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I am recently watching some tutorials for deep learning from Dr Andrew Ng on Youtube. Link is hereThe Youtube video

There is a concept of number of features in convolutional neural network in TensorFlow's tutorial.

I don't quite understand why the feature is 32 or 64 here in conv layer1 or layer2?

Then I came to the video, there is also the concept of Quadratic features. It is calculated as 3 million however. But how is it calculated?

Are the two features related in concept?

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  • $\begingroup$ it's common to use powers of two for sizing filters or dense layers in neural networks $\endgroup$
    – Vadim Smolyakov
    Aug 15 '17 at 18:26
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Sorry for being so late in the response. I have just read your message.

The instructor is using $\frac{x^2}2$. So $\frac{2500\times 2500}2$, and this will get approx $3$ millions features.

BR

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What this means is:

  1. 50x50 pixels image means 2500 unit pixels in your image. Then each unit is a feature, i.e.,

$x_1=\text{pixel}_1$, $x_2 = \text{pixel}_2$, $\cdots$, $x_{2500} = \text{pixel}_{2500}$. A quadratic function means that you take all possible combinations of $x_ix_j$, for example $x_1^2$, $\ x_1x_2$, $\ x_1x_3$, $\ \cdots$, $\ x_1x_{2500}$, etc... this will give you a better outcome than considering just a linear function..., just get all the possible combinations and count them and you will get around $\frac{n^2}{2} = \frac{2500\ \times\ 2500}{2} \approx 3M$

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