what is the first initialized weight in pytorch convolutional layer

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I do self-studying in Udacity PyTorch
Regarding to the last paragraph




Learning



In the code you've been working with, you've been setting the values of filter weights explicitly, but neural networks will actually learn the best filter weights as they train on a set of image data. You'll learn all about this type of neural network later in this section, but know that high-pass and low-pass filters are what define the behavior of a network like this, and you know how to code those from scratch!



In practice, you'll also find that many neural networks learn to detect the edges of images because the edges of object contain valuable information about the shape of an object.




I have studied all through the last 44th sections. But I couldn't be able to answer the following questions




  1. What is the initialized weight when I do torch.nn.Conv2d? And how to define it myself?

  2. How does PyTorch update weights in the convolutional layer?










share|improve this question



























    0















    I do self-studying in Udacity PyTorch
    Regarding to the last paragraph




    Learning



    In the code you've been working with, you've been setting the values of filter weights explicitly, but neural networks will actually learn the best filter weights as they train on a set of image data. You'll learn all about this type of neural network later in this section, but know that high-pass and low-pass filters are what define the behavior of a network like this, and you know how to code those from scratch!



    In practice, you'll also find that many neural networks learn to detect the edges of images because the edges of object contain valuable information about the shape of an object.




    I have studied all through the last 44th sections. But I couldn't be able to answer the following questions




    1. What is the initialized weight when I do torch.nn.Conv2d? And how to define it myself?

    2. How does PyTorch update weights in the convolutional layer?










    share|improve this question

























      0












      0








      0








      I do self-studying in Udacity PyTorch
      Regarding to the last paragraph




      Learning



      In the code you've been working with, you've been setting the values of filter weights explicitly, but neural networks will actually learn the best filter weights as they train on a set of image data. You'll learn all about this type of neural network later in this section, but know that high-pass and low-pass filters are what define the behavior of a network like this, and you know how to code those from scratch!



      In practice, you'll also find that many neural networks learn to detect the edges of images because the edges of object contain valuable information about the shape of an object.




      I have studied all through the last 44th sections. But I couldn't be able to answer the following questions




      1. What is the initialized weight when I do torch.nn.Conv2d? And how to define it myself?

      2. How does PyTorch update weights in the convolutional layer?










      share|improve this question














      I do self-studying in Udacity PyTorch
      Regarding to the last paragraph




      Learning



      In the code you've been working with, you've been setting the values of filter weights explicitly, but neural networks will actually learn the best filter weights as they train on a set of image data. You'll learn all about this type of neural network later in this section, but know that high-pass and low-pass filters are what define the behavior of a network like this, and you know how to code those from scratch!



      In practice, you'll also find that many neural networks learn to detect the edges of images because the edges of object contain valuable information about the shape of an object.




      I have studied all through the last 44th sections. But I couldn't be able to answer the following questions




      1. What is the initialized weight when I do torch.nn.Conv2d? And how to define it myself?

      2. How does PyTorch update weights in the convolutional layer?







      python neural-network conv-neural-network pytorch






      share|improve this question













      share|improve this question











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      share|improve this question










      asked Dec 31 '18 at 19:04









      SaritSarit

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          When you declared nn.Conv2d the weights are initialized via this code.



          In particular, if you give bias it uses initialization as proposed by Kaiming et.al. It initializes as uniform distribution between (-bound, bound) where bound=sqrt{6/((1+a^2)fan_in)} (See here).



          You can initialize weight manually too. This has been answered elsewhere (See here) and I won't repeat it.



          When you call optimizer.step and optimizer has parameters of convolutional filter registered they are updated.






          share|improve this answer

























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            1 Answer
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            active

            oldest

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            1 Answer
            1






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes









            2














            When you declared nn.Conv2d the weights are initialized via this code.



            In particular, if you give bias it uses initialization as proposed by Kaiming et.al. It initializes as uniform distribution between (-bound, bound) where bound=sqrt{6/((1+a^2)fan_in)} (See here).



            You can initialize weight manually too. This has been answered elsewhere (See here) and I won't repeat it.



            When you call optimizer.step and optimizer has parameters of convolutional filter registered they are updated.






            share|improve this answer






























              2














              When you declared nn.Conv2d the weights are initialized via this code.



              In particular, if you give bias it uses initialization as proposed by Kaiming et.al. It initializes as uniform distribution between (-bound, bound) where bound=sqrt{6/((1+a^2)fan_in)} (See here).



              You can initialize weight manually too. This has been answered elsewhere (See here) and I won't repeat it.



              When you call optimizer.step and optimizer has parameters of convolutional filter registered they are updated.






              share|improve this answer




























                2












                2








                2







                When you declared nn.Conv2d the weights are initialized via this code.



                In particular, if you give bias it uses initialization as proposed by Kaiming et.al. It initializes as uniform distribution between (-bound, bound) where bound=sqrt{6/((1+a^2)fan_in)} (See here).



                You can initialize weight manually too. This has been answered elsewhere (See here) and I won't repeat it.



                When you call optimizer.step and optimizer has parameters of convolutional filter registered they are updated.






                share|improve this answer















                When you declared nn.Conv2d the weights are initialized via this code.



                In particular, if you give bias it uses initialization as proposed by Kaiming et.al. It initializes as uniform distribution between (-bound, bound) where bound=sqrt{6/((1+a^2)fan_in)} (See here).



                You can initialize weight manually too. This has been answered elsewhere (See here) and I won't repeat it.



                When you call optimizer.step and optimizer has parameters of convolutional filter registered they are updated.







                share|improve this answer














                share|improve this answer



                share|improve this answer








                edited Jan 1 at 9:02

























                answered Jan 1 at 1:41









                Umang GuptaUmang Gupta

                3,34611637




                3,34611637
































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