Keras中LSTM输入维度的问题

data_1 -> shape (1150,50) 
data_2 -> shape (1150,50)
y_train -> shape (1150,50)

input_1 = Input(shape=data_1.shape)
LSTM_1 = LSTM(100)(input_1)

input_2 = Input(shape=data_2.shape)
LSTM_2 = LSTM(100)(input_2)

concat = Concatenate(axis=-1)
x = concat([LSTM_1, LSTM_2])
dense_layer = Dense(1, activation='sigmoid')(x)
model = keras.models.Model(inputs=[input_1, input_2], outputs=[dense_layer])

model.compile(loss='binary_crossentropy',
                      optimizer='adam',
                      metrics=['acc'])

model.fit([data_1, data_2], y_train, epochs=10)

当我运行此代码时,我得到一个ValueError:

ValueError:检查模型输入时出错:预期输入_1有3个维度,但得到了具有形状的数组(1150,50)

有人能解决这个问题吗?