我正试图用输入(123,45,4(和输出(123,4,5,1(实现一个LSTM，其中4个整数作为输入，一个数字作为输出。我使用的是Mac操作系统、Google Colab和TF 2.3.0版本。
这是我的型号：
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_h, 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，划时代=50(时，我得到错误：断言错误：无法计算输出张量("dense_2/truediv:0"，shape=(None，None，1(，dtype=float32(。有人知道怎么解决这个问题吗？
以下是重现问题的代码：
负载数据：
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((154,45,4),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((154,45,1), 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)
列车型号：
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, y_train, epochs = 50)
输入数据：training_data_input.txttraining_data_output.txt
在model = Model([encoder_inputs, decoder_inputs], decoder_outputs)中指定2个输入，而在fit中只传递1个。