In [ ]:
%matplotlib inline
%reload_ext autoreload
%autoreload 2
Please note that this notebook is most likely going to cause a stuck process. So if you are going to run it, please make sure to restart your jupyter notebook as soon as you completed running it.
The bug happens inside the fastText library, which we have no control over. You can check the status of this issue: here and here.
For the future, note that there're 3 separate implementations of fasttext, perhaps one of them works: https://github.com/facebookresearch/fastText/tree/master/python https://pypi.org/project/fasttext/ https://radimrehurek.com/gensim/models/fasttext.html#module-gensim.models.fasttext
Translation files¶
In [ ]:
from fastai.text import *
French/English parallel texts from http://www.statmt.org/wmt15/translation-task.html . It was created by Chris Callison-Burch, who crawled millions of web pages and then used a set of simple heuristics to transform French URLs onto English URLs (i.e. replacing "fr" with "en" and about 40 other hand-written rules), and assume that these documents are translations of each other.
In [ ]:
PATH = Path('data/translate')
TMP_PATH = PATH/'tmp'
TMP_PATH.mkdir(exist_ok=True)
fname='giga-fren.release2.fixed'
en_fname = PATH/f'{fname}.en'
fr_fname = PATH/f'{fname}.fr'
In [ ]:
re_eq = re.compile('^(Wh[^?.!]+\?)')
re_fq = re.compile('^([^?.!]+\?)')
lines = ((re_eq.search(eq), re_fq.search(fq))
for eq, fq in zip(open(en_fname, encoding='utf-8'), open(fr_fname, encoding='utf-8')))
qs = [(e.group(), f.group()) for e,f in lines if e and f]
In [ ]:
pickle.dump(qs, (PATH/'fr-en-qs.pkl').open('wb'))
In [ ]:
qs = pickle.load((PATH/'fr-en-qs.pkl').open('rb'))
In [ ]:
qs[:5], len(qs)
Out[ ]:
([('What is light ?', 'Qu’est-ce que la lumière?'),
('Who are we?', 'Où sommes-nous?'),
('Where did we come from?', "D'où venons-nous?"),
('What would we do without it?', 'Que ferions-nous sans elle ?'),
('What is the absolute location (latitude and longitude) of Badger, Newfoundland and Labrador?',
'Quelle sont les coordonnées (latitude et longitude) de Badger, à Terre-Neuve-etLabrador?')],
52331)
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en_qs,fr_qs = zip(*qs)
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en_tok = Tokenizer.proc_all_mp(partition_by_cores(en_qs))
In [ ]:
fr_tok = Tokenizer.proc_all_mp(partition_by_cores(fr_qs), 'fr')
In [ ]:
en_tok[0], fr_tok[0]
Out[ ]:
(['what', 'is', 'light', '?'], ['qu’', 'est', '-ce', 'que', 'la', 'lumière', '?'])
In [ ]:
np.percentile([len(o) for o in en_tok], 90), np.percentile([len(o) for o in fr_tok], 90)
Out[ ]:
(23.0, 28.0)
In [ ]:
keep = np.array([len(o)<30 for o in en_tok])
In [ ]:
en_tok = np.array(en_tok)[keep]
fr_tok = np.array(fr_tok)[keep]
In [ ]:
pickle.dump(en_tok, (PATH/'en_tok.pkl').open('wb'))
pickle.dump(fr_tok, (PATH/'fr_tok.pkl').open('wb'))
In [ ]:
en_tok = pickle.load((PATH/'en_tok.pkl').open('rb'))
fr_tok = pickle.load((PATH/'fr_tok.pkl').open('rb'))
In [ ]:
def toks2ids(tok,pre):
