66 lines
2.3 KiB
Python
66 lines
2.3 KiB
Python
# Copyright 2017 The TensorFlow Agents 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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"""Compute a streaming estimation of the mean of submitted tensors."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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class StreamingMean(object):
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"""Compute a streaming estimation of the mean of submitted tensors."""
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def __init__(self, shape, dtype):
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"""Specify the shape and dtype of the mean to be estimated.
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Note that a float mean to zero submitted elements is NaN, while computing
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the integer mean of zero elements raises a division by zero error.
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Args:
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shape: Shape of the mean to compute.
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dtype: Data type of the mean to compute.
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"""
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self._dtype = dtype
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self._sum = tf.Variable(lambda: tf.zeros(shape, dtype), False)
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self._count = tf.Variable(lambda: 0, trainable=False)
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@property
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def value(self):
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"""The current value of the mean."""
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return self._sum / tf.cast(self._count, self._dtype)
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@property
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def count(self):
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"""The number of submitted samples."""
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return self._count
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def submit(self, value):
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"""Submit a single or batch tensor to refine the streaming mean."""
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# Add a batch dimension if necessary.
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if value.shape.ndims == self._sum.shape.ndims:
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value = value[None, ...]
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return tf.group(self._sum.assign_add(tf.reduce_sum(value, 0)),
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self._count.assign_add(tf.shape(value)[0]))
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def clear(self):
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"""Return the mean estimate and reset the streaming statistics."""
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value = self._sum / tf.cast(self._count, self._dtype)
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with tf.control_dependencies([value]):
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reset_value = self._sum.assign(tf.zeros_like(self._sum))
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reset_count = self._count.assign(0)
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with tf.control_dependencies([reset_value, reset_count]):
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return tf.identity(value)
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