Data Warehousing and Data Mining207910 marksNumerical: backpropagation neural networkClassification by backpropagation
When Multilayer Perceptron is Better Choice Over Other Classification Algorithms Consider a multilayer feed-forward neural network given below. Let the learning rate be 0.5. Assume initial values of weights and biases as given in the table below. Train the network for the training tuples (1, 1, 0) a
When Multilayer Perceptron is Better Choice Over Other Classification Algorithms
Consider a multilayer feed-forward neural network given below. Let the learning rate be 0.5. Assume initial values of weights and biases as given in the table below. Train the network for the training tuples (1, 1, 0) and (0, 1, 1), where last number is target output. Show weight and bias updates by using back-propagation algorithm. Assume that sigmoid activation function is used in the network.
$$\begin{array}{c|c|c|c|c|c|c|c|c} w_{13} & w_{14} & w_{23} & w_{24} & w_{35} & w_{45} & b_3 & b_4 & b_5 \ \hline 0.5 & 0.2 & -0.3 & 0.5 & 0.1 & 0.3 & 0.6 & -0.4 & 0.8 \ \end{array}$$
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- Inputs: node 1 ($x1$), node 2 ($x2$); Hidden: nodes 3, 4; Output: node 5 - Weights: $w{13}=0.5,\ w{14}=0.2,\ w{23}=-0.3,\ w{24}=0.5,\ w{35}=0.1,\ w{45}=0.3$ - Biases: $b3=0.6,\ b4=-0.4,\ b5=0.8$ - Learning rate: $\alpha=0.5$ - Activation: sigmoid $\sigma(x)=\dfrac{1}{1+e^{-x}}$ - Training tuple...