python-3.x – keras LSTM模型中的尺寸不匹配

输入张量:(数据长度,训练系列数,回顾时间步长)

输出张量:(数据长度,要预测的序列数,向前看的时间步长)

Note: I want to keep the dimensions exactly like that,no
transposition.

重现问题的虚拟数据代码是：

import numpy as np
from keras.models import Sequential
from keras.layers import Dense,TimeDistributed,LSTM

epoch_number = 100
batch_size = 20
input_dim = 4
output_dim = 3
look_back = 24
look_ahead = 24
n = 100

trainX = np.random.rand(n,input_dim,look_back)
trainY = np.random.rand(n,output_dim,look_ahead)
print('test X:',trainX.shape)
print('test Y:',trainY.shape)

model = Sequential()

# Add the first LSTM layer (The intermediate layers need to pass the sequences to the next layer)
model.add(LSTM(10,batch_input_shape=(None,look_back),return_sequences=True))

# add the first LSTM layer (the dimensions are only needed in the first layer)
model.add(LSTM(10,return_sequences=True))

# the TimeDistributed object allows a 3D output
model.add(TimeDistributed(Dense(look_ahead)))

model.compile(loss='mean_squared_error',optimizer='adam',metrics=['accuracy'])
model.fit(trainX,trainY,nb_epoch=epoch_number,batch_size=batch_size,verbose=1)

这个流程：

Exception: Error when checking model target: expected
timedistributed_1 to have shape (None,4,24) but got array with shape
(100,3,24)

问题似乎是在定义TimeDistributed层时.

如何定义TimeDistributed层以便编译和训练？