def main():
data_loader = InputHelper()
data_loader.create_dictionary(FLAGS.data_dir + '/' + FLAGS.train_file,
FLAGS.data_dir + '/')
FLAGS.vocab_size = data_loader.vocab_size
FLAGS.n_classes = data_loader.n_classes
model = BiRNN(FLAGS.rnn_size, FLAGS.layer_size, FLAGS.vocab_size,
FLAGS.batch_size, FLAGS.sequence_length, FLAGS.n_classes,
FLAGS.grad_clip)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables())
ckpt = tf.train.get_checkpoint_state(FLAGS.save_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
while True:
# x = raw_input('请输入一个地址:\n')
x = ''
x = [data_loader.transform_raw(x, FLAGS.sequence_length)]
labels = model.inference(sess, data_loader.labels, x)
print(labels)
def main():
data_loader = InputHelper()
data_loader.create_dictionary(FLAGS.data_dir + '/' + FLAGS.train_file,
FLAGS.data_dir + '/')
FLAGS.vocab_size = data_loader.vocab_size
FLAGS.n_classes = data_loader.n_classes
wl = load_wl()
# Define specified Model
model = BiRNN(embedding_size=FLAGS.embedding_size,
rnn_size=FLAGS.rnn_size,
layer_size=FLAGS.layer_size,
vocab_size=FLAGS.vocab_size,
attn_size=FLAGS.attn_size,
sequence_length=FLAGS.sequence_length,
n_classes=FLAGS.n_classes,
grad_clip=FLAGS.grad_clip,
learning_rate=FLAGS.learning_rate)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables())
ckpt = tf.train.get_checkpoint_state(FLAGS.save_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
now = 0
for file in open('./data/total_txt_img_cat.list'):
word_list = {}
arr = np.zeros([len(wl), 50])
lab = file.split('\t')[2]
for line in open('./data/text_seqs/' + file.split()[0] + '.xml'):
seq = line.split('\t')[0]
x, w = data_loader.transform_raw(seq, FLAGS.sequence_length)
_, out_features = model.inference(sess, data_loader.labels,
[x])
for i, j in enumerate(w):
punc = '[,.!\'%*+-/=><]'
j = re.sub(punc, '', j)
if j in word_list:
word_list[j] += out_features[i]
else:
word_list[j] = out_features[i]
count = 0
for w in word_list:
if w in wl:
arr[wl[w]] = word_list[w]
count += 1
print('now:', now, 'count:', count, 'shape:', arr.shape)
s = str(now)
while len(s) < 4:
s = '0' + s
np.save('./text_lstm/text_' + s + '_' + lab.strip() + '.npy', arr)
now += 1
def main():
data_loader = InputHelper('data/stop_words.pkl')
data_loader.load_dictionary(FLAGS.data_dir + '/dictionary')
FLAGS.vocab_size = data_loader.vocab_size
FLAGS.n_classes = data_loader.n_classes
# Define specified Model
model = AttentionBiRNN(FLAGS.embedding_size, FLAGS.rnn_size, FLAGS.layer_size, FLAGS.vocab_size, FLAGS.attn_size,
FLAGS.sequence_length, FLAGS.n_classes, FLAGS.grad_clip, FLAGS.learning_rate)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables())
ckpt = tf.train.get_checkpoint_state(FLAGS.save_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
# while True:
# x = raw_input('请输入一个地址:\n')
with open('./data/test.txt', 'r') as f:
lines = f.readlines()
for line in lines:
text = line.split('\t')
l = text[0]
try:
x = [data_loader.transform_raw(l, FLAGS.sequence_length)]
words = data_loader.words
labels, alphas, _ = model.inference(sess, data_loader.labels, x)
print(labels, text[1].replace('\n', ''), len(words))
words_weights = []
for word, alpha in zip(words, alphas[0] / alphas[0][0:len(words)].max()):
words_weights.append(word + ':' + str(alpha))
print(str(words_weights).decode('unicode-escape'))
except:
print(l)