The architecture of a neural network is provided. The input is a 3D feature vector, and the output is a 2D vector. The weights are also shown on the edges, connecting two nodes in two different layers. For each node in the 2 hidden layers and the output layer, a sigmoid function g() is used as an activation function. What are the equations for y1 and y2?
The architecture of a neural network is provided. The input is a 3D feature vector, and the output is a 2D vector. The weights are also shown on the edges, connecting two nodes in two different layers. For each node in the 2 hidden layers and the output layer, a sigmoid function g() is used as an activation function. What are the equations for y1 and y2?
Computer Networking: A Top-Down Approach (7th Edition)
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ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
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The architecture of a neural network is provided. The input is a 3D feature
What are the equations for y1 and y2?
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S3
Ws3V1
Ws153
X1
Wx1S1
y1
S1
Ws1S4
Wx251
S4
X2
WsaV2
Ws254
Y2
Wx282
S2
Ws255
S5
Wx3s2
X3
Layer 2
Hidden Layer 2
Layer 3
Output Layer
Layer 0
Input Layer
Layer 1
Hidden Layer 1"
Transcribed Image Text:Flow of Information
S3
Ws3V1
Ws153
X1
Wx1S1
y1
S1
Ws1S4
Wx251
S4
X2
WsaV2
Ws254
Y2
Wx282
S2
Ws255
S5
Wx3s2
X3
Layer 2
Hidden Layer 2
Layer 3
Output Layer
Layer 0
Input Layer
Layer 1
Hidden Layer 1
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