Keras 如何在LSTM中提供批量大小？

我存档以查找如何将此（粗体文本）批处理形状转换为正确形式的信息。我知道提供了尺寸、时间步长和尺寸。我的时间步是1231步。我想建立一个合并模型，其中一个LSTM是状态完整的，另一个是无状态的

from numpy import array
from numpy import hstack
from keras.models import Model
from keras.layers import Input
from keras.layers import Dense
from keras.layers.merge import concatenate


def split_sequences(sequences, n_steps):
    X, y = list(), list()
    for i in range(len(sequences)):
      # 
      end_ix = i + n_steps
     
       if end_ix > len(sequences):
        break
    
       seq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1, -1]
       X.append(seq_x)
       y.append(seq_y)
   return array(X), array(y)


in_seq1 = X_left
in_seq2 = X_right
out_seq = Y_train# array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))])

in_seq1 = in_seq1.reshape((len(in_seq1), 5))
in_seq2 = in_seq2.reshape((len(in_seq2), 1))
out_seq = out_seq.reshape((len(out_seq), 1))
 
dataset = hstack((in_seq1, in_seq2, out_seq))
  
n_steps = 1
# convert into input/output
X, y = split_sequences(dataset, n_steps)
  
X1 = X[:, :, 0]
X2 = X[:, :, 1]

visible1 = Input(shape=(n_steps,1))
lstm1 = LSTM(100, input_shape =(100,1),**batch_input_shape=(32,100,1)**, stateful=True)(visible1)

visible2 = Input(shape=(n_steps,1))
lstm2 = LSTM(100, input_shape =(100,1))(visible2)

merge = concatenate([lstm1, lstm2])
output = Dense(1)(merge)
model = Model(inputs=[visible1, visible2], outputs=output)
model.compile(optimizer='adam', loss='mape', metrics=['accuracy'])
# fit model
model.fit([X1, X2], y, epochs=2000, verbose=1, batch_size=64, 
validation_split=0.1)

yhat = model.predict([X1, X2], verbose=0)
print(yhat)

塔克斯

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