Assignment 2 Neural Networks with One Hidden Layer Due: 11:5SPM, November 8, 2018 Issued: October 23, 2018 First, go over the attached "Assignment2 codes ipynb" which implement neural networks with one hidden layer. Please make sure to fully understand the codes between ..### START KEY CODE HERE WAW" and "WI END KEY CODE HERE An ipynb file is a notebook document used by Jupyter Notebook (http://jupyter.org'install). an interactive computational environment designed to help scientists work with the Python language and their data. .Then, replace the activation function tanh for the hidden layer with ReLU as illustrated in the figure below O0.24 0 Hint: you will find the functions useful Grading: We will review and comment on your submission regarding the style of your python code. You must attempt every question in order to receive credit. Submission: Write the following items in a pdf document and subait the pdf file to the Cyber.ewha ac.kr assignment dropbox by the deadline Summarize how you modified the codes to implement ReLU in the hidden layer. Report your prediction accuracy(%) using section "4,5 Predictions." 3. Make a choice of an optimal number hidden layers (i.e, the best hidden layer size) for the given dataset using "4.6 Tuning hidden layer size. Zip your source codes (Assignment2 codes.ipynb) with the pdf, and submit a single zip file. Please upload only the sources related to the task, except for the unnecessary ones. Assignment 2 Neural Networks with One Hidden Layer Due: 11:5SPM, November 8, 2018 Issued: October 23, 2018 First, go over the attached "Assignment2 codes ipynb" which implement neural networks with one hidden layer. Please make sure to fully understand the codes between ..### START KEY CODE HERE WAW" and "WI END KEY CODE HERE An ipynb file is a notebook document used by Jupyter Notebook (http://jupyter.org'install). an interactive computational environment designed to help scientists work with the Python language and their data. .Then, replace the activation function tanh for the hidden layer with ReLU as illustrated in the figure below O0.24 0 Hint: you will find the functions useful Grading: We will review and comment on your submission regarding the style of your python code. You must attempt every question in order to receive credit. Submission: Write the following items in a pdf document and subait the pdf file to the Cyber.ewha ac.kr assignment dropbox by the deadline Summarize how you modified the codes to implement ReLU in the hidden layer. Report your prediction accuracy(%) using section "4,5 Predictions." 3. Make a choice of an optimal number hidden layers (i.e, the best hidden layer size) for the given dataset using "4.6 Tuning hidden layer size. Zip your source codes (Assignment2 codes.ipynb) with the pdf, and submit a single zip file. Please upload only the sources related to the task, except for the unnecessary ones