ValueError：尺寸必须相等，但对于输入形状为 [?,13], [?,13,3076] 的“loss/dense_1_loss/mul”（操作：“Mul”），尺寸为 13 和 3076

大佬教程收集整理的这篇文章主要介绍了ValueError：尺寸必须相等，但对于输入形状为 [?,13], [?,13,3076] 的“loss/dense_1_loss/mul”（操作：“Mul”），尺寸为 13 和 3076，大佬教程大佬觉得挺不错的，现在分享给大家，也给大家做个参考。

如何解决ValueError：尺寸必须相等，但对于输入形状为 [?,13], [?,13,3076] 的“loss/dense_1_loss/mul”（操作：“Mul”），尺寸为 13 和 3076？

我正在 seq2seq 模型之上构建一个聊天机器人。尺寸有误。请帮忙。 这是 .py 文件：https://drive.google.com/drive/u/0/folders/1ljeLb_LYO6qd9azqly31T3luv0oACXfO

import re


lines = open('movIE_lines.txt',enCoding = 'utf-8',errors = 'ignore').read().split('\n')
converse = open('movIE_conversations.txt',errors = 'ignore').read().split('\n')



exchn = []
for conver in converse:
    exchn.append(conver.split(' +++$+++ ')[-1][1:-1].replace("'"," ").replace(",","").split())

diag = {}
for line in lines:
    diag[line.split(' +++$+++ ')[0]] = line.split(' +++$+++ ')[-1]

questions = []
answers = []

for conver in exchn:
    for i in range(len(conver) - 1):
        questions.append(diag[conver[i]])
        answers.append(diag[conver[i+1]])

sorted_ques = []
sorted_ans = []
for i in range(len(questions)):
    if len(questions[i]) < 13:
        sorted_ques.append(questions[i])
        sorted_ans.append(answers[i])



def clean_text(txt):
    txt = txt.lower()
    txt = re.sub(r"i'm","i am",txt)
    txt = re.sub(r"he's","he is",txt)
    txt = re.sub(r"she's","she is",txt)
    txt = re.sub(r"that's","that is",txt)
    txt = re.sub(r"what's","what is",txt)
    txt = re.sub(r"where's","where is",txt)
    txt = re.sub(r"\'ll"," will",txt)
    txt = re.sub(r"\'ve"," have",txt)
    txt = re.sub(r"\'re"," are",txt)
    txt = re.sub(r"\'d"," would",txt)
    txt = re.sub(r"won't","will not",txt)
    txt = re.sub(r"can't","can not",txt)
    txt = re.sub(r"[^\w\s]","",txt)
    return txt

clean_ques = []
clean_ans = []

for line in sorted_ques:
    clean_ques.append(clean_text(linE))
        
for line in sorted_ans:
    clean_ans.append(clean_text(linE))



## delete
del(answers,questions,linE)



for i in range(len(clean_ans)):
    clean_ans[i] = ' '.join(clean_ans[i].split()[:11])



## trimming
clean_ans=clean_ans[:30000]
clean_ques=clean_ques[:30000]
## delete


###  count occurences ###
word2count = {}

for line in clean_ques:
    for word in line.split():
        if word not in word2count:
            word2count[word] = 1
        else:
            word2count[word] += 1
for line in clean_ans:
    for word in line.split():
        if word not in word2count:
            word2count[word] = 1
        else:
            word2count[word] += 1

## delete
del(word,linE)


###  remove less frequent ###
thresh = 5

vocab = {}
word_num = 0
for word,count in word2count.items():
    if count >= thresh:
        vocab[word] = word_num
        word_num += 1
        
## delete
del(word2count,word,count,thresh)       
del(word_num)        



for i in range(len(clean_ans)):
    clean_ans[i] = '<SOS> ' + clean_ans[i] + ' <EOS>'

tokens = ['<PAD>','<EOS>','<OUT>','<SOS>']
x = len(vocab)
for token in tokens:
    vocab[token] = x
    x += 1
    
    

vocab['cameron'] = vocab['<PAD>']
vocab['<PAD>'] = 0


### inv answers Dict ###
inv_vocab = {w:v for v,w in vocab.items()}




encoder_inp = []
for line in clean_ques:
    lst = []
    for word in line.split():
        if word not in vocab:
            lst.append(vocab['<OUT>'])
        else:
            lst.append(vocab[word])
        
    encoder_inp.append(lst)

decoder_inp = []
for line in clean_ans:
    lst = []
    for word in line.split():
        if word not in vocab:
            lst.append(vocab['<OUT>'])
        else:
            lst.append(vocab[word])        
    decoder_inp.append(lst)

from tensorflow.keras.preprocessing.sequence import pad_sequences
encoder_inp = pad_sequences(encoder_inp,13,padding='post',truncaTing='post')
decoder_inp = pad_sequences(decoder_inp,truncaTing='post')    

decoder_final_output = []
for i in decoder_inp:
    decoder_final_output.append(I[1:]) 

decoder_final_output = pad_sequences(decoder_final_output,truncaTing='post')

