不同样本上的多对多lstm模型

我最近开始学习如何为多元时间序列数据建立LSTM模型。我在这里和这里已经找到了如何填充序列和实现多对多LSTM模型的方法。我已经创建了一个数据框来测试模型，但是仍然出现错误（如下）。

d = {'ID':['a12', 'a12','a12','a12','a12','b33','b33','b33','b33','v55','v55','v55','v55','v55','v55'], 'Exp_A':[2.2,2.2,2.2,2.2,2.2,3.1,3.1,3.1,3.1,1.5,1.5,1.5,1.5,1.5,1.5], 
     'Exp_B':[2.4,2.4,2.4,2.4,2.4,1.2,1.2,1.2,1.2,1.5,1.5,1.5,1.5,1.5,1.5], 
     'A':[0,0,1,0,1,0,1,0,1,0,1,1,1,0,1], 'B':[0,0,1,1,1,0,0,1,1,1,0,0,1,0,1],
     'Time_Interval': ['11:00:00', '11:10:00', '11:20:00', '11:30:00', '11:40:00',
                '11:00:00', '11:10:00', '11:20:00', '11:30:00',
                '11:00:00', '11:10:00', '11:20:00', '11:30:00', '11:40:00', '11:50:00']}

df = pd.DataFrame(d)
df.set_index('Time_Interval', inplace=True)

我试图用蛮力来垫垫：

from keras.preprocessing.sequence import pad_sequences

x1 = df['A'][df['ID']== 'a12']
x2 = df['A'][df['ID']== 'b33']
x3 = df['A'][df['ID']== 'v55']

mx = df['ID'].size().max() # Find the largest group
seq1 = [x1, x2, x3]
padded1 = np.array(pad_sequences(seq1, maxlen=6, dtype='float32')).reshape(-1,mx,1)

我以类似的方式创建了padded2，padded3并padded4针对每个功能：

padded_data = np.dstack((padded1, padded1, padded3, padded4))
padded_data.shape = (3, 6, 4)

padded_data

array([[[0. , 0. , 0. , 0. ],
        [0. , 0. , 2.2, 2.4],
        [0. , 0. , 2.2, 2.4],
        [1. , 1. , 2.2, 2.4],
        [0. , 0. , 2.2, 2.4],
        [1. , 1. , 2.2, 2.4]],

       [[0. , 0. , 0. , 0. ],
        [0. , 0. , 0. , 0. ],
        [0. , 0. , 3.1, 1.2],
        [1. , 1. , 3.1, 1.2],
        [0. , 0. , 3.1, 1.2],
        [1. , 1. , 3.1, 1.2]],

       [[0. , 0. , 1.5, 1.5],
        [1. , 1. , 1.5, 1.5],
        [1. , 1. , 1.5, 1.5],
        [1. , 1. , 1.5, 1.5],
        [0. , 0. , 1.5, 1.5],
        [1. , 1. , 1.5, 1.5]]], dtype=float32)

编辑

#split into train/test
train = pad_1[:2]   # train on the 1st two samples.
test = pad_1[-1:]    

train_X = train[:,:-1]  # one step ahead prediction.
train_y = train[:,1:]

test_X = test[:,:-1]  # test on the last sample
test_y = test[:,1:]
# check shapes
print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)
#(2, 5, 4) (2, 5, 4) (1, 5, 4) (1, 5, 4)

# design network
model = Sequential()
model.add(Masking(mask_value=0., input_shape=(train.shape[1], train.shape[2])))
model.add(LSTM(32, input_shape=(train.shape[1], train.shape[2]), return_sequences=True))
model.add(Dense(4))
model.compile(loss='mae', optimizer='adam', metrics=['accuracy'])
model.summary()

# fit network
history = model.fit(train, test, epochs=300, validation_data=(test_X, test_y), verbose=2, shuffle=False)

[！[在此处输入图片描述] [3]] [3]

所以我的问题是：

1. 当然，必须有一种有效的数据转换方法吗？
2. 假设我要对未来序列进行单个时间步长预测，我有

first time-step=array([[[0.5 , 0.9 , 2.5, 3.5]]], dtype=float32)第一个时间步是序列的单个“帧”。如何调整模型以纳入此范围？