CNN tensor input shape and feature maps Welcome back to this series on neural network programming. It only takes a minute to sign up.I do not understand the connection between layer $\ell$ and layer $\ell+1$. But I think that above code will result in 64 feature maps.Let's say you have a grayscale image input to the first layer and 32 kernels of shape The output of this layer has 32 channels (1 per each kernel). But in general I still don't understand. Featured on Meta

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Typically I would think that with 64 filters and 32 feature maps from the previous layer we would get 64*32 feature maps in the next layer (all features are connected to each filter). By using our site, you acknowledge that you have read and understand our Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Did I understood well that actually each layer output can be seen as one image with a certain channel size?

This means your feature map only has 1/4th of the original size. Thank you so much for your nice explanation and quick answer @jan kukacka!

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By using our site, you acknowledge that you have read and understand our To start, the CNN receives an input feature map: a three-dimensional matrix where the size of the first two dimensions corresponds to the length and width of the images in pixels. I am reading a paper which implementing CNN but i dont understand this sentence Naively speaking, a filter of a CNN works by moving the filter matrix (e.g. This is exactly the same way how pooling reduces the feature map size.

A similar question and answer clearly responds to his particular example: How are filters and activation maps connected in Convolutional Neural Networks? !Yes, that's correct.

In fact, convolutional filters can learn average and max pooling.Thanks for contributing an answer to Stack Overflow! In a convolutional neural network units within a hidden layer are segmented into "feature maps" where the units within a feature map share the weight matrix, or in simple terms look for the same feature. In the second layer in your example, you have 64 kernels of shape Typically I would think that with 64 filters and 32 feature maps from the previous layer we would get 64*32 feature maps in the next layer (all features are connected to each filter).I hope that from the above explanation it is clear that you are not applying each of the 64 kernels on each of the 32 feature maps individually.

But in general I still don't understand.

(e.g., pad 1 pixel for a filter size of 3) • On each feature map, the response at (0, 0) has a receptive field centered at (0, 0) on the image • On each feature map, the response at ( , )has a receptive field centered at ( , )on the image (stride ) • A general solution See [Karel Lenc & Andrea Vedaldi] “R- NN minus R”. The size of the third dimension is 3 (corresponding to the 3 channels of a color image: red, green, and blue). Don't forget to mark the answer and upvote if it indeed solved your problem :) It is the same as if you would simply subsample the result. Stack Overflow works best with JavaScript enabled


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