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Consider a two-layer neural network with two inputs a and b, one hidden unit c, and one output unit d. This network has five weights (wac» Wbc+ Woc• Wcd» Woa), where woc and wod represent the weights associated with bias inputs. Assume that: (1) all the weights are initialized to 0.1; (2) the learning rate is 0.3; (3) the input for the two biases is -1; and (4) c and d are sigmoid units. Use the following approximate values for o where necessary below: o(x) -0.5 < x < - 2.5 -2.5 < x < - 0.05 -0. 05 < x < 0 0.001 0.20 0.49 0.5 0 < x < 0. 05 0. 05 < x < 2.5 2.5 < x < 5.0 0.51 0.80 0.999 Consider the following training example for the network described above: a = 1, b = 0, d = 1

Consider a two-layer neural network with two inputs a and b, one hidden unit c, and one output unit d. This network has five weights (wac» Wbc+ Woc• Wcd» Woa), where woc and wod represent the weights associated with bias inputs. Assume that: (1) all the weights are initialized to 0.1; (2) the learning rate is 0.3; (3) the input for the two biases is -1; and (4) c and d are sigmoid units. Use the following approximate values for o where necessary below: o(x) -0.5 < x < - 2.5 -2.5 < x < - 0.05 -0. 05 < x < 0 0.001 0.20 0.49 0.5 0 < x < 0. 05 0. 05 < x < 2.5 2.5 < x < 5.0 0.51 0.80 0.999 Consider the following training example for the network described above: a = 1, b = 0, d = 1

Database System Concepts
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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Show all the updated weights. All answers should round to 3 decimals, e.g., 0.300.

dzd =

dwcd = 

dw0d = 
dzc =

dwac = 

Consider a two-layer neural network with two inputs a and b, one hidden unit c, and one output unit d. This network has five weights (wacs Wbc, Woco Wcd» Wod), where woc and represent the weights associated with bias inputs. Assume that: (1) all the weights are initialized to 0.1; (2) the learning rate is 0.3; (3) the input for the two biases is -1; and (4) c and d are sigmoid units. Use the following approximate values for o where necessary Wod below: |0(x) -0.5 < -2.5 < x < -0. 05 < < 0 < - 2.5 - 0.05 0.001 0.20 0.49 0.5 0 < x < 0. 05 0. 05 < x < 2.5 2. 5 < x < 5.0 0.51 0.80 0.999 Consider the following training example for the network described above: a = 1, b = 0, d = 1
Transcribed Image Text:Consider a two-layer neural network with two inputs a and b, one hidden unit c, and one output unit d. This network has five weights (wacs Wbc, Woco Wcd» Wod), where woc and represent the weights associated with bias inputs. Assume that: (1) all the weights are initialized to 0.1; (2) the learning rate is 0.3; (3) the input for the two biases is -1; and (4) c and d are sigmoid units. Use the following approximate values for o where necessary Wod below: |0(x) -0.5 < -2.5 < x < -0. 05 < < 0 < - 2.5 - 0.05 0.001 0.20 0.49 0.5 0 < x < 0. 05 0. 05 < x < 2.5 2. 5 < x < 5.0 0.51 0.80 0.999 Consider the following training example for the network described above: a = 1, b = 0, d = 1
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