freq = Counter(p for o in tok for p in o)
itos = [o for o,c in freq.most_common(40000)]
itos.insert(0, '_bos_')
itos.insert(1, '_pad_')
itos.insert(2, '_eos_')
itos.insert(3, '_unk')
stoi = collections.defaultdict(lambda: 3, {v:k for k,v in enumerate(itos)})
ids = np.array([([stoi[o] for o in p] + [2]) for p in tok])
np.save(TMP_PATH/f'{pre}_ids.npy', ids)
pickle.dump(itos, open(TMP_PATH/f'{pre}_itos.pkl', 'wb'))
return ids,itos,stoi
In [ ]:
en_ids,en_itos,en_stoi = toks2ids(en_tok,'en')
fr_ids,fr_itos,fr_stoi = toks2ids(fr_tok,'fr')
In [ ]:
def load_ids(pre):
ids = np.load(TMP_PATH/f'{pre}_ids.npy')
itos = pickle.load(open(TMP_PATH/f'{pre}_itos.pkl', 'rb'))
stoi = collections.defaultdict(lambda: 3, {v:k for k,v in enumerate(itos)})
return ids,itos,stoi
In [ ]:
en_ids,en_itos,en_stoi = load_ids('en')
fr_ids,fr_itos,fr_stoi = load_ids('fr')
In [ ]:
[fr_itos[o] for o in fr_ids[0]], len(en_itos), len(fr_itos)
Out[ ]:
(['qu’', 'est', '-ce', 'que', 'la', 'lumière', '?', '_eos_'], 17573, 24793)
Word vectors¶
fasttext word vectors available from https://fasttext.cc/docs/en/english-vectors.html
In [ ]:
# ! pip install git+https://github.com/facebookresearch/fastText.git
In [ ]:
import fastText as ft
To use the fastText library, you'll need to download fasttext word vectors for your language (download the 'bin plus text' ones).
In [ ]:
en_vecs = ft.load_model(str((PATH/'wiki.en.bin')))
In [ ]:
fr_vecs = ft.load_model(str((PATH/'wiki.fr.bin')))
In [ ]:
def get_vecs(lang, ft_vecs):
vecd = {w:ft_vecs.get_word_vector(w) for w in ft_vecs.get_words()}
pickle.dump(vecd, open(PATH/f'wiki.{lang}.pkl','wb'))
return vecd
In [ ]:
en_vecd = get_vecs('en', en_vecs)
fr_vecd = get_vecs('fr', fr_vecs)
In [ ]:
en_vecd = pickle.load(open(PATH/'wiki.en.pkl','rb'))
fr_vecd = pickle.load(open(PATH/'wiki.fr.pkl','rb'))
In [ ]:
ft_words = en_vecs.get_words(include_freq=True)
ft_word_dict = {k:v for k,v in zip(*ft_words)}
ft_words = sorted(ft_word_dict.keys(), key=lambda x: ft_word_dict[x])
len(ft_words)
In [ ]:
dim_en_vec = len(en_vecd[','])
dim_fr_vec = len(fr_vecd[','])
dim_en_vec,dim_fr_vec
Out[ ]:
(300, 300)
In [ ]:
en_vecs = np.stack(list(en_vecd.values()))
en_vecs.mean(),en_vecs.std()
Out[ ]:
(0.0075652334, 0.29283327)
Model data¶
In [ ]:
enlen_90 = int(np.percentile([len(o) for o in en_ids], 99))
frlen_90 = int(np.percentile([len(o) for o in fr_ids], 97))
enlen_90,frlen_90
Out[ ]:
(29, 33)
In [ ]:
en_ids_tr = np.array([o[:enlen_90] for o in en_ids])
fr_ids_tr = np.array([o[:frlen_90] for o in fr_ids])
In [ ]:
class Seq2SeqDataset(Dataset):
def __init__(self, x, y): self.x,self.y = x,y
def __getitem__(self, idx): return A(self.x[idx], self.y[idx])
def __len__(self): return len(self.x)
In [ ]:
np.random.seed(42)
trn_keep = np.random.rand(len(en_ids_tr))>0.1
en_trn,fr_trn = en_ids_tr[trn_keep],fr_ids_tr[trn_keep]
en_val,fr_val = en_ids_tr[~trn_keep],fr_ids_tr[~trn_keep]
len(en_trn),len(en_val)
Out[ ]:
(45219, 5041)
In [ ]:
trn_ds = Seq2SeqDataset(fr_trn,en_trn)
val_ds = Seq2SeqDataset(fr_val,en_val)
