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| """Quality metrics for the model."""
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| import tensorflow as tf
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| def char_accuracy(predictions, targets, rej_char, streaming=False):
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| """Computes character level accuracy.
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| Both predictions and targets should have the same shape
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| [batch_size x seq_length].
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| Args:
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| predictions: predicted characters ids.
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| targets: ground truth character ids.
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| rej_char: the character id used to mark an empty element (end of sequence).
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| streaming: if True, uses the streaming mean from the slim.metric module.
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| Returns:
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| a update_ops for execution and value tensor whose value on evaluation
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| returns the total character accuracy.
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| """
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| with tf.compat.v1.variable_scope('CharAccuracy'):
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| predictions.get_shape().assert_is_compatible_with(targets.get_shape())
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| targets = tf.cast(targets, dtype=tf.int32)
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| const_rej_char = tf.constant(rej_char, shape=targets.get_shape())
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| weights = tf.cast(tf.not_equal(targets, const_rej_char), dtype=tf.float32)
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| correct_chars = tf.cast(tf.equal(predictions, targets), dtype=tf.float32)
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| accuracy_per_example = tf.compat.v1.div(
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| tf.reduce_sum(input_tensor=tf.multiply(
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| correct_chars, weights), axis=1),
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| tf.reduce_sum(input_tensor=weights, axis=1))
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| if streaming:
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| return tf.metrics.mean(accuracy_per_example)
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| else:
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| return tf.reduce_mean(input_tensor=accuracy_per_example)
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| def sequence_accuracy(predictions, targets, rej_char, streaming=False):
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| """Computes sequence level accuracy.
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| Both input tensors should have the same shape: [batch_size x seq_length].
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| Args:
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| predictions: predicted character classes.
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| targets: ground truth character classes.
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| rej_char: the character id used to mark empty element (end of sequence).
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| streaming: if True, uses the streaming mean from the slim.metric module.
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| Returns:
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| a update_ops for execution and value tensor whose value on evaluation
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| returns the total sequence accuracy.
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| """
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|
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| with tf.compat.v1.variable_scope('SequenceAccuracy'):
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| predictions.get_shape().assert_is_compatible_with(targets.get_shape())
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|
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| targets = tf.cast(targets, dtype=tf.int32)
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| const_rej_char = tf.constant(
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| rej_char, shape=targets.get_shape(), dtype=tf.int32)
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| include_mask = tf.not_equal(targets, const_rej_char)
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| include_predictions = tf.cast(
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| tf.compat.v1.where(include_mask, predictions,
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| tf.zeros_like(predictions) + rej_char), dtype=tf.int32)
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| correct_chars = tf.cast(
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| tf.equal(include_predictions, targets), dtype=tf.float32)
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| correct_chars_counts = tf.cast(
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| tf.reduce_sum(input_tensor=correct_chars, axis=[1]), dtype=tf.int32)
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| target_length = targets.get_shape().dims[1].value
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| target_chars_counts = tf.constant(
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| target_length, shape=correct_chars_counts.get_shape())
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| accuracy_per_example = tf.cast(
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| tf.equal(correct_chars_counts, target_chars_counts), dtype=tf.float32)
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| if streaming:
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| return tf.metrics.mean(accuracy_per_example)
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| else:
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| return tf.reduce_mean(input_tensor=accuracy_per_example)
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|
