.. _sec_word2vec_pretraining:
word2vec Ön Eğitimi
===================
:numref:`sec_word2vec` içinde tanımlanan skip-gram modelini uygulamaya
devam ediyoruz. Ardından, PTB veri kümesindeki negatif örnekleme
kullanarak word2vec ön eğiteceğiz. Her şeyden önce,
:numref:`sec_word2vec_data` içinde açıklanan ``d2l.load_data_ptb``
işlevini çağırarak veri yineleyicisini ve bu veri kümesi için sözcük
dağarcığını elde edelim.
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
import math
from d2l import mxnet as d2l
from mxnet import autograd, gluon, np, npx
from mxnet.gluon import nn
npx.set_np()
batch_size, max_window_size, num_noise_words = 512, 5, 5
data_iter, vocab = d2l.load_data_ptb(batch_size, max_window_size,
num_noise_words)
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
import math
import torch
from torch import nn
from d2l import torch as d2l
batch_size, max_window_size, num_noise_words = 512, 5, 5
data_iter, vocab = d2l.load_data_ptb(batch_size, max_window_size,
num_noise_words)
.. raw:: html
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
embed = nn.Embedding(input_dim=20, output_dim=4)
embed.initialize()
embed.weight
.. raw:: latex
\diilbookstyleoutputcell
.. parsed-literal::
:class: output
Parameter embedding0_weight (shape=(20, 4), dtype=float32)
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
embed = nn.Embedding(num_embeddings=20, embedding_dim=4)
print(f'Parameter embedding_weight ({embed.weight.shape}, '
f'dtype={embed.weight.dtype})')
.. raw:: latex
\diilbookstyleoutputcell
.. parsed-literal::
:class: output
Parameter embedding_weight (torch.Size([20, 4]), dtype=torch.float32)
.. raw:: html
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
x = np.array([[1, 2, 3], [4, 5, 6]])
embed(x)
.. raw:: latex
\diilbookstyleoutputcell
.. parsed-literal::
:class: output
array([[[ 0.01438687, 0.05011239, 0.00628365, 0.04861524],
[-0.01068833, 0.01729892, 0.02042518, -0.01618656],
[-0.00873779, -0.02834515, 0.05484822, -0.06206018]],
[[ 0.06491279, -0.03182812, -0.01631819, -0.00312688],
[ 0.0408415 , 0.04370362, 0.00404529, -0.0028032 ],
[ 0.00952624, -0.01501013, 0.05958354, 0.04705103]]])
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
x = torch.tensor([[1, 2, 3], [4, 5, 6]])
embed(x)
.. raw:: latex
\diilbookstyleoutputcell
.. parsed-literal::
:class: output
tensor([[[-1.7168, 1.5441, -0.8471, 0.6239],
[-0.8008, 0.1647, -0.0691, -0.4562],
[-0.4796, 1.1483, 1.5281, 0.7885]],
[[ 1.3627, -0.1596, -0.4472, -1.0897],
[-0.2692, 0.7414, 0.3256, -0.1186],
[ 0.2164, -0.8939, 0.3935, 2.7814]]], grad_fn=)
.. raw:: html
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
def skip_gram(center, contexts_and_negatives, embed_v, embed_u):
v = embed_v(center)
u = embed_u(contexts_and_negatives)
pred = npx.batch_dot(v, u.swapaxes(1, 2))
return pred
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
def skip_gram(center, contexts_and_negatives, embed_v, embed_u):
v = embed_v(center)
u = embed_u(contexts_and_negatives)
pred = torch.bmm(v, u.permute(0, 2, 1))
return pred
.. raw:: html
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
skip_gram(np.ones((2, 1)), np.ones((2, 4)), embed, embed).shape
.. raw:: latex
\diilbookstyleoutputcell
.. parsed-literal::
:class: output
(2, 1, 4)
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
skip_gram(torch.ones((2, 1), dtype=torch.long),
torch.ones((2, 4), dtype=torch.long), embed, embed).shape
.. raw:: latex
