Assignment # 3: RNN Language Model Solution

$30.00

Description

Submission: You must submit two files through MarkUs2: a PDF file containing your writeup, titled a3-writeup.pdf, and your code file a3_code.py. Your writeup must be typeset using LATEX.

The programming assignments are individual work. See the Course Information handout3 for de- tailed policies.

You should attempt all questions for this assignment. Most of them can be answered at least partially even if you were unable to finish earlier questions. If you were unable to run the experiments, please discuss what outcomes you might hypothetically expect from the experiments. If you think your computational results are incorrect, please say so; that may help you get partial credit.

Overview

In thie project, you will work on extending min-char-rnn.py, the vanilla RNN language model implementation we covered in tutorial. This was written by Andrej Karpathy4. You will experiment with the Shakespeare dataset, which is shakespeare.txt in the starter code.

Part 1 (20%)

The RNN language model uses a softmax activation function for its output distribution at each time step. Its possible to modify the distribution by multiplying the logits by a constant :

y = softmax(z).

Here, 1/ can be thought of as a “temperature”, i.e. lower values of correspond to a “hotter

distribution. (This terminology comes from an algorithm called simulated annealing.)

Write a function to sample text from the model using dierent temperatures (i.e., 1/alpha on Slide 5). Try dierent temperatures, and, in your report, include examples of texts generated using dierent temperatures. Briefly discuss what dierence the temperature makes.

Include the listing (i.e., source code) of the function you wrote/modified to accomplish the task in the report.

You should either train the RNN yourself, or use the weights from Part 3 up to you.

Part 2 (50%)

Write a function that uses an RNN to complete a string. That is, the RNN should generate text that is a plausible continuation of a given starter string. In order to do that, you will need to

compute the hidden activity h at the end of the starter string, and then to start generating new text.

Include 5 interesting examples of outputs that your network generated using a starter string. (This part need not be easily reproducible).

Include the listing (i.e., source code) of the function you wrote in the report.

You should either train the RNN yourself, or use the weights from Part 3 up to you.

Part 3 (30%)

The weights for a trained RNN are included as char-rnn-snapshot.npz. Some samples from the RNN (at temperature 1/ = 1) are included as samples.txt, and code to read in the weights is included as read_in_npz.py (if this doesnt work, try the pickle file, and get a using

import cPickle as pickle; a = pickle.load(open(“char-rnn-snapshot.pkl”)).)

In the samples that the RNN generated, it seems that a newline or a space usually follows the colon (i.e., “:”) character. In the weight data provided, identify the specific weights that are responsible for this behaviour by the RNN. In your report, specify the coordinates and values of the weights you identified, and explain how those weights make the RNN generate newlines and spaces after colons.

Part 4 (10% bonus)

Identify another interesting behaviour of the RNN, identify the weights that are responsible for it. Specify the coordinates and the values of the weights, and explain how those weights lead to the behaviour that you identified. To earn bonus points, the behaviour has to be more interesting than the behaviour in Part 3 (i.e., character A following character B).

What to submit

The project should be implemented using Python 2. Your report should be in PDF format, generated with LATEX. You will submit three files: a3_code.py, and a3-writeup.pdf. You can submit other Python files as well: we should have all the code thats needed to run your experiments.

Reproducibility counts! We should be able to obtain all the graphs and figures in your report by running your code. Submissions that are not reproducible will not receive full marks. If your graphs/reported numbers cannot be reproduced by running the code, you may be docked up to

20%. (Of course, if the code is simply incomplete, you may lose even more.) Suggestion: if you are using randomness anywhere, use numpy.random.seed().