papers | Papers for the C Standards Committee , WG21 | Math library

I was reading a decent paper S-DCNet and I fell upon a section (page3,table1,classifier) where a convolution layer has been used on the feature map in order to produce a binary classification output as part of an internal process. Since I am a noob and when someone talks to me about classification I automatically make a synapse relating to FCs combined with softmax, I started wondering ... Is this a possible thing to do? Can indeed a convolutional layer be used to classify a binary outcome? The whole concept triggered my imagination so much that I insist on getting answers...

Honestly, how does this actually work? What is the difference between using a convolution filter instead of a fully connected layer for classification purposes?

Edit (Uncertain answer on how does it work): I asked a colleague and he told me that using a filter of the same shape as the length-width shape of the feature map at the current stage, may lead to a learnable binary output (considering that you also reduce the #channels of the feature map to a single channel). But I still don't understand the motivations behind such a technique ..

The Python program generates rock, paper, scissors game. The game works; however, I am having trouble keeping up with the score. I use the count method to calculate the amount times the user wins, cpu wins, # of rocks/paper/scissors that have been used.

I looked at other similar questions similar to mine. I am stuck because I am using functions. I want to keep the function format for practice.

I tried setting the counter to equal to 0's as globals. That gave a lot of traceback errors.

I tried changing the while loop within the game() function, but that produced an infinite loop. I kept the while loop within the main() function.

What is the best way to approach this? I want to be able to keep scores and for the count to update until the user quits the program. Thank you!

Suppose you have a neural network with 2 layers A and B. A gets the network input. A and B are consecutive (A's output is fed into B as input). Both A and B output predictions (prediction1 and prediction2) Picture of the described architecture You calculate a loss (loss1) directly after the first layer (A) with a target (target1). You also calculate a loss after the second layer (loss2) with its own target (target2).

Does it make sense to use the sum of loss1 and loss2 as the error function and back propagate this loss through the entire network? If so, why is it "allowed" to back propagate loss1 through B even though it has nothing to do with it?

This question is related to this question https://datascience.stackexchange.com/questions/37022/intuition-importance-of-intermediate-supervision-in-deep-learning but it does not answer my question sufficiently. In my case, A and B are unrelated modules. In the aforementioned question, A and B would be identical. The targets would be the same, too.

(Additional information) The reason why I'm asking is that I'm trying to understand LCNN (https://github.com/zhou13/lcnn) from this paper. LCNN is made up of an Hourglass backbone, which then gets fed into MultiTask Learner (creates loss1), which in turn gets fed into a LineVectorizer Module (loss2). Both loss1 and loss2 are then summed up here and then back propagated through the entire network here.

Even though I've visited several deep learning lectures, I didn't know this was "allowed" or makes sense to do. I would have expected to use two loss.backward(), one for each loss. Or is the pytorch computational graph doing something magical here? LCNN converges and outperforms other neural networks which try to solve the same task.

The "Template non-type arguments" paragraph of the article „Template parameters and template arguments“ states:

The only exceptions are that non-type template parameters of reference or pointer type and non-static data members of reference or pointer type in a non-type template parameter of class type and its subobjects (since C++20) cannot refer to/be the address of

• a temporary object (including one created during reference initialization);
• a string literal;
• the result of typeid;
• the predefined variable __func__;
• or a subobject (including non-static class member, base subobject, or array element) of one of the above (since C++20).

Emphasis is mine.

And below there is an example

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/* Here is the HTML form Code*/

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