\diilbookstyleoutputcell
.. parsed-literal::
:class: output
torch.Size([2, 1, 4])
.. raw:: html
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
loss = gluon.loss.SigmoidBCELoss()
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
class SigmoidBCELoss(nn.Module):
# Maskeleme ile ikili çapraz entropi kaybı
def __init__(self):
super().__init__()
def forward(self, inputs, target, mask=None):
out = nn.functional.binary_cross_entropy_with_logits(
inputs, target, weight=mask, reduction="none")
return out.mean(dim=1)
loss = SigmoidBCELoss()
.. raw:: html
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
pred = np.array([[1.1, -2.2, 3.3, -4.4]] * 2)
label = np.array([[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0]])
mask = np.array([[1, 1, 1, 1], [1, 1, 0, 0]])
loss(pred, label, mask) * mask.shape[1] / mask.sum(axis=1)
.. raw:: latex
\diilbookstyleoutputcell
.. parsed-literal::
:class: output
array([0.93521017, 1.8462094 ])
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
pred = torch.tensor([[1.1, -2.2, 3.3, -4.4]] * 2)
label = torch.tensor([[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0]])
mask = torch.tensor([[1, 1, 1, 1], [1, 1, 0, 0]])
loss(pred, label, mask) * mask.shape[1] / mask.sum(axis=1)
.. raw:: latex
\diilbookstyleoutputcell
.. parsed-literal::
:class: output
tensor([0.9352, 1.8462])
.. raw:: html
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
def sigmd(x):
return -math.log(1 / (1 + math.exp(-x)))
print(f'{(sigmd(1.1) + sigmd(2.2) + sigmd(-3.3) + sigmd(4.4)) / 4:.4f}')
print(f'{(sigmd(-1.1) + sigmd(-2.2)) / 2:.4f}')
.. raw:: latex
\diilbookstyleoutputcell
.. parsed-literal::
:class: output
0.9352
1.8462
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
def sigmd(x):
return -math.log(1 / (1 + math.exp(-x)))
print(f'{(sigmd(1.1) + sigmd(2.2) + sigmd(-3.3) + sigmd(4.4)) / 4:.4f}')
print(f'{(sigmd(-1.1) + sigmd(-2.2)) / 2:.4f}')
.. raw:: latex
\diilbookstyleoutputcell
.. parsed-literal::
:class: output
0.9352
1.8462
.. raw:: html
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
embed_size = 100
net = nn.Sequential()
net.add(nn.Embedding(input_dim=len(vocab), output_dim=embed_size),
nn.Embedding(input_dim=len(vocab), output_dim=embed_size))
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
embed_size = 100
net = nn.Sequential(nn.Embedding(num_embeddings=len(vocab),
embedding_dim=embed_size),
nn.Embedding(num_embeddings=len(vocab),
embedding_dim=embed_size))
.. raw:: html
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
def train(net, data_iter, lr, num_epochs, device=d2l.try_gpu()):
net.initialize(ctx=device, force_reinit=True)
trainer = gluon.Trainer(net.collect_params(), 'adam',
{'learning_rate': lr})
animator = d2l.Animator(xlabel='epoch', ylabel='loss',
xlim=[1, num_epochs])
# Normalleştirilmiş kayıpların toplamı, normalleştirilmiş kayıpların sayısı
metric = d2l.Accumulator(2)
for epoch in range(num_epochs):
timer, num_batches = d2l.Timer(), len(data_iter)
for i, batch in enumerate(data_iter):
center, context_negative, mask, label = [
data.as_in_ctx(device) for data in batch]
with autograd.record():
pred = skip_gram(center, context_negative, net[0], net[1])
l = (loss(pred.reshape(label.shape), label, mask) *
mask.shape[1] / mask.sum(axis=1))
l.backward()
trainer.step(batch_size)
metric.add(l.sum(), l.size)
if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
animator.add(epoch + (i + 1) / num_batches,