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense,Embedding,LSTM,input


enc_inp = input(shape=(13,))
dec_inp = input(shape=(13,))


VOCAB_SIZE = len(vocab)
embed = Embedding(VOCAB_SIZE+1,output_dim=50,input_length=13,Trainable=True                  
                  )


enc_embed = embed(enc_inp)
enc_lstm = LSTM(400,return_sequences=True,return_state=TruE)
enc_op,h,c = enc_lstm(enc_embed)
enc_states = [h,c]



dec_embed = embed(dec_inp)
dec_lstm = LSTM(400,return_state=TruE)
dec_op,_,_ = dec_lstm(dec_embed,initial_state=enc_states)

dense = Dense(VOCAB_SIZE,activation='softmax')

dense_op = dense(dec_op)

model = Model([enc_inp,dec_inp],dense_op)




model.compile(loss='categorical_crossentropy',metrics=['acc'],optimizer='adam')

model.fit([encoder_inp,decoder_inp],decoder_final_output,epochs=4)

错误是：尺寸必须相等，但对于输入形状为 [?,13],[?,3027] 的 'loss/dense_loss/mul'（操作：'Mul'），尺寸为 13 和 3027 .

Train on 30000 samples
Epoch 1/4
   32/30000 [..............................] - ETA: 9:26
---------------------------------------------------------------------------
InvalIDArgumentError                      TraceBACk (most recent call last)
~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\framework\ops.py in _create_c_op(graph,node_def,inputs,control_inputs)
   1618   try:
-> 1619     c_op = c_API.TF_FinishOperation(op_desc)
   1620   except errors.InvalIDArgumentError as e:

InvalIDArgumentError: Dimensions must be equal,but are 13 and 3027 for 'loss/dense_loss/mul' (op: 'Mul') with input shapes: [?,3027].

During handling of the above exception,another exception occurred:

ValueError                                TraceBACk (most recent call last)
<ipython-input-5-75c3ca63e6a3> in <module>
    201 model.compile(loss='categorical_crossentropy',optimizer='adam')
    202 
--> 203 model.fit([encoder_inp,epochs=4)

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\keras\ENGIne\Training.py in fit(self,x,y,batch_size,epochs,verbose,callBACks,valIDation_split,valIDation_data,shuffle,class_weight,sample_weight,initial_epoch,steps_per_epoch,valIDation_steps,valIDation_freq,max_queue_size,workers,use_multiprocessing,**kwargs)
    817         max_queue_size=max_queue_size,818         workers=workers,--> 819         use_multiprocessing=use_multiprocessing)
    820 
    821   def evaluate(self,~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\keras\ENGIne\Training_v2.py in fit(self,model,**kwargs)
    340                 mode=ModeKeys.TraiN,341                 Training_context=Training_context,--> 342                 @R_287_10586@l_epochs=epochs)
    343             cbks.make_logs(model,epoch_logs,Training_result,ModeKeys.TraiN)
    344 

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\keras\ENGIne\Training_v2.py in run_one_epoch(model,iterator,execution_function,dataset_size,strategy,num_samples,mode,Training_context,@R_287_10586@l_epochs)
    126         step=step,mode=mode,size=currenT_Batch_sizE) as batch_logs:
    127       try:
--> 128         batch_outs = execution_function(iterator)
    129       except (stopiteration,errors.OutOfRangeError):
    130         # Todo(kaftan): file BUG about tf function and errors.OutOfRangeError?

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\keras\ENGIne\Training_v2_utils.py in execution_function(input_fn)
     96     # `numpy` translates Tensors to values in Eager mode.
     97     return nest.map_structure(_non_none_constant_value,---> 98                               diStributed_function(input_fn))
     99 
    100   return execution_function

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\eager\def_function.py in __call__(self,*args,**kwds)
    566         xla_context.Exit()
    567     else:
--> 568       result = self._call(*args,**kwds)
    569 
    570     if tracing_count == self._get_tracing_count():

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\eager\def_function.py in _call(self,**kwds)
    613       # This is the first call of __call__,so we have to initialize.
    614       initializers = []
--> 615       self._initialize(args,kwds,add_initializers_to=initializers)
    616     finally:
    617       # At this point we kNow that the initialization is complete (or less

