Table of Contents
Question
With an artificial neural network what is the point of having a cost function?
A. It helps the network determine the cost of the error so they can make larger or smaller adjustments to its guesses.
B. It helps the network determine whether there should be many more hidden layers in the network.
C. It shows that the network should make the same level of adjustment whether it’s 67% right or 99% right.
D. It shows that at some point the processing power cost will be too great for the neural network to make accurate predictions.
Answer
A. It helps the network determine the cost of the error so they can make larger or smaller adjustments to its guesses.
Explanation
The correct answer is A. It helps the network determine the cost of the error so they can make larger or smaller adjustments to its guesses.
A cost function is a measure of “how good” a neural network did with respect to its given training sample and the expected output. It also may depend on variables such as weights and biases. A cost function is a single value, not a vector, because it rates how good the neural network did as a whole.
A cost function is used to evaluate the performance of a neural network and to guide the learning process. By comparing the network’s predictions with the actual outputs, the cost function calculates the error or the difference between them. The goal of learning is to minimize this error by adjusting the network’s parameters (weights and biases) using an optimization algorithm such as gradient descent.
The cost function helps the network determine the cost of the error so they can make larger or smaller adjustments to its guesses. The larger the error, the larger the cost, and the larger the adjustments. The smaller the error, the smaller the cost, and the smaller the adjustments. The network tries to find the optimal values of the parameters that minimize the cost function and produce accurate predictions.
The other options are incorrect because they do not describe the point of having a cost function.
- B. It helps the network determine whether there should be many more hidden layers in the network. The number of hidden layers in a neural network is a design choice that depends on the complexity of the problem and the amount of data available. The cost function does not directly determine how many hidden layers are needed, although it can be used to compare different architectures and select the best one.
- C. It shows that the network should make the same level of adjustment whether it’s 67% right or 99% right. The network should not make the same level of adjustment regardless of how accurate it is. The network should make larger adjustments when it is less accurate and smaller adjustments when it is more accurate, as explained above.
- D. It shows that at some point the processing power cost will be too great for the neural network to make accurate predictions. The processing power cost refers to the computational resources required to train and run a neural network, such as memory, CPU, or GPU. The cost function does not directly measure or affect this cost, although it may be related to factors such as the size and complexity of the network, or the number of iterations needed to converge.
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