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(log=log)
data_loader.load_embedding(FLAGS.embedding_file, FLAGS.embedding_size)
data_loader.load_label_dictionary(FLAGS.label_dic)
x, y, x_w_p, x_s_p = data_loader.load_valid(FLAGS.valid_file,
FLAGS.interaction_rounds,
FLAGS.sequence_length)
FLAGS.embeddings = data_loader.embeddings
FLAGS.vocab_size = len(data_loader.word2idx)
FLAGS.n_classes = len(data_loader.label_dictionary)
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,
interaction_rounds=FLAGS.interaction_rounds,
batch_size=FLAGS.batch_size,
embeddings=FLAGS.embeddings,
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)
model_path = FLAGS.save_dir + '/model.ckpt-45'
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, model_path)
labels = model.inference(sess, y, x, x_w_p, x_s_p)
corrcet_num = 0
for i in range(len(labels)):
if labels[i] == y[i]:
corrcet_num += 1
print('eval_acc = {:.3f}'.format(corrcet_num * 1.0 / len(labels)))
data_loader.output_result(labels, FLAGS.valid_file, FLAGS.result_file)
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