BYU CS classes

Gain exposure to state of the art attention mechanisms.

For this lab, you will need to read, modify, and extend the Transformer model from this paper. You will use this new network build a Spanish to English translation system.

This lab is again different than those seen previously. You will be working with an annotated jupyter notebook from Harvard. This annotated notebook contains the paper text, along with code snippets to explain certain aspects of this network.

You should turn in your completed jupyter notebook (built upon the annotated version from Harvard), which includes several example snippets of translated Spanish to English sentences.

Please turn in your samples inside of the jupyter notebook, not in a separate file.

Since much of this lab is reading someone else's code, the grading standard is slightly different.

Your code/image will be graded on the following:

• 50% Read and Study the annotation found in the Harvard notebook (on your honor)
• 20% Clean, transform, load, and train on provided General Conference NMT dataset
• 20% Try 1, 2, 4, 6 layers for both encoder and decoder pieces, report results in a few short paragraphs
• 10% Good coding style, readable output

For this lab, you will modify the Annotated Transformer. There is link to a coloab notebook in the jupyter notebook that you can use. The code is slightly different between the notebook linked above, and the colab like provided by Harvard. Both will work, you may very likely need to mix and match pieces from each to get a working implementation. While this may feel slightly frustrating, it is good practice for deep learning research strategies.

Often when implementing a novel deep learning method, you will start by using someone's implementation as a reference. This is an extremely valuable, and potentially time-saving skill, for producing workable solutions to many problems solved by deep learning methods.

There are 3 main parts to this lab

Part 1: Reading and Study A large portion of your time spent in this lab will be reading and understanding the topology, attention, dot products, etc introduced in this paper. Since this will be time consuming, and potentially new to some of you, there is no report or grading scheme. Simply do your best to read and understand the material (it will make part 2 and 3 easier if you have a good understanding).

Part 2: Extend their work to build a General Conference machine translation system

Part 2: Sample text and Training information

We now want to be able to train our network, and sample text after training.

This function outlines how training a sequence style network goes. Fill in the pieces.

def train(inp, target):
    ## initialize hidden layers, set up gradient and loss 
      # your code here
    ## /
    loss = 0
    for c in range(chunk_len):
        output, hidden = # run the forward pass of your rnn with proper input
        loss += criterion(output, target[c].unsqueeze(0))
 
    ## calculate backwards loss and step the optimizer (globally)
      # your code here
    ## /
 
    return loss.item() / chunk_len

You can at this time, if you choose, also write out your train loop boilerplate that samples random sequences and trains your RNN. This will be helpful to have working before writing your own GRU class.

If you are finished training, or during training, and you want to sample from the network you may consider using the following function. If your RNN model is instantiated as `decoder`then this will probabilistically sample a sequence of length `predict_len`

def evaluate(prime_str='A', predict_len=100, temperature=0.8):
    ## initialize hidden variable, initialize other useful variables 
      # your code here
    ## /
 
    prime_input = char_tensor(prime_str)
 
    # Use priming string to "build up" hidden state
    for p in range(len(prime_str) - 1):
        _, hidden = decoder(prime_input[p], hidden)
    inp = prime_input[-1]
 
    for p in range(predict_len):
        output, hidden = #run your RNN/decoder forward on the input
 
        # Sample from the network as a multinomial distribution
        output_dist = output.data.view(-1).div(temperature).exp()
        top_i = torch.multinomial(output_dist, 1)[0]
 
        ## get character from your list of all characters, add it to your output str sequence, set input
        ## for the next pass through the model
         # your code here
        ## /
 
    return predicted

Part 3: Creating your own GRU cell

The cell that you used in Part 1 was a pre-defined Pytorch layer. Now, write your own GRU class using the same parameters as the built-in Pytorch class does.

Please try not to look at the GRU cell definition. The answer is right there in the code, and in theory, you could just cut-and-paste it. This bit is on your honor!

Part 4: Run it and generate your final text!

Assuming everything has gone well, you should be able to run the main function in the scaffold code, using either your custom GRU cell or the built in layer, and see output something like this. I trained on the “lotr.txt” dataset, using chunk_length=200, hidden_size=100 for 2000 epochs gave.

[0m 9s (100 5%) 2.2169]
Whaiss Mainde 

'

he and the 



'od and roulll and Are say the 
rere. 
'Wor 
'Iow anond wes ou 

'Yi 

[0m 19s (200 10%) 2.0371]
Whimbe. 

'Thhe 
on not of they was thou hit of 
sil ubat thith hy the seare 
as sower and of len beda 

[0m 29s (300 15%) 2.0051]
Whis the cart. Whe courn!' 'Bu't of they aid dou giter of fintard of the not you ous, 
'Thas orntie it 

[0m 38s (400 20%) 1.8617]
Wh win took be to the know the gost bing to kno wide dought, and he as of they thin. 

The Gonhis gura 

[0m 48s (500 25%) 1.9821]
When of they singly call the and thave thing 
they the nowly we'tly by ands, of less be grarmines of t 

[0m 58s (600 30%) 1.8170]
Whinds to mass of I 
not ken we ting and dour 
and they. 


'Wat res swe Ring set shat scmaid. The 
ha 

[1m 7s (700 35%) 2.0367]
Whad ded troud wanty agy. Ve tanle gour the gone veart on hear, as dent far of the Ridgees.' 

'The Ri 

[1m 17s (800 40%) 1.9458]
Whis is brouch Heared this lack and was weself, for on't 
abothom my and go staid it 
they curse arsh  

[1m 27s (900 45%) 1.7522]
Whout bear the 
Evening 
the pace spood, Arright the spaines beren the and Wish was was on the more yo 

[1m 37s (1000 50%) 1.6444]
Whe Swarn. at colk. N(r)rce or they he 
wearing. And the on the he was are he said Pipin. 

'Yes and i 

[1m 47s (1100 55%) 1.8770]
Whing at they and thins the Wil might 
happened you dlack rusting and thousting fy them, there lifted  

[1m 57s (1200 60%) 1.9401]
Wh the said Frodo eary him that the herremans! 

'I the Lager into came and broveener he sanly 
for 
s 

[2m 7s (1300 65%) 1.8095]
When lest 
- in sound fair, and 
the Did dark he in the gose cilling the stand I in the sight. Frodo y 

[2m 16s (1400 70%) 1.9229]
Whing in a shade and Mowarse round and parse could pass not a have partainly. ' for as I come of I 
le 

[2m 26s (1500 75%) 1.8169]
Whese one her of in a lief that, 
but. 'We repagessed, 
wandere in these fair of long one have here my 

[2m 36s (1600 80%) 1.6635]
Where fread in thougraned in woohis, on the the green the 
pohered alked tore becaming was seen what c 

[2m 46s (1700 85%) 1.7868]
Whil neat 
came to 
is laked, 
and fourst on him grey now they as pass away aren have in the border sw 

[2m 56s (1800 90%) 1.6343]
Wh magered. 

Then tell some tame had bear that 
came as it nome in 
to houbbirnen and to heardy. 


' 

[3m 6s (1900 95%) 1.8191]
Who expey to must away be to the master felkly and for, what shours was alons? I had be the long to fo 

[3m 16s (2000 100%) 1.8725]
White, and his of his in before that for brown before can then took on the fainter smass about rifall