In [ ]:
bs=125
In [ ]:
trn_samp = SortishSampler(en_trn, key=lambda x: len(en_trn[x]), bs=bs)
val_samp = SortSampler(en_val, key=lambda x: len(en_val[x]))
In [ ]:
trn_dl = DataLoader(trn_ds, bs, transpose=True, transpose_y=True, num_workers=1,
pad_idx=1, pre_pad=False, sampler=trn_samp)
val_dl = DataLoader(val_ds, int(bs*1.6), transpose=True, transpose_y=True, num_workers=1,
pad_idx=1, pre_pad=False, sampler=val_samp)
md = ModelData(PATH, trn_dl, val_dl)
In [ ]:
it = iter(trn_dl)
its = [next(it) for i in range(5)]
[(len(x),len(y)) for x,y in its]
Out[ ]:
[(33, 29), (21, 7), (21, 8), (33, 13), (33, 21)]
Initial model¶
In [ ]:
def create_emb(vecs, itos, em_sz):
emb = nn.Embedding(len(itos), em_sz, padding_idx=1)
wgts = emb.weight.data
miss = []
for i,w in enumerate(itos):
try: wgts[i] = torch.from_numpy(vecs[w]*3)
except: miss.append(w)
print(len(miss),miss[5:10])
return emb
In [ ]:
nh,nl = 256,2
In [ ]:
class Seq2SeqRNN(nn.Module):
def __init__(self, vecs_enc, itos_enc, em_sz_enc, vecs_dec, itos_dec, em_sz_dec, nh, out_sl, nl=2):
super().__init__()
self.nl,self.nh,self.out_sl = nl,nh,out_sl
self.emb_enc = create_emb(vecs_enc, itos_enc, em_sz_enc)
self.emb_enc_drop = nn.Dropout(0.15)
self.gru_enc = nn.GRU(em_sz_enc, nh, num_layers=nl, dropout=0.25)
self.out_enc = nn.Linear(nh, em_sz_dec, bias=False)
self.emb_dec = create_emb(vecs_dec, itos_dec, em_sz_dec)
self.gru_dec = nn.GRU(em_sz_dec, em_sz_dec, num_layers=nl, dropout=0.1)
self.out_drop = nn.Dropout(0.35)
self.out = nn.Linear(em_sz_dec, len(itos_dec))
self.out.weight.data = self.emb_dec.weight.data
def forward(self, inp):
sl,bs = inp.size()
h = self.initHidden(bs)
emb = self.emb_enc_drop(self.emb_enc(inp))
enc_out, h = self.gru_enc(emb, h)
h = self.out_enc(h)
dec_inp = V(torch.zeros(bs).long())
res = []
for i in range(self.out_sl):
emb = self.emb_dec(dec_inp).unsqueeze(0)
outp, h = self.gru_dec(emb, h)
outp = self.out(self.out_drop(outp[0]))
res.append(outp)
dec_inp = V(outp.data.max(1)[1])
if (dec_inp==1).all(): break
return torch.stack(res)
def initHidden(self, bs): return V(torch.zeros(self.nl, bs, self.nh))
In [ ]:
def seq2seq_loss(input, target):
sl,bs = target.size()
sl_in,bs_in,nc = input.size()
if sl>sl_in: input = F.pad(input, (0,0,0,0,0,sl-sl_in))
input = input[:sl]
return F.cross_entropy(input.view(-1,nc), target.view(-1))#, ignore_index=1)
In [ ]:
opt_fn = partial(optim.Adam, betas=(0.8, 0.99))
In [ ]:
rnn = Seq2SeqRNN(fr_vecd, fr_itos, dim_fr_vec, en_vecd, en_itos, dim_en_vec, nh, enlen_90)
learn = RNN_Learner(md, SingleModel(to_gpu(rnn)), opt_fn=opt_fn)
learn.crit = seq2seq_loss
3097 ['l’', "d'", 't_up', 'd’', "qu'"] 1285 ["'s", '’s', "n't", 'n’t', ':']
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learn.lr_find()
learn.sched.plot()
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16%|█▋ | 62/377 [00:09<00:47, 6.60it/s, loss=11.4] 17%|█▋ | 64/377 [00:09<00:47, 6.62it/s, loss=11.2]
Exception in thread Thread-242:
Traceback (most recent call last):
