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Objective Gain experience empirically determining hyperparameters and evaluating models Instructions Complete this assignment individually Use an accepted style (e.g., APA, Chicago, etc.) for citing...

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Objective

Gain experience empirically determining hyperparameters and evaluating models

Instructions

  • Complete this assignment individually
  • Use an accepted style (e.g., APA, Chicago, etc.) for citing any materials
  • Submit a PDF file containing your submission for this assignment
  • Please view the accompanying Assignment 4 video
  • Use the provided PDF containing the Assignment 4 notebook and the output data generated by the Assignment 4 notebook
    • Alternatively, you may use the attached Assignment 4 notebook to generate your own outputs:
      • Download the Assignment4.ipynb notebook file to your local machine.
      • Connect to the Gateway on Scholar directly or via Scholar's landing page
      • Launch a Jupyter Notebook on Scholar using the gpu queue.
      • Upload the notebook file to Jupyter Notebook and view it.
      • Restart the kernel as is and run all cells (“Restart & Run All” under the Kernel tab).
  • Using the time and validation accuracy values generated via the Assignment 4 notebook, determine the optimal values for the following hyperparameters:
    • Number of filters
    • Batch size
  • Provide a rationale/justification to support your hyperparameter value selections
  • Include a three dimensional, graphical representation of the data to support your hyperparameter value selections
  • Include any related literature to support your hyperparameter value selection
  • Reflect on the impact that a specific domain/application would have on your selections
assignment4-1-0lfs1agr.pdf assignment4-1-3l04st5z.ipynb assignment4graphics-1-ji1shm5a.ipynb cnndata-1ehd2rm1.csv assignment-rubric-2jdtkbep-awo1irrd.png
Answered 1 days After Feb 26, 2021

Solution

Sandeep Kumar answered on Feb 28 2021
153 Votes
Since the model used here is a Convolutional neural network (2D), the optimum filters range from 32 to 128 here, the higher the filters the more the network will learn. But exceeding the number of filters can lead to overfitting of the model. Also, in a CNN model, batch size is generally 32 to 256...
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