import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), '../'))
from qaPairsRelationClassification.DSSM.model import DSSM
import tensorflow as tf
from qaPairsRelationClassification.utils.load_data import load_char_data
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
p, h, y = load_char_data('data/test.csv', data_size=None)
model = DSSM()
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, '../output/dssm/dssm_49.ckpt')
loss, acc = sess.run([model.loss, model.acc],
feed_dict={
model.p: p,
model.h: h,
model.y: y,
model.keep_prob: 1
})
print('loss: ', loss, ' acc:', acc)
import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), '../'))
from qaPairsRelationClassification.ABCNN_V1.model import ABCNN
import tensorflow as tf
from qaPairsRelationClassification.utils.load_data import load_char_data
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
p, h, y = load_char_data('input/test.csv', data_size=None)
model = ABCNN(True, True)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, '../output/abcnn/abcnn_23.ckpt')
loss, acc = sess.run([model.loss, model.acc],
feed_dict={
model.p: p,
model.h: h,
model.y: y,
model.keep_prob: 1
})
print('loss: ', loss, ' acc:', acc)
import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), '../'))
from qaPairsRelationClassification.ABCNN_V1.model import ABCNN
import tensorflow as tf
from qaPairsRelationClassification.utils.load_data import load_char_data
from qaPairsRelationClassification.ABCNN_V1 import args
p, h, y = load_char_data('data/train.csv', data_size=None)
p_eval, h_eval, y_eval = load_char_data('data/dev.csv', data_size=1000)
p_holder = tf.placeholder(dtype=tf.int32,
shape=(None, args.seq_length),
name='p')
h_holder = tf.placeholder(dtype=tf.int32,
shape=(None, args.seq_length),
name='h')
y_holder = tf.placeholder(dtype=tf.int32, shape=None, name='y')
dataset = tf.data.Dataset.from_tensor_slices((p_holder, h_holder, y_holder))
dataset = dataset.batch(args.batch_size).repeat(args.epochs)
iterator = dataset.make_initializable_iterator()
next_element = iterator.get_next()
# model = Graph(False, False)
model = ABCNN(True, True)
saver = tf.train.Saver()
config = tf.ConfigProto()
import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), '../'))
from qaPairsRelationClassification.CONVNET.model import CONVNET
import tensorflow as tf
from qaPairsRelationClassification.utils.load_data import load_char_data
from qaPairsRelationClassification.CONVNET import args
p, h, y = load_char_data('input/train.csv', data_size=None)
p_eval, h_eval, y_eval = load_char_data('input/dev.csv',
data_size=args.batch_size)
p_holder = tf.placeholder(dtype=tf.int32,
shape=(None, args.seq_length),
name='p')
h_holder = tf.placeholder(dtype=tf.int32,
shape=(None, args.seq_length),
name='h')
y_holder = tf.placeholder(dtype=tf.int32, shape=None, name='y')
dataset = tf.data.Dataset.from_tensor_slices((p_holder, h_holder, y_holder))
dataset = dataset.batch(args.batch_size).repeat(args.epochs)
iterator = dataset.make_initializable_iterator()
next_element = iterator.get_next()
model = CONVNET()
saver = tf.train.Saver()
config = tf.ConfigProto()