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82 lines
3.2 KiB
Python
82 lines
3.2 KiB
Python
#
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# Copyright (c) 2017 Intel Corporation
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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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#
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import tensorflow as tf
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import numpy as np
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class SharedRunningStats(object):
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def __init__(self, tuning_parameters, replicated_device, epsilon=1e-2, shape=(), name=""):
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self.tp = tuning_parameters
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with tf.device(replicated_device):
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with tf.variable_scope(name):
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self._sum = tf.get_variable(
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dtype=tf.float64,
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shape=shape,
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initializer=tf.constant_initializer(0.0),
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name="running_sum", trainable=False)
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self._sum_squared = tf.get_variable(
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dtype=tf.float64,
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shape=shape,
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initializer=tf.constant_initializer(epsilon),
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name="running_sum_squared", trainable=False)
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self._count = tf.get_variable(
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dtype=tf.float64,
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shape=(),
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initializer=tf.constant_initializer(epsilon),
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name="count", trainable=False)
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self._shape = shape
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self._mean = self._sum / self._count
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self._std = tf.sqrt(tf.maximum((self._sum_squared - self._count*tf.square(self._mean))
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/ tf.maximum(self._count-1, 1), epsilon))
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self.new_sum = tf.placeholder(shape=self.shape, dtype=tf.float64, name='sum')
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self.new_sum_squared = tf.placeholder(shape=self.shape, dtype=tf.float64, name='var')
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self.newcount = tf.placeholder(shape=[], dtype=tf.float64, name='count')
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self._inc_sum = tf.assign_add(self._sum, self.new_sum, use_locking=True)
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self._inc_sum_squared = tf.assign_add(self._sum_squared, self.new_sum_squared, use_locking=True)
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self._inc_count = tf.assign_add(self._count, self.newcount, use_locking=True)
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def push(self, x):
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x = x.astype('float64')
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self.tp.sess.run([self._inc_sum, self._inc_sum_squared, self._inc_count],
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feed_dict={
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self.new_sum: x.sum(axis=0).ravel(),
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self.new_sum_squared: np.square(x).sum(axis=0).ravel(),
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self.newcount: np.array(len(x), dtype='float64')
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})
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@property
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def n(self):
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return self.tp.sess.run(self._count)
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@property
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def mean(self):
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return self.tp.sess.run(self._mean)
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@property
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def var(self):
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return self.std ** 2
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@property
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def std(self):
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return self.tp.sess.run(self._std)
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@property
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def shape(self):
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return self._shape |