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train_classifier.py
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train_classifier.py
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import os
import sys
import argparse
import time
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import dataloader
from local_models.classification_models import ClassificationModel
def eval_model(niter, model, input_x, input_y):
model.eval()
# N = len(valid_x)
# criterion = nn.CrossEntropyLoss()
correct = 0.0
cnt = 0.
# total_loss = 0.0
with torch.no_grad():
for x, y in zip(input_x, input_y):
x, y = Variable(x, volatile=True), Variable(y)
output = model(x)
# loss = criterion(output, y)
# total_loss += loss.item()*x.size(1)
pred = output.data.max(1)[1]
correct += pred.eq(y.data).cpu().sum()
cnt += y.numel()
model.train_snli()
return correct.item()/cnt
def train_model(epoch, model, optimizer,
train_x, train_y,
test_x, test_y,
best_test, save_path):
model.train_snli()
niter = epoch*len(train_x)
criterion = nn.CrossEntropyLoss()
cnt = 0
for x, y in zip(train_x, train_y):
niter += 1
cnt += 1
model.zero_grad()
x, y = Variable(x), Variable(y)
output = model(x)
loss = criterion(output, y)
loss.backward()
optimizer.step()
test_acc = eval_model(niter, model, test_x, test_y)
sys.stdout.write("Epoch={} iter={} lr={:.6f} train_loss={:.6f} test_err={:.6f}\n".format(
epoch, niter,
optimizer.param_groups[0]['lr'],
loss.item(),
test_acc
))
if test_acc > best_test:
best_test = test_acc
if save_path:
torch.save(model.state_dict(), save_path)
# test_err = eval_model(niter, model, test_x, test_y)
sys.stdout.write("\n")
return best_test
def save_data(data, labels, path, type='train'):
with open(os.path.join(path, type+'.txt'), 'w') as ofile:
for text, label in zip(data, labels):
ofile.write('{} {}\n'.format(label, ' '.join(text)))
def main(args):
if args.dataset == 'mr':
# data, label = dataloader.read_MR(args.path)
# train_x, train_y, test_x, test_y = dataloader.cv_split2(
# data, label,
# nfold=10,
# valid_id=args.cv
# )
#
# if args.save_data_split:
# save_data(train_x, train_y, args.path, 'train')
# save_data(test_x, test_y, args.path, 'test')
train_x, train_y = dataloader.read_corpus('/data/datasets/mr/train.txt')
test_x, test_y = dataloader.read_corpus('/data/datasets/mr/test.txt')
elif args.dataset == 'imdb':
train_x, train_y = dataloader.read_corpus(os.path.join('/data/nlp/datasets/imdb',
'train_tok.csv'),
clean=False, MR=True, shuffle=True)
test_x, test_y = dataloader.read_corpus(os.path.join('/data/nlp/datasets/imdb',
'test_tok.csv'),
clean=False, MR=True, shuffle=True)
else:
train_x, train_y = dataloader.read_corpus('proj/to_di/data/{}/'
'train_tok.csv'.format(args.dataset),
clean=False, MR=False, shuffle=True)
test_x, test_y = dataloader.read_corpus('proj/to_di/data/{}/'
'test_tok.csv'.format(args.dataset),
clean=False, MR=False, shuffle=True)
nclasses = max(train_y) + 1
# elif args.dataset == 'subj':
# data, label = dataloader.read_SUBJ(args.path)
# elif args.dataset == 'cr':
# data, label = dataloader.read_CR(args.path)
# elif args.dataset == 'mpqa':
# data, label = dataloader.read_MPQA(args.path)
# elif args.dataset == 'trec':
# train_x, train_y, test_x, test_y = dataloader.read_TREC(args.path)
# data = train_x + test_x
# label = None
# elif args.dataset == 'sst':
# train_x, train_y, valid_x, valid_y, test_x, test_y = dataloader.read_SST(args.path)
# data = train_x + valid_x + test_x
# label = None
# else:
# raise Exception("unknown dataset: {}".format(args.dataset))
# if args.dataset == 'trec':
# elif args.dataset != 'sst':
# train_x, train_y, valid_x, valid_y, test_x, test_y = dataloader.cv_split(
# data, label,
# nfold = 10,
# test_id = args.cv
# )
model = ClassificationModel(args.embedding, args.d, args.depth, args.dropout, args.cnn, nclasses).cuda()
need_grad = lambda x: x.requires_grad
optimizer = optim.Adam(
filter(need_grad, model.parameters()),
lr = args.lr
)
train_x, train_y = dataloader.create_batches(
train_x, train_y,
args.batch_size,
model.word2id,
)
# valid_x, valid_y = dataloader.create_batches(
# valid_x, valid_y,
# args.batch_size,
# emb_layer.word2id,
# )
test_x, test_y = dataloader.create_batches(
test_x, test_y,
args.batch_size,
model.word2id,
)
best_test = 0
# test_err = 1e+8
for epoch in range(args.max_epoch):
best_test = train_model(epoch, model, optimizer,
train_x, train_y,
# valid_x, valid_y,
test_x, test_y,
best_test, args.save_path
)
if args.lr_decay>0:
optimizer.param_groups[0]['lr'] *= args.lr_decay
# sys.stdout.write("best_valid: {:.6f}\n".format(
# best_valid
# ))
sys.stdout.write("test_err: {:.6f}\n".format(
best_test
))
if __name__ == "__main__":
argparser = argparse.ArgumentParser(sys.argv[0], conflict_handler='resolve')
argparser.add_argument("--cnn", action='store_true', help="whether to use cnn")
argparser.add_argument("--lstm", action='store_true', help="whether to use lstm")
argparser.add_argument("--dataset", type=str, default="mr", help="which dataset")
argparser.add_argument("--embedding", type=str, required=True, help="word vectors")
argparser.add_argument("--batch_size", "--batch", type=int, default=32)
argparser.add_argument("--max_epoch", type=int, default=70)
argparser.add_argument("--d", type=int, default=150)
argparser.add_argument("--dropout", type=float, default=0.3)
argparser.add_argument("--depth", type=int, default=1)
argparser.add_argument("--lr", type=float, default=0.001)
argparser.add_argument("--lr_decay", type=float, default=0)
argparser.add_argument("--cv", type=int, default=0)
argparser.add_argument("--save_path", type=str, default='')
argparser.add_argument("--save_data_split", action='store_true', help="whether to save train/test split")
argparser.add_argument("--gpu_id", type=int, default=0)
args = argparser.parse_args()
# args.save_path = os.path.join(args.save_path, args.dataset)
print (args)
torch.cuda.set_device(args.gpu_id)
main(args)