Consider the following table for the connections between the input neurons and the hidden layer neurons.
| Input neuron | Hidden layer neurons | Connection weithts |
| 1 | 1 | 1 |
| 1 | 2 | 0.1 |
| 1 | 3 | -1 |
| 2 | 1 | 1 |
| 2 | 2 | -1 |
| 2 | 3 | -1 |
| 3 | 1 | 0.2 |
| 3 | 2 | 0.3 |
| 3 | 3 | 0.6 |
The connections weights from Hidden layer neurons to the output neurons are 0.5, 0.3 and 0.6 for first, second and third neurons respectively corresponding threshold value for output layer is 0.5 and for hidden layer 1.8, 0.05 and – 0.2 for first, second and third neuron respectively,
(a) Draw the diagraph of the network.
(b) Write the results of activation and interpret.
Assumption: the input neurons have values $x_1=1,\;x_2=1,\;x_3=1$ (not specified in the problem, so I use 1 for each input). The network structure (diagraph) and the numerical activation calculations follow. Diagraph (ASCII): Inputs: I1 I2 I3 Hidden: H1 H2 H3 Output: O Connections (I→H): I1→H1 (weight 1), I1→H2 (0.1), I1→H3 (−1) I2→H1 (1), I2→H2 (−1), I2→H3 (−1) I3→H1 (0.2), I3→H2 (0.3), I3→H3 (0.6) ______ _____ _______ ____ _____ ________.
_________ ________ _______ __________ ______.
___ _____ ______ __________ _____ ______ ____ ________ _______ __________.
________ ________ ____ __________ _________ _____ ___ _______ ______ ________.
_____ ________ _____ _____ ________ ________ ___ _____ ___ __________.
_________ ________ _________ ________ ________ _________ ___ ______ _________.
____ __________ _________ _____ ___ ____ _________ ___ ________ ___ ______ __________.
___ ___ _________ ________ ___ ________ _______ ___.
______ __________ ______ __________ _________ ___ ___ ________ ___.
________ _____ _____ ______ ____ _________ ___ _______ ______ ____ ________ _____.
________ ________ ___ _______ _______ ________ _________ __________ _________ _________.
_________ _____ ___ __________ ______ ___ ____ ________ _________.
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______ _________ _______ ___ _________ _______ ____ ________ ___ ___ _______ ____.
____ _________ ___ _____ ____.
______ ____ _________ __________ ____ ____ ____ ___ ________ ________ ________ _______.
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Consider the following table for the connections between the input neurons and the hidden layer neurons.
| Input neuron | Hidden layer neurons | Connection weithts |
| 1 | 1 | 1 |
| 1 | 2 | 0.1 |
| 1 | 3 | -1 |
| 2 | 1 | 1 |
| 2 | 2 | -1 |
| 2 | 3 | -1 |
| 3 | 1 | 0.2 |
| 3 | 2 | 0.3 |
| 3 | 3 | 0.6 |
Reinforcement Learning,
Semi-supervised.
What is the role of loss function in machine learning algorithms? Explain any two commonly used loss functions in machine learning algorithms.
Supervised Learning,
If the input vectors arand initial values of three weight vectors are
then calculate the resulting weight found after training the competitive layer with Kohonen’s rule and a learning rate
of 0.5 on the input-series in order I1,I2,and I3,
Unsupervised learning,