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| # 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') | |
| } | |