具有KERA和可变输入量的深度学习(lstm)

我正在尝试用keras实现一个lstm模型。问题是我有不同形状的数据。我的数据如下所示:

col1 col2 col3 col4 col5

[1,2,3][2,3,4][3,4,5][5,6,7][4,5,9]

[0,2][1,5][1,24][11,7][1,4]

[0,2,4,5][1,5,7,8][1,24,-7,6][11,7,4,5][1,4,1,2]

我的代码是

import numpy as np
import pandas as pd
import h5py
from sklearn.model_selection import train_test_split
from keras.layers import Dense
from keras.layers import Input, LSTM
from keras.models import Model

X_train, X_test, y_train, y_test = train_test_split(X, y_target, test_size=0.2, random_state=1)  
batch_size = 32 
timesteps = 300 
output_size = 1
epochs=120

inputs = Input(batch_shape=(batch_size, timesteps, output_size))
lay1 = LSTM(10, stateful=True, return_sequences=True)(inputs)
lay2 = LSTM(10, stateful=True, return_sequences=True)(lay1)
output = Dense(units = output_size)(lay2)

regressor = Model(inputs=inputs, outputs = output)
regressor.compile(optimizer='adam', loss = 'mae')
regressor.summary()

for i in range(epochs):
    print("Epoch: " + str(i))
    regressor.fit(X_train, y_train, shuffle=False, epochs = 1, batch_size = batch_size)
    regressor.reset_states()

运行代码时出现的错误是:

ValueError: Error when checking input: expected input_5 to have 3 dimensions, but got array with shape (11200, 5) #11200 lines, 5 columns

谢谢