我在Tensorflow（2.3版本）中编写了一段代码来执行自定义操作，但遇到了问题。虽然代码通常运行正常，但在某些情况下，即使输入相同，也会抛出意外的错误和异常。
我尝试排查问题，几乎可以确定这是评估依赖性问题。我尝试添加了一些依赖性控制，但没有效果。抱歉代码有点长，但我实在无法在更小的示例中重现这个问题。以下是我的代码：
import tensorflow.compat.v1 as tftf.compat.v1.disable_eager_execution()tf.disable_v2_behavior()myTensor_values = tf.placeholder(dtype=tf.float32)myTensor_l2_splits = tf.placeholder(dtype=tf.int32)myTensor_l1_splits = tf.placeholder(dtype=tf.int32)def innerloop_processing(begin_index , end_index , input1) : innerloop_counter = begin_index ta = tf.TensorArray(tf.float32, size=0, dynamic_size=True, clear_after_read=False , infer_shape=False ) def innerloop_body(counter , begin_index , end_index , input1 , ta) : inner_being_index = input1[1][counter] inner_end_index = input1[1][counter+1] row = tf.slice(input1[0] , [inner_being_index] , [inner_end_index-inner_being_index]) ta = ta.write(counter-begin_index , row) counter = counter + 1 return counter , begin_index , end_index , input1 , ta def innerloop_cond(counter , begin_index , end_index , input1 , ta ) : return input1[1][counter] < input1[1][end_index] -1 #stop at the next pointer of the l2_splits results = tf.while_loop(innerloop_cond , innerloop_body , [innerloop_counter , begin_index , end_index , input1 , ta] ) print_resutls = tf.print("this is the component result :" , results[4].stack()) return results[4].stack()def generateL1Tensor_writeback(start_offest,step,num): counter=tf.constant(0,tf.int32) values = tf.TensorArray(tf.int32, size=0, dynamic_size=True, clear_after_read=False , infer_shape=False ) def cond(values , start_offest , num ,counter) : return counter*step <= num*step def body(values , start_offest , num ,counter) : values = values.write(counter,[(counter*step)+start_offest]) counter = counter+1 return values , start_offest , num ,counter final_values , _ , _ , _ = tf.while_loop(cond,body,[values , start_offest , num , counter]) final = final_values.concat() #print_line = tf.print(" xxxxx This is the is the split : " , final) return finaldef multiply2n_ragged(tensor1 , tensor2) : #this function multiplies two ragged tesnsors of rank 2 . the most outer ranks of the two tensros must be equal . #setting variables and constats outerloop_counter = tf.constant(0 , dtype=tf.int32) carry_on = tf.constant(0 , dtype=tf.int32) taValues = tf.TensorArray(tf.float32, size=0, dynamic_size=True, clear_after_read=False , infer_shape=False ) taL2Splits = tf.TensorArray(tf.int32, size=0, dynamic_size=True, clear_after_read=False , infer_shape=False ) taL1Splits = tf.TensorArray(tf.int32, size=0, dynamic_size=True, clear_after_read=False , infer_shape=False ) taL1Splits = taL1Splits.write(0,[0]) ## required intialization for L1 split only innerloop_processing_graphed = tf.function(innerloop_processing) generateL1Tensor_writeback_graphed = tf.function(generateL1Tensor_writeback) def outerloop_cond(counter,input1,input2 ,taValues ,taL2Splits , taL1Splits , carry_on ) : value = tf.shape(input1[2])[0]-1 return counter < value ## this is the length of the outermost dimision , stop of this def outloop_body(counter,input1,input2, taValues ,taL2Splits , taL1Splits , carry_on) : l1_comp_begin = input1[2][counter] ## this is begin position of the current row in the outer split ( ie. the ith value in the outer row split tensor ) l1_comp_end = input1[2][counter+1] ## this is end position of the current row in the outer split (ie. the ith + 1 value in the outer row split tensor) l1_comp2_begin = input2[2][counter] ## we do the same for the second components l1_comp2_end = input2[2][counter+1] ## we do the same for the second components comp = innerloop_processing_graphed(l1_comp_begin ,l1_comp_end ,input1 ) ## now retrive the data to be procesed for the selected rows from vector1 comp2 =innerloop_processing_graphed(l1_comp2_begin ,l1_comp2_end ,input2 ) ## do the same for vector 2 comp2 = tf.transpose(comp2) ### desired operation multiply =tf.matmul(comp , comp2) #### This is the desired operation myshape= tf.shape(multiply) ## calculate the shape of the result in order to prepare to write the result in a ragged tensor format. offset = tf.cond( taValues.size() >0 ,lambda: tf.shape(taValues.concat())[0] , lambda : [0]) ### this is a hack, TensorArray.concat returns an error if the array is empty. Thus we check before calling this. l2v = generateL1Tensor_writeback_graphed(offset,myshape[1],myshape[0]) # generate the inner row split of the result for the current element taL2Splits=taL2Splits.write(counter,l2v) # write back the inner rowlplit to a TensorArray taValues=taValues.write(counter,tf.reshape(multiply , [-1])) # wirte back the actual ragged tensor elemnts in a another TensorArray carry_on=carry_on+myshape[0] ## required to calculate the outer row splite taL1Splits=taL1Splits.write(counter+1,[carry_on]) ## This is the outmost row split. counter = counter+1 return counter , input1,input2, taValues ,taL2Splits , taL1Splits , carry_on outerloop_finalcounter , _ , _ , ta1,ta2,ta3,_ = tf.while_loop(outerloop_cond,outloop_body,[outerloop_counter , tensor1 , tensor2 ,taValues ,taL2Splits , taL1Splits,carry_on]) uinquie_ta2 , _ = tf.unique(ta2.concat()) # this is required since some values might be duplicate in the row split itself final_values = ta1.concat() , uinquie_ta2 ,ta3.concat() return final_valuest = myTensor_values , myTensor_l2_splits , myTensor_l1_splitsoo =multiply2n_ragged(t,t)new_oo = multiply2n_ragged(oo,oo)sess = tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True)))sess.run(tf.global_variables_initializer())vals =np.array([1.0, 2.2 , 1.1 , 4.0, 5.0 , 1.1 , 6.0, 7.0 , 1.1 , 8.0, 9.0 , 1.1 ,10.0, 11.0 , 1.1 ])l2_splits = np.array([0,3,6,9,12,15])l1_splits = np.array([0, 2, 5 ]) re = sess.run([new_oo ] , feed_dict={myTensor_values:vals ,myTensor_l1_splits:l1_splits ,myTensor_l2_splits:l2_splits } )print(re)如我所说，代码在很多情况下运行正常，但有时对于相同的输入会生成以下错误。我得到的一些不同错误的堆栈跟踪如下：
this is the component result : [[1 2.2 1.1] [4 5 1.1]]this is the component result : [[1 2.2 1.1] [4 5 1.1]]this is the component result : [[6 7 1.1] [8 9 1.1] [10 11 1.1]]this is the component result : [[6 7 1.1] [8 9 1.1] [10 11 1.1]]this is the component result : [[7.05 16.21] [16.21 42.21]]this is the component result : [[7.05 16.21] [16.21 42.21]]---------------------------------------------------------------------------InvalidArgumentError Traceback (most recent call last)C:\ProgramData\Anaconda3\envs\AutoEncoder\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args) 1364 try:-> 1365 return fn(*args) 1366 except errors.OpError as e:C:\ProgramData\Anaconda3\envs\AutoEncoder\lib\site-packages\tensorflow\python\client\session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata) 1349 return self._call_tf_sessionrun(options, feed_dict, fetch_list,-> 1350 target_list, run_metadata) 1351 C:\ProgramData\Anaconda3\envs\AutoEncoder\lib\site-packages\tensorflow\python\client\session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata) 1442 fetch_list, target_list,-> 1443 run_metadata) 1444 InvalidArgumentError: {{function_node __inference_innerloop_processing_13658}} {{function_node __inference_innerloop_processing_13658}} Expected size[0] in [0, 0], but got 3 [[{{node while/body/_1/while/Slice}}]] [[while_33/StatefulPartitionedCall_1]]During handling of the above exception, another exception occurred:InvalidArgumentError Traceback (most recent call last)<ipython-input-18-238a2ce9a03a> in <module> 94 l2_splits = np.array([0,3,6,9,12,15]) 95 l1_splits = np.array([0, 2, 5 ])---> 96 re = sess.run([new_oo ] , feed_dict={myTensor_values:vals ,myTensor_l1_splits:l1_splits ,myTensor_l2_splits:l2_splits } ) 97 print(re)C:\ProgramData\Anaconda3\envs\AutoEncoder\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata) 956 try: 957 result = self._run(None, fetches, feed_dict, options_ptr,--> 958 run_metadata_ptr) 959 if