advanced deep-learning best practices every branch has the same stride value ( )which is necessary to keep all branch outputs the same size so you can concatenate them in this branchthe striding occurs in the spatial convolution layer from keras import layers branch_a layers conv ( activation='relu'strides= )(xbranch_b layers conv ( activation='relu')(xbranch_b layers conv ( activation='relu'strides= )(branch_bbranch_c layers averagepooling ( strides= )(xbranch_c layers conv ( activation='relu')(branch_cbranch_d layers conv ( activation='relu')(xbranch_d layers conv ( activation='relu')(branch_dbranch_d layers conv ( activation='relu'strides= )(branch_doutput layers concatenate[branch_abranch_bbranch_cbranch_d]axis=- in this branchthe striding occurs in the average pooling layer concatenates the branch outputs to obtain the module output note that the full inception architecture is available in keras as keras applications inception_v inceptionv including weights pretrained on the imagenet dataset another closely related model available as part of the keras applications module is xception xceptionwhich stands for extreme inceptionis convnet architecture loosely inspired by inception it takes the idea of separating the learning of channel-wise and space-wise features to its logical extremeand replaces inception modules with depthwise separable convolutions consisting of depthwise convolution ( spatial convolution where every input channel is handled separatelyfollowed by pointwise convolution ( convolution)--effectivelyan extreme form of an inception modulewhere spatial features and channel-wise features are fully separated xception has roughly the same number of parameters as inception but it shows better runtime performance and higher accuracy on imagenet as well as other large-scale datasetsdue to more efficient use of model parameters residual connections residual connections are common graph-like network component found in many post network architecturesincluding xception they were introduced by he et al from microsoft in their winning entry in the ilsvrc imagenet challenge in late they tackle two common problems that plague any large-scale deep-learning modelvanishing gradients and representational bottlenecks in generaladding residual connections to any model that has more than layers is likely to be beneficial francois chollet"xceptiondeep learning with depthwise separable convolutions,conference on computer vision and pattern recognition ( )he et al "deep residual learning for image recognition,licensed to