Consider the neural network shown below: Z 2 Z W The weight matrix, W, is: [1, 1, -1, 0.5,…

Consider the neural network shown below: Z 2 Z W The weight matrix, W, is: [1, 1, -1, 0.5,…

Question:

Transcribed Image Text:

Consider the neural network shown below:
Z₁
2₂
Z₂
W₂
The weight matrix, W, is: [1, 1, -1, 0.5, 1, 2]. Assume that the hidden layer uses RelU and the
output layer uses Sigmoid activation function. Assume squared error loss, i.e.,
Loss = (y – y)².
The input x = 4, and the output y = 0.
Using this information, answer the questions below:
(Show all work, and all answers should be rounded to 2 decimal places OR POINTS WILL
BE TAKEN OFF!)
(a) [2 points] Use forward propagation to compute the predicted output.
(b) [1 point] What is the loss or error value?
(c) [4 points] Using backpropagation, compute the gradient of the weight vector, that is, compute
the partial derivative of the error with respect to all of the weights.

Expert Answer:

Answer rating: 100% (QA)

Part a Forward propagation Compute the weighted sum of the inputs in the hidden layer Z1 4 1 4 1 1 0
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