please code in python, I really need to know this works.. kindly help me out... pleaseuse can use any sample dataset for demoYou may use PyTorch or Keras libraries for building deep learning models.A) Implement a single-layer neural network model that is used to classify the "CAN intrusion data samples"<you can you can sample data> into normal and anomalous classes (0: normal, 1: attack). Use the standarized training set from Q1E) to train the network.The details of the model are as follows:Use a Sigmoid as the output layer acitivation function to enable non-linearity.Use the binary cross-entropy loss as a training criterion.Use Stochastic gradient descent optimizer with a learning rate of 0.1.Run the model for 10 iterations/epochs.Record the loss for each iteration, and make a plot of iterations/epochs vs loss(Binary Cross Entropy). Print out the precision, recall, and F1-score of the test set. Print out the model execution time (including both training and testing time) in milliseconds. Please keep two decimal places.

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Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

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please code in python, I really need to know this works.. kindly help me out... please

use can use any sample dataset for demo

You may use PyTorch or Keras libraries for building deep learning models.

A) Implement a single-layer neural network model that is used to classify the "CAN intrusion data samples"<you can you can sample data> into normal and anomalous classes (0: normal, 1: attack). Use the standarized training set from Q1E) to train the network.

The details of the model are as follows:

Use a Sigmoid as the output layer acitivation function to enable non-linearity.
Use the binary cross-entropy loss as a training criterion.
Use Stochastic gradient descent optimizer with a learning rate of 0.1.
Run the model for 10 iterations/epochs.

Record the loss for each iteration, and make a plot of iterations/epochs vs loss(Binary Cross Entropy). Print out the precision, recall, and F1-score of the test set. Print out the model execution time (including both training and testing time) in milliseconds. Please keep two decimal places.

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