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Let us introduce the following setting. Denote by CNN ( in _ { ch } , out _ { ch } , kernel = (
Let us introduce the following setting. Denote by CNNinch outch kernel kh kw stride swsh padding pw ph the convolutional layer. Here, we use the following notations: inch the number of input channels, outch the number of output channels, kernel dimensions of the convolutional kernel kh height, kw width stride convolution pixel step two dimensions padding number of pixels in two dimensions that are placed to expand the boundary of the input. For further convenience, let us use one value in the case if the two dimensions that are placed to expand the boundary of the input. For further convenience, let us use the one value in the case if the two dimensional parameter takes equal values in each of its dimensions, eg kernel means kernel After convolutional layer we use some nonlinear function, as it is supposed to be To conclude, CNN is introduced in the most standardized way, as it seems possible. Also let us consider the standard max polling layer, with kernel size x and denoted by MLP Therefore the setting is complete. Consider the following neural network diagram.
CNN MPL CNN CNN MLP
We put through this model one tensor of form channel height, width After that we flatten the output. Thus we get the d vector. What is the length of this vector?
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b
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d
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