错误：AssertionError：无法计算输出Tensor“ dense_2 / truediv：0”，shape =None，None，1，dtype = float32

问题描述

我试图用输入（123,45,4）和输出（123,1）来实现一个LSTM，该LSTM的输入是4个整数，输出是一个整数。我正在使用Mac OS，Google Colab和TF版本2.3.0。

这是我的模特：

def define_models(n_input,n_output,n_units):
    # define training encoder
    encoder_inputs = Input(shape=(None,n_input))
    encoder = LSTM(n_units,return_state=True)
    encoder_outputs,state_h,state_c = encoder(encoder_inputs)
    encoder_states = [state_h,state_c]
    # define training decoder
    decoder_inputs = Input(shape=(None,n_output))
    decoder_lstm = LSTM(n_units,return_sequences=True,return_state=True)
    decoder_outputs,_,_ = decoder_lstm(decoder_inputs,initial_state=encoder_states)
    decoder_dense = Dense(n_output,activation='softmax')
    decoder_outputs = decoder_dense(decoder_outputs)
    model = Model([encoder_inputs,decoder_inputs],decoder_outputs)
    # define inference encoder
    encoder_model = Model(encoder_inputs,encoder_states)
    # define inference decoder
    decoder_state_input_h = Input(shape=(n_units,))
    decoder_state_input_c = Input(shape=(n_units,))
    decoder_states_inputs = [decoder_state_input_h,decoder_state_input_c]
    decoder_outputs,state_c = decoder_lstm(decoder_inputs,initial_state=decoder_states_inputs)
    decoder_states = [state_h,state_c]
    decoder_outputs = decoder_dense(decoder_outputs)
    decoder_model = Model([decoder_inputs] + decoder_states_inputs,[decoder_outputs] + decoder_states)
    # return all models
    return model,encoder_model,decoder_model

当我尝试运行代码：model.fit（x_train，y_train，epochs = 50）时出现错误： AssertionError：无法计算输出Tensor（“ dense_2 / truediv：0”，shape =（None，None，1），dtype = float32） 。有人知道如何解决这个问题吗？

以下是重现此问题的代码：

加载数据：

with open("training_data_input.txt") as fopen:
  with open("training_data_output.txt") as fopen2:
    for line in fopen:
      myList = line.strip().split()
      myList[0] = myList[0].replace("[","")
      if myList[0] == "":
        myList = myList[1:]
      if "][" in myList[3]:
        j = 0
        print(myList[3])
        myList[3] = myList[3].replace(']][[',"")
        if len(myList[3]) > 3:
          myList[3] = (myList[3][:3])
        myList = myList[:4]
      myList[len(myList)-1] = myList[len(myList)-1].replace("]","")
      x = np.empty((154,4),dtype=np.float32)
      i = 0
      j = 0
      if j >=45:
        j = 0
      print(myList)
      x[i][j] = myList
      i+=1
      j+=1
    for line in fopen2:
      myList = line.strip().split()
      x_out = np.empty((154,1),dtype=np.float32)
      myList[0] = myList[0].replace("[","")
      if myList[0] == "":
        myList = myList[1:]
      if "][" in myList[0]:
        j = 0
        myList[0] = myList[0].replace(']][[',"")
        if len(myList[0]) > 3:
          myList[0] = (myList[0][:2])
        myList = myList[:1]
      myList[len(myList)-1] = myList[len(myList)-1].replace("]","")
      i = 0
      j = 0
      if j >=45:
        j = 0
      x_out[i][j] = myList
      i+=1
      
print(x.shape)
print(x_out.shape)

火车模型：

from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(x,x_out,test_size = 0.2,random_state = 4)
print(x_train.shape)
print(y_train.shape)

model.fit(x_train,epochs = 50)

输入数据： training_data_input.txt training_data_output.txt

解决方法

在model = Model([encoder_inputs,decoder_inputs],decoder_outputs)中，您指定2个输入，而在适合时，您仅传递1个。