(metric[0] / metric[1],))
print(f'loss {metric[0] / metric[1]:.3f}, '
f'{metric[1] / timer.stop():.1f} tokens/sec on {str(device)}')
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
def train(net, data_iter, lr, num_epochs, device=d2l.try_gpu()):
def init_weights(m):
if type(m) == nn.Embedding:
nn.init.xavier_uniform_(m.weight)
net.apply(init_weights)
net = net.to(device)
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
animator = d2l.Animator(xlabel='epoch', ylabel='loss',
xlim=[1, num_epochs])
# Normalleştirilmiş kayıpların toplamı, normalleştirilmiş kayıpların sayısı
metric = d2l.Accumulator(2)
for epoch in range(num_epochs):
timer, num_batches = d2l.Timer(), len(data_iter)
for i, batch in enumerate(data_iter):
optimizer.zero_grad()
center, context_negative, mask, label = [
data.to(device) for data in batch]
pred = skip_gram(center, context_negative, net[0], net[1])
l = (loss(pred.reshape(label.shape).float(), label.float(), mask)
/ mask.sum(axis=1) * mask.shape[1])
l.sum().backward()
optimizer.step()
metric.add(l.sum(), l.numel())
if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
animator.add(epoch + (i + 1) / num_batches,
(metric[0] / metric[1],))
print(f'loss {metric[0] / metric[1]:.3f}, '
f'{metric[1] / timer.stop():.1f} tokens/sec on {str(device)}')
.. raw:: html
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
lr, num_epochs = 0.002, 5
train(net, data_iter, lr, num_epochs)
.. raw:: latex
\diilbookstyleoutputcell
.. parsed-literal::
:class: output
loss 0.407, 98407.8 tokens/sec on gpu(0)
.. figure:: output_word2vec-pretraining_d81279_93_1.svg
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
lr, num_epochs = 0.002, 5
train(net, data_iter, lr, num_epochs)
.. raw:: latex
\diilbookstyleoutputcell
.. parsed-literal::
:class: output
loss 0.410, 361758.5 tokens/sec on cuda:0
.. figure:: output_word2vec-pretraining_d81279_96_1.svg
.. raw:: html
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
def get_similar_tokens(query_token, k, embed):
W = embed.weight.data()
x = W[vocab[query_token]]
# Kosinüs benzerliğini hesaplayın. Sayısal kararlılık için 1e-9 ekleyin
cos = np.dot(W, x) / np.sqrt(np.sum(W * W, axis=1) * np.sum(x * x) + 1e-9)
topk = npx.topk(cos, k=k+1, ret_typ='indices').asnumpy().astype('int32')
for i in topk[1:]: # Remove the input words
print(f'cosine sim={float(cos[i]):.3f}: {vocab.to_tokens(i)}')
get_similar_tokens('chip', 3, net[0])
.. raw:: latex
\diilbookstyleoutputcell
.. parsed-literal::
:class: output
cosine sim=0.668: intel
cosine sim=0.638: microprocessor
cosine sim=0.616: workstations
.. raw:: html
.. raw:: html
.. raw:: latex
\diilbookstyleinputcell
.. code:: python
def get_similar_tokens(query_token, k, embed):
W = embed.weight.data
x = W[vocab[query_token]]
# Kosinüs benzerliğini hesaplayın. Sayısal kararlılık için 1e-9 ekleyin
cos = torch.mv(W, x) / torch.sqrt(torch.sum(W * W, dim=1) *
torch.sum(x * x) + 1e-9)
topk = torch.topk(cos, k=k+1)[1].cpu().numpy().astype('int32')
for i in topk[1:]: # Remove the input words
print(f'cosine sim={float(cos[i]):.3f}: {vocab.to_tokens(i)}')
get_similar_tokens('chip', 3, net[0])
.. raw:: latex
\diilbookstyleoutputcell
.. parsed-literal::
:class: output
cosine sim=0.679: microprocessor
cosine sim=0.665: intel
cosine sim=0.626: processes
.. raw:: html
.. raw:: html
.. raw:: html
`Tartışmalar `__
.. raw:: html
.. raw:: html
`Tartışmalar `__
.. raw:: html
.. raw:: html