我如何结合卷积神经网络和递归神经网络进行图像分割？
我的模型摘要：
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_34 (InputLayer) (None, 128, 128, 3) 0
__________________________________________________________________________________________________
conv2d_277 (Conv2D) (None, 128, 128, 16) 448 input_34[0][0]
__________________________________________________________________________________________________
max_pooling2d_109 (MaxPooling2D (None, 64, 64, 16) 0 conv2d_277[0][0]
__________________________________________________________________________________________________
conv2d_278 (Conv2D) (None, 64, 64, 32) 4640 max_pooling2d_109[0][0]
__________________________________________________________________________________________________
max_pooling2d_110 (MaxPooling2D (None, 32, 32, 32) 0 conv2d_278[0][0]
__________________________________________________________________________________________________
conv2d_279 (Conv2D) (None, 32, 32, 64) 18496 max_pooling2d_110[0][0]
__________________________________________________________________________________________________
max_pooling2d_111 (MaxPooling2D (None, 16, 16, 64) 0 conv2d_279[0][0]
__________________________________________________________________________________________________
conv2d_280 (Conv2D) (None, 16, 16, 128) 73856 max_pooling2d_111[0][0]
__________________________________________________________________________________________________
max_pooling2d_112 (MaxPooling2D (None, 8, 8, 128) 0 conv2d_280[0][0]
__________________________________________________________________________________________________
conv2d_281 (Conv2D) (None, 8, 8, 256) 295168 max_pooling2d_112[0][0]
__________________________________________________________________________________________________
up_sampling2d_109 (UpSampling2D (None, 16, 16, 256) 0 conv2d_281[0][0]
__________________________________________________________________________________________________
concatenate_109 (Concatenate) (None, 16, 16, 384) 0 up_sampling2d_109[0][0]
conv2d_280[0][0]
__________________________________________________________________________________________________
conv2d_282 (Conv2D) (None, 16, 16, 128) 442496 concatenate_109[0][0]
__________________________________________________________________________________________________
up_sampling2d_110 (UpSampling2D (None, 32, 32, 128) 0 conv2d_282[0][0]
__________________________________________________________________________________________________
concatenate_110 (Concatenate) (None, 32, 32, 192) 0 up_sampling2d_110[0][0]
conv2d_279[0][0]
__________________________________________________________________________________________________
conv2d_283 (Conv2D) (None, 32, 32, 64) 110656 concatenate_110[0][0]
__________________________________________________________________________________________________
up_sampling2d_111 (UpSampling2D (None, 64, 64, 64) 0 conv2d_283[0][0]
__________________________________________________________________________________________________
concatenate_111 (Concatenate) (None, 64, 64, 96) 0 up_sampling2d_111[0][0]
conv2d_278[0][0]
__________________________________________________________________________________________________
conv2d_284 (Conv2D) (None, 64, 64, 32) 27680 concatenate_111[0][0]
__________________________________________________________________________________________________
up_sampling2d_112 (UpSampling2D (None, 128, 128, 32) 0 conv2d_284[0][0]
__________________________________________________________________________________________________
concatenate_112 (Concatenate) (None, 128, 128, 48) 0 up_sampling2d_112[0][0]
conv2d_277[0][0]
__________________________________________________________________________________________________
conv2d_285 (Conv2D) (None, 128, 128, 16) 6928 concatenate_112[0][0]
__________________________________________________________________________________________________
conv2d_286 (Conv2D) (None, 128, 128, 1) 17 conv2d_285[0][0]
__________________________________________________________________________________________________
lambda_49 (Lambda) (16384, 1, 1) 0 conv2d_286[0][0]
__________________________________________________________________________________________________
lstm_28 (LSTM) (16384, 1) 12 lambda_49[0][0]
__________________________________________________________________________________________________
dense_26 (Dense) (16384, 1) 2 lstm_28[0][0]
__________________________________________________________________________________________________
lambda_50 (Lambda) (128, 128, 1) 0 dense_26[0][0]
==================================================================================================
Total params: 980,399
Trainable params: 980,399
Non-trainable params: 0
我想将卷积神经网络的输出作为递归神经网络的输入...但是它在此行model = keras.models.Model(inputs, outputs)中显示错误
error：```AttributeError：'tuple'对象没有属性'ndim'