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Suppose we have a neural net, which we denote as a function F(x; 0), that performs binary logistic regression to classify between images of cats and dogs. The final layer of F is a sigmoid, so that the output is a number between 0 and 1. Cats are given the label 0, and dogs are given the label 1. We use the NLL loss as our loss function: L(F(x; 0), y) = − [y log F(x; 0) + (1 − y) log(1 — F(x; 0))] We have an image x under consideration and wish to modify our predictions about it. Is the following true? If we want our net to predict 'cat' we should try taking a gradient step new = x − ▼xF(x; 0).

Suppose we have a neural net, which we denote as a function F(x; 0), that performs binary logistic regression to classify between images of cats and dogs. The final layer of F is a sigmoid, so that the output is a number between 0 and 1. Cats are given the label 0, and dogs are given the label 1. We use the NLL loss as our loss function: L(F(x; 0), y) = − [y log F(x; 0) + (1 − y) log(1 — F(x; 0))] We have an image x under consideration and wish to modify our predictions about it. Is the following true? If we want our net to predict 'cat' we should try taking a gradient step new = x − ▼xF(x; 0).

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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Suppose we have a neural net, which we denote as a function F(x; 0), that performs binary logistic regression to classify between images of cats and dogs. The final layer of F is a sigmoid, so that the output is a number between 0 and 1. Cats are given the label 0, and dogs are given the label 1. We use the NLL loss as our loss function: L(F(x; 0), y) = − [y log F(x; 0) + (1 − y) log(1 – F(x; 0))] We have an image x under consideration and wish to modify our predictions about it. Is the following true? If we want our net to predict 'cat' we should try taking a gradient step new = x − ▼xF(x; 0).
Transcribed Image Text:Suppose we have a neural net, which we denote as a function F(x; 0), that performs binary logistic regression to classify between images of cats and dogs. The final layer of F is a sigmoid, so that the output is a number between 0 and 1. Cats are given the label 0, and dogs are given the label 1. We use the NLL loss as our loss function: L(F(x; 0), y) = − [y log F(x; 0) + (1 − y) log(1 – F(x; 0))] We have an image x under consideration and wish to modify our predictions about it. Is the following true? If we want our net to predict 'cat' we should try taking a gradient step new = x − ▼xF(x; 0).
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