gs gs126 Neural Networks - Random Initialization - Quiz No.1
gs gs126 Neural Networks Quiz
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Question 1: Arrange the following steps in sequence used in the course of training a neural network after initializing weight and biases.
I. Backward propagation
II. Forward propagation
III. Compute the loss function
IV. Repeat the steps for n iterations till we have minimized the loss function, without overfitting the train data
I. Backward propagation
II. Forward propagation
III. Compute the loss function
IV. Repeat the steps for n iterations till we have minimized the loss function, without overfitting the train data
I → II → III → IV
II → III → I → IV
III → II → I → IV
II → I → III → IV
Question 2: Which of the following statement is not true about random initialization?
Weights are randomly distributed with a mean of zero
Weights are randomly distributed with a standard deviation of one
To break symmetry the weights are initialized randomly
Following random initialization, each neuron can then proceed to learn the same function of its input
Question 3: Which of the following initialization method is used often in order to recommend the weights during weight initialization?
Zero initialization method
Random initialization method
Xavier initialization method
He-et-al initialization method
Question 4: Among the below distributions pair which one can be used to attain random initialization?
Uniform distribution and Normal distribution
Standard distribution and Normal distribution
Uniform distribution and Zero distribution
Zero distribution and Normal distribution
Question 5: What will be the result if we use the identical set of weights whenever we train the network?
The learning algorithm would work fine
The learning algorithm would make changes to the network weights
The learning algorithm would fail to make changes to the network weights, and the model will be stuck
The learning algorithm would make changes to the network weights, and the model will be stuck
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