Expert Answer:project

Answer & Explanation:check the file to get more info about the project >>HINTS FOR DOE PROJECT
hints_for_doe_project.docx

neural_networks.pptx

Unformatted Attachment Preview

HINTS FOR DOE PROJECT
1. Open the link
https://colab.research.google.com/drive/1l5ViDafDktbCLWH_0-EVl4dr1yFovgCo
a.
b.
c.
d.
Click on File
Click on New Python 3 notebook
Click on run time
Change Runtime type to GPU.
Copy the given codes and paste in new cells. Press shift Enter to run. Or click Run
button.
2. Run the codes as it is and treat this as your base experiment.
3. Our goal here is to use DOE to find the best combination of hyperparameters that gives
the highest validation accuracy in a Neural Network. Now, keep all other
hyperparameters as it is and change Kernel (filter) size Conv2D to 1, run the codes and
note the validation accuracy. Again, change the Kernel (filter) size Conv2D to 2, run the
codes and note the validation accuracy. Repeat this for all possible value of Kernel (filter)
size Conv2D and note validation accuracy for each value of Kernel (filter) size Conv2D.
Now plot the graph between different values of Kernel (filter) size Conv2D (x-axis) and
validation accuracies (y-axis). Looking at this graph choose two values of the Kernel
(filter) size Conv2D that denotes the highest difference in validation accuracy. (i.e. PICK
TWO LEVELS of this hyperparameter)
4. Repeat the same process for all the hyperparameters and find two levels for each
hyperparameter from graph.
5. Design your 2𝑘 experiment. (fractional factorial designs)
6. Collect data from your Neural network for single replicate analysis.
7. Find the significance of each hyperparameter and build a regression model. Test your
regression model. (The output of your regression model should be close to 50-60%)
8. Validate your model with neural network. Select the different combination of
hyperparameter values and find the validation accuracy using your regression model. Run
the same combination of hyperparameters in neural network and see the difference
between your result and the result from neural network. Explain the reason for the
difference in the result.
Examples
Everyday examples of NN
• Youtube
Everyday examples of NN (Contd.)
• FaceApp
Everyday examples of NN (Contd.)
• Autonomous driving
Everyday examples of NN (Contd.)
• Autonomous driving
Everyday examples of NN (Contd.)
• Medical image segmentation
Who do they do it?
A NN is nothing but function:
𝑦1 , 𝑦2 , … , 𝑦𝑚 = 𝑓 𝑥1 , 𝑥2 , … , 𝑥𝑛
𝑧 = 𝑤1 𝑥1 + 𝑤2 𝑥2 + ⋯ + 𝑤𝑛 𝑥𝑛 + 𝑏
𝑦=
1
1 + 𝑒 −𝑧
Basic Unit: Neuron
Activation functions
Deep neural network
Deep neural network
𝑧 = 𝑤1 𝑥1 + 𝑤2 𝑥2 + ⋯ + 𝑤𝑛 𝑥𝑛 + 𝑏
𝑦=
1
1 + 𝑒 −𝑧
Training a DNN
Convolutional neural network (CNN)
CNN (Contd.)
Hyperparameters optimization
The performance of a neural network depends on many hyperparameters
For example:
1) Activation function
2) Number of neurons
3) Number of layers
4) Filter size
5) Number of filters
Your project!
https://colab.research.google.com/drive/1l5ViDafDktbCLWH_0-EVl4dr1yFovgCo
Learning rate
http://www.benfrederickson.com/numericaloptimization/
…
Purchase answer to see full
attachment