代写data编程、Python编程设计代做

项目预算：开发周期：发布时间：要求地区：

Assignment 1
Deadline: 10.24.2023 - 11.59pm
By the deadline, you are required to submit the following:
Report. Prepare a single PDF file that presents clear and concise evidence of
your completion of each of the listed tasks. Use the overleaf template available on iCorsi.
Source code. Create a single zipped Python script using the template available on iCorsi, performing each of the specified tasks. If a task is accomplished
in the code but not documented in the report, it will be considered incomplete.
Therefore, make sure to thoroughly report all your work in the submitted report. Jupyter notebook files will not be accepted.
General hints. The question marked with * is more challenging, and we recommend possibly leaving them as the last questions to solve. To obtain the
maximum grade, not only should all exercises be completed correctly, but the
plots must also be clear, have a legend, and have labels on the axes and the
text should be written in clear English. For saving plots in high quality, consider using the command matplotlib.pyplot.savefig. Clarity of plots
and text will account in total for 5 points.
Submission rules: As already discussed in class, we will stick to the following rules.
• Submit ONE pdf file containing your report and ONE (zipped) .py file
containing the code. Use the templates and name your files
NAME SURNAME.pdf and NAME SURNAME.py. We will read and
correct only these files. Other files present in the folder will not be read.
If such files are missing, they will be evaluated with a score of 0.
• Code either not written in Python or not using PyTorch receives a grade
of 0. Of course you can use auxiliary packages when needed (matplotlib,
numpy,. . . ), but for the learning part, you must use PyTorch.
• Submission between 00.00am - 00.10 of Wednesday 25th will be accepted
with no reduction.
• If plagiarism is suspected, TAs and I will thoroughly investigate the situation, and we will summon the student for a face-to-face clarification
regarding certain answers they provided. In case of plagiarism, a score
1
reduction will be applied to all the people involved, depending on their
level of involvement.
• If extensive usage of AI tools is detected, we will summon the student
for a face-to-face clarification regarding certain answers they provided.
If the answers are not adequately supported with in-person answers, we
will proceed to apply a penalty to the evaluation, ranging from 10% to
100%.
Polynomial regression (with gradient descent) [95 points]
Let z ∈ R and consider the polynomial
p(z) = z
4
100
− z
3 + z
2 − 10z =
X
4
k=1
z
kwk (1)
where w = [w1, w2, w3, w4]
T = [−10, 1, −1,
1
100 ]
T
. This polynomial can be also
expressed as the dot product of two vectors, namely
p(z) = wT x x = [z, z2
, z3
, z4
]
T
(2)
Consider an independent and identically distributed (i.i.d.) dataset D = {(zi
, yi)}
N
i=1,
where yi = p(zi) + εi
, and each εi
is drawn from a normal distribution with
mean zero and standard deviation σ.
Now, assuming that the vector w is unknown, linear regression could estimate it using the dot-product form presented in Equation 2. To achieve this we
can move to another dataset
D
′
:= {(xi
, yi)}
N
i=1 x
i = [zi
, z2
i
, z3
i
, z4
i
]
T
The task of this assignment is to perform polynomial regression using gradient descent with PyTorch, even if a closed-form solution exists.
1. (5 pts) Define a function
plot_polynomial(coeffs, z_range, color=’b’)
Where coeffs is a np.array containing [w0, w1, w2, w3, w4]
T
(w0 in this
case is equal to 0) z_range is the interval [zmin, zmax] of the z variable;
color represent a color. Use the function to plot the polynomial. Report
and comment on the plot.
2. (10 pts) Write a function
create_dataset(coeffs, z_range, sample_size, sigma, seed=42)
that generates the dataset D′
. Here coeffs is a np.array containing
[w0, w1, w2, w3, w4]
T
, z_range is the interval [zmin, zmax] of the z variable;
sample_size is the dimension of the sample; sigma is the standard
deviation of the normal distrbution from which εi are sampled; seed is
the seed for the random procedure.
