Question

Modelling Neural Network
Consider the following neural network:
Though the weights between the layers are not shown for clarity, you must consider that every neuron of the layer K is connected to every neuron of the layer K + 1. For the architecture given above, answer the following questions:
• Write the dimensions of the input vector, the weight matrix, the bias matrix, the preactivation matrix, the activation matrix, and the output vector of every layer
• Suppose that the initial values of the inputs are [1,2,3,4,5] and the weight and bias matrices are initialized to 0.5, show the calculation of every neuron (both the preactivations and activations) for one forward pass. The activation function is sigmoid
• Draw the computation graph of the given architecture for the calculation of derivatives of the loss function with respect to the weights and using sigmoid as an activation function.
• Suppose that you have two training examples x1 = [1,2,3,4,5] and x2 = [5,4,3,2,1] and y1 = [1] and y2 = [0]. Also suppose that you have only the 2nd hidden layer present in the architecture given above i.e. there is no hidden layer 1. Show two iterations of the backpropagation algorithms with all the equations and results

Answer 1

Answer:

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