File "/home/jhoward/anaconda3/lib/python3.6/threading.py", line 916, in _bootstrap_inner
self.run()
File "/home/jhoward/anaconda3/lib/python3.6/site-packages/tqdm/_tqdm.py", line 144, in run
for instance in self.tqdm_cls._instances:
File "/home/jhoward/anaconda3/lib/python3.6/_weakrefset.py", line 60, in __iter__
for itemref in self.data:
RuntimeError: Set changed size during iteration
70%|███████ | 265/377 [00:39<00:16, 6.64it/s, loss=30]
In [ ]:
lr=3e-3
In [ ]:
learn.fit(lr, 1, cycle_len=12, use_clr=(20,10))
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6%|▌ | 22/377 [00:04<01:06, 5.34it/s, loss=10.8]
Exception in thread Thread-20:
Traceback (most recent call last):
File "/home/jhoward/anaconda3/lib/python3.6/threading.py", line 916, in _bootstrap_inner
self.run()
File "/home/jhoward/anaconda3/lib/python3.6/site-packages/tqdm/_tqdm.py", line 144, in run
for instance in self.tqdm_cls._instances:
File "/home/jhoward/anaconda3/lib/python3.6/_weakrefset.py", line 60, in __iter__
for itemref in self.data:
RuntimeError: Set changed size during iteration
epoch trn_loss val_loss
0 5.48978 5.462648
1 4.616437 4.770539
2 4.345884 4.37726
3 3.857125 4.136014
4 3.612306 3.941867
5 3.375064 3.839872
6 3.383987 3.708972
7 3.224772 3.664173
8 3.238523 3.604765
9 2.962041 3.587814
10 2.96163 3.574888
11 2.866477 3.581224
Out[ ]:
[3.5812237]
In [ ]:
learn.save('initial')
In [ ]:
learn.load('initial')
Test¶
In [ ]:
x,y = next(iter(val_dl))
probs = learn.model(V(x))
preds = to_np(probs.max(2)[1])
for i in range(180,190):
print(' '.join([fr_itos[o] for o in x[:,i] if o != 1]))
print(' '.join([en_itos[o] for o in y[:,i] if o != 1]))
print(' '.join([en_itos[o] for o in preds[:,i] if o!=1]))
print()
quels facteurs pourraient influer sur le choix de leur emplacement ? _eos_ what factors influencetheir location ? _eos_ what factors might might influence on the their ? ? _eos_ qu’ est -ce qui ne peut pas changer ? _eos_ what can not change ? _eos_ what not change change ? _eos_ que faites - vous ? _eos_ what do you do ? _eos_ what do you do ? _eos_ qui réglemente les pylônes d' antennes ? _eos_ who regulates antenna towers ? _eos_ who regulates the doors doors ? _eos_ où sont - ils situés ? _eos_ where are they located ? _eos_ where are the located ? _eos_ quelles sont leurs compétences ? _eos_ what are their qualifications ? _eos_ what are their skills ? _eos_ qui est victime de harcèlement sexuel ? _eos_ who experiences sexual harassment ? _eos_ who is victim sexual sexual ? ? _eos_ quelles sont les personnes qui visitent les communautés autochtones ? _eos_ who visits indigenous communities ? _eos_ who are people people aboriginal aboriginal ? _eos_ pourquoi ces trois points en particulier ? _eos_ why these specific three ? _eos_ why are these two different ? ? _eos_ pourquoi ou pourquoi pas ? _eos_ why or why not ? _eos_ why or why not _eos_
Bidir¶
In [ ]:
class Seq2SeqRNN_Bidir(nn.Module):
def __init__(self, vecs_enc, itos_enc, em_sz_enc, vecs_dec, itos_dec, em_sz_dec, nh, out_sl, nl=2):
