How to calculate feed-forward (forward-propagation) in neural network for classification?

Wed, 02 Oct 2024 14:47:26 +0000

For the following neural network, calculate accuracy of classification, given these settings

How to calculate feed-forward (forward-propagation) in neural network?

Thu, 04 Apr 2019 15:54:17 +0000

In the figure below, a neural network is shown. Calculate the following:

1) How many neurons do we have in the input layer and the output layer?

2) How many hidden layers do we have?

3) If all the weights initialized with 1 ($w1=w2=w3=...=w19=1$), what is the output of this network after feed-forward for the sample shown in the figure (X = (x1,x2,x3) = (2,5,3) and y=10)? What is the error of the network ($\text { Error }=\frac{1}{2}(\hat{y}-y)^{2}$)? Assume activation functions for all neurons except the output neuron is $f(z)=z$.

4) If we change the activation function of all the neurons in the second hidden layer to Sigmoid ($S(x)=\frac{1}{1+e^{-x}}=\frac{e^{x}}{e^{x}+1}$), what would be the output of the network after this change? Calculate the error as well.

https://i.imgur.com/rtqPiRa.jpg

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How to calculate feed-forward (forward-propagation) in neural network for classification?

How to calculate feed-forward (forward-propagation) in neural network?