run_metadata: 960 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)C:\ProgramData\Anaconda3\envs\AutoEncoder\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata) 1179 if final_fetches or final_targets or (handle and feed_dict_tensor): 1180 results = self._do_run(handle, final_targets, final_fetches,-> 1181 feed_dict_tensor, options, run_metadata) 1182 else: 1183 results = []C:\ProgramData\Anaconda3\envs\AutoEncoder\lib\site-packages\tensorflow\python\client\session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata) 1357 if handle is None: 1358 return self._do_call(_run_fn, feeds, fetches, targets, options,-> 1359 run_metadata) 1360 else: 1361 return self._do_call(_prun_fn, handle, feeds, fetches)C:\ProgramData\Anaconda3\envs\AutoEncoder\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args) 1382 '\nsession_config.graph_options.rewrite_options.' 1383 'disable_meta_optimizer = True')-> 1384 raise type(e)(node_def, op, message) 1385 1386 def _extend_graph(self):InvalidArgumentError: Expected size[0] in [0, 0], but got 3 [[{{node while/body/_1/while/Slice}}]] [[while_33/StatefulPartitionedCall_1]]以及以下错误：
CancelledError Traceback (most recent call last)C:\ProgramData\Anaconda3\envs\AutoEncoder\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args) 1364 try:-> 1365 return fn(*args) 1366 except errors.OpError as e:C:\ProgramData\Anaconda3\envs\AutoEncoder\lib\site-packages\tensorflow\python\client\session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata) 1349 return self._call_tf_sessionrun(options, feed_dict, fetch_list,-> 1350 target_list, run_metadata) 1351 C:\ProgramData\Anaconda3\envs\AutoEncoder\lib\site-packages\tensorflow\python\client\session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata) 1442 fetch_list, target_list,-> 1443 run_metadata) 1444 CancelledError: {{function_node __inference_innerloop_processing_11240}} {{function_node __inference_innerloop_processing_11240}} [_Derived_]Loop execution was cancelled. [[{{node while/LoopCond/_20}}]] [[while_27/StatefulPartitionedCall_1]]During handling of the above exception, another exception occurred:CancelledError Traceback (most recent call last)<ipython-input-15-238a2ce9a03a> in <module> 94 l2_splits = np.array([0,3,6,9,12,15]) 95 l1_splits = np.array([0, 2, 5 ])---> 96 re = sess.run([new_oo ] , feed_dict={myTensor_values:vals ,myTensor_l1_splits:l1_splits ,myTensor_l2_splits:l2_splits } ) 97 print(re)C:\ProgramData\Anaconda3\envs\AutoEncoder\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata) 956 try: 957 result = self._run(None, fetches, feed_dict, options_ptr,--> 958 run_metadata_ptr) 959 if run_metadata: 960 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)C:\ProgramData\Anaconda3\envs\AutoEncoder\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata) 1179 if final_fetches or final_targets or (handle and feed_dict_tensor): 1180 results = self._do_run(handle, final_targets, final_fetches,-> 1181 feed_dict_tensor, options, run_metadata) 1182 else: 1183 results = []C:\ProgramData\Anaconda3\envs\AutoEncoder\lib\site-packages\tensorflow\python\client\session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata) 1357 if handle is None: 1358 return self._do_call(_run_fn, feeds, fetches, targets, options,-> 1359 run_metadata) 1360 else: 1361 return self._do_call(_prun_fn, handle, feeds, fetches)C:\ProgramData\Anaconda3\envs\AutoEncoder\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args) 1382 '\nsession_config.graph_options.rewrite_options.' 1383 'disable_meta_optimizer = True')-> 1384 raise type(e)(node_def, op, message) 1385 1386 def _extend_graph(self):CancelledError: [_Derived_]Loop execution was cancelled. [[{{node while/LoopCond/_20}}]] [[while_27/StatefulPartitionedCall_1]]我认为所有错误都是在innerloop_processing中抛出的。我还在Tensorflow的GitHub上这里开了一个issue。
回答：
看起来问题出自tf.Cond，幸运的是这个问题在Tensorflow2中已被重新实现。因此，移除以下调用：
tf.disable_v2_behavior()可以解决这个问题。
