Upstream changes from internal

README.md

@@ -12,16 +12,18 @@ data flow graphs.  Nodes in the graph represent mathematical operations, while
 the graph edges represent the multidimensional data arrays (tensors) that flow
 between them.  This flexible architecture lets you deploy computation to one
 or more CPUs or GPUs in a desktop, server, or mobile device without rewriting
-code.  TensorFlow was originally developed by researchers and engineers
+code.  TensorFlow also includes TensorBoard, a data visualization toolkit.
+
+TensorFlow was originally developed by researchers and engineers
 working on the Google Brain team within Google's Machine Intelligence research
 organization for the purposes of conducting machine learning and deep neural
 networks research.  The system is general enough to be applicable in a wide
 variety of other domains, as well.
 
-**If you'd like to contribute to tensorflow, be sure to review the [contribution
+**If you'd like to contribute to TensorFlow, be sure to review the [contribution
 guidelines](CONTRIBUTING.md).**
 
-**We use [github issues](https://github.com/tensorflow/tensorflow/issues) for
+**We use [GitHub issues](https://github.com/tensorflow/tensorflow/issues) for
 tracking requests and bugs, but please see
 [Community](tensorflow/g3doc/resources/index.md#community) for general questions
 and discussion.**

WORKSPACE

@@ -103,7 +103,7 @@ new_git_repository(
   name = "iron_collapse",
   build_file = "bower.BUILD",
   remote = "https://github.com/polymerelements/iron-collapse.git",
-  tag = "v1.0.6",
+  tag = "v1.0.8",
 )
 
 new_git_repository(
@@ -159,7 +159,7 @@ new_git_repository(
   name = "iron_input",
   build_file = "bower.BUILD",
   remote = "https://github.com/polymerelements/iron-input.git",
-  tag = "v1.0.9",
+  tag = "1.0.10",
 )
 
 new_git_repository(
@@ -187,7 +187,7 @@ new_git_repository(
   name = "iron_overlay_behavior",
   build_file = "bower.BUILD",
   remote = "https://github.com/polymerelements/iron-overlay-behavior.git",
-  tag = "v1.6.2",
+  tag = "v1.6.3",
 )
 
 new_git_repository(
@@ -253,6 +253,20 @@ new_git_repository(
   tag = "v1.1.3",
 )
 
+new_git_repository(
+  name = "paper_dialog",
+  build_file = "bower.BUILD",
+  remote = "https://github.com/polymerelements/paper-dialog.git",
+  tag = "v1.0.4",
+)
+
+new_git_repository(
+  name = "paper_dialog_behavior",
+  build_file = "bower.BUILD",
+  remote = "https://github.com/polymerelements/paper-dialog-behavior.git",
+  tag = "v1.2.5",
+)
+
 new_git_repository(
   name = "paper_dropdown_menu",
   build_file = "bower.BUILD",
@@ -320,7 +334,7 @@ new_git_repository(
   name = "paper_radio_button",
   build_file = "bower.BUILD",
   remote = "https://github.com/polymerelements/paper-radio-button.git",
-  tag = "v1.1.1",
+  tag = "v1.1.2",
 )
 
 new_git_repository(
@@ -404,5 +418,5 @@ new_git_repository(
   name = "webcomponentsjs",
   build_file = "bower.BUILD",
   remote = "https://github.com/polymer/webcomponentsjs.git",
-  tag = "v0.7.21",
+  tag = "v0.7.22",
 )

bower.BUILD

@@ -348,6 +348,24 @@ filegroup(
     ],
 )
 
+filegroup(
+    name = "paper_dialog",
+    srcs = [
+        "index.html",
+        "paper-dialog.html",
+    ],
+)
+
+filegroup(
+    name = "paper_dialog_behavior",
+    srcs = [
+        "index.html",
+        "paper-dialog-behavior.html",
+        "paper-dialog-common.css",
+        "paper-dialog-shared-styles.html",
+    ],
+)
+
 filegroup(
     name = "paper_dropdown_menu",
     srcs = [

tensorflow/BUILD

@@ -77,12 +77,12 @@ filegroup(
         "//tensorflow/contrib/distributions:all_files",
         "//tensorflow/contrib/framework:all_files",
         "//tensorflow/contrib/layers:all_files",
+        "//tensorflow/contrib/learn:all_files",
         "//tensorflow/contrib/linear_optimizer:all_files",
         "//tensorflow/contrib/linear_optimizer/kernels:all_files",
         "//tensorflow/contrib/lookup:all_files",
         "//tensorflow/contrib/losses:all_files",
         "//tensorflow/contrib/metrics:all_files",
-        "//tensorflow/contrib/skflow:all_files",
         "//tensorflow/contrib/tensor_forest:all_files",
         "//tensorflow/contrib/testing:all_files",
         "//tensorflow/contrib/util:all_files",
@@ -97,6 +97,7 @@ filegroup(
         "//tensorflow/examples/how_tos/reading_data:all_files",
         "//tensorflow/examples/image_retraining:all_files",
         "//tensorflow/examples/label_image:all_files",
+        "//tensorflow/examples/skflow:all_files",
         "//tensorflow/examples/tutorials/mnist:all_files",
         "//tensorflow/examples/tutorials/word2vec:all_files",
         "//tensorflow/g3doc/how_tos/adding_an_op:all_files",

tensorflow/contrib/distributions/BUILD

@@ -1,5 +1,5 @@
 # Description:
-#   Contains ops to train linear models on top of TensorFlow.
+#   Contains ops for statistical distributions (with pdf, cdf, sample, etc...).
 #   APIs here are meant to evolve over time.
 
