Suppose that we use sigmod activation function and square loss function: (t- o)2. The learning rate is a. Derive the formulae for updating Vij and Wij using the gradient descent learning strategy in the backward propagation. For simplicity, we don't have bias in our neural network.

Suppose that we use sigmod activation function and square loss function: (t- o)2. The learning rate is a. Derive the formulae for updating Vij and Wij using the gradient descent learning strategy in the backward propagation. For simplicity, we don't have bias in our neural network.

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

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Concept explainers

Use of XOR function

XOR function or exclusive OR function was introduced in Excel 2013. This function is available under excel logical functions. For the given statement, the XOR function will return true only when either of the statements is true. It returns false when both the statements are true or when both the statements are false.

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1. Suppose that we use sigmod activation function and square loss function: (t-
o)². The learning rate is a. Derive the formulae for updating Vij and Wij using the
gradient descent learning strategy in the backward propagation. For simplicity,
we don't have bias in our neural network.

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Step 1: Gradient Descent in Neural Networks with Sigmoid Activation and Square Loss Function

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2: Back propagation: Consider the following neural network. Single-circled nodes denote variables (e.g. æ1 is an input variable, hy is an intermediate variable, y is an output variable), and double-circled nodes denote functions (e.g. Σ takes the sum of its inputs, and ♂ denotes the logistic function σ(x) = 1+e-x. In the network below, hi = l+e-T1w1T2W2 · 1 1 W1 = -1.7 Cloon Σ W2 = 0.1 1 h - 0.6 oblood Σ S2 W4 = - 1.8 W5 = ww 0.2 = 0.5 3
Artificial Neural Network: USE KOHONEN SOM to cluster 4 vectors. The maximum number of clusters to be formed is 2. The learning rate (or, gain term ɳ(t)=0.8. Do the training for 5 epochs and compute the final weights. ɳ(t+1)= ɳ(t)/2
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