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Really love your series - I would really appreciate it if you can go through what the backpropation functions do: optimizer.zero_grad() loss.backward() optimizer.step()
Hi John. Great video and really clear explanation. Just one question, is there a better way of classifying the flower types. It seems like doing it numerically with all types on the same scale is a bit strange and could lead to some confusion for less accurate models. For example, the machine might give a score of 1.1 because it is unsure whether a flower is Setosa (0.0) or Virginica (2.0). However, a score of 1.1 would lead us to conclude that it is most likely Versicolor. Is there a way to avoid this? I hope I'm being clear.
13:13 When I run this code, I get something else. I am not sure where or what my error is. /usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py Return an iterator over module parameters. This is typically passed to an optimizer. Args: recurse (bool): if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module. Yields: Parameter: module parameter Example:: >>> # xdoctest: +SKIP("undefined vars") >>> for param in model.parameters(): >>> print(type(param), param.size()) (20L,) (20L, 1L, 5L, 5L)
Sir, I have covered full playlist of kivy and it is really amazing, but when we make the kivy program we can only excess it on system which contains the compiler but what if I want to share it or want to convert it into apk can we do it. Please make one more amazing videos and solve this problem, I have already tried Google cllab but file get crashed again and again.
Thank you so much. However, I have a problem with my Colab; I am not able to visualize the epochs like you did around the timestamp 19 minutes and 30 seconds. How do I set up my Colab like that?
Where is the text of your code in the pinned comment? I am not finding any code anywhere in that 1st pinned comment. Not finding any code in any of your vids... I am probably missing something obvious... the tutorial is fantastic...
@@Codemycom Where in the pinned comment... I am not finding any code anywhere in that 1st pinned comment. Not finding any code in any of your vids... I am probably missing something obvious
i just got this error can you please fix it ? RuntimeError: expected scalar type Long but found Float the part that show an error is the convert is part when we convert array to FloatTensor. i did check everything and not found a fix ?
hi guys i just started playing with neural networks and i have a little problem with this tutorial and no matter what i do my losses and epochs graph is stationary on one value. PLS help
Can you share your code? One thing I could think of is that you run the training itself multiple times - that is something you shouldn't do as the model already learned some weights and biases. You are basically overfitting the model. Run the whole notebook instead
For me this was because I had a type on class forward: ``` def forward(self, x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.out(x) # remove the ReLU activation here return x```
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▶ Get The Code
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Really love your series - I would really appreciate it if you can go through what the backpropation functions do:
optimizer.zero_grad()
loss.backward()
optimizer.step()
Awesome tutorial, it's great to see that you are using sklearn for data partitioning, thanks for the tutorial! Keep up the good work👍
Thanks
This saved my life. Thank you!
Welcome
Thanks for the video!!!really helpful to lot of learners
Glad it was helpful!
torch.FloatTensor(x) not converting x to torch tensor but creating a torch tensor x dimension!!! . Not sure if I am missing anything?
Amazing learning with your videos and then i got deep dive learning.. anjaay
Glad you enjoyed it!
@@Codemycom can't wait your next videos...
@@shahrizal0715 The completed and final list of all the pytorch videos is in the pinned comment. There won't be any more.
Hi John. Great video and really clear explanation. Just one question, is there a better way of classifying the flower types. It seems like doing it numerically with all types on the same scale is a bit strange and could lead to some confusion for less accurate models. For example, the machine might give a score of 1.1 because it is unsure whether a flower is Setosa (0.0) or Virginica (2.0). However, a score of 1.1 would lead us to conclude that it is most likely Versicolor. Is there a way to avoid this? I hope I'm being clear.
make a system for that it's easy come on
John you are the best... Wish you will do llm and bert
I'm planning on it
13:13 When I run this code, I get something else. I am not sure where or what my error is.
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py
Return an iterator over module parameters.
This is typically passed to an optimizer.
Args:
recurse (bool): if True, then yields parameters of this module
and all submodules. Otherwise, yields only parameters that
are direct members of this module.
Yields:
Parameter: module parameter
Example::
>>> # xdoctest: +SKIP("undefined vars")
>>> for param in model.parameters():
>>> print(type(param), param.size())
(20L,)
(20L, 1L, 5L, 5L)
Sir, I have covered full playlist of kivy and it is really amazing, but when we make the kivy program we can only excess it on system which contains the compiler but what if I want to share it or want to convert it into apk can we do it. Please make one more amazing videos and solve this problem, I have already tried Google cllab but file get crashed again and again.
Really impressive 😧 .. Very nice tutorial but i return to Django soon 🤕
ha
I Love you!
Thanks!
Now I have another issue with the model.parameters that is on the tutorial is not showing the fc1, fc2, and out. How do I fix that?
Did u by any chance type model.parameter() ?
19:15 we had the same error. I caught it early haha
ha
Thank you so much. However, I have a problem with my Colab; I am not able to visualize the epochs like you did around the timestamp 19 minutes and 30 seconds. How do I set up my Colab like that?
I figured it out professor!
thank you .
Can you do a playlist list on computer vision with openCV?😇
Sorry, no plans for that.
y_train = torch.LongTensor(y_train) is not coverting and is giving me an error am I doing something wrong?
I figured it out
glad you got it sorted
Where is the text of your code in the pinned comment? I am not finding any code anywhere in that 1st pinned comment. Not finding any code in any of your vids... I am probably missing something obvious... the tutorial is fantastic...
The code is a link in the pinned comment. It's a link to the code that you click on.
@@Codemycomok... I had not clicked "Read more"... the link for code then did show up... thanks!
@@tracysmith2914 Ah, glad you got it sorted out!
Do you have any github repo for this tutorial?
Yep, pinned comment as always
I've been typing your code into my PC to try but it's a slow process. Can I get a text file with your code anywhere?
Code is always in the pinned comment
@@Codemycom Where in the pinned comment... I am not finding any code anywhere in that 1st pinned comment. Not finding any code in any of your vids... I am probably missing something obvious
@@tracysmith2914 Look for line that says " Get The Code" in the pinned comment.
i just got this error can you please fix it ?
RuntimeError: expected scalar type Long but found Float
the part that show an error is the convert is part when we convert array to FloatTensor.
i did check everything and not found a fix ?
i also had same error , but then i did exactly copied from him , just for learning purpose , and it worked
hi guys i just started playing with neural networks and i have a little problem with this tutorial and no matter what i do my losses and epochs graph is stationary on one value. PLS help
Can you share your code? One thing I could think of is that you run the training itself multiple times - that is something you shouldn't do as the model already learned some weights and biases. You are basically overfitting the model. Run the whole notebook instead
For me this was because I had a type on class forward:
``` def forward(self, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.out(x) # remove the ReLU activation here
return x```
Thank you, that fixed mine@@MattVibes
Freaky 🥵
lol
Hi. This one is a little bit too heavy. It would be better to split it into 2 short videos. Great tutorials overall. Thanks
Or you can watch it twice
Hello sir please