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Deep Learning for NLP Coding Assignment 2 CS 544

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Deep Learning for NLP
Coding Assignment 2
CS 544

Introduction
The goal of this coding assignment to get you expertise in TensorFlow, especially in developing code from the grounds-up. This assignment will be not much hand-held: you have to
do most things from scratch, including creating a tf.Session. In this task, you will be implementing a sequence-to-sequence Recursive Neural Network (RNN) model in TensorFlow.
You will be using the same data from the HMM Coding Assignment 3 for Part-of-Speech
tagging. In particular, you are expected to:
• Populate the starter code in designated places marked by TODO(student).
– The starter code is on: http://sami.haija.org/cs544/DL5/starter.py.
• Write code for reading the data files and producing numpy arrays that will be used for
training [this has to produce expected outcomes, as measured by grading scripts]. This
should be implemented in class DatasetReader.
• Write code for constructing and training the model [here, you should be creative, per
grading scheme below!]. This should be implemented in class SequenceModel.
– You can optionally fill-in the main() code-block, so that you can run locally (or in
vocareum without submitting i.e. for debugging). However, the main() function
will not be run through the submission script.
– You must implement the functions that are annotated in the starter code. The
grading script will train your model for exactly K seconds1
. Therefore, you must
explore good hyperparameters for this training budget [e.g. batch size, learning
rate], which are always a function of your model architecture and the training
algorithm (there is no one answer that fits all!)
Grading
The grading of this assignment is more-in-line with the first 3 coding assignments, except that
you are competing with one-another, not with teaching staff, to some extent. You can also
receive bonus credits. In fact, the theoretical maximum grade for the assignment
is 180%, which would be given to the single highest-accuracy student on unseen
language, if her/his performance is ≈ 100% on Italian and Japanese.
There will be no late days for this assignment, since there is a competition. No exceptions
will be made.
The grading scheme is subject to change, and will be finalized by Friday April 12.
1Likely, K will be 120 to 200 seconds
1
Prepared By:
Sami & Ron
Deep Learning for NLP
Coding Assignment 2
CS 544
Due: Sun April 28
[30 points] Task 1: Data Processing
20 points Implement ReadFile. This function is used by ReadData. The function header is
copied here for your convenience
d e f Re adFile ( s e l f , fil e n am e , te rm index , t a g i n d e x ) :
“””Reads file into dataset , while populating term_index and tag_index.
Args:
filename: Path of text file containing sentences and tags. Each line is a
sentence and each term is followed by “/tag”. Note: some terms might
have a “/” e.g. my/word/tag — the term is “my/word” and the last “/”
separates the tag.
term_index: dictionary to be populated with every unique term (i.e. before
the last “/”) to point to an integer. All integers must be utilized from
0 to number of unique terms – 1, without any gaps nor repetitions.
tag_index: same as term_index , but for tags.
Return:
The parsed file as a list of lists: [parsedLine1 , parsedLine2 , …]
each parsedLine is a list: [(term1 , tag1), (term2 , tag2), …]
“””
10 points Implement BuildMatrices. The function header is copied here for your convenience
d e f B uil dM a t ri c e s ( d a t a s e t ) :
“””Converts dataset [returned by ReadFile] to np arrays for tags , terms , lengths.
Args:
dataset: Returned by method ReadFile. It is a list (length N) of lists:
[sentence1 , sentence2 , …] , where every sentence is a list:
[(word1 , tag1), (word2 , tag2), …] , where every word and tag are integers.
Returns:
Tuple of 3 numpy arrays: (terms_matrix , tags_matrix , lengths_arr)
terms_matrix: shape (N, T) int64 numpy array. Row i contains the word
indices in dataset[i].
tags_matrix: shape (N, T) int64 numpy array. Row i contains the tag
indices in dataset[i].
lengths: shape (N) int64 numpy array. Entry i contains the length of
sentence in dataset[i].
T is the maximum length. For example , calling as:
BuildMatrices([[(1 ,2) , (4 ,10)] , [(13 , 20), (3, 6), (7, 8), (3, 20)]])
i.e. with two sentences , first with length 2 and second with length 4,
should return the tuple:
(
2
Prepared By:
Sami & Ron
Deep Learning for NLP
Coding Assignment 2
CS 544
Due: Sun April 28
[[1, 4, 0, 0], # Note: 0 padding.