2
3. (5 pts) Use the code of the previous point to generate data with the following parameters
• Each zi should be in the interval [−3, 3]
• σ = 0.5
• Use a sample size of 500 for training data and a seed of 0
• Use a sample size of 500 for evaluation data and a seed of 1
4. (5 pts) Define a function
visualize_data(X, y, coeffs, z_range, title="")
that plot the polynomial p(z) and the generated data, (train and evaluation), where X, y are as returned from the function create_dataset,
coeffs are the coefficient of the polynomial, z_range is the interval
[zmin, zmax] of the z variable and title may be helpful to distinguish between the training and the evaluation plots. What we mean with this
question, is the following: Provide two plots containing both the true
polynomial; In one, add a scatter plot with the training data, and in the
other a scatter plot with the evaluation data. Use the function to visualize
the data. Report and comment on the plots.
5. (20 pts) Perform polynomial regression on D using linear regression on
D′ and reports some comments on the training procedure. Consider your
training procedure good when the training loss is less than 0.5. This
works in my code with a number of steps equal to 3000. In particular,
explain:
• How you preprocessed the data.
• Which learning rate do you use, and why. What happens if the
learning rate is too small; what happens if the learning rate is too
high, and why.
• If bias should be set as True of False in torch.nn.Linear and
why.
You should use the data you generated at point 3. Note: If you plan to reuse code explained during the lecture, explicitly indicate which parts are
being reused or readjusted/modified. In any case, the steps must always
be explained and understood in detail.
6. (10 pts) Plot the training and evaluation loss as functions of the iterations
and report them in the same plot. If you use both steps and epochs, you
can choose either of the two, as long as it is clear from the plot and the
plot reports what we expect - namely, that the loss functions decrease.
7. (5 pts) Plot the polynomial you got with your estimated coefficient as
well as the original one in the same plot.
3
8. (10 pts) Plot the value of the parameters at each iteration as well as the
true value. What we expect is a plot having on the x axis the number of
steps/epochs and on the y axis the value of the parameters while they
are learned. Then, in the same graph, plot a horizontal line with the true
value.
9. (15 pts) Re-train the model with the following parameters:
• Each zi should be in the interval [−3, 3]
• σ = 0.5
• Use a sample size of 10 for training data and a seed of 0
• Use a sample size of 500 for evaluation data and a seed of 1
• Keep the learning rate you chose at the previous point.
Report: A plot with both the training and evaluation loss as functions of
the iterations in the same plot and the polynomial you got with your estimated coefficient as well as the original one in the same plot. Comment
on what is going on. Note: Do not overwrite the code, keep the original
training and this new one as two separate parts.
10. (5 pts*) This part is less trivial and should be considered as a separate
exercise. Suppose you have to learn the function
f(x) = 5 sin(x) + 3
and you know that your data are x
i belong to the interval [−a, a]. Which
performances would you expect using linear regression in the following
cases?
(a) a = 0.01
(b) a = 5
Note: We want to see some code that generates noisy data from f, do the
linear regression and we want to see the loss value on it. If you know
the mathematics behind it, you can use your knowledge to answer and
validate the data, but as this is a Lab exam, we want to see empirical
evidence.
Questions [5 points]
Please answer these questions in max 10 lines. In Lecture 3, we have seen an
explicit step of the parameter update for one-dimensional linear regression,
namely
wt+1 = wt − α
∂L
∂w (wt, bt) (3)
bt+1 = bt − α
∂L
∂b (wt, bt) (4)
Hint: Do we have any information about the gradient of the loss function with
respect to the parameter at the very beginning of the training loop?
4
(a) Do you think that could be beneficial to choose two different values of α
(learning rate) for Equation (3) and (4)?
(b) Explain the difference between choosing two different learning rates in
principle and what is instead done by the adaptive methods - e.g, Adagrad. If you don’t know the method, you can study one of the following
resources:
• Section 8.5.1 of Deep Learning Book
• Section 12.7 of Dive Into Deep Learning
5