super().__init__()
self.emb_enc = create_emb(vecs_enc, itos_enc, em_sz_enc)
self.nl,self.nh,self.out_sl = nl,nh,out_sl
self.gru_enc = nn.GRU(em_sz_enc, nh, num_layers=nl, dropout=0.25, bidirectional=True)
self.out_enc = nn.Linear(nh*2, em_sz_dec, bias=False)
self.drop_enc = nn.Dropout(0.05)
self.emb_dec = create_emb(vecs_dec, itos_dec, em_sz_dec)
self.gru_dec = nn.GRU(em_sz_dec, em_sz_dec, num_layers=nl, dropout=0.1)
self.emb_enc_drop = nn.Dropout(0.15)
self.out_drop = nn.Dropout(0.35)
self.out = nn.Linear(em_sz_dec, len(itos_dec))
self.out.weight.data = self.emb_dec.weight.data
def forward(self, inp):
sl,bs = inp.size()
h = self.initHidden(bs)
emb = self.emb_enc_drop(self.emb_enc(inp))
enc_out, h = self.gru_enc(emb, h)
h = h.view(2,2,bs,-1).permute(0,2,1,3).contiguous().view(2,bs,-1)
h = self.out_enc(self.drop_enc(h))
dec_inp = V(torch.zeros(bs).long())
res = []
for i in range(self.out_sl):
emb = self.emb_dec(dec_inp).unsqueeze(0)
outp, h = self.gru_dec(emb, h)
outp = self.out(self.out_drop(outp[0]))
res.append(outp)
dec_inp = V(outp.data.max(1)[1])
if (dec_inp==1).all(): break
return torch.stack(res)
def initHidden(self, bs): return V(torch.zeros(self.nl*2, bs, self.nh))
In [ ]:
rnn = Seq2SeqRNN_Bidir(fr_vecd, fr_itos, dim_fr_vec, en_vecd, en_itos, dim_en_vec, nh, enlen_90)
learn = RNN_Learner(md, SingleModel(to_gpu(rnn)), opt_fn=opt_fn)
learn.crit = seq2seq_loss
In [ ]:
learn.fit(lr, 1, cycle_len=12, use_clr=(20,10))
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epoch trn_loss val_loss
0 4.896942 4.761351
1 4.323335 4.260878
2 3.962747 4.06161
3 3.596254 3.940087
4 3.432788 3.944787
5 3.310895 3.686629
6 3.454976 3.638168
7 3.093827 3.588456
8 3.257495 3.610536
9 3.033345 3.540344
10 2.967694 3.516766
11 2.718945 3.513977
Out[ ]:
[3.5139771]
In [ ]:
learn.save('bidir')
Teacher forcing¶
In [ ]:
class Seq2SeqStepper(Stepper):
def step(self, xs, y, epoch):
self.m.pr_force = (10-epoch)*0.1 if epoch<10 else 0
xtra = []
output = self.m(*xs, y)
if isinstance(output,tuple): output,*xtra = output
self.opt.zero_grad()
loss = raw_loss = self.crit(output, y)
if self.reg_fn: loss = self.reg_fn(output, xtra, raw_loss)
loss.backward()
if self.clip: # Gradient clipping
nn.utils.clip_grad_norm(trainable_params_(self.m), self.clip)
self.opt.step()
return raw_loss.data[0]
In [ ]:
class Seq2SeqRNN_TeacherForcing(nn.Module):
def __init__(self, vecs_enc, itos_enc, em_sz_enc, vecs_dec, itos_dec, em_sz_dec, nh, out_sl, nl=2):
super().__init__()
self.emb_enc = create_emb(vecs_enc, itos_enc, em_sz_enc)
self.nl,self.nh,self.out_sl = nl,nh,out_sl
self.gru_enc = nn.GRU(em_sz_enc, nh, num_layers=nl, dropout=0.25)
self.out_enc = nn.Linear(nh, em_sz_dec, bias=False)
self.emb_dec = create_emb(vecs_dec, itos_dec, em_sz_dec)
self.gru_dec = nn.GRU(em_sz_dec, em_sz_dec, num_layers=nl, dropout=0.1)
self.emb_enc_drop = nn.Dropout(0.15)
self.out_drop = nn.Dropout(0.35)
self.out = nn.Linear(em_sz_dec, len(itos_dec))
self.out.weight.data = self.emb_dec.weight.data
self.pr_force = 1.
