam optimizer as mentioned in the paper
>       optimizer = tf.train.AdamOptimizer()
>       #This is the train operation for our objective
>       train_op = optimizer.minimize(self.loss)    
>       #Operation to initialize all variables
>       init = tf.global_variables_initializer()
>       print("Begin training...")
>       with self.sess as sess:
>           #Initialize all variables
>           sess.run(init)
>           test_exists = self.test_data
>           #create summary writer for train
>           train_writer = tf.summary.FileWriter(save_dir+"/train",sess.graph)
>           #If we're using a test set, include another summary writer for that
>           if test_exists:
>               test_writer = tf.summary.FileWriter(save_dir+"/test",sess.graph)
>               test_x,test_y = self.test_data(*self.test_args)
>               test_feed = {self.input:test_x,self.target:test_y}
>           #This is our training loop
>           for i in tqdm(range(iterations)):
>               #Use the data function we were passed to get a batch every iteration
>               x,y = self.data(*self.args)
>               #Create feed dictionary for the batch
>               feed = {
>                   self.input:x,
>                   self.target:y
>               }
>               #Run the train op and calculate the train summary
>               summary,_ = sess.run([merged,train_op],feed)
>               #If we're testing, don't train on test set. But do calculate summary
>               if test_exists:
>                   t_summary = sess.run(merged,test_feed)
>                   #Write test summary
>                   test_writer.add_summary(t_summary,i)
>               #Write train summary for this step
>               train_writer.add_summary(summary,i)
>           #Save our trained model     
>           self.save()

File "train.py", line 18, in <module>
    network.train(args.iterations,args.savedir)
  File "/home/psoni/EDSR-Tensorflow/model.py", line 222, in train
    t_summary = sess.run(merged,test_feed)
  File "/home/psoni/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 895, in run
    run_metadata_ptr)
  File "/home/psoni/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1093, in _run
    np_val = np.asarray(subfeed_val, dtype=subfeed_dtype)
  File "/home/psoni/anaconda2/lib/python2.7/site-packages/numpy/core/numeric.py", line 531, in asarray
    return array(a, dtype, copy=False, order=order)

训练时，此错误消息会在几个时期后持续弹出。在查看之前的帖子后，我认为这是一个错误，因为可以通过更改dtype =来编辑不正确的数据结构。这发生在训练的前十个时期内，所以也许是因为记忆问题？我看到另一个用户遇到了这个问题。非常感谢任何和所有帮助。

tf.Session.run给出ValueError：设置一个带序列的数组元素

0 个答案: