# Homework 2 600.482/682 Deep Learning

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Homework 2
600.482/682 Deep Learning

Please submit a report (LaTeX generated PDF) and
to Gradescope with entry code 9G83Y7
(submit the code as programming assignment)
1. The goal of this problem is to minimize a function given a certain input using gradient
descent by breaking down the overall function into smaller components via a computation
graph. The function is defined as:
f(x1, x2, w1, w2) = 1
1 + e−(w1x1+w2x2)
+ 0.5(w
2
1 + w
2
2
).
∂w1
,
∂f
∂w2
,
∂f
∂x1
,
∂f
∂x2
.
Solution:
∂f
∂w1
=
x1 · e
−(w1x1+w2x2)
(1 + e−(w1x1+w2x2))
2
+ w1
∂f
∂w2
=
x2 · e
−(w1x1+w2x2)
(1 + e−(w1x1+w2x2))
2
+ w2
∂f
∂x1
=
w1 · e
−(w1x1+w2x2)
(1 + e−(w1x1+w2x2))
2
∂f
∂x2
=
w2 · e
−(w1x1+w2x2)
(1 + e−(w1x1+w2x2))
2
(b) Start with the following initialization: w1 = 0.3, w2 = −0.5, x1 = 0.2, x2 = 0.4, draw
the computation graph. Please use backpropagation as we did in class.
You can draw the graph on paper and insert a photo into your report.
The goal is for you to practice working with computation graphs. As a consequence,
you must include the intermediate values during the forward and backward pass.
Solution:
The computation graph is shown as below. All number above the lines are values in
forward pass. All numbers below the lines are values in backward pass.
1
(c) Implement the above computation graph in the complimentary Colab Notebook using
numpy. Use the values of (b) to initialize the weights and fix the input. Use a constant
step size of 0.01. Plot the weight value w1 and w2 for 30 iterations in a single figure in
the report.
Solution:
2. The goal of this problem is to understand the classification ability of a neural network.
Specifically, we consider the XOR problem. Go to the link in footnote1 and answer the
following questions. Hint: hit reset the network right next to the run button after you change
the architecture.
(a) Can a linear classifier, without any hidden layers, solve the XOR problem?
Solution: No. Since there’s only one layer,it is only capable of distinguish all data with
a line. It is apparently not possible to divide the data in XOR problem with a line.
1https://playground.tensorflow.org/#activation=relu&batchSize=10&dataset=xor&regDataset=
reg-plane&learningRate=0.01&regularizationRate=0&noise=0&networkShape=&seed=0.10699&showTestData=
false&discretize=true&percTrainData=80&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&
cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=
false&hideText=false
2
(b) With one hidden layer and ReLU(x) = max(0, x), how many neurons in the hidden
layer do you need to solve the XOR problem? Describe the training loss and estimated
prediction accuracy when using 2, 3 and 4 neurons. Discuss the intuition of why a
certain number of neurons is necessary to solve XOR.
Solution:
When using 2 neurons, the training loss is 0.268, the estimated prediction accuracy is
78
100 = 0.78. The picture is shown as below.
When using 3 neurons, the training loss is 0.260, the estimated prediction accuracy is
73
100 = 0.73. The picture is shown as below.
When using 4 neurons, the training loss is 0.002, the estimated prediction accuracy is
100
100 = 1.00. The picture is shown as below.
3
I think that there are 2 status for x1 and 2 status for x2. Since a layer of neurons can
only perform 1 manipulation, we need 2 = 4 neurons to represent the 4 conditions when
x1 xor x2. Therefore, we can use the four neurons in the hidden layers to make to right
prediction.
3. In this problem, we want to build a neural network from scratch using Numpy for a realworld problem. We consider the MNIST dataset (http://yann.lecun.com/exdb/mnist/),
a hand-written digit classification dataset. Please follow the formula in the complimentary
Colab Notebook. Hint: Make sure you pass the loss and gradient check in the notebook.
(a) Implement the loss and gradient of a linear classifier (python function
linear classifier forward and backward).
(b) Implement the loss and gradient of a multilayer perceptron with one hidden layer and
ReLU(x) = max(0, x) (python function mlp single hidden forward and backward).
(c) Implement the loss and gradient of a multilayer perceptron with two hidden layer, skip
connection and ReLU(x) = max(0, x) (python function mlp two hidden forward and backward).
(d) Plot the development accuracy of each epoch of three models in a single figure using
the following hyperparameters: the batch size is 50, the learning rate is 0.005 and the
number of epochs is 20.
Solution:
4
(e) Try using other hyperparameters and select a set of best hyperparameters using development accuracy. Once you pick the best model and hyperparameters, include
the development accuracy of each epoch into the above figure (make a new figure) and
report the test accuracy of the selected model and hyperparameters.
Solution: The best parameter I currently find is BS = 100, LR = 0.01, NB EPOCH =
20. The development accuracy is 97.30%, higher than the original MLP with 2 hidden layers dev loss,
which is 97.29%.
The picture is shown as below:
The test accuracy is 97.18%
5

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