def forward(self, inp, y=None):
sl,bs = inp.size()
h = self.initHidden(bs)
emb = self.emb_enc_drop(self.emb_enc(inp))
enc_out, h = self.gru_enc(emb, h)
h = self.out_enc(h)
dec_inp = V(torch.zeros(bs).long())
res = []
for i in range(self.out_sl):
emb = self.emb_dec(dec_inp).unsqueeze(0)
outp, h = self.gru_dec(emb, h)
outp = self.out(self.out_drop(outp[0]))
res.append(outp)
dec_inp = V(outp.data.max(1)[1])
if (dec_inp==1).all(): break
if (y is not None) and (random.random()<self.pr_force):
if i>=len(y): break
dec_inp = y[i]
return torch.stack(res)
def initHidden(self, bs): return V(torch.zeros(self.nl, bs, self.nh))
In [ ]:
rnn = Seq2SeqRNN_TeacherForcing(fr_vecd, fr_itos, dim_fr_vec, en_vecd, en_itos, dim_en_vec, nh, enlen_90)
learn = RNN_Learner(md, SingleModel(to_gpu(rnn)), opt_fn=opt_fn)
learn.crit = seq2seq_loss
In [ ]:
learn.fit(lr, 1, cycle_len=12, use_clr=(20,10), stepper=Seq2SeqStepper)
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epoch trn_loss val_loss
0 4.460622 12.661013
1 3.468132 7.138729
2 3.235244 6.202878
3 3.101616 5.454283
4 3.135989 4.823736
5 2.980696 4.933402
6 2.91562 4.287475
7 3.032661 3.975346
8 3.103834 3.790773
9 3.121457 3.578682
10 2.917534 3.532427
11 3.326946 3.490643
Out[ ]:
[3.490643]
In [ ]:
learn.save('forcing')
Attentional model¶
In [ ]:
def rand_t(*sz): return torch.randn(sz)/math.sqrt(sz[0])
def rand_p(*sz): return nn.Parameter(rand_t(*sz))
In [ ]:
class Seq2SeqAttnRNN(nn.Module):
def __init__(self, vecs_enc, itos_enc, em_sz_enc, vecs_dec, itos_dec, em_sz_dec, nh, out_sl, nl=2):
super().__init__()
self.emb_enc = create_emb(vecs_enc, itos_enc, em_sz_enc)
self.nl,self.nh,self.out_sl = nl,nh,out_sl
self.gru_enc = nn.GRU(em_sz_enc, nh, num_layers=nl, dropout=0.25)
self.out_enc = nn.Linear(nh, em_sz_dec, bias=False)
self.emb_dec = create_emb(vecs_dec, itos_dec, em_sz_dec)
self.gru_dec = nn.GRU(em_sz_dec, em_sz_dec, num_layers=nl, dropout=0.1)
self.emb_enc_drop = nn.Dropout(0.15)
self.out_drop = nn.Dropout(0.35)
self.out = nn.Linear(em_sz_dec, len(itos_dec))
self.out.weight.data = self.emb_dec.weight.data
self.W1 = rand_p(nh, em_sz_dec)
self.l2 = nn.Linear(em_sz_dec, em_sz_dec)
self.l3 = nn.Linear(em_sz_dec+nh, em_sz_dec)
self.V = rand_p(em_sz_dec)
def forward(self, inp, y=None, ret_attn=False):
sl,bs = inp.size()
h = self.initHidden(bs)
emb = self.emb_enc_drop(self.emb_enc(inp))
enc_out, h = self.gru_enc(emb, h)
h = self.out_enc(h)
dec_inp = V(torch.zeros(bs).long())
res,attns = [],[]
w1e = enc_out @ self.W1
for i in range(self.out_sl):
w2h = self.l2(h[-1])
u = F.tanh(w1e + w2h)
a = F.softmax(u @ self.V, 0)
attns.append(a)
Xa = (a.unsqueeze(2) * enc_out).sum(0)
emb = self.emb_dec(dec_inp)
wgt_enc = self.l3(torch.cat([emb, Xa], 1))
outp, h = self.gru_dec(wgt_enc.unsqueeze(0), h)
outp = self.out(self.out_drop(outp[0]))
res.append(outp)
dec_inp = V(outp.data.max(1)[1])
if (dec_inp==1).all(): break
if (y is not None) and (random.random()<self.pr_force):
if i>=len(y): break
dec_inp = y[i]
res = torch.stack(res)
if ret_attn: res = res,torch.stack(attns)
return res
def initHidden(self, bs): return V(torch.zeros(self.nl, bs, self.nh))
