Load the attached csv file in python. Each row consists of feature 1, feature 2, class label. Train two single/double hidden layer deep networks by varying the number of hidden nodes (4, 8, 12, 16) in each layer with 70% training and 30% validation data. Use appropriate learning rate, activation, and loss functions and also mention the reason for choosing the same. Report, compare, and explain the observed accuracy and minimum loss achieved. Visually observe the dataset and design an appropriate feature transformation (derived feature) such that after feature transformation, the dataset can be classified using a minimal network architecture (minimum number of parameters). Design, train this minimal network, and report training and validation errors, and trained the parameters of the network. Use 70% training and 30% validation data, appropriate learning rate, activation and loss functions. Explain the final results. Content of the CSV File 0.16276 0.010281 0 0.224371 0.02846 0 0.284088 0.054416 0 0.341179 0.087967 0 0.394927 0.128871 0 0.444636 0.176823 0 -15.3398 2.938268 0 -15.5571 1.973302 0 -15.7136 0.992594 0 -15.808 1.16E-12 0 0.1 0 1 0.036842 0.002327 1 -0.02596 -0.00329 1 -0.08766 -0.01679 1 -0.14751 -0.03803 1 -0.20479 -0.06683 1 -0.25879 -0.10292 1 14.5381 -4.744 1 14.86934 -3.8338 1 15.14341 -2.90064 1 15.35874 -1.94813 1 15.51396 -0.97999 1 15.60796 -1.15E-12 1

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

Problem 1PE

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Load the attached csv file in python. Each row consists of feature 1, feature 2, class label.

Train two single/double hidden layer deep networks by varying the number of hidden nodes (4, 8, 12, 16) in each layer with 70% training and 30% validation data. Use appropriate learning rate, activation, and loss functions and also mention the reason for choosing the same. Report, compare, and explain the observed accuracy and minimum loss achieved.

Visually observe the dataset and design an appropriate feature transformation (derived feature) such that after feature transformation, the dataset can be classified using a minimal network architecture (minimum number of parameters). Design, train this minimal network, and report training and validation errors, and trained the parameters of the network. Use 70% training and 30% validation data, appropriate learning rate, activation and loss functions. Explain the final results.

Content of the CSV File

0.16276	0.010281	0
0.224371	0.02846	0
0.284088	0.054416	0
0.341179	0.087967	0
0.394927	0.128871	0
0.444636	0.176823	0
-15.3398	2.938268	0
-15.5571	1.973302	0
-15.7136	0.992594	0
-15.808	1.16E-12	0
0.1	0	1
0.036842	0.002327	1
-0.02596	-0.00329	1
-0.08766	-0.01679	1
-0.14751	-0.03803	1
-0.20479	-0.06683	1
-0.25879	-0.10292	1
14.5381	-4.744	1
14.86934	-3.8338	1
15.14341	-2.90064	1
15.35874	-1.94813	1
15.51396	-0.97999	1
15.60796	-1.15E-12	1

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