gs gs127 Feedforward Neural Networks - Backpropagation Algorithm - Quiz No.1
gs gs127 SQLite Quiz
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Question 1: What will be the values of net input of h1 and net input of h2 from the below figure, given that w1 = 0.2, w2 = 0.15, w3 = 0.3, w4 = 0.25, w5 = 0.45, w6 =0.4, w7 = 0.55, w8 = 0.5?
0.385, 0.378
0.358, 0.682
0.853, 0.387
0.583, 0.826
Question 2: What will be the outputs of h from the below figure, given that the net input h1 = 0.38 and net input h2 = 0.37 using the logistic function?
0.514, 0.511
0.594, 0.591
0.494, 0.525
0.454, 0.256
Question 3: What will be the net inputs of the output layer in the below figure, given that the w5 = 0.45, w6 =0.4, w7 = 0.55, w8 = 0.5, Out h1 = 0.6 and Out h2 = 0.65?
1.628, 1.365
1.228, 1.165
1.528, 1.665
1.428, 1.465
Question 4: What will be the outputs of output layer from the below figure, given that the net input O1 = 1.3 and net input O2 = 1.41 using the logistic function?
0.867, 0.408
0.876, 0.048
0.768, 0.084
0.786, 0.804
Question 6: Given the below neural network with Out O1 = 0.15 and Out O2 = 0.25. How much does the total error change with respect to the outputs?
0.2, 0.4
0.75, 0.55
0.35, 0.25
0.72, 0.38
Question 15: Which of the following is not a step in backpropagation algorithm?
Feed-forward computation
Backpropagation to the output layer
Backpropagation to the input layer
Backpropagation to the hidden layer
Question 16: Which of the following statements is not true about backpropagation?
Initially the weights are assigned at random
The algorithm iterates through many cycles of two processes until a stopping criterion has reached
Each epoch includes a forward phase and a backward phase
The whole cycles of execution together are known as an epoch
Question 17: Which of the following statements is not true about forward phase in backpropagation in neural network?
The neurons are activated in sequence from the input layer to the output layer
Applying each neuron’s weights and activation function from the input layer to the output layer
Applying each neuron’s weights and activation function from the output layer to the input layer
Upon reaching the final layer, an output signal is produced
Question 18: Which of the following statements is not true about backward phase in backpropagation in neural network?
The network’s output signal resulting from the forward phase is compared to the true target value in the training data
The difference between the networks’s output signal and the true value results in an error
The error is propagated backwards in the network to modify the connection weights between neurons
Modifying the weights cannot reduce the future errors
Question 19: Which of the following statements is not true about the backpropagation architecture?
Backpropogation is a multilayer feed forward network with one layer of z-hidden units
The input layer is connected to hidden layer by means of interconnecction weights
The hidden layer is connected to output layer by means of interconnecction weights
Only the hidden units can have bias
Question 21: Which of the following statements is not true about the backpropagation architecture?
It resembles a multi layered feed forward network
The increasing number of hidden layers results in the computational complexity of the network
The increasing number of hidden layers may results in the time taken for minimizing the error as low
The increasing number of hidden layers may results in the time taken for convergence as very high
Question 23: Which of the following is not a feature of back propagation algorithm?
It is fast, simple and easy to program
It does not need any special mention of the features of the function to be learned
It has more parameters to tune apart from the numbers of input
It is a flexible method as it does not require prior knowledge about the network
Question 24: Which of the following statements is not true about the backpropagation?
Static backpropagation produces a mapping of a static input for static output
Static backpropagation is useful to solve static classification issues like optical character recognition
Recurrent backpropagation is fed forward until a fixed value is achieved and after that, the error is computed and propagated backward
The mapping is static in both static back-propagation and recurrent backpropagation
Question 25: What will be the values of net input of h1 and net input of h2 from the below figure, given that w1 = 0.2, w2 = 0.15, w3 = 0.3, w4 = 0.25, w5 = 0.45, w6 =0.4, w7 = 0.55, w8 = 0.5?
0.385, 0.378
0.358, 0.682
0.853, 0.387
0.583, 0.826
Question 26: What will be the outputs of h from the below figure, given that the net input h1 = 0.38 and net input h2 = 0.37 using the logistic function?
0.514, 0.511
0.594, 0.591
0.494, 0.525
0.454, 0.256
Question 27: What will be the net inputs of the output layer in the below figure, given that the w5 = 0.45, w6 =0.4, w7 = 0.55, w8 = 0.5, Out h1 = 0.6 and Out h2 = 0.65?
1.628, 1.365
1.228, 1.165
1.528, 1.665
1.428, 1.465
Question 28: What will be the outputs of output layer from the below figure, given that the net input O1 = 1.3 and net input O2 = 1.41 using the logistic function?
0.867, 0.408
0.876, 0.048
0.768, 0.084
0.786, 0.804
Question 30: Given the below neural network with Out O1 = 0.15 and Out O2 = 0.25. How much does the total error change with respect to the outputs?
0.2, 0.4
0.75, 0.55
0.35, 0.25
0.72, 0.38
