|
|
@@ -1,8 +1,14 @@ |
|
|
|
import gensim |
|
|
|
from gensim import models |
|
|
|
|
|
|
|
import os |
|
|
|
import pickle |
|
|
|
|
|
|
|
import matplotlib.pyplot as plt |
|
|
|
import matplotlib.ticker as ticker |
|
|
|
|
|
|
|
import nltk |
|
|
|
import numpy as np |
|
|
|
import torch |
|
|
|
|
|
|
|
from model import * |
|
|
|
|
|
|
|
class SampleIter: |
|
|
|
def __init__(self, dirname): |
|
|
@@ -32,35 +38,6 @@ def train_word_vec(): |
|
|
|
model = models.Word2Vec(sentences=sents, size=200, sg=0, workers=4, min_count=5) |
|
|
|
model.save('yelp.word2vec') |
|
|
|
|
|
|
|
|
|
|
|
''' |
|
|
|
Train process |
|
|
|
''' |
|
|
|
import math |
|
|
|
import os |
|
|
|
import copy |
|
|
|
import pickle |
|
|
|
|
|
|
|
import matplotlib.pyplot as plt |
|
|
|
import matplotlib.ticker as ticker |
|
|
|
import numpy as np |
|
|
|
import json |
|
|
|
import nltk |
|
|
|
from gensim.models import Word2Vec |
|
|
|
import torch |
|
|
|
from torch.utils.data import DataLoader, Dataset |
|
|
|
|
|
|
|
from model import * |
|
|
|
|
|
|
|
net = HAN(input_size=200, output_size=5, |
|
|
|
word_hidden_size=50, word_num_layers=1, word_context_size=100, |
|
|
|
sent_hidden_size=50, sent_num_layers=1, sent_context_size=100) |
|
|
|
|
|
|
|
optimizer = torch.optim.SGD(net.parameters(), lr=0.01) |
|
|
|
criterion = nn.NLLLoss() |
|
|
|
num_epoch = 1 |
|
|
|
batch_size = 64 |
|
|
|
|
|
|
|
class Embedding_layer: |
|
|
|
def __init__(self, wv, vector_size): |
|
|
|
self.wv = wv |
|
|
@@ -73,10 +50,8 @@ class Embedding_layer: |
|
|
|
v = np.zeros(self.vector_size) |
|
|
|
return v |
|
|
|
|
|
|
|
embed_model = Word2Vec.load('yelp.word2vec') |
|
|
|
embedding = Embedding_layer(embed_model.wv, embed_model.wv.vector_size) |
|
|
|
del embed_model |
|
|
|
|
|
|
|
from torch.utils.data import DataLoader, Dataset |
|
|
|
class YelpDocSet(Dataset): |
|
|
|
def __init__(self, dirname, num_files, embedding): |
|
|
|
self.dirname = dirname |
|
|
@@ -103,12 +78,28 @@ def collate(iterable): |
|
|
|
x_list.append(x) |
|
|
|
return x_list, torch.LongTensor(y_list) |
|
|
|
|
|
|
|
if __name__ == '__main__': |
|
|
|
def train(net, num_epoch, batch_size, print_size=10, use_cuda=False): |
|
|
|
from gensim.models import Word2Vec |
|
|
|
import torch |
|
|
|
import gensim |
|
|
|
from gensim import models |
|
|
|
|
|
|
|
embed_model = Word2Vec.load('yelp.word2vec') |
|
|
|
embedding = Embedding_layer(embed_model.wv, embed_model.wv.vector_size) |
|
|
|
del embed_model |
|
|
|
|
|
|
|
optimizer = torch.optim.SGD(net.parameters(), lr=0.01) |
|
|
|
criterion = nn.NLLLoss() |
|
|
|
|
|
|
|
dirname = 'reviews' |
|
|
|
dataloader = DataLoader(YelpDocSet(dirname, 238, embedding), batch_size=batch_size, collate_fn=collate) |
|
|
|
dataloader = DataLoader(YelpDocSet(dirname, 238, embedding), |
|
|
|
batch_size=batch_size, |
|
|
|
collate_fn=collate, |
|
|
|
num_workers=4) |
|
|
|
running_loss = 0.0 |
|
|
|
print_size = 10 |
|
|
|
|
|
|
|
if use_cuda: |
|
|
|
net.cuda() |
|
|
|
for epoch in range(num_epoch): |
|
|
|
for i, batch_samples in enumerate(dataloader): |
|
|
|
x, y = batch_samples |
|
|
@@ -119,11 +110,16 @@ if __name__ == '__main__': |
|
|
|
sent_vec = [] |
|
|
|
for word in sent: |
|
|
|
vec = embedding.get_vec(word) |
|
|
|
sent_vec.append(torch.Tensor(vec.reshape((1, -1)))) |
|
|
|
vec = torch.Tensor(vec.reshape((1, -1))) |
|
|
|
if use_cuda: |
|
|
|
vec = vec.cuda() |
|
|
|
sent_vec.append(vec) |
|
|
|
sent_vec = torch.cat(sent_vec, dim=0) |
|
|
|
# print(sent_vec.size()) |
|
|
|
doc.append(Variable(sent_vec)) |
|
|
|
doc_list.append(doc) |
|
|
|
if use_cuda: |
|
|
|
y = y.cuda() |
|
|
|
y = Variable(y) |
|
|
|
predict = net(doc_list) |
|
|
|
loss = criterion(predict, y) |
|
|
@@ -131,8 +127,21 @@ if __name__ == '__main__': |
|
|
|
loss.backward() |
|
|
|
optimizer.step() |
|
|
|
running_loss += loss.data[0] |
|
|
|
print(loss.data[0]) |
|
|
|
if i % print_size == print_size-1: |
|
|
|
print(running_loss/print_size) |
|
|
|
running_loss = 0.0 |
|
|
|
|
|
|
|
torch.save(net.state_dict(), 'model.dict') |
|
|
|
torch.save(net.state_dict(), 'model.dict') |
|
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__': |
|
|
|
''' |
|
|
|
Train process |
|
|
|
''' |
|
|
|
|
|
|
|
|
|
|
|
net = HAN(input_size=200, output_size=5, |
|
|
|
word_hidden_size=50, word_num_layers=1, word_context_size=100, |
|
|
|
sent_hidden_size=50, sent_num_layers=1, sent_context_size=100) |
|
|
|
|
|
|
|
train(net, num_epoch=1, batch_size=64, use_cuda=True) |