google-research

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# coding=utf-8
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# Copyright 2024 The Google Research Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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#     http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Utility functions for learning code."""
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import functools
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import tensorflow.compat.v1 as tf
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from tensorflow.contrib import labeled_tensor
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@functools.partial(lt.define_reduce_op, 'reduce_nanmean')
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def reduce_nanmean(tensor, axes=None, keepdims=False, name=None):
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  """Take the mean of a tensor, skipping NaNs.
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  Args:
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    tensor: tensor to reduce.
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    axes: optional list of axes to reduce.
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    keepdims: optional boolean indicating whether to keep dimensions or not.
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    name: optional op name.
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  Returns:
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    tf.Tensor with reduce values.
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  """
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  masked = tf.is_nan(tensor)
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  valid_tensor = tf.where(masked, tf.zeros_like(tensor), tensor)
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  total = tf.reduce_sum(valid_tensor, axes, keepdims=keepdims)
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  counts = tf.reduce_sum(tf.cast(tf.logical_not(masked), tensor.dtype),
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                         axes, keepdims=keepdims)
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  return tf.div(total, counts, name=name)
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