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1)- We have a 50 x 25 grid with 5 separate quantities for each entry of the grid. Each of the 5 quantities is further represented by 100 values.
If we want to represent the 50 such grids using a tensor, what is the best way to represent such a tensor (defined using NumPy),
say X? Given below are the dimensions of 4 tensors. Choose the best option.Answer- (50, 50, 25, 5, 100)
2)- From the data described in the above question, what will be the dimensions of the following tensor:
Y = X[20:, 10:25, :50, :100, :50]
Answer- (30, 15, 25, 5, 50)
3)- Let’s take the same tensor X from Q1 and run the following code:
Y = numpy.mean(X, axis = 3)
Answer- None of the above
4)- Given a tensor t, load the tensor into a tensorflow’s tf.data.Dataset and run the reduce operation with parameters initial_state = 1 and
reduce_func = lambda x, y: x + y on the dataset. Choose the result of the operations from the options below:t = [[1, 2, 3, 4, 5, 6, 7], [2, 3, 4, 5, 6, 7, 8], [3, 4, 5, 6, 7, 8, 9]]
Answer- [7 10 13 16 19 22 25]
5)- Using the same tensor from Q4, we load the tensor into a tensorflow’s tf.data.Dataset and run the map operation with map_func = lambda
x: (x + tf.reduce_mean(x)) . We then run the reduce operation with parameters initial_state = 5 and reduce_func = lambda
x, y: 3*x – 4*y on the dataset. Choose the result of the operations from the options below:Answer- [-165, -217, -269, -321, -373, -425, -477]
6)- What will be the first, last elements and the size of the output array of the following code:
x= tf.data.dataset.range(1, 10)
x_= x.repeat(2).batch(20)
n= next(iter(x_))
print(n.numpy())
Answer- 1, 9, 18
7)- For the given raw data, which of the following preprocessing steps do you think were necessarily applied before providing it for the training of a neural network? Hint: Only three are correct.
time, status, age, month_year, col1, col2
10, A, 76, Jul-1972, 6.76, yes
30, B, 56, Jan-1968,, No
35, A, 41, Nov-1977, 1.34,
99, C, 71,, 2.9, No
185, B, 52, Mar-1965, 12.08, Yes
Answer- Handle missing values, Column month_year split, One-hot encoding
8)- Suppose the shape of a given data is (100, 15). Our task is to perform regression on the last 5 columns using others 10. We assigned 20% of this data for validation while training process, 20% for evaluating the model after it is trained. What are the correct dimensions of
x_train, y_train, x_valid, y_valid, x_test, y_test respectively?
Answer- (60, 10), (60, 5), (20, 10), (20, 5), (20, 10), (20, 5)
9)- We are building a two-layer neural network with 7 units in the input layer, 4 units in the hidden layer and 1 unit in the output layer.
The hidden layer uses ReLU activation and the output layer uses sigmoid activation. Find below the weights and biases of the network. Wij is the weight of the j(th) unit in the i(layer) The bias terms b and output terms o follow the same pattern.W11 = [-0.05, 0.1, 0.1, 0.2, 0.35, 0.6, -0.9, -0.1] b11 = -0.8
W12 = [-0.5, 0.1, 0.1, 0.02, 0.3, 0.36, 0.9, 0.1] b12 = -0.1
W13 = [-0.05, 0.1, 0.1, 0.2, 0.35, 0.6, 0.9, -0.1] b13 = -0.1
W14 = [-0.5, 0.1, 0.1, 0.02, 0.3, 0.36, -0.9, 0.1] b14 = -0.1Given input x = [1, 0.2, 0.4, 0.9, 1, 0, 0.6, 0.3], what are the outputs (rounded) of the hidden layer?
Answer- [0, 0.348, 0.95, 0]
10)- In continuation of the previous question, given the weights and bias for the output layer, what is the output of the neural network?
W21 = [-0.05, 0.1, 0.1, 0.2]; b21 = 0
Answer- 0.532
11)- Which of the following will help in reducing variance in a neural network?
Answer- Increasing the dropout rate, Increasing lambda (a parameter that controls L2 regularization), Increasing the size of training data by using data augmentation
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