neural networks and deep learning coursera week 2 quiz answers
Quiz - Neural Network Basics
1. In logistic regression given the input x, and parameters w € R". b € R, how do we generate the output g?
- aWx+b).
- a(Wx)
- tanh(W x + b)
- Wx+b
3. Consider the Numpy array x:
x=np.array([[[1], [2], [3], [4]])
What is the shape of x?
- (4,)
- (1, 2, 2)
- (2, 2, 1)
- (2, 2)
4. Consider the following random arrays a and b, and c:
a = np.random.randn(3,3) # a.shape = (3,3)
b= np.random.randn(2,1) #b.shape = (2,1)
c=a+b
What will be the shape of c?
- c.shape = (3,3)
- c.shape = (2, 3, 3)
- c.shape = (2, 1)
- The computation cannot happen because it is not possible to broadcast more than one dimension
5. Consider the two following random arrays a and b:
a = np.random.randn(4, 3) # a.shape = (4, 3)
b = np.random.randn(1,3) # b.shape = (1, 3)
c = a*b
What will be the shape of c?
- c.shape = (1, 3)
- The computation cannot happen because the sizes don’t match.
- The computation cannot happen because it is not possible to broadcast more than one dimension.
- c.shape = (4, 3)
6. Suppose our input batch consists of 8 grayscale images, each of dimension 8x8. We reshape these images into feature column vectors x'. Remember that X= x(1)g(2) ... x8) . What is the dimension of X?
- (64, 8)
- (512, 1)
- (8, 8, 8)
- (8, 64)
7. Recall that np.dot (a, b) performs a matrix multiplication on a and b
whereas a * b performs an element-wise multiplication.
Consider the two following random arrays a and b:
a = np.random.randn(12288,150)
#a.shape = (12288, 150)
b = np.random.randn(150,45)
#b.shape = (150,45)
c = np.dot(a, b)
What is the shape of c?
- c.shape = (150,150)
- c.shape = (12288, 150)
- The computation cannot happen because the sizes don’t match. It’s going to be “Error”!
- C.Shape = (12288. 45)
Consider the following code snippet:
a.shape = (4,3)
b.shape = (4,1)
for i in range(3):
for j in range(4):
c[i][j] = a[j][i] + b[j]
How do you vectorize this?
- c= a + b
- c = a.T + b
- c = a + b.T
- c = a.T + b.T
9. Consider the code snippet:
a.shape = (3,3)
b.shape = (3,3)
C= a**2 + b.T**2
Which of the following gives an equivalent output for C?
- for i in range(3):
for j in range(3):
c[i][j] = a[i][j]**2 + b[i][j]**2 - for i in range(3):
c[i] = a[i]**2 + b[i]**2 - The computation cannot happen because the sizes don’t match. It’s going to be an “Error”!
- for i in range(3):
for i in range(3):
c[i][j] = a[i][j]**2 + b[j][i]**2
10. Consider the following computational graph.
What is the output of J?
- ab + bc + ac
- (a – 1), (b + c)
- (a + c), (b – 1)
- (c – 1), (a + c)