 licenses(["notice"])  # Apache 2.0
@@ -16,6 +16,16 @@ py_library(
     srcs_version = "PY2AND3",
 )
 
+cuda_py_tests(
+    name = "dirichlet_multinomial_test",
+    srcs = ["python/kernel_tests/dirichlet_multinomial_test.py"],
+    additional_deps = [
+        ":distributions_py",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 cuda_py_tests(
     name = "gaussian_test",
     size = "small",

tensorflow/contrib/distributions/__init__.py

@@ -23,6 +23,6 @@
 
 # pylint: disable=unused-import,wildcard-import, line-too-long
 from tensorflow.contrib.distributions.python.ops import gaussian_conjugate_posteriors
+from tensorflow.contrib.distributions.python.ops.dirichlet_multinomial import *
 from tensorflow.contrib.distributions.python.ops.gaussian import *
 # from tensorflow.contrib.distributions.python.ops.dirichlet import *  # pylint: disable=line-too-long
-# from tensorflow.contrib.distributions.python.ops.dirichlet_multinomial import *  # pylint: disable=line-too-long

tensorflow/contrib/distributions/python/kernel_tests/dirichlet_multinomial_test.py

@@ -0,0 +1,192 @@
+# Copyright 2016 Google Inc. All Rights Reserved.
+#
+# 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.
+# ==============================================================================
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import tensorflow as tf
+
+
+class DirichletMultinomialTest(tf.test.TestCase):
+
+  def test_num_classes(self):
+    with self.test_session():
+      for num_classes in range(3):
+        alpha = np.random.rand(3, num_classes)
+        dist = tf.contrib.distributions.DirichletMultinomial(alpha)
+        self.assertEqual([], dist.num_classes.get_shape())
+        self.assertEqual(num_classes, dist.num_classes.eval())
+
+  def test_alpha_property(self):
+    alpha = np.array([[1., 2, 3]])
+    with self.test_session():
+      dist = tf.contrib.distributions.DirichletMultinomial(alpha)
+      self.assertEqual([1, 3], dist.alpha.get_shape())
+      self.assertAllClose(alpha, dist.alpha.eval())
+
+  def test_empty_alpha_and_empty_counts_returns_empty(self):
+    with self.test_session():
+      alpha = [[]]
+      counts = [[]]
+      dist = tf.contrib.distributions.DirichletMultinomial(alpha)
+      self.assertAllEqual([], dist.pmf(counts).eval())
+      self.assertAllEqual([0], dist.pmf(counts).get_shape())
+      self.assertAllEqual([], dist.log_pmf(counts).eval())
+      self.assertAllEqual([0], dist.log_pmf(counts).get_shape())
+      self.assertAllEqual([[]], dist.mean.eval())
+      self.assertAllEqual([1, 0], dist.mean.get_shape())
+      self.assertAllEqual(0, dist.num_classes.eval())
+      self.assertAllEqual([], dist.num_classes.get_shape())
+
+  def test_pmf_both_zero_batches(self):
+    # The probabilities of one vote falling into class k is the mean for class
+    # k.
+    with self.test_session():
+      # Both zero-batches.  No broadcast
+      alpha = [1., 2]
+      counts = [1, 0.]
+      dist = tf.contrib.distributions.DirichletMultinomial(alpha)
+      pmf = dist.pmf(counts)
+      self.assertAllClose(1 / 3., pmf.eval())
+      self.assertEqual((), pmf.get_shape())
+
+  def test_pmf_alpha_stretched_in_broadcast_when_same_rank(self):
+    # The probabilities of one vote falling into class k is the mean for class
+    # k.
+    with self.test_session():
+      alpha = [[1., 2]]
+      counts = [[1, 0.], [0, 1.]]
+      dist = tf.contrib.distributions.DirichletMultinomial(alpha)
+      pmf = dist.pmf(counts)
+      self.assertAllClose([1 / 3., 2 / 3.], pmf.eval())
+      self.assertEqual((2), pmf.get_shape())
+
+  def test_pmf_alpha_stretched_in_broadcast_when_lower_rank(self):
+    # The probabilities of one vote falling into class k is the mean for class
+    # k.
+    with self.test_session():
+      alpha = [1., 2]
+      counts = [[1, 0.], [0, 1.]]
+      pmf = tf.contrib.distributions.DirichletMultinomial(alpha).pmf(counts)
+      self.assertAllClose([1 / 3., 2 / 3.], pmf.eval())
+      self.assertEqual((2), pmf.get_shape())
+
+  def test_pmf_counts_stretched_in_broadcast_when_same_rank(self):
+    # The probabilities of one vote falling into class k is the mean for class
