我试图用keras中的索引池修改segnet，但是当我将每个conv块的conv内核数量增加四倍时，在训练时就出错了。我不知道为什么。请给我一些帮助吗？预先感谢。
回溯
tensorflow.python.framework.errors_impl.InvalidArgumentError：不兼容的形状：[2,64,64,256]与[512] [[{{节点副本_0 / model_3 / max_unpooling2d_2 / max_unpooling2d_2 / mul_3}}]] [[{{node loss / mul}}]]
model.summary（）
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 512, 512, 3) 0
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 512, 512, 64) 1792 input_1[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 512, 512, 64) 256 conv2d_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 512, 512, 64) 36928 batch_normalization_1[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 512, 512, 64) 256 conv2d_2[0][0]
__________________________________________________________________________________________________
max_pooling_with_argmax2d_1 (Ma [(None, 256, 256, 64 0 batch_normalization_2[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 256, 256, 128 73856 max_pooling_with_argmax2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 256, 256, 128 512 conv2d_3[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 256, 256, 128 147584 batch_normalization_3[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 256, 256, 128 512 conv2d_4[0][0]
__________________________________________________________________________________________________
max_pooling_with_argmax2d_2 (Ma [(None, 128, 128, 12 0 batch_normalization_4[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 128, 128, 256 295168 max_pooling_with_argmax2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 128, 128, 256 1024 conv2d_5[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 128, 128, 256 590080 batch_normalization_5[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 128, 128, 256 1024 conv2d_6[0][0]
__________________________________________________________________________________________________
max_pooling_with_argmax2d_3 (Ma [(None, 64, 64, 256) 0 batch_normalization_6[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 64, 64, 512) 1180160 max_pooling_with_argmax2d_3[0][0]
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 64, 64, 512) 2048 conv2d_7[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 64, 64, 512) 2359808 batch_normalization_7[0][0]
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 64, 64, 512) 2048 conv2d_8[0][0]
__________________________________________________________________________________________________
max_pooling_with_argmax2d_4 (Ma [(None, 32, 32, 512) 0 batch_normalization_8[0][0]
__________________________________________________________________________________________________
max_unpooling2d_1 (MaxUnpooling (None, 64, 64, 512) 0 max_pooling_with_argmax2d_4[0][0]
max_pooling_with_argmax2d_4[0][1]
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 64, 64, 512) 2359808 max_unpooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 64, 64, 512) 2048 conv2d_13[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, 64, 64, 512) 2359808 batch_normalization_9[0][0]
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 64, 64, 512) 2048 conv2d_14[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, 64, 64, 512) 2359808 batch_normalization_10[0][0]
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 64, 64, 512) 2048 conv2d_15[0][0]
__________________________________________________________________________________________________
max_unpooling2d_2 (MaxUnpooling (None, 128, 128, 256 0 batch_normalization_11[0][0]
max_pooling_with_argmax2d_3[0][1]
__________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, 128, 128, 256 590080 max_unpooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 128, 128, 256 1024 conv2d_16[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, 128, 128, 256 590080 batch_normalization_12[0][0]
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 128, 128, 256 1024 conv2d_17[0][0]
__________________________________________________________________________________________________
max_unpooling2d_3 (MaxUnpooling (None, 256, 256, 128 0 batch_normalization_13[0][0]
max_pooling_with_argmax2d_2[0][1]
__________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, 256, 256, 128 147584 max_unpooling2d_3[0][0]
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 256, 256, 128 512 conv2d_18[0][0]
__________________________________________________________________________________________________
conv2d_19 (Conv2D) (None, 256, 256, 128 147584 batch_normalization_14[0][0]
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 256, 256, 128 512 conv2d_19[0][0]
__________________________________________________________________________________________________
max_unpooling2d_4 (MaxUnpooling (None, 512, 512, 64) 0 batch_normalization_15[0][0]
max_pooling_with_argmax2d_1[0][1]
__________________________________________________________________________________________________
conv2d_20 (Conv2D) (None, 512, 512, 64) 36928 max_unpooling2d_4[0][0]
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 512, 512, 64) 256 conv2d_20[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 512, 512, 1) 65 batch_normalization_2[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 256, 256, 1) 129 batch_normalization_4[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 128, 128, 1) 257 batch_normalization_6[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 64, 64, 1) 513 batch_normalization_8[0][0]
__________________________________________________________________________________________________
conv2d_21 (Conv2D) (None, 64, 64, 1) 513 batch_normalization_10[0][0]
__________________________________________________________________________________________________
conv2d_22 (Conv2D) (None, 128, 128, 1) 257 batch_normalization_12[0][0]
__________________________________________________________________________________________________
conv2d_23 (Conv2D) (None, 256, 256, 1) 129 batch_normalization_14[0][0]
__________________________________________________________________________________________________
conv2d_24 (Conv2D) (None, 512, 512, 1) 65 batch_normalization_16[0][0]
__________________________________________________________________________________________________
conv2d_transpose_1 (Conv2DTrans (None, 512, 512, 1) 2 conv2d_9[0][0]
__________________________________________________________________________________________________
conv2d_transpose_2 (Conv2DTrans (None, 512, 512, 1) 5 conv2d_10[0][0]
__________________________________________________________________________________________________
conv2d_transpose_3 (Conv2DTrans (None, 512, 512, 1) 17 conv2d_11[0][0]
__________________________________________________________________________________________________
conv2d_transpose_4 (Conv2DTrans (None, 512, 512, 1) 65 conv2d_12[0][0]
__________________________________________________________________________________________________
conv2d_transpose_5 (Conv2DTrans (None, 512, 512, 1) 65 conv2d_21[0][0]
__________________________________________________________________________________________________
conv2d_transpose_6 (Conv2DTrans (None, 512, 512, 1) 17 conv2d_22[0][0]
__________________________________________________________________________________________________
conv2d_transpose_7 (Conv2DTrans (None, 512, 512, 1) 5 conv2d_23[0][0]
__________________________________________________________________________________________________
conv2d_transpose_8 (Conv2DTrans (None, 512, 512, 1) 2 conv2d_24[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 512, 512, 4) 0 conv2d_transpose_1[0][0]
conv2d_transpose_2[0][0]
conv2d_transpose_3[0][0]
conv2d_transpose_4[0][0]
__________________________________________________________________________________________________
concatenate_2 (Concatenate) (None, 512, 512, 4) 0 conv2d_transpose_5[0][0]
conv2d_transpose_6[0][0]
conv2d_transpose_7[0][0]
conv2d_transpose_8[0][0]
__________________________________________________________________________________________________
concatenate_3 (Concatenate) (None, 512, 512, 8) 0 concatenate_1[0][0]
concatenate_2[0][0]
__________________________________________________________________________________________________
conv2d_25 (Conv2D) (None, 512, 512, 2) 18 concatenate_3[0][0]
__________________________________________________________________________________________________
reshape_1 (Reshape) (None, 262144, 2) 0 conv2d_25[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 262144, 2) 0 reshape_1[0][0]
==================================================================================================
Total params: 13,296,332
Trainable params: 13,287,756
Non-trainable params: 8,576
keras 2.2.4 张量流1.13.1