http://d2l.ai/chapter_convolutional-neural-networks/lenet.html

â€śEach 2Ă—2 pooling operation (stride 2) reduces dimensionality by a factor of 4 via spatial downsamplingâ€ť. From 28x28 to 14x14, how does it reduce by a factor of 4? Since the dimensionality of a matrix is rows x cols, is it, 28x28=784; 14x14=196, hence 784-196=588 and 588 is divisible by 4, so it reduces by a factor of 4? Sorry for asking a silly question.

Hey @rezahabibi96, your question is not silly at all. Asking question is always better than keeping quiet! The origin size is 28x28=784, and the after pooling size is 14x14=196. If we calculate 784 / 196 = 4, that is where the factor â€ś4â€ť coming from!

How would we do #4 for the exercises in 6.6?

â€śdisplay the activation functionsâ€ť (ie sweaters and coats)?

Can we just interject `visualize_activation(mx.gluon.nn.Activation('sigmoid'))`

somewhere and it work?

Hey @smizerex, sorry for a bit confusing here. It was asking â€śDisplay the features after the first and second convolution layers of LeNet for different inputs (e.g., sweaters and coats).â€ť Let me know if that makes sense to you.

The input was 28x28 , We are applying a 2x2 pooling operation of stride 2 , Here stride simply means how many cells were shifted horizontally after the first pooling and vertically after the first horizontal pooling is finished . The output after performing pooling is 14x14,

The formula behind is (28-2)/2+1=14

Here 1 is the bias term and the denominator term denotes the stride .So we finally get a output of size 14x14.

I have a problem with the activations of the first two conv layers; it appears that the first layer shows sort of a complete object (e.g. shoe) rather than showing edges. I understand that the first layers are meant to capture simple features like edges??!

Hi @osamaGkhafagy, excellent question! In general, the earlier layers try to capture the local features (such as the edge by the color contrast). However, it doesnâ€™t need to be necessary an edge, it can be any low level coarse features for the later layers to learn.