Deep Learning Test

Statement 1: It is possible to train a network well by initializing all the weights as 0
Statement 2: It is possible to train a network well by initializing biases as 0

Which of the statements given above is true?

The number of nodes in the input layer is 10 and the hidden layer is 5. The maximum number of connections from the input layer to the hidden layer are

The input image has been converted into a matrix of size 28 X 28 and a kernel/filter of size 7 X 7 with a stride of 1. What will be the size of the convoluted matrix?

In a simple MLP model with 8 neurons in the input layer, 5 neurons in the hidden layer and 1 neuron in the output layer. What is the size of the weight matrices between hidden output layer and input hidden layer?

Which of the following functions can be used as an activation function in the output layer if we wish to predict the probabilities of n classes (p1, p2..pk) such that sum of p over all n equals to 1?

Assume a simple MLP model with 3 neurons and inputs= 1,2,3. The weights to the input neurons are 4,5 and 6 respectively. Assume the activation function is a linear constant value of 3. What will be the output ?

Which of following activation function can't be used at output layer to classify an image ?

[True | False] In the neural network, every parameter can have their different learning rate.

Dropout can be applied at visible layer of Neural Network model?

I am working with the fully connected architecture having one hidden layer with 3 neurons and one output neuron to solve a binary classification challenge. Below is the structure of input and output:

Input dataset: [ [1,0,1,0] , [1,0,1,1] , [0,1,0,1] ]

Output: [ [1] , [1] , [0] ]

To train the model, I have initialized all weights for hidden and output layer with 1.

What do you say model will able to learn the pattern in the data?