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\eager\def_function.py in _initialize(self,args,add_initializers_to)
    495     self._concrete_stateful_fn = (
    496         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
--> 497             *args,**kwds))
    498 
    499     def invalID_creator_scope(*unused_args,**unused_kwds):

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self,**kwargs)
   2387       args,kwargs = None,None
   2388     with self._lock:
-> 2389       graph_function,_ = self._maybe_define_function(args,kwargs)
   2390     return graph_function
   2391 

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\eager\function.py in _maybe_define_function(self,kwargs)
   2701 
   2702       self._function_cache.missed.add(call_context_key)
-> 2703       graph_function = self._create_graph_function(args,kwargs)
   2704       self._function_cache.priMary[cache_key] = graph_function
   2705       return graph_function,kwargs

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\eager\function.py in _create_graph_function(self,kwargs,overrIDe_flat_arg_shapes)
   2591             arg_names=arg_names,2592             overrIDe_flat_arg_shapes=overrIDe_flat_arg_shapes,-> 2593             capture_by_value=self._capture_by_value),2594         self._function_attributes,2595         # Tell the ConcreteFunction to clean up its graph once it goes out of

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\framework\func_graph.py in func_graph_from_py_func(name,python_func,signature,func_graph,autograph,autograph_options,add_control_dependencIEs,arg_names,op_return_value,collections,capture_by_value,overrIDe_flat_arg_shapes)
    976                                           converted_funC)
    977 
--> 978       func_outputs = python_func(*func_args,**func_kwargs)
    979 
    980       # invariant: `func_outputs` contains only Tensors,CompositeTensors,~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\eager\def_function.py in wrapped_fn(*args,**kwds)
    437         # __wrapped__ allows autoGraph to swap in a converted function. We give
    438         # the function a weak reference to itself to avoID a reference cycle.
--> 439         return weak_wrapped_fn().__wrapped__(*args,**kwds)
    440     weak_wrapped_fn = weakref.ref(wrapped_fn)
    441 

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\keras\ENGIne\Training_v2_utils.py in diStributed_function(input_iterator)
     83     args = _prepare_Feed_values(model,input_iterator,strategy)
     84     outputs = strategy.experimental_run_v2(
---> 85         per_replica_function,args=args)
     86     # Out of PerReplica outputs reduce or pick values to return.
     87     all_outputs = dist_utilS.Unwrap_output_Dict(

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\diStribute\diStribute_lib.py in experimental_run_v2(self,fn,kwargs)
    761       fn = autograph.tf_convert(fn,ag_ctx.control_status_ctx(),762                                 converT_By_default=falsE)
--> 763       return self._extended.call_for_each_replica(fn,args=args,kwargs=kwargs)
    764 
    765   def reduce(self,reduce_op,value,axis):

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\diStribute\diStribute_lib.py in call_for_each_replica(self,kwargs)
   1817       kwargs = {}
   1818     with self._container_strategy().scope():
-> 1819       return self._call_for_each_replica(fn,kwargs)
   1820 
   1821   def _call_for_each_replica(self,kwargs):

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\diStribute\diStribute_lib.py in _call_for_each_replica(self,kwargs)
   2162         self._container_strategy(),2163         replica_ID_in_sync_group=constant_op.constant(0,dtypes.int32)):
-> 2164       return fn(*args,**kwargs)
   2165 
   2166   def _reduce_to(self,desTinations):

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\autograph\impl\API.py in wrapper(*args,**kwargs)
    290   def wrapper(*args,**kwargs):
    291     with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.Disabled):
--> 292       return func(*args,**kwargs)
    293 
    294   if inspect.isfunction(funC) or inspect.ismethod(funC):

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\keras\ENGIne\Training_v2_utils.py in Train_on_batch(model,reset_metrics,standalonE)
    431       y,432       sample_weights=sample_weights,--> 433       output_loss_metrics=model._output_loss_metrics)
    434 
    435   if reset_metrics:

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\keras\ENGIne\Training_eager.py in Train_on_batch(model,targets,sample_weights,output_loss_metrics)
    310           sample_weights=sample_weights,311           Training=True,--> 312           output_loss_metrics=output_loss_metrics))
    313   if not isinstance(outs,List):
    314     outs = [outs]

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\keras\ENGIne\Training_eager.py in _process_single_batch(model,output_loss_metrics,Training)
    251               output_loss_metrics=output_loss_metrics,252               sample_weights=sample_weights,--> 253               Training=Training))
    254       if @R_287_10586@l_loss is None:
    255         raise ValueError('The model cAnnot be run '

