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122 lines
4.7 KiB
Python
122 lines
4.7 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 numpy as np
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import tensorflow as tf
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class SharedRunningStats(object):
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def __init__(self, replicated_device=None, epsilon=1e-2, name="", create_ops=True):
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self.sess = None
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self.name = name
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self.replicated_device = replicated_device
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self.epsilon = epsilon
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self.ops_were_created = False
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if create_ops:
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with tf.device(replicated_device):
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self.create_ops()
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def create_ops(self, shape=[1], clip_values=None):
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self.clip_values = clip_values
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with tf.variable_scope(self.name):
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self._sum = tf.get_variable(
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dtype=tf.float64,
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initializer=tf.constant_initializer(0.0),
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name="running_sum", trainable=False, shape=shape, validate_shape=False,
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collections=[tf.GraphKeys.GLOBAL_VARIABLES])
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self._sum_squared = tf.get_variable(
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dtype=tf.float64,
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initializer=tf.constant_initializer(self.epsilon),
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name="running_sum_squared", trainable=False, shape=shape, validate_shape=False,
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collections=[tf.GraphKeys.GLOBAL_VARIABLES])
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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(self.epsilon),
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name="count", trainable=False, collections=[tf.GraphKeys.GLOBAL_VARIABLES])
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self._shape = None
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self._mean = tf.div(self._sum, self._count, name="mean")
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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), self.epsilon), name="stdev")
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self.tf_mean = tf.cast(self._mean, 'float32')
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self.tf_std = tf.cast(self._std, 'float32')
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self.new_sum = tf.placeholder(dtype=tf.float64, name='sum')
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self.new_sum_squared = tf.placeholder(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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self.raw_obs = tf.placeholder(dtype=tf.float64, name='raw_obs')
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self.normalized_obs = (self.raw_obs - self._mean) / self._std
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if self.clip_values is not None:
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self.clipped_obs = tf.clip_by_value(self.normalized_obs, self.clip_values[0], self.clip_values[1])
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self.ops_were_created = True
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def set_session(self, sess):
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self.sess = sess
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def push(self, x):
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x = x.astype('float64')
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self.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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if self._shape is None:
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self._shape = x.shape
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@property
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def n(self):
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return self.sess.run(self._count)
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@property
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def mean(self):
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return self.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.sess.run(self._std)
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@property
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def shape(self):
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return self._shape
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@shape.setter
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def shape(self, val):
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self._shape = val
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self.new_sum.set_shape(val)
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self.new_sum_squared.set_shape(val)
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self.tf_mean.set_shape(val)
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self.tf_std.set_shape(val)
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self._sum.set_shape(val)
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self._sum_squared.set_shape(val)
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def normalize(self, batch):
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if self.clip_values is not None:
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return self.sess.run(self.clipped_obs, feed_dict={self.raw_obs: batch})
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else:
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return self.sess.run(self.normalized_obs, feed_dict={self.raw_obs: batch})
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