Row 3647

Row ID: 3647 | Dataset Entry | Axioma AXP Content Repository

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Hi everyone, I´m new in this AI stuff and I'm engineering student too. So, I´m trying to code a multilayer perceptron with two hidden layers and one neuron in output layer to do a regression task without any library, just with linear algebra. I think that I understood the math behind the back propagation, but I have a doubt when i'm coding.

During the backward pass I wrote something similar to this( just to know, I´m writing the code in samsung notes before coding):

% second hidden layer

dE\_dY = grad3\*w3

dY\_dI = g(L2\_input,derivative = "True")

grad2 = dE\_dy\*dI\_dy

dI\_dW = L1\_output

W2 = W2 -(learning\_rate)\*(-grad2\*dI\_dW)

This part of my code is updating the weigths of second hidden layer. My problem is with the dimensions. because when i calculate grad2, that in my case is the multiplication of 2 collunm vectors of same dimensions,something like (1xn)\*(1xn), and as you know, I can't multiply this.

Due to the fact that I know the dimension of my weight matrix, e.g MxN, I konw that --> (learning\_rate)\*(-grad2\*dI\_dW) <--- needs to have this same dimensions. With this, the only possibility to achieve the dimension MxN is if I realize the operation dI\_dy.\*dI\_dy, where the operator ".\*" will multiply each one of the elements of the dI\_dy by each element of dI\_dy, resulting in the (1xn).

My doubt is: In the math, -->dI\_dy\*dI\_dy<--- is just a multiplication, but when I'm coding, it appears that I will need to use the ".\*", but I don't know if this is correct.

Just to know, I´m programming in MATLAB.

Sorry by the long text and if I didn't was clear enough, please let me know (I'm not an native english speaker).

Field	Value
text	Hi everyone, I´m new in this AI stuff and I'm engineering student too. So, I´m trying to code a multilayer perceptron with two hidden layers and one neuron in output layer to do a regression task without any library, just with linear algebra. I think that I understood the math behind the back propagation, but I have a doubt when i'm coding. During the backward pass I wrote something similar to this( just to know, I´m writing the code in samsung notes before coding): % secon…
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datetime	2024-03-18
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Raw Record

{
  "text": "Hi everyone, I´m new in this AI stuff and I'm engineering student too. So, I´m trying to code a multilayer perceptron with two hidden layers and one neuron in output layer to do a regression task without any library, just with linear algebra. I think that I understood the math behind the back propagation, but I have a doubt when i'm coding.\n\n&#x200B;\n\nDuring the backward pass I wrote something similar to this( just to know, I´m writing the code in samsung notes before coding):\n\n&#x200B;\n\n% second hidden layer\n\ndE\\_dY = grad3\\*w3\n\ndY\\_dI = g(L2\\_input,derivative = \"True\")\n\ngrad2 = dE\\_dy\\*dI\\_dy\n\ndI\\_dW = L1\\_output\n\n&#x200B;\n\nW2 = W2 -(learning\\_rate)\\*(-grad2\\*dI\\_dW)\n\n&#x200B;\n\nThis part of my code is updating the weigths of second hidden layer. My problem is with the dimensions. because when i calculate grad2, that in my case is the multiplication of 2 collunm vectors of same dimensions,something like (1xn)\\*(1xn), and as you know, I can't multiply this.\n\n&#x200B;\n\nDue to the fact that I know the dimension of my weight matrix, e.g MxN, I konw that --> (learning\\_rate)\\*(-grad2\\*dI\\_dW) <--- needs to have this same dimensions. With this, the only possibility to achieve the dimension MxN is if I realize the operation dI\\_dy.\\*dI\\_dy, where the operator \".\\*\" will multiply each one of the elements of the dI\\_dy by each element of dI\\_dy, resulting in the (1xn).\n\n&#x200B;\n\nMy doubt is: In the math, -->dI\\_dy\\*dI\\_dy<--- is just a multiplication, but when I'm coding, it appears that I will need to use the \".\\*\", but I don't know if this is correct.\n\n&#x200B;\n\nJust to know, I´m programming in MATLAB.\n\nSorry by the long text and if I didn't was clear enough, please let me know (I'm not an native english speaker).\n\n&#x200B;",
  "label": "r/neuralnetworks",
  "dataType": "post",
  "communityName": "r/neuralnetworks",
  "datetime": "2024-03-18",
  "username_encoded": "Z0FBQUFBQm5LakwxVVNlZ1V4aF8yUEtFWHl5OHkxTnRNclp5N2ZaMUsxWFJWcjJFcFk0SV92R3h5blhLYkprZDZGUmZxQmFzaEYyd1Y3ZzNqWHQ2ZXpvTGowV1I5WWg5eFE9PQ==",
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}

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Entry Information

Entry ID: 3647
Repository: Axioma AXP
Dataset: arrmlet/reddit_dataset_36
Total Entries: 100,000