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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Author:James Kurose, Keith Ross
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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?

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
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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