a (1) Consider a fully connected, feed-forward neural network, with inputs x € RD, a hidden layer consisting of M neuron

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A 1 Consider A Fully Connected Feed Forward Neural Network With Inputs X Rd A Hidden Layer Consisting Of M Neuron 1 (78.08 KiB) Viewed 80 times

a (1) Consider a fully connected, feed-forward neural network, with inputs x € RD, a hidden layer consisting of M neurons, Z ERM, and an output û € R. The network is trained by minimization of the sum-of-squares loss over the training data {(xi, yi)}{1, i.e. loss = 1+,(yi – ġ(x;)). (a) Assuming each neuron in the hidden layer and the output also take an additional unit 'bias' input, how many weights are needed to define the network? [2 marks] (b) If the activation functions in the hidden layer are linear, show that this three layer network is equivalent to a two-layer model with no hidden layer. How are the weights of the network related to the weights of a standard linear model ĝ = Wo+w.x? [6 marks)