程序代写案例-APS360H1
Winter 2019 Midterm Practice Questions APS360H1
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Do not turn this page until you have received the signal to start.
In the meantime, please read the instructions below carefully.
This test consists of 5 questions on 8 pages (including this one), printed
on both sides of the paper. When you receive the signal to start, please make
sure that your copy of the test is complete, fill in the identification section
above, and write your name on the back of the last page.
Answer each question directly on the test paper, in the space provided.
If you need more space for one of your solutions, use the extra pages at the
end of the test paper and indicate clearly the part of your work that should
be marked.
Write up your solutions carefully! If you are giving only one part of an
answer, indicate clearly what you are doing. Part marks might be given for
incomplete solutions where it is clearly indicated what parts are missing.
You must write the test in pen if you would like to potentially request
for the test to be regraded.
Marking Guide
# 1: / 5
# 2: / 5
# 3: /12
# 4: / 6
# 5: /12
TOTAL: /40
Page 1 of 8 Good Luck! over. . .
Midterm Practice Questions
Question 1. [5 marks]
Circle the best answer for each of the questions below.
Part (a) [1 mark]
Batching is straightforward in all but the
following type of network:
(A) Word2Vec
(B) Fully Convolutional neural networks
(C) Feed-forward neural networks
(D) Convolutional neural networks
(E) Recurrent neural networks
Part (b) [1 mark]
Which of the following does not help re-
duce overfitting?
(A) Using dropout layers.
(B) Using weight decay to penalize large weights.
(C) Augmenting the test data by adding small, random per-
turbations.
(D) Stopping training when the validation accuracy stops im-
proving.
(E) Augmenting the training data by adding small, random
perturbations.
Part (c) [1 mark]
Consider the following PyTorch code:
x = img.view(-1, 28 * 28). What
shape does the tensor img have to be for
the code to throw an error?
(A) [1, 28, 28]
(B) [10, 14, 56]
(C) [4, 784]
(D) [28, 28, 28]
(E) None of the above
Part (d) [1 mark]
A transpose convolution is used in the fol-
lowing context:
(A) A pixel-wise prediction problem.
(B) A recurrent neural network that takes images as input.
(C) An image autoencoder.
(D) All of the above.
(E) Only (a) and (c).
Part (e) [1 mark]
Which of the following is true about re-
current neural networks?
(A) The size of the RNN output vector is the same as the
size of the hidden state.
(B) The size of the RNN output vector is the same as the
size of the input embedding.
(C) The PyTorch module nn.RNN is more often used than
nn.LSTM.
(D) The PyTorch module nn.LSTM uses weight sharing, and
thus has fewer parameters than nn.RNN.
(E) The size of the hidden state is the same as the size of
the input embedding.
Page 2 of 8 cont’d. . .
Winter 2019 Midterm Practice Questions APS360H1
Question 2. [5 marks]
Explain what could cause the training curve to have the below shape.
Page 3 of 8 over. . .
Midterm Practice Questions
Question 3. [12 marks]
Consider the following two networks NetworkA and NetworkB:
class NetworkA(nn.Module):
def __init__(self):
super(NetworkA, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=5, kernel_size=3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(in_channels=5, out_channels=10, kernel_size=3, padding=1)
self.fc = nn.Linear(10 * 5 * 5, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 10 * 5 * 5)
x = self.fc(x)
x = x.squeeze(1) # Flatten to [batch_size]
return x
class NetworkB(nn.Module):
def __init__(self):
super(NetworkA, self).__init__()
self.fc1 = nn.Linear(20 * 20, 300)
self.fc2 = nn.Linear(300, 100)
self.fc3 = nn.Linear(100, 10)
def forward(self, x):
x = x.view(-1, 20 * 20)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
Part (a) [5 marks]
How many parameters are in NetworkA?
Part (b) [5 marks]
How many parameters are in NetworkB?
Part (c) [2 marks]
Which of the two networks is more likely to overfit?
Page 4 of 8 cont’d. . .
Winter 2019 Midterm Practice Questions APS360H1
Question 4. [6 marks]
Consider the following neural network model written in PyTorch. Identify three issues with the code.
class Classifier(nn.Module):
def __init__(self):
super(Classifier, self).__init__()
self.layer1 = nn.Linear(28 * 28, 40)
self.layer2 = nn.Linear(30, 5)
def forward(self, img):
flattened = img.view(-1, 28 * 28)
activation1 = self.layer1(flattened)
activation2 = self.layer2(activation1)
return torch.softmax(activation2)
1.
2.
3.
Page 5 of 8 over. . .
Midterm Practice Questions
Question 5. [12 marks]
Part (a) [4 marks]
Describe the architecture of a word2vec model.
Part (b) [4 marks]
Describe two possible subsampling methods used in convolutional neural networks.
Part (c) [4 marks]
Suppose you are training an RNN to determine the gender of the author of a tweet, but you have a small
training set. What techniques can you use?
Page 6 of 8 cont’d. . .
Winter 2019 Midterm Practice Questions APS360H1
Additional page for answers
Page 7 of 8 over. . .
On this page, please write nothing except your name.
Family Name(s):
Given Name(s):
Page 8 of 8 Total Marks = 40 End of Midterm Practice Questions

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