[Day 43] Coding up LeNet, VGG, InceptionNet, UNet from scratch

 Hello :)
Today is Day 43!


Quick summary of today:
  • write LeNet from scratch
  • write VGG from scratch
  • write InceptioNet from scratch
  • write UNet from scratch (again)


Wanting to understand the popular models a bit more, I decided to do the above. 

1) Let's begin with LeNet. 

A basic framework developed in the 1990s, basic but set the groundwork for networks like AlexNet, VGG and ResNet.


consists of 2 conv layers, each followed by a maxpool, and then ending with 2 fully connected (linear) layer. 

2) Next is VGG

The paper proposes numerous versions, VGG11, VGG13, VGG16, VGG19 but from a google search VGG16 seems most popular (version D in the pic). It is deeper than the earlier LeNet, consisting of multiple conv+maxpool layers, each increasing the amount of filters, and decreasing the size of the image. 
instead of 1 version, a general model was created so that it can adapt to the desired VGG architecture

below is the implementation. I think this is a nice set-up for testing the 4 versions on 1 dataset of my choosing. 


3) GoogLeNet / InceptionNet


This is a long one... haha. Funny that a research paper references a meme (and it got the name inception from it)
GoogleNet features inception modules - which consist of parallel conv branches of different receptive field sizes.Also 1x1 convolutions reduce dimensionality and improve efficiency.Towards the end, global avgpool replaces fully connected layers for fixed-length feature vectors.

and the implementation:

individual convolution block

individual inception block

And the final GoogLeNet


4) UNet

Yesterday I attemped it and I think I got a good kind-of working model, but I was not sure if it is correct or not because I translated it from tensorflow to pytorch. Today I found another version of UNet online and pasted it into my records (thank you to this youtuber)


Pretty similar to yesterday. Here is the double conv part, and the init of the Unet itself.

and then the forward


Actually I decided to even test this UNet on a human image dataset in kaggle


After 50 epochs, with learning_rate 0.01 and batch_size of 16, using DiceLoss and BCEWithLogitsLoss, the best model I got is: epoch: 42, train loss: 0.2380, valid loss: 0.3435. I am running a 100 epoch one with a learning rate scheduler. It will take long but the results (hopefully good) can be seen here in Kaggle. 
Sample outputs from the 50 epoch model are:
I should try IoU, or maybe another combination of losses too. 


Side note! Turns out I did not properly install pytorch on my laptop and was running things on cpu, which is a disaster, but today I set it up properly. (but still using kaggle is a bit faster haha).


That is all for today!

See you tomorrow :) 

Popular posts from this blog

[Day 198] Transactions Data Streaming Pipeline Porject [v1 completed]

Image
 Hello :) Today is Day 198! A quick summary of today: data streaming pipeline project [v1 done] Here is a link to the project's repo. Well ... I did not know I can do it in a day (~14 hours) after yesterday's issues but here we are. Turns out in order to insert the full (~70 variables with nested/list structure), I need the proper pyspark schema. And yesterday I did not have that and that is why when I was reading data in the kafka producer I was getting NULL in the columns - my schema was wrong. Well today I not only fixed the schema for the 4 variables I had yesterday, but included *all* the variables that come from the Stripe API ~ 70 (for completeness).  When I run docker-compose, the data streams and is input into the postgres db (and is still running). Unfortunately, the free Stripe API for creating realistic transactions has a limit of 25, so every 3 seconds, 25 new transactions are sent to the db. It has been running half the day (since I got that set up) and as I am wr
Read more

[Day 107] Transforming natural language to charts

Image
 Hello :) Today is Day 107! A quick summary of today: created text2chart - transforming natural language to charts [ github ] [ webapp ] I am super sick, but in the around 3 hours of my fever not being high, I managed to study a bit. I was again thinking about creating some nlp project but was stuck on the idea of how to host a model. After looking around I found this article where they create a similar app called chat2vis  and they use codellama. I know codellama is an open source model that I can get from huggingface and I wondered, how did they host it. Turns out - they ask the user to input a huggingface api key, which then allows for querying and generating content. (AWESOME! this is applicable to some of the other PDF chat apps that I have, but I will look to make an app of them later).  My fever eased a bit, and I sat on my chair to do some work ^^ Here is a breakdown of the app. I used streamlit. Also, 2 default datasets during dev: Financial Statements of Major Companies(2009-
Read more

[Day 54] I became a backprop ninja! (woohoo)

Image
 Hello :) Today is Day 54! A quick summary of today: Read through cs231n's neural network lectures [1] [2] [3] Spent 8 hours going over Andrej Karpathy's backprop ninja tutorial After yesterday's confidence boost in doing backprop, I came in today with the intention of becoming of, as Andrej Karpathy calls it, a backprop ninja.  The cs231n NN lectures were the usual about how NN became to be, param initialization, activation functions, loss functions, regularization and optimizers. Nothing out of the ordinary, but still was good to reassure myself of that knowledge. But that was definitely not the main even of today. As I mentioned, I wanted to become (or at least attempt to become) a backprop ninja today. I have watched that particular tutorial twice, the 1st time I had no idea what is going on, the 2nd time I could follow, and now it was the 3rd time - and now, I wanted to participate and actually see the why and the how of the things we have to do, when we do manual back
Read more