residual connection consists of making the output of an earlier layer available as input to later layereffectively creating shortcut in sequential network rather than being concatenated to the later activationthe earlier output is summed with the later activationwhich assumes that both activations are the same size if they're different sizesyou can use linear transformation to reshape the earlier activation into the target shape (for examplea dense layer without an activation orfor convolutional feature mapsa convolution without an activationhere' how to implement residual connection in keras when the feature-map sizes are the sameusing identity residual connections this example assumes the existence of input tensor xapplies transformation to from keras import layers layers conv ( activation='relu'padding='same')(xy layers conv ( activation='relu'padding='same')(yy layers conv ( activation='relu'padding='same')(yy layers add([yx]adds the original back to the output features and the following implements residual connection when the feature-map sizes differusing linear residual connection (againassuming the existence of input tensor )uses convolution to linearly downsample the original tensor to the same shape as from keras import layers layers conv ( activation='relu'padding='same')(xy layers conv ( activation='relu'padding='same')(yy layers maxpooling ( strides= )(yresidual layers conv ( strides= padding='same')(xy layers add([yresidual]adds the residual tensor back to the output features representational bot tlenecks in deep learning in sequential modeleach successive representation layer is built on top of the previous onewhich means it only has access to information contained in the activation of the previous layer if one layer is too small (for exampleit has features that are too low-dimensional)then the model will be constrained by how much information can be crammed into the activations of this layer licensed to