According to the Neural Network structure shown in the following figure, and assume that: 1. All weights are set as: 0.5 0.2*x, x < 0 2. Activation function is LeakyReLU: f(x) = {0.2 * n - x, x ≥0 3. Loss function is defined as: L = ½-½ 11(y; — ŷ;)², where y; is the ground truth and ŷi is the output of the Neural Network. W₁₁ (Σ (Σ x1 Σ Σ Σ ŷ (Σ (Σ) Given a dataset D = {(x1,y1), (x2, y2), (X3, Y3), (x4, Y4)}, and • x₁ = (1,3), y₁ = 5 • x2 = (-1,2), y₂ = 3 • x3 = (2,6), y3 = 7 • x4 = (4,2), y = 2 Q1: [15%] [Forward Propagation] calculate the corresponding output ŷ1, ŷ2, Ŷ3, ŷ4 JL Q2: [20%] [Backward Propagation] calculate the loss function and gradient: (red weight in the structure) aw 1
According to the Neural Network structure shown in the following figure, and assume that: 1. All weights are set as: 0.5 0.2*x, x < 0 2. Activation function is LeakyReLU: f(x) = {0.2 * n - x, x ≥0 3. Loss function is defined as: L = ½-½ 11(y; — ŷ;)², where y; is the ground truth and ŷi is the output of the Neural Network. W₁₁ (Σ (Σ x1 Σ Σ Σ ŷ (Σ (Σ) Given a dataset D = {(x1,y1), (x2, y2), (X3, Y3), (x4, Y4)}, and • x₁ = (1,3), y₁ = 5 • x2 = (-1,2), y₂ = 3 • x3 = (2,6), y3 = 7 • x4 = (4,2), y = 2 Q1: [15%] [Forward Propagation] calculate the corresponding output ŷ1, ŷ2, Ŷ3, ŷ4 JL Q2: [20%] [Backward Propagation] calculate the loss function and gradient: (red weight in the structure) aw 1
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
Alert dont submit
data mining
![According to the Neural Network structure shown in the following figure, and assume
that:
1. All weights are set as: 0.5
0.2*x, x < 0
2. Activation function is LeakyReLU: f(x) = {0.2 *
n
-
x, x ≥0
3. Loss function is defined as: L = ½-½ 11(y; — ŷ;)², where y; is the ground truth and ŷi
is the output of the Neural Network.
W₁₁
(Σ
(Σ
x1
Σ
Σ
Σ
ŷ
(Σ
(Σ)
Given a dataset D = {(x1,y1), (x2, y2), (X3, Y3), (x4, Y4)}, and
• x₁ = (1,3), y₁ = 5
• x2 = (-1,2), y₂ = 3
• x3 = (2,6), y3 = 7
•
x4 = (4,2), y = 2
Q1: [15%] [Forward Propagation] calculate the corresponding output ŷ1, ŷ2, Ŷ3, ŷ4
JL
Q2: [20%] [Backward Propagation] calculate the loss function and gradient: (red
weight in the structure)
aw 1](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F9287e908-7d71-4130-a3a7-c569e28ecc52%2Fcae5481f-3d1d-4208-8363-d19f6b0091ad%2Ffe8xwf_processed.png&w=3840&q=75)
Transcribed Image Text:According to the Neural Network structure shown in the following figure, and assume
that:
1. All weights are set as: 0.5
0.2*x, x < 0
2. Activation function is LeakyReLU: f(x) = {0.2 *
n
-
x, x ≥0
3. Loss function is defined as: L = ½-½ 11(y; — ŷ;)², where y; is the ground truth and ŷi
is the output of the Neural Network.
W₁₁
(Σ
(Σ
x1
Σ
Σ
Σ
ŷ
(Σ
(Σ)
Given a dataset D = {(x1,y1), (x2, y2), (X3, Y3), (x4, Y4)}, and
• x₁ = (1,3), y₁ = 5
• x2 = (-1,2), y₂ = 3
• x3 = (2,6), y3 = 7
•
x4 = (4,2), y = 2
Q1: [15%] [Forward Propagation] calculate the corresponding output ŷ1, ŷ2, Ŷ3, ŷ4
JL
Q2: [20%] [Backward Propagation] calculate the loss function and gradient: (red
weight in the structure)
aw 1
Expert Solution
This question has been solved!
Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.
This is a popular solution!
Trending now
This is a popular solution!
Step by step
Solved in 2 steps
Recommended textbooks for you
Database System Concepts
Computer Science
ISBN:
9780078022159
Author:
Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:
McGraw-Hill Education
Starting Out with Python (4th Edition)
Computer Science
ISBN:
9780134444321
Author:
Tony Gaddis
Publisher:
PEARSON
Digital Fundamentals (11th Edition)
Computer Science
ISBN:
9780132737968
Author:
Thomas L. Floyd
Publisher:
PEARSON
Database System Concepts
Computer Science
ISBN:
9780078022159
Author:
Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:
McGraw-Hill Education
Starting Out with Python (4th Edition)
Computer Science
ISBN:
9780134444321
Author:
Tony Gaddis
Publisher:
PEARSON
Digital Fundamentals (11th Edition)
Computer Science
ISBN:
9780132737968
Author:
Thomas L. Floyd
Publisher:
PEARSON
C How to Program (8th Edition)
Computer Science
ISBN:
9780133976892
Author:
Paul J. Deitel, Harvey Deitel
Publisher:
PEARSON
Database Systems: Design, Implementation, & Manag…
Computer Science
ISBN:
9781337627900
Author:
Carlos Coronel, Steven Morris
Publisher:
Cengage Learning
Programmable Logic Controllers
Computer Science
ISBN:
9780073373843
Author:
Frank D. Petruzella
Publisher:
McGraw-Hill Education