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I've read this articles http://www.codeproject.com/Articles/143059/Neural-Network-for-Recognition-of-Handwritten-Di and when I turn to this one: Layer #0: is the gray scale image of the handwritten character in the MNIST database which is padded to 29x29 pixel. There are 29x29= 841 neurons in the input layer. Layer #1: is a convolutional layer with six (6) feature maps. There are 13x13x6 = 1014 neurons, (5x5+1)x6 = 156 weights, and 1014x26 = 26364 connections from layer #1 to the previous layer.

How can we get the six(6) feature maps just from convolution on image ? I think we just get only one feature map. Or am i wrong ?

PhiVH
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  • really good explanation here: https://stackoverflow.com/questions/42786717/how-to-calculate-the-number-of-parameters-for-convolutional-neural-network – hpep Aug 14 '18 at 18:40

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I'm doing my research around convolution neural network.

Six different kernels(or filters) are convoluted on the same image to generate six feature map.

Layer #0: Input image with 29x29 pixels thus have 29*29=841 neuron(input neuron)

Layer #1: Convolutional layer uses 6 different kernels(or filters) of size 5x5 pixel and stride length 2(amount of shift while convoluting input with kernals or filters) which are convoluted with the input image(29x29) generating 6 different feature maps(13x13) thus 13x13x6=1014 neuron.

Filter size 5x5 and a bias(for weight correction) thus (5x5)+1 neuron and was we have 6 kernals(or filters), gives 6*[(5x5)+1]= 156 neuron.

During convolution we move kernels(or filters) 26 times(13 horizontal move + 13 vertical move) and finally 1014*26=26364 connections from Layer #0 to Layer #1.

You should go through this research paper by Y LeCun, L Bottou, Y Bengio: Gradient- Based learing applied to document recognition Section II to understand convolution neural network(I recommend to read the whole paper).

Another place where you can find detailed explanation and python implementation fo CNN is here. If you have time I recommend to go through this site for more details about deep learning.

Thank you.

Alwyn Mathew
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you get six feature maps by convolving with six different kernel on the same image.

Zaw Lin
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