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)
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Chapter1: Computer Networks And The Internet
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Question
![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).](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Ffbb4f64a-abda-416b-9af6-fa6be3ae4141%2F7a348e75-355b-4286-838b-73481a798ab9%2Fe4a40xt_processed.png&w=3840&q=75)
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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