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the weight matrices between the input and hidden, and hidder respectively, be: W = (w₁, w2, w3) = (1, 1,−1) W = (w₁, w₂, w3) T Assume that the hidden layer uses ReLU, whereas the output activation. Assume SSE error. Answer the following questions, v

the weight matrices between the input and hidden, and hidder respectively, be: W = (w₁, w2, w3) = (1, 1,−1) W = (w₁, w₂, w3) T Assume that the hidden layer uses ReLU, whereas the output activation. Assume SSE error. Answer the following questions, v

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Q1. 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 = (w₁, w, w3) = (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: W1 W2 W3 Z1 Z2 w/₂ w/₂ 23 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 80 for the output layer. (d) Compute the net gradient vector 8h for the hidden layer.
Transcribed Image Text:Q1. 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 = (w₁, w, w3) = (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: W1 W2 W3 Z1 Z2 w/₂ w/₂ 23 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 80 for the output layer. (d) Compute the net gradient vector 8h for the hidden layer.
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