File size: 1,989 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 |
# Copyright 2018 The TensorFlow Authors 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.
# ==============================================================================
"""Placeholders for non-task-specific model inputs."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
class Inputs(object):
def __init__(self, config):
self._config = config
self.keep_prob = tf.placeholder(tf.float32, name='keep_prob')
self.label_smoothing = tf.placeholder(tf.float32, name='label_smoothing')
self.lengths = tf.placeholder(tf.int32, shape=[None], name='lengths')
self.mask = tf.placeholder(tf.float32, [None, None], name='mask')
self.words = tf.placeholder(tf.int32, shape=[None, None], name='words')
self.chars = tf.placeholder(tf.int32, shape=[None, None, None],
name='chars')
def create_feed_dict(self, mb, is_training):
cvt = mb.task_name == 'unlabeled'
return {
self.keep_prob: 1.0 if not is_training else
(self._config.unlabeled_keep_prob if cvt else
self._config.labeled_keep_prob),
self.label_smoothing: self._config.label_smoothing
if (is_training and not cvt) else 0.0,
self.lengths: mb.lengths,
self.words: mb.words,
self.chars: mb.chars,
self.mask: mb.mask.astype('float32')
}
|