In [ ]:
rnn = Seq2SeqAttnRNN(fr_vecd, fr_itos, dim_fr_vec, en_vecd, en_itos, dim_en_vec, nh, enlen_90)
learn = RNN_Learner(md, SingleModel(to_gpu(rnn)), opt_fn=opt_fn)
learn.crit = seq2seq_loss
In [ ]:
lr=2e-3
In [ ]:
learn.fit(lr, 1, cycle_len=15, use_clr=(20,10), stepper=Seq2SeqStepper)
A Jupyter Widget
epoch trn_loss val_loss
0 3.882168 11.125291
1 3.599992 6.667136
2 3.236066 5.552943
3 3.050283 4.919096
4 2.99024 4.500383
5 3.07999 4.000295
6 2.891087 4.024115
7 2.854725 3.673913
8 2.979285 3.590668
9 3.109851 3.459867
10 2.92878 3.517598
11 2.778292 3.390253
12 2.795427 3.388423
13 2.809757 3.353334
14 2.6723 3.368584
Out[ ]:
[3.3685837]
In [ ]:
learn.save('attn')
In [ ]:
learn.load('attn')
Test¶
In [ ]:
x,y = next(iter(val_dl))
probs,attns = learn.model(V(x),ret_attn=True)
preds = to_np(probs.max(2)[1])
In [ ]:
for i in range(180,190):
print(' '.join([fr_itos[o] for o in x[:,i] if o != 1]))
print(' '.join([en_itos[o] for o in y[:,i] if o != 1]))
print(' '.join([en_itos[o] for o in preds[:,i] if o!=1]))
print()
quels facteurs pourraient influer sur le choix de leur emplacement ? _eos_ what factors influencetheir location ? _eos_ what factors might influence the their their their ? _eos_ qu’ est -ce qui ne peut pas changer ? _eos_ what can not change ? _eos_ what can not change change ? _eos_ que faites - vous ? _eos_ what do you do ? _eos_ what do you do ? _eos_ qui réglemente les pylônes d' antennes ? _eos_ who regulates antenna towers ? _eos_ who regulates the lights ? ? _eos_ où sont - ils situés ? _eos_ where are they located ? _eos_ where are they located ? _eos_ quelles sont leurs compétences ? _eos_ what are their qualifications ? _eos_ what are their skills ? _eos_ qui est victime de harcèlement sexuel ? _eos_ who experiences sexual harassment ? _eos_ who is victim sexual sexual ? _eos_ quelles sont les personnes qui visitent les communautés autochtones ? _eos_ who visits indigenous communities ? _eos_ who is people people aboriginal people ? _eos_ pourquoi ces trois points en particulier ? _eos_ why these specific three ? _eos_ why are these three three ? ? _eos_ pourquoi ou pourquoi pas ? _eos_ why or why not ? _eos_ why or why not ? _eos_
In [ ]:
attn = to_np(attns[...,180])
In [ ]:
fig, axes = plt.subplots(3, 3, figsize=(15, 10))
for i,ax in enumerate(axes.flat):
ax.plot(attn[i])
All¶
In [ ]:
class Seq2SeqRNN_All(nn.Module):
def __init__(self, vecs_enc, itos_enc, em_sz_enc, vecs_dec, itos_dec, em_sz_dec, nh, out_sl, nl=2):
super().__init__()
self.emb_enc = create_emb(vecs_enc, itos_enc, em_sz_enc)
self.nl,self.nh,self.out_sl = nl,nh,out_sl
self.gru_enc = nn.GRU(em_sz_enc, nh, num_layers=nl, dropout=0.25, bidirectional=True)
self.out_enc = nn.Linear(nh*2, em_sz_dec, bias=False)
self.drop_enc = nn.Dropout(0.25)
self.emb_dec = create_emb(vecs_dec, itos_dec, em_sz_dec)
self.gru_dec = nn.GRU(em_sz_dec, em_sz_dec, num_layers=nl, dropout=0.1)
self.emb_enc_drop = nn.Dropout(0.15)
self.out_drop = nn.Dropout(0.35)
self.out = nn.Linear(em_sz_dec, len(itos_dec))
self.out.weight.data = self.emb_dec.weight.data
self.W1 = rand_p(nh*2, em_sz_dec)
