deep learning for computer vision stride in order to downsample the feature maps by factor of on the other handconvolution is typically done with windows and no stride (stride why downsample feature maps this waywhy not remove the max-pooling layers and keep fairly large feature maps all the way uplet' look at this option the convolutional base of the model would then look like thismodel_no_max_pool models sequential(model_no_max_pool add(layers conv ( ( )activation='relu'input_shape=( ))model_no_max_pool add(layers conv ( ( )activation='relu')model_no_max_pool add(layers conv ( ( )activation='relu')here' summary of the modelmodel_no_max_pool summary(layer (typeoutput shape param ===============================================================conv d_ (conv (none conv d_ (conv (none conv d_ (conv (none ===============================================================total params , trainable params , non-trainable params what' wrong with this setuptwo thingsit isn' conducive to learning spatial hierarchy of features the windows in the third layer will only contain information coming from windows in the initial input the high-level patterns learned by the convnet will still be very small with regard to the initial inputwhich may not be enough to learn to classify digits (try recognizing digit by only looking at it through windows that are pixels!we need the features from the last convolution layer to contain information about the totality of the input the final feature map has , total coefficients per sample this is huge if you were to flatten it to stick dense layer of size on topthat layer would have million parameters this is far too large for such small model and would result in intense overfitting in shortthe reason to use downsampling is to reduce the number of feature-map coefficients to processas well as to induce spatial-filter hierarchies by making successive convolution layers look at increasingly large windows (in terms of the fraction of the original input they covernote that max pooling isn' the only way you can achieve such downsampling as you already knowyou can also use strides in the prior convolution layer and you can licensed to