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\keras\ENGIne\Training_eager.py in _model_loss(model,Training)
    165 
    166         if hasattr(loss_fn,'reduction'):
--> 167           per_sample_losses = loss_fn.call(targets[i],outs[i])
    168           weighted_losses = losses_utils.compute_weighted_loss(
    169               per_sample_losses,~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\keras\losses.py in call(self,y_true,y_pred)
    219       y_pred,y_true = tf_losses_util.squeeze_or_expand_dimensions(
    220           y_pred,y_truE)
--> 221     return self.fn(y_true,y_pred,**self._fn_kwargs)
    222 
    223   def get_config(self):

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\keras\losses.py in categorical_crossentropy(y_true,from_logits,label_smoothing)
    969   y_true = smart_cond.smart_cond(label_smoothing,970                                  _smooth_labels,lambda: y_truE)
--> 971   return K.categorical_crossentropy(y_true,from_logits=from_logits)
    972 
    973 

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\keras\BACkend.py in categorical_crossentropy(target,output,axis)
   4493       epsilon_ = _constant_to_tensor(epsilon(),output.dtype.base_dtypE)
   4494       output = clip_ops.clip_by_value(output,epsilon_,1. - epsilon_)
-> 4495       return -math_ops.reduce_sum(target * math_ops.log(output),axis)
   4496     else:
   4497       # When softmax activation function is used for output operation,we

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\ops\math_ops.py in binary_op_wrapper(x,y)
    900     with ops.name_scope(None,op_name,[x,y]) as name:
    901       if isinstance(x,ops.Tensor) and isinstance(y,ops.Tensor):
--> 902         return func(x,name=Name)
    903       elif not isinstance(y,sparse_tensor.SparseTensor):
    904         try:

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\ops\math_ops.py in _mul_dispatch(x,Name)
   1199   is_tensor_y = isinstance(y,ops.Tensor)
   1200   if is_tensor_y:
-> 1201     return gen_math_ops.mul(x,name=Name)
   1202   else:
   1203     assert isinstance(y,sparse_tensor.SparseTensor)  # Case: Dense * Sparse.

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\ops\gen_math_ops.py in mul(x,Name)
   6122   # Add nodes to the TensorFlow graph.
   6123   _,_op,_outputs = _op_def_library._apply_op_Helper(
-> 6124         "Mul",x=x,y=y,name=Name)
   6125   _result = _outputs[:]
   6126   if _execute.must_record_gradIEnt():

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\framework\op_def_library.py in _apply_op_Helper(op_type_name,name,**keywords)
    740       op = g._create_op_internal(op_type_name,dtypes=None,741                                  name=scope,input_types=input_types,--> 742                                  attrs=attr_protos,op_def=op_def)
    743 
    744     # `outputs` is returned as a separate return value so that the output

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\framework\func_graph.py in _create_op_internal(self,op_type,dtypes,input_types,attrs,op_def,compute_devicE)
    593     return super(FuncGraph,self)._create_op_internal(  # pylint: disable=protected-access
    594         op_type,--> 595         compute_devicE)
    596 
    597   def capture(self,tensor,name=None,shape=NonE):

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\framework\ops.py in _create_op_internal(self,compute_devicE)
   3320           input_types=input_types,3321           original_op=self._default_original_op,-> 3322           op_def=op_def)
   3323       self._create_op_Helper(ret,compute_device=compute_devicE)
   3324     return ret

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\framework\ops.py in __init__(self,g,output_types,control_inputs,original_op,op_def)
   1784           op_def,node_def.attr)
   1785       self._c_op = _create_c_op(self._graph,grouped_inputs,-> 1786                                 control_input_ops)
   1787       name = compat.as_str(node_def.Name)
   1788     # pylint: enable=protected-access

~\Anaconda3\envs\Pushkar\lib\site-packages\tensorflow_core\python\framework\ops.py in _create_c_op(graph,control_inputs)
   1620   except errors.InvalIDArgumentError as e:
   1621     # Convert to ValueError for BACkWARDs compatibility.
-> 1622     raise ValueError(str(E))
   1623 
   1624   return c_op

ValueError: Dimensions must be equal,3027].

解决方法

enc_inp = Input(shape=(13,))
dec_inp = Input(shape=(13,))

大佬总结

以上是大佬教程为你收集整理的ValueError：尺寸必须相等，但对于输入形状为 [?,13], [?,13,3076] 的“loss/dense_1_loss/mul”（操作：“Mul”），尺寸为 13 和 3076全部内容，希望文章能够帮你解决ValueError：尺寸必须相等，但对于输入形状为 [?,13], [?,13,3076] 的“loss/dense_1_loss/mul”（操作：“Mul”），尺寸为 13 和 3076所遇到的程序开发问题。

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