Consider the neural network in Figure 25.13. Let bias values be fixed at 0, and let the weight matrices between the input and hidden, and hidden and output layers, respectively, be: W = (wn, w2, ws) =(1, 1,-1) w = (u, wg, w)" = (0.5, 1,2)" Assume that the hidden layer uses ReLU, whereas the output layer uses sigmoid activation. Assume SSE error. Answer the following questions, when the input is x 4 and the true response is y 0: 22 Figure 25.13. Neural network for Q1. (a) Use forward propagation to compute the predicted output. (b) What is the loss or error value? (c) Compute the net gradient vector 8º for the output layer. (d) Compute the net gradient vector & for the hidden layer.
Consider the neural network in Figure 25.13. Let bias values be fixed at 0, and let the weight matrices between the input and hidden, and hidden and output layers, respectively, be: W = (wn, w2, ws) =(1, 1,-1) w = (u, wg, w)" = (0.5, 1,2)" Assume that the hidden layer uses ReLU, whereas the output layer uses sigmoid activation. Assume SSE error. Answer the following questions, when the input is x 4 and the true response is y 0: 22 Figure 25.13. Neural network for Q1. (a) Use forward propagation to compute the predicted output. (b) What is the loss or error value? (c) Compute the net gradient vector 8º for the output layer. (d) Compute the net gradient vector & for the hidden layer.
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
Related questions
Question
![Consider the neural network in Figure 25.13. Let bias values be fixed at 0, and let
the weight matrices between the input and hidden, and hidden and output layers,
respectively, be:
W = (w1 , w2, w3) =(1,1,-1)
w = (w, ws, w" = (0.5, 1,2)"
Assume that the hidden layer uses RELU, whereas the output layer uses sigmoid
activation. Assume SSE error. Answer the following questions, when the input is x =
4 and the true response is y = 0:
21
2
w3
Figure 25.13. Neural network for Q1.
(a) Use forward propagation to compute the predicted output.
(b) What is the loss or error value?
(c) Compute the net gradient vector 8° for the output layer.
(d) Compute the net gradient vector 8* for the hidden layer.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fbf7665ca-c91e-41f5-9fcc-211071bb6e7a%2Fb970257f-1f6a-4b89-987a-049f11065bf9%2F13hajo_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Consider the neural network in Figure 25.13. Let bias values be fixed at 0, and let
the weight matrices between the input and hidden, and hidden and output layers,
respectively, be:
W = (w1 , w2, w3) =(1,1,-1)
w = (w, ws, w" = (0.5, 1,2)"
Assume that the hidden layer uses RELU, whereas the output layer uses sigmoid
activation. Assume SSE error. Answer the following questions, when the input is x =
4 and the true response is y = 0:
21
2
w3
Figure 25.13. Neural network for Q1.
(a) Use forward propagation to compute the predicted output.
(b) What is the loss or error value?
(c) Compute the net gradient vector 8° for the output layer.
(d) Compute the net gradient vector 8* for the hidden layer.
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