self.l2 = nn.Linear(em_sz_dec, em_sz_dec)
self.l3 = nn.Linear(em_sz_dec+nh*2, em_sz_dec)
self.V = rand_p(em_sz_dec)
def forward(self, inp, y=None):
sl,bs = inp.size()
h = self.initHidden(bs)
emb = self.emb_enc_drop(self.emb_enc(inp))
enc_out, h = self.gru_enc(emb, h)
h = h.view(2,2,bs,-1).permute(0,2,1,3).contiguous().view(2,bs,-1)
h = self.out_enc(self.drop_enc(h))
dec_inp = V(torch.zeros(bs).long())
res,attns = [],[]
w1e = enc_out @ self.W1
for i in range(self.out_sl):
w2h = self.l2(h[-1])
u = F.tanh(w1e + w2h)
a = F.softmax(u @ self.V, 0)
attns.append(a)
Xa = (a.unsqueeze(2) * enc_out).sum(0)
emb = self.emb_dec(dec_inp)
wgt_enc = self.l3(torch.cat([emb, Xa], 1))
outp, h = self.gru_dec(wgt_enc.unsqueeze(0), h)
outp = self.out(self.out_drop(outp[0]))
res.append(outp)
dec_inp = V(outp.data.max(1)[1])
if (dec_inp==1).all(): break
if (y is not None) and (random.random()<self.pr_force):
if i>=len(y): break
dec_inp = y[i]
return torch.stack(res)
def initHidden(self, bs): return V(torch.zeros(self.nl*2, bs, self.nh))
In [ ]:
rnn = Seq2SeqRNN_All(fr_vecd, fr_itos, dim_fr_vec, en_vecd, en_itos, dim_en_vec, nh, enlen_90)
learn = RNN_Learner(md, SingleModel(to_gpu(rnn)), opt_fn=opt_fn)
learn.crit = seq2seq_loss
In [ ]:
learn.fit(lr, 1, cycle_len=15, use_clr=(20,10), stepper=Seq2SeqStepper)
A Jupyter Widget
epoch trn_loss val_loss
0 3.817306 7.527982
1 3.239813 5.82099
2 3.06717 5.437195
3 3.077923 4.718295
4 2.952973 4.337892
5 3.018182 3.994012
6 2.761607 3.777056
7 2.913683 3.595531
8 2.91521 3.46984
9 2.921533 3.370839
10 2.913826 3.336167
11 2.746896 3.37274
12 2.695839 3.332427
13 2.531583 3.341861
14 2.524642 3.324184
Out[ ]:
[3.3241842]
Test¶
In [ ]:
x,y = next(iter(val_dl))
probs = learn.model(V(x))
preds = to_np(probs.max(2)[1])
for i in range(180,190):
print(' '.join([fr_itos[o] for o in x[:,i] if o != 1]))
print(' '.join([en_itos[o] for o in y[:,i] if o != 1]))
print(' '.join([en_itos[o] for o in preds[:,i] if o!=1]))
print()
quels facteurs pourraient influer sur le choix de leur emplacement ? _eos_ what factors influencetheir location ? _eos_ what factors might affect the choice of their ? ? _eos_ qu’ est -ce qui ne peut pas changer ? _eos_ what can not change ? _eos_ what can not change change _eos_ que faites - vous ? _eos_ what do you do ? _eos_ what do you do ? _eos_ qui réglemente les pylônes d' antennes ? _eos_ who regulates antenna towers ? _eos_ who regulates the antenna ? ? _eos_ où sont - ils situés ? _eos_ where are they located ? _eos_ where are they located ? _eos_ quelles sont leurs compétences ? _eos_ what are their qualifications ? _eos_ what are their skills ? _eos_ qui est victime de harcèlement sexuel ? _eos_ who experiences sexual harassment ? _eos_ who is victim harassment harassment ? _eos_ quelles sont les personnes qui visitent les communautés autochtones ? _eos_ who visits indigenous communities ? _eos_ who are the people people ? ? pourquoi ces trois points en particulier ? _eos_ why these specific three ? _eos_ why are these three specific ? _eos_ pourquoi ou pourquoi pas ? _eos_ why or why not ? _eos_ why or why not ? _eos_