Course 4: Week 1: Reading: CNN Example *CORRECTION*

hi, i’m trying to get my head around the figures given for the parameters in the cnn for the conv1 and conv2 layer

Here are screenshots for reference:

From my understanding
for the first conv layer,
the calculation should be (5 * 5 * 3 + 1) * 6.
i am not sure why the figure of 8 is used in the typo…
you have a 5 *5 *3 filter convolving on the input image.
there are 6 such filters, and each filter has a bias term, so (5 *5 *3+1) * 6

somewhat similarly in the 2nd conv layer
i get (5* 5* 6+1) * 16
the figure 8 in used in the typo in place of the 6.
from my understanding, you have a 5* 5* 6 filter convolving on the input image of size 14 * 14 * 6.
there are 16 such filters, and each filter has a bias term, so (5*5 *6+1) * 16.
i saw this topic raised in the forums but was directed here. there doesn’t seem to be an obvious way of searching for a topic so apologies if this has already been answered…
would appreciate some clarification on this!


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Hi @user73 ,

Thanks for reporting the problem. You’re right, the #of channels in LeNet-5 Conv1 layer is 6 rather than 8. The issue is same as this.


Would be helpful to mention this explicitly in correction slide. Had to look around why my computed number of params are different.

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You solved my question. Thanks!

i was wondering the same thing; great to spot the errors