+    # k.
+    with self.test_session():
+      alpha = [[1., 2], [2., 3]]
+      counts = [[1, 0.]]
+      pmf = tf.contrib.distributions.DirichletMultinomial(alpha).pmf(counts)
+      self.assertAllClose([1 / 3., 2 / 5.], pmf.eval())
+      self.assertEqual((2), pmf.get_shape())
+
+  def test_pmf_counts_stretched_in_broadcast_when_lower_rank(self):
+    # The probabilities of one vote falling into class k is the mean for class
+    # k.
+    with self.test_session():
+      alpha = [[1., 2], [2., 3]]
+      counts = [1, 0.]
+      pmf = tf.contrib.distributions.DirichletMultinomial(alpha).pmf(counts)
+      self.assertAllClose([1 / 3., 2 / 5.], pmf.eval())
+      self.assertEqual((2), pmf.get_shape())
+
+  def test_pmf_for_one_vote_is_the_mean_with_one_record_input(self):
+    # The probabilities of one vote falling into class k is the mean for class
+    # k.
+    alpha = [1., 2, 3]
+    with self.test_session():
+      for class_num in range(3):
+        counts = np.zeros((3), dtype=np.float32)
+        counts[class_num] = 1.0
+        dist = tf.contrib.distributions.DirichletMultinomial(alpha)
+        mean = dist.mean.eval()
+        pmf = dist.pmf(counts).eval()
+
+        self.assertAllClose(mean[class_num], pmf)
+        self.assertTupleEqual((3,), mean.shape)
+        self.assertTupleEqual((), pmf.shape)
+
+  def test_zero_counts_results_in_pmf_equal_to_one(self):
+    # There is only one way for zero items to be selected, and this happens with
+    # probability 1.
+    alpha = [5, 0.5]
+    counts = [0., 0.]
+    with self.test_session():
+      dist = tf.contrib.distributions.DirichletMultinomial(alpha)
+      pmf = dist.pmf(counts)
+      self.assertAllClose(1.0, pmf.eval())
+      self.assertEqual((), pmf.get_shape())
+
+  def test_large_tau_gives_precise_probabilities(self):
+    # If tau is large, we are doing coin flips with probability mu.
+    mu = np.array([0.1, 0.1, 0.8], dtype=np.float32)
+    tau = np.array([100.], dtype=np.float32)
+    alpha = tau * mu
+
+    # One (three sided) coin flip.  Prob[coin 3] = 0.8.
+    # Note that since it was one flip, value of tau didn't matter.
+    counts = [0., 0, 1]
+    with self.test_session():
+      dist = tf.contrib.distributions.DirichletMultinomial(alpha)
+      pmf = dist.pmf(counts)
+      self.assertAllClose(0.8, pmf.eval(), atol=1e-4)
+      self.assertEqual((), pmf.get_shape())
+
+    # Two (three sided) coin flips.  Prob[coin 3] = 0.8.
+    counts = [0., 0, 2]
+    with self.test_session():
+      dist = tf.contrib.distributions.DirichletMultinomial(alpha)
+      pmf = dist.pmf(counts)
+      self.assertAllClose(0.8**2, pmf.eval(), atol=1e-2)
+      self.assertEqual((), pmf.get_shape())
+
+    # Three (three sided) coin flips.
+    counts = [1., 0, 2]
+    with self.test_session():
+      dist = tf.contrib.distributions.DirichletMultinomial(alpha)
+      pmf = dist.pmf(counts)
+      self.assertAllClose(3 * 0.1 * 0.8 * 0.8, pmf.eval(), atol=1e-2)
+      self.assertEqual((), pmf.get_shape())
+
+  def test_small_tau_prefers_correlated_results(self):
+    # If tau is small, then correlation between draws is large, so draws that
+    # are both of the same class are more likely.
+    mu = np.array([0.5, 0.5], dtype=np.float32)
+    tau = np.array([0.1], dtype=np.float32)
+    alpha = tau * mu
+
+    # If there is only one draw, it is still a coin flip, even with small tau.
+    counts = [1, 0.]
+    with self.test_session():
+      dist = tf.contrib.distributions.DirichletMultinomial(alpha)
+      pmf = dist.pmf(counts)
+      self.assertAllClose(0.5, pmf.eval())
+      self.assertEqual((), pmf.get_shape())
+
+    # If there are two draws, it is much more likely that they are the same.
+    counts_same = [2, 0.]
+    counts_different = [1, 1.]
+    with self.test_session():
+      dist = tf.contrib.distributions.DirichletMultinomial(alpha)
+      pmf_same = dist.pmf(counts_same)
+      pmf_different = dist.pmf(counts_different)
+      self.assertLess(5 * pmf_different.eval(), pmf_same.eval())
+      self.assertEqual((), pmf_same.get_shape())
+
+
+if __name__ == '__main__':
+  tf.test.main()