Question

1. Answer the following questions appropriately.For an image of size 16 x 16 pixels consisting of digits, i.e. each image is of a hand-written digit. Assume that there are as many hidden neuron in the only hidden layer as in the input layer. The output layer is used to predict the digit written in the impute image. The digit can be from 0 to 9. Answer the following questions:                              [5 x 4]

1. What should be the number of neurons in output layer?
2. What is the total number of input units?
3. What should be the total number of neurons in the network including input layers and bias weights?
4. What is the total number of weight links in the neuron?
5. What activation functions will you use on hidden and output activations?

Answer 1

Solution 1: The number of neurons in the output layer actually depends upon the number of classes in which the inputs are actually going to be classified. In this case, the number of classes is actually equally equal to 10 since the digits have to be classified into 0-9 digits.

Solution 2: The number of inputs neurons in a neural network actually depends upon the number of inputs and in this case, the number of neurons actually depends upon the size of the image which is equal to 16 X 16 = 256, this is because the image is nothing but a matrix that contains at least 256 values inside it. Therefore, the input neurons in this case are equal to 256.

Solution 3: Total number of neurons = 256 + 256 + 10 = 522. The bias weights actually depend upon your choice and how much you want to regularize the network.

Solution 4: Total number of neural links would be equal to 256 X 256 X 10 = 655360.

Solution 5: For the hidden layer, you must use the ReLu activation function because it prevents the neural network from suffering through the exploding gradient or vanishing gradient problem. You should use the softmax classifier as the activation function in the output layer to optimally classify all the digits.

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