def data_extract():
global numwords
print("1. Start data_extract")
print('Load review (%d)...' % (num_review))
datas = gd.get_dataset('../dataset/movie_data.csv', 'UTF8', num_review)
w2i, i2w, freqtable, train_set = gd.get_unigram_voca(datas, num_review)
numwords = len(w2i)
def initialize_train_dataset(self):
train_dataset, _ = get_dataset('train', self.vocab)
train_dataset = train_dataset.shuffle(buffer_size=1000, seed=321)
train_dataset = train_dataset.padded_batch(
FLAGS.batch_size,
padded_shapes=([FLAGS.num_timesteps], (), ()),
drop_remainder=True).repeat().prefetch(3 * FLAGS.batch_size)
train_dataset = train_dataset.make_one_shot_iterator()
self.train_sentence, self.train_sentence_len, self.train_label = train_dataset.get_next(
)
def initialize_dev_dataset(self):
dev_dataset, _ = get_dataset('dev', self.vocab)
dev_dataset = dev_dataset.padded_batch(
FLAGS.
batch_size, # because default padding_value and pad_idx are 0
padded_shapes=([FLAGS.num_timesteps], (),
())).prefetch(FLAGS.batch_size * 3)
dev_iterator = dev_dataset.make_one_shot_iterator()
self.dev_init_op = dev_iterator.make_initializer(dev_dataset,
name='dev_init')
self.dev_sentence, self.dev_sentence_len, self.dev_label = dev_iterator.get_next(
)
def main():
start = timeit.default_timer()
datas = gd.get_dataset('../dataset/movie_data.csv', 'UTF8', num_review)
print("train data : bigram_tokenizing...")
s2i, i2class, bigram2i, s_bag = bigram_tokenize(datas)
input_set = []
test_lists = []
for k in s_bag.keys():
input_set.append([i2class[k], k])
print("test data : bigram_tokenizing...")
test_set = test_bigram_tokenize(datas, bigram2i)
print()
print("training...")
#emb1.shape = (N, D), emb2.shape = (4, D)
emb1, emb2 = trainer(input_set, bigram2i, s_bag, dimension=DIMEN, learning_rate=RATE, epoch=EPOCH)
print("testing...")
print("# of tesing samples")
print(len(test_set))
print()
acc = []
f = open("result.txt", "w")
i = 0
for test in test_set:
i += 1
_, _, _, predict, list_ = classification(test[0], test[1], emb1, emb2)
f.write("sentence %d : %s\npredict : %d, real : %d\n" %(i, test[2], list_[0], list_[1]))
if predict is 1:
acc.append(1)
else:
acc.append(0)
stop = timeit.default_timer()
print("==============================================")
print("train_data : all")
print("test_data : all")
print("# of bigram : %d" %(len(bigram2i)))
print("epoch : %d" %EPOCH)
print("dimen : %d" %DIMEN)
print("learning_rate : %.3f" %RATE)
print("computing time : %.2f" %(stop-start))
print("correct / total : %d / %d" %(sum(acc), len(acc)))
print("test_set accuracy : %.2f" %(sum(acc)/len(acc) * 100))
print("==============================================")
def initialize_vocab(self):
_, self.vocab = get_dataset('train')
self.vocab_freqs = self.vocab.get_freq()
self.vocab_size = self.vocab.size
def classification():
datas = gd.get_dataset('../dataset/movie_data.csv', 'UTF8', num_review)
plt.xlabel('Num of Epochs')
plt.ylabel('Accuracy')
plt.legend(['train', 'validation'], loc='best')
plt.subplot(1, 2, 2)
plt.plot(np.arange(1, len(history['loss']) + 1), history['loss'], 'r')
plt.plot(np.arange(1,
len(history['val_loss']) + 1), history['val_loss'], 'g')
plt.xticks(np.arange(0, epochs + 1, epochs / 10))
plt.title('Training Loss vs. Validation Loss')
plt.xlabel('Num of Epochs')
plt.ylabel('Loss')
plt.legend(['train', 'validation'], loc='best')
plt.show()
X_train, X_test, y_train, y_test = get_dataset()
model = get_model(num_classes=5)
model = train_model(model,
X_train,
X_test,
y_train,
y_test,
batch_size=32,
num_epochs=50)
save_model(model)
