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| # Copyright 2017 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. | |
| # ============================================================================== | |
| """Quality metrics for the model.""" | |
| import tensorflow as tf | |
| def char_accuracy(predictions, targets, rej_char, streaming=False): | |
| """Computes character level accuracy. | |
| Both predictions and targets should have the same shape | |
| [batch_size x seq_length]. | |
| Args: | |
| predictions: predicted characters ids. | |
| targets: ground truth character ids. | |
| rej_char: the character id used to mark an empty element (end of sequence). | |
| streaming: if True, uses the streaming mean from the slim.metric module. | |
| Returns: | |
| a update_ops for execution and value tensor whose value on evaluation | |
| returns the total character accuracy. | |
| """ | |
| with tf.variable_scope('CharAccuracy'): | |
| predictions.get_shape().assert_is_compatible_with(targets.get_shape()) | |
| targets = tf.to_int32(targets) | |
| const_rej_char = tf.constant(rej_char, shape=targets.get_shape()) | |
| weights = tf.to_float(tf.not_equal(targets, const_rej_char)) | |
| correct_chars = tf.to_float(tf.equal(predictions, targets)) | |
| accuracy_per_example = tf.div( | |
| tf.reduce_sum(tf.multiply(correct_chars, weights), 1), | |
| tf.reduce_sum(weights, 1)) | |
| if streaming: | |
| return tf.contrib.metrics.streaming_mean(accuracy_per_example) | |
| else: | |
| return tf.reduce_mean(accuracy_per_example) | |
| def sequence_accuracy(predictions, targets, rej_char, streaming=False): | |
| """Computes sequence level accuracy. | |
| Both input tensors should have the same shape: [batch_size x seq_length]. | |
| Args: | |
| predictions: predicted character classes. | |
| targets: ground truth character classes. | |
| rej_char: the character id used to mark empty element (end of sequence). | |
| streaming: if True, uses the streaming mean from the slim.metric module. | |
| Returns: | |
| a update_ops for execution and value tensor whose value on evaluation | |
| returns the total sequence accuracy. | |
| """ | |
| with tf.variable_scope('SequenceAccuracy'): | |
| predictions.get_shape().assert_is_compatible_with(targets.get_shape()) | |
| targets = tf.to_int32(targets) | |
| const_rej_char = tf.constant( | |
| rej_char, shape=targets.get_shape(), dtype=tf.int32) | |
| include_mask = tf.not_equal(targets, const_rej_char) | |
| include_predictions = tf.to_int32( | |
| tf.where(include_mask, predictions, | |
| tf.zeros_like(predictions) + rej_char)) | |
| correct_chars = tf.to_float(tf.equal(include_predictions, targets)) | |
| correct_chars_counts = tf.cast( | |
| tf.reduce_sum(correct_chars, reduction_indices=[1]), dtype=tf.int32) | |
| target_length = targets.get_shape().dims[1].value | |
| target_chars_counts = tf.constant( | |
| target_length, shape=correct_chars_counts.get_shape()) | |
| accuracy_per_example = tf.to_float( | |
| tf.equal(correct_chars_counts, target_chars_counts)) | |
| if streaming: | |
| return tf.contrib.metrics.streaming_mean(accuracy_per_example) | |
| else: | |
| return tf.reduce_mean(accuracy_per_example) | |