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Part - 1: Backward Propagation as laid out in asgmt_backpropagation.xlsx

Step 1

Here we are defining all the nodes of the neural network so we can use it to further along the way to the next step in backward propagation

Step 2

Here we are calculating the partial derivatives line 2: Since E2 is not dependent on w5, we are excluding it and calculate only for E1 which is dependent on w5 line 4: we find the partial derivative E1 w.r.t a_o1 line 5: we find the partial derivative of o1 w.r.t of w5

Step 3

Here we are calculating the E_Total w.r.t w5, w6, w7 and w8

Step 4

Here we are calculating the E_Total w.r.t w5, w6, w7 and w8

NOTE: Rest of the steps have been marked with post-it like sticky comments in the Excel file

Part - 2:

Purpose: Session 6 assignment is to try to achieve the following:

  • Reduce the parameters
  • Use any or all the techniques about CNN layers, loss functions etc.,
  • Achieve 99.3% accuracy

Based on MNIST dataset

Create a simple Convolutional Neural Network model and predict

Project Setup:

Clone the project as shown below:-

$ git clone [email protected]:pankaja0285/era_v1_session6_pankaja.git
$ cd era_v1_session6_pankaja

About the file structure
|__asgmt_backpropagation.xlsx
|__README.md
|__S6.ipynb

NOTE: List of libraries required: torch and torchsummary, tqdm for progress bar, which are installed using requirements.txt

One of 2 ways to run the S6.ipynb notebook:

  1. Using Anaconda prompt - Run as an administrator start jupyter notebook from the folder era_v1_session5_pankaja and run it off of your localhost
    NOTE: Without Admin privileges, the installs will not be correct and further import libraries will fail. 
jupyter notebook
  1. Upload the notebook folder era_v1_session6_pankaja to google colab at colab.google.com and run it on colab

NOTE: Follow along the S6.ipynb - notebook cells and run sequentially to see the outputs.

Notebook execution sequence of sections is as follows:

File used: S6.ipynb

  1. Following the section Model - 1 for all the set up of params and training and plotting the accuracies and losses
  2. Following the section Model - 2 for all the set up of params and training and plotting the accuracies and losses

Contributing:

For any questions, bug(even typos) and/or features requests do not hesitate to contact me or open an issue!