Homework Help is Here – Start Your Trial Now!
Engineering
Computer Engineering
the addition operator is called a residual connection (or shortcut connection). With residual blocks, inputs can forward propagate faster through the residual connections across layers. Activation function Activation function f(x) Rx) -x Weight layer Weight layer Activation function Activation function Waight layer Weight layer Fig. 7.6.2: A regular block (left) and a residual block (right). ResNet follows VGG's full 3 x 3 convolutional layer design. The residual block has two 3 x 3 con- volutional layers with the same number of output channels. Each convolutional layer is followed by a batch normalization layer and a ReLU activation function. Then, we skip these two convolu- tion operations and add the input directly before the final ReLU activation function. This kind of design requires that the output of the two convolutional layers has to be of the same shape as the input, so that they can be added together. If we want to change the number of channels, we need to introduce an additional 1 x 1 convolutional layer to transform the input into the desired shape for the addition operation. Let us have a look at the code below.

the addition operator is called a residual connection (or shortcut connection). With residual blocks, inputs can forward propagate faster through the residual connections across layers. Activation function Activation function f(x) Rx) -x Weight layer Weight layer Activation function Activation function Waight layer Weight layer Fig. 7.6.2: A regular block (left) and a residual block (right). ResNet follows VGG's full 3 x 3 convolutional layer design. The residual block has two 3 x 3 con- volutional layers with the same number of output channels. Each convolutional layer is followed by a batch normalization layer and a ReLU activation function. Then, we skip these two convolu- tion operations and add the input directly before the final ReLU activation function. This kind of design requires that the output of the two convolutional layers has to be of the same shape as the input, so that they can be added together. If we want to change the number of channels, we need to introduce an additional 1 x 1 convolutional layer to transform the input into the desired shape for the addition operation. Let us have a look at the code below.

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...
See similar textbooks
icon
Related questions
Question
Complete solution of tis complete qun
the addition operator is called a residual connection (or shortcut connection). With residual blocks, inputs can forward propagate faster through the residual connections across layers. Activation function Activation function f(x) Rx) – x Weight layer Weight layer Activation function Activation function Weight layer Waight layer Fig. 7.6.2: A regular block (left) and a residual block (right). ResNet follows VGG's full 3 x 3 convolutional layer design. The residual block has two 3 x 3 con- volutional layers with the same number of output channels. Each convolutional layer is followed by a batch normalization layer and a ReLU activation function. Then, we skip these two convolu- tion operations and add the input directly before the final RELU activation function. This kind of design requires that the output of the two convolutional layers has to be of the same shape as the input, so that they can be added together. If we want to change the number of channels, we need to introduce an additional 1 x 1 convolutional layer to transform the input into the desired shape for the addition operation. Let us have a look at the code below.
Transcribed Image Text:the addition operator is called a residual connection (or shortcut connection). With residual blocks, inputs can forward propagate faster through the residual connections across layers. Activation function Activation function f(x) Rx) – x Weight layer Weight layer Activation function Activation function Weight layer Waight layer Fig. 7.6.2: A regular block (left) and a residual block (right). ResNet follows VGG's full 3 x 3 convolutional layer design. The residual block has two 3 x 3 con- volutional layers with the same number of output channels. Each convolutional layer is followed by a batch normalization layer and a ReLU activation function. Then, we skip these two convolu- tion operations and add the input directly before the final RELU activation function. This kind of design requires that the output of the two convolutional layers has to be of the same shape as the input, so that they can be added together. If we want to change the number of channels, we need to introduce an additional 1 x 1 convolutional layer to transform the input into the desired shape for the addition operation. Let us have a look at the code below.
Expert Solution
steps

Step by step

Solved in 2 steps

SEE SOLUTIONCheck out a sample Q&A here
Blurred answer
Recommended textbooks for you
Computer Networking: A Top-Down Approach (7th Edi…
Computer Networking: A Top-Down Approach (7th Edi…
Computer Engineering
ISBN:
9780133594140
Author:
James Kurose, Keith Ross
Publisher:
PEARSON
Computer Organization and Design MIPS Edition, Fi…
Computer Organization and Design MIPS Edition, Fi…
Computer Engineering
ISBN:
9780124077263
Author:
David A. Patterson, John L. Hennessy
Publisher:
Elsevier Science
Network+ Guide to Networks (MindTap Course List)
Network+ Guide to Networks (MindTap Course List)
Computer Engineering
ISBN:
9781337569330
Author:
Jill West, Tamara Dean, Jean Andrews
Publisher:
Cengage Learning
Concepts of Database Management
Concepts of Database Management
Computer Engineering
ISBN:
9781337093422
Author:
Joy L. Starks, Philip J. Pratt, Mary Z. Last
Publisher:
Cengage Learning
Prelude to Programming
Prelude to Programming
Computer Engineering
ISBN:
9780133750423
Author:
VENIT, Stewart
Publisher:
Pearson Education
Sc Business Data Communications and Networking, T…
Sc Business Data Communications and Networking, T…
Computer Engineering
ISBN:
9781119368830
Author:
FITZGERALD
Publisher:
WILEY
SEE MORE TEXTBOOKS