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A) Consider a fully connected network (each hidden node is connected to all inputs and all outputs) with 2 dimensional real input and one hidden layer with sigmoid activation function. no bias is used in any nodes. The network is trained so that target output = input. At iteration t, the weights are shown in the following network architecture along with the input vector (x1=2, x2=-2). What will be value of actual output and the loss function at iteration t? First choose the most appropriate activation function at the output node and loss function.

A) Consider a fully connected network (each hidden node is connected to all inputs and all outputs) with 2 dimensional real input and one hidden layer with sigmoid activation function. no bias is used in any nodes. The network is trained so that target output = input. At iteration t, the weights are shown in the following network architecture along with the input vector (x1=2, x2=-2). What will be value of actual output and the loss function at iteration t? First choose the most appropriate activation function at the output node and loss function.

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...
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Advanced Physics A) Consider a fully connected network (each hidden node is connected to all inputs and all outputs) with 2 dimensional real input and one hidden layer with sigmoid activation function. no bias is used in any nodes. The network is trained so that target output = input. At iteration t, the weights are shown in the following network architecture along with the input vector (x1=2, x2=-2). What will be value of actual output and the loss function at iteration t? First choose the most appropriate activation function at the output node and loss function. w3=1 wl=0 w4-0 w2=1 x1 B) What will be the weights w3 and w1 in iteration t+1 assuming 1) gradient descent and 2) Nestorov accelerated gradient descent? Assume learning rate = 0.25 and momentum constant = 0.75, and at (t-1), w1=-0.5, w2=0.5, w3=0.5 and w4=-0.5
Transcribed Image Text:Advanced Physics A) Consider a fully connected network (each hidden node is connected to all inputs and all outputs) with 2 dimensional real input and one hidden layer with sigmoid activation function. no bias is used in any nodes. The network is trained so that target output = input. At iteration t, the weights are shown in the following network architecture along with the input vector (x1=2, x2=-2). What will be value of actual output and the loss function at iteration t? First choose the most appropriate activation function at the output node and loss function. w3=1 wl=0 w4-0 w2=1 x1 B) What will be the weights w3 and w1 in iteration t+1 assuming 1) gradient descent and 2) Nestorov accelerated gradient descent? Assume learning rate = 0.25 and momentum constant = 0.75, and at (t-1), w1=-0.5, w2=0.5, w3=0.5 and w4=-0.5
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