coach v0.8.0

architectures/tensorflow_components/shared_variables.py

@@ -0,0 +1,81 @@
+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import tensorflow as tf
+import numpy as np
+
+
+class SharedRunningStats(object):
+    def __init__(self, tuning_parameters, replicated_device, epsilon=1e-2, shape=(), name=""):
+        self.tp = tuning_parameters
+        with tf.device(replicated_device):
+            with tf.variable_scope(name):
+                self._sum = tf.get_variable(
+                    dtype=tf.float64,
+                    shape=shape,
+                    initializer=tf.constant_initializer(0.0),
+                    name="running_sum", trainable=False)
+                self._sum_squared = tf.get_variable(
+                    dtype=tf.float64,
+                    shape=shape,
+                    initializer=tf.constant_initializer(epsilon),
+                    name="running_sum_squared", trainable=False)
+                self._count = tf.get_variable(
+                    dtype=tf.float64,
+                    shape=(),
+                    initializer=tf.constant_initializer(epsilon),
+                    name="count", trainable=False)
+
+                self._shape = shape
+                self._mean = tf.to_float(self._sum / self._count)
+                self._std = tf.sqrt(tf.maximum(tf.to_float(self._sum_squared / self._count) - tf.square(self._mean), 1e-2))
+
+                self.new_sum = tf.placeholder(shape=self.shape, dtype=tf.float64, name='sum')
+                self.new_sum_squared = tf.placeholder(shape=self.shape, dtype=tf.float64, name='var')
+                self.newcount = tf.placeholder(shape=[], dtype=tf.float64, name='count')
+
+                self._inc_sum = tf.assign_add(self._sum, self.new_sum, use_locking=True)
+                self._inc_sum_squared = tf.assign_add(self._sum_squared, self.new_sum_squared, use_locking=True)
+                self._inc_count = tf.assign_add(self._count, self.newcount, use_locking=True)
+
+    def push(self, x):
+        x = x.astype('float64')
+        self.tp.sess.run([self._inc_sum, self._inc_sum_squared, self._inc_count],
+                         feed_dict={
+                             self.new_sum: x.sum(axis=0).ravel(),
+                             self.new_sum_squared: np.square(x).sum(axis=0).ravel(),
+                             self.newcount: np.array(len(x), dtype='float64')
+                         })
+
+    @property
+    def n(self):
+        return self.tp.sess.run(self._count)
+
+    @property
+    def mean(self):
+        return self.tp.sess.run(self._mean)
+
+    @property
+    def var(self):
+        return self.std ** 2
+
+    @property
+    def std(self):
+        return self.tp.sess.run(self._std)
+
+    @property
+    def shape(self):
+        return self._shape