Question 1 (Short Answers, 08 points):
1- How does splitting a dataset into train, dev and test sets help identify overfitting? (2pts).
We must make sure that train and dev have the same distribution , also if model
fits the training so much, it can’t learn very well . And we can identified by low
train error and high dev error.
2- You are designing a deep learning system to detect driver fatigue in cars. It is crucial that
that your model detects fatigue, to prevent any accidents. Which of the following is the
most appropriate evaluation metric: Accuracy, Precision, Recall, Loss Value. Explain
your choice (1pt).
All of them because we must consider all situations of drivers fatigue.
3- Which of the following techniques does NOT prevent a model from overfitting? (1pts).
(i) Data augmentation
(ii) Dropout
(iii) Early stopping
(iv) None of the above **
4- You are given a dataset of 10×10 grayscale images. Your goal is to build a 5-class classifier.
You have to adopt one of the following two options (2pts):
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The input is flattened into a 100-dimensional vector, followed by a fully-connected layer with 5
neurons •
The input is directly given to a convolutional layer with five 10 × 10 filters
The second option is better because we don’t have to flatten the input.
5- You are doing full batch gradient descent using the entire training set (not stochastic gradient
descent). Is it necessary to shuffle the training data? Explain your answer. (2pts).
No, it is not necessary because each frequency runs through all dataset and the order
don’t matter.
Question 2 (Architectures and training, 12 points):
A. Convolution:
(a) How many values will be generated if we forward propagate the image over the given
convolution kernel? (01pts)
9 values
(b) Calculate these values. (03pts)
109
108
110
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92
85
74
72
74
79
(a) Consider the figure below (1pts):
(h=4,w=4,c=5)
B. Linearity (1 pts):
z = np.matmul(W, a_prev) + b = np.dot(W, a prev) + b
C. Convolutional neural network (04 pts):
Layer
Input
CONV3-8
Leaky ReLU
POOL-2
BATCHNORM
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CONV3-16
Leaky ReLU
POOL-2
FLATTEN
FC-10
D. Training (02pts):
Activation Volume
Number of param
32 × 32 × 3
32 x 32 x 8
32 x 32 x 8
16 x 16 x 8
16 x 16 x 8
16 x 16 x 16
16 x 16 x 16
8 x 8 x 16
1688
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10
0
8*(3x3x3 + 1) = 224
0
0
2*8
16*(3x3x8 + 1) = 1168
0
0
0
(8x8x16 + 1) * 10 = 10250
Yes he is right , because the loss function moves a lot when passing from type of image to
another that’s why optimization is so hard when using minibatch gradient descent