[13, 3, 7, 3]],
[[2, 10, 0, 0], # Note: 0 padding.
[20, 6, 8, 20]] ,
[2, 4]
)
“””
Note: this task is completely independent of the second task. You are graded
each in isolation. In fact, when grading Task 2, we use our implementation of
these functions.
[60 + ? points] Task 2: Coding Sequence Models
In this task, you are expected to populate class SequenceModel.
You might find these functions useful:
• SimpleRNNCell. You construct the class once, and you can call it to map prev-state
tensor and current values tensor, to next state tensor.
• tf.reshape: Changes the number of dimensions of a tensor.
• tf.nn.embedding lookup: Takes a float matrix tensor (embeddings) of shape (N, d),
and int64 any-dimension (= D) tensor (indices): returns float tensor of D + [d].
• tf.get variable: creates a variable (E.g. an embedding variable) and defaults it to
“trainable”.
Grades:
• You will receive zero credit if you do not implement any of the required methods:
– save model: Saves the trained model to a file.
– load model: Loads the trained model from a file. For this one and the above, you
might find the first Deep Learning material useful: http://sami.haija.org/
cs544/DL1
– run inference: Given sentences (i.e. matrices of term IDs and their lengths),
return the part-of-speech tag ID for every word in every sentence.
• If your class does not implement lengths vector to binary matrix correctly, then
will get -10 points (though the minimum grade for the task is 0).
3
Prepared By:
Sami & Ron
Deep Learning for NLP
Coding Assignment 2
CS 544
Due: Sun April 28
• If you implement the required methods, then your grade depends on your model’s
accuracy on unseen data. The following grade table applies for the Italian and the
Japanese texts:
Accuracy: < 50% 50% ≤ a ≤ 85% 85% a ≥ 85%
Grade: 0
a−50
35 × 30 30 30 + a−85
7.5 × 30
Bonus Points: The top 5 performers get additional grades. The performance is measured
on unseen language.
Rank 1 2 3 4 5
+Bonus Points 60 50 40 30 20
Note: We will only consider for bonus students who receive > 92.5% test accuracy on the
Italian and Japanese languages. In addition, the bonus points will only be awarded for people
who explain their method through a 2-page PDF or a couple of slides. The criteria for getting
the bonus credits, is that the teaching staff must be able to understand how replicate the
method.
You might find this code useful:
xemb = t f . nn . embedding lookup ( . . . )
r n n c e l l = t f . k e r a s . l a y e r s . SimpleRNNCell ( s t a t e s i z e ) # Callable instance.
s t a t e s = [ ]
c u r s t a t e = t f . z e r o s ( shape =[1 , s t a t e s i z e ] )
f o r i i n xrange ( max length ) :
c u r s t a t e = r n n c e l l ( xemb [ : , i , : ] , [ c u r s t a t e ] ) [ 0 ] # shape (batch , state_size)
s t a t e s . append ( c u r s t a t e )
s t a c k e d s t a t e s = t f . s t a c k ( s t a t e s , a x i s =1) # Shape (batch , max_length , state_size)
Finally, your model will be tested by the grader script, which invokes 2 programs, the training
program then the testing program. They will run as:
# TRAINING PROGRAM
model = SequenceModel ( max length , num terms , num tags )
model . b u i l d i n f e r e n c e ( )
model . b u i l d t r a i n i n g ( )
w hil e ( tim e s p e n t < K) :
model . t r ai n e p o c h ( terms , t a g s , l e n g t h s )
model . s ave m odel ( ’ /some/ f i l e /path ’ )
# TESTING PROGRAM [runs in a separate shell command , after training program]
model = SequenceModel ( max length , num terms , num tags )
model . l o ad m odel ( ’ /some/ f i l e /path ’ )
model . b u i l d i n f e r e n c e ( )
model . r u n i n f e r e n c e ( t e s t t a g s , t e s t l e n g t h s ) # and compare with ground -truth
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