import matplotlib.pyplot as pltLSTM Language Model from scratch
This notebook was borrowed from Christina Kouridis’ github. The notation is different than the notation used in the LTSM section of the notes and will be changed in a next version of this page.
1. Imports
2. Data Preparation
data = open('HP1.txt').read().lower()chars = set(data)
vocab_size = len(chars)
print('data has %d characters, %d unique' % (len(data), vocab_size))data has 442743 characters, 54 unique# creating dictionaries for mapping chars to ints and vice versa
char_to_idx = {w: i for i,w in enumerate(chars)}
idx_to_char = {i: w for i,w in enumerate(chars)}3. Load and run model
%run model.pymodel = LSTM(char_to_idx, idx_to_char, vocab_size, epochs = 10, lr = 0.0005)
J, params = model.train(data)**********************************
Gradient check...
--------- Wf ---------
Approximate: -1.740830e-05, Exact: -1.737468e-05 => Error: 9.663498e-04
--------- bf ---------
Approximate: -1.745160e-02, Exact: -1.745160e-02 => Error: 8.959478e-08
--------- Wi ---------
Approximate: -2.405187e-04, Exact: -2.405360e-04 => Error: 3.594129e-05
--------- bi ---------
Approximate: -8.715965e-03, Exact: -8.716004e-03 => Error: 2.221818e-06
--------- Wc ---------
Approximate: -3.100993e-03, Exact: -3.100992e-03 => Error: 2.596673e-07
--------- bc ---------
Approximate: 4.223633e-01, Exact: 4.223632e-01 => Error: 7.624846e-08
--------- Wo ---------
Approximate: 2.267981e-04, Exact: 2.267891e-04 => Error: 1.982771e-05
--------- bo ---------
Approximate: -7.785133e-04, Exact: -7.784895e-04 => Error: 1.523026e-05
--------- Wv ---------
Approximate: -9.925721e-03, Exact: -9.925717e-03 => Error: 1.702052e-07
--------- bv ---------
Approximate: -4.163758e-02, Exact: -4.163762e-02 => Error: 3.824062e-07
Test successful!
**********************************
Epoch: 0 Batch: 0 - 25 Loss: 99.72
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Epoch: 0 Batch: 400000 - 400025 Loss: 47.77
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Epoch: 1 Batch: 0 - 25 Loss: 47.38
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Epoch: 1 Batch: 400000 - 400025 Loss: 42.27
en againicl, it. by kneanhed widded the mught harry sall, be blowrcunals in lofk. he say as fillo the now" said ron, beca thimotthing was thead just pilesper lown then crowped on any. it's tert his beeved all?"
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Epoch: 2 Batch: 0 - 25 Loss: 42.49
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Epoch: 2 Batch: 400000 - 400025 Loss: 39.39
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Epoch: 3 Batch: 0 - 25 Loss: 39.83
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"he was dumgonly. you're was i peblay, slatcel, you folt an amound up a planes a
Epoch: 3 Batch: 400000 - 400025 Loss: 37.39
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Epoch: 4 Batch: 0 - 25 Loss: 37.95
ing on the saune him, think aron' ie."
"but ofything? you to which was a ron she's stor, pot ron, male, at ohan -- they way bas anyther?"
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Epoch: 4 Batch: 400000 - 400025 Loss: 35.98
ow as the foof somathing alm the blanst the thon in enew end getter his anyenly there two hed. they eppially. he stoved dene.
"so finsmere at the toom, ip you saupt extilie, he mouffly," said herm out.
"it's abon' bations tome," said haspered wan
Epoch: 5 Batch: 0 - 25 Loss: 36.61
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he was hume a but looked but that it said smilebout, but mumby in, he had he vellat laot behand to suf mararon, voldy trything nighirgand a, hairfung whistine, wrion it was ever at at leaws tome any standed to
Epoch: 5 Batch: 400000 - 400025 Loss: 34.93
isted, lit of urbanas,. there? all right it, harry loop let and suddenly, it wall, tham.
"linting as shather!" said harry, atting it regenard were placible.
"i plintion, you lecked his," said harry, a day...... it was snapes are baraking the fort
Epoch: 6 Batch: 0 - 25 Loss: 35.63
ing expseptaiting around aprife, they sittar.
"she unlergoim to, while exat you, but -- i got nearly breat, litter, any you.. professor school bo told sfeem up anythin me tome his hinder, and harry scensce, if a start out hermiound wathen oncober a
Epoch: 6 Batch: 400000 - 400025 Loss: 34.1
sserefup, at horward most. it's noo caber at it was look, but ron, rone ry foold had to daying at the worrs, the cloak his tallow who elppy let the hoot wite as the high? jumped, we've noinded anyous the bort stone, up to. make i the good. "you're de
Epoch: 7 Batch: 0 - 25 Loss: 34.86
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"course," said harry.
"air yould be now ron nervous -- key leaves and sitian was and aly, professor drying, but gont
Epoch: 7 Batch: 400000 - 400025 Loss: 33.42
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he had if an achosteming, now.
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"harry arentay catched and thel," said hermione a foot stop been looked at them bo
Epoch: 8 Batch: 0 - 25 Loss: 34.23
oon of harry, and the room to harry.
"oh, neville, you th told of that will your minub.".
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Epoch: 8 Batch: 400000 - 400025 Loss: 32.85
speat, usianding her free. then saves. he's good of them.
"professor mcgonagall's few do make.."
"-- bounhing norbert for them. ron, he was, they was priving, thon, i don't dogf -- you lound you pook again, and i'r event sony with as there, leeve
Epoch: 9 Batch: 0 - 25 Loss: 33.71
ed at little as dien't beard whispering a mean and harry, "peanber whurbus al op as," said dumbiertur. fasted and it was insibe.
"are my crearly look at them on the ron of cupposs you nomes, the glusmy. they were a mistrof of gay expested thought s
Epoch: 9 Batch: 400000 - 400025 Loss: 32.37
asenfor and hermione into the trallow you a like and pastnice.
"she was a fell pastuch rided."
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4. Plot results
plt.plot([i for i in range(len(J))], J)
plt.xlabel("#training iterations")
plt.ylabel("training loss")Text(0, 0.5, 'training loss')