File size: 3,488 Bytes
97b6013 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
#!/usr/bin/env python
# Copyright 2017, 2018 Google, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Extracts paths that are indicative of each relation."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tensorflow as tf
from . import path_model
from . import lexnet_common
tf.flags.DEFINE_string(
'dataset_dir', 'datasets',
'Dataset base directory')
tf.flags.DEFINE_string(
'dataset',
'tratz/fine_grained',
'Subdirectory containing the corpus directories: '
'subdirectory of dataset_dir')
tf.flags.DEFINE_string(
'corpus', 'random/wiki',
'Subdirectory containing the corpus and split: '
'subdirectory of dataset_dir/dataset')
tf.flags.DEFINE_string(
'embeddings_base_path', 'embeddings',
'Embeddings base directory')
tf.flags.DEFINE_string(
'logdir', 'logdir',
'Directory of model output files')
tf.flags.DEFINE_integer(
'top_k', 20, 'Number of top paths to extract')
tf.flags.DEFINE_float(
'threshold', 0.8, 'Threshold above which to consider paths as indicative')
FLAGS = tf.flags.FLAGS
def main(_):
hparams = path_model.PathBasedModel.default_hparams()
# First things first. Load the path data.
path_embeddings_file = 'path_embeddings/{dataset}/{corpus}'.format(
dataset=FLAGS.dataset,
corpus=FLAGS.corpus)
path_dim = (hparams.lemma_dim + hparams.pos_dim +
hparams.dep_dim + hparams.dir_dim)
path_embeddings, path_to_index = path_model.load_path_embeddings(
os.path.join(FLAGS.embeddings_base_path, path_embeddings_file),
path_dim)
# Load and count the classes so we can correctly instantiate the model.
classes_filename = os.path.join(
FLAGS.dataset_dir, FLAGS.dataset, 'classes.txt')
with open(classes_filename) as f_in:
classes = f_in.read().splitlines()
hparams.num_classes = len(classes)
# We need the word embeddings to instantiate the model, too.
print('Loading word embeddings...')
lemma_embeddings = lexnet_common.load_word_embeddings(
FLAGS.embeddings_base_path, hparams.lemma_embeddings_file)
# Instantiate the model.
with tf.Graph().as_default():
with tf.variable_scope('lexnet'):
instance = tf.placeholder(dtype=tf.string)
model = path_model.PathBasedModel(
hparams, lemma_embeddings, instance)
with tf.Session() as session:
model_dir = '{logdir}/results/{dataset}/path/{corpus}'.format(
logdir=FLAGS.logdir,
dataset=FLAGS.dataset,
corpus=FLAGS.corpus)
saver = tf.train.Saver()
saver.restore(session, os.path.join(model_dir, 'best.ckpt'))
path_model.get_indicative_paths(
model, session, path_to_index, path_embeddings, classes,
model_dir, FLAGS.top_k, FLAGS.threshold)
if __name__ == '__main__':
tf.app.run()
|