# MergeVertex behaviour with both Convolutional and Regular layers

**URL:** <https://community.konduit.ai/t/mergevertex-behaviour-with-both-convolutional-and-regular-layers/2641>\
**Category:** DL4J\
**Created:** [June 23, 2023, 8:35am UTC](https://community.konduit.ai/t/mergevertex-behaviour-with-both-convolutional-and-regular-layers/2641 "2023-06-23T08:35:16Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![EquanimeAugello](https://avatars.discourse-cdn.com/v4/letter/e/e9c0ed/32.png) [@EquanimeAugello](https://community.konduit.ai/u/EquanimeAugello)\
**Post date:** [June 23, 2023, 8:35am UTC](https://community.konduit.ai/t/mergevertex-behaviour-with-both-convolutional-and-regular-layers/2641/1 "2023-06-23T08:35:16Z")

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I’ve read the MergeVertex concatenates the activations, and that the concaenation is along the depth in case of Convolutional networks. I’d like to know if this last scenario is the case also if we are merging a ConvolutionLayer with a DenseLayer, thank you very much.

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**Author:** ![agibsonccc](https://yyz1.discourse-cdn.com/flex035/user_avatar/community.konduit.ai/agibsonccc/32/697_2.png) [@agibsonccc](https://community.konduit.ai/u/agibsonccc)\
**Post date:** [June 23, 2023, 11:29am UTC](https://community.konduit.ai/t/mergevertex-behaviour-with-both-convolutional-and-regular-layers/2641/2 "2023-06-23T11:29:17Z")

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@EquanimeAugello you’ll need to reshape everything to match then pass it to merge.