Cherrypicks for 1.1

tensorflow/python/kernel_tests/bincount_op_test.py

@@ -73,13 +73,13 @@ def test_random_with_weights(self):
         else:
           weights = np.random.random(num_samples)
         self.assertAllEqual(
-            math_ops.bincount(arr, weights=weights).eval(),
+            math_ops.bincount(arr, weights).eval(),
             np.bincount(arr, weights))
 
   def test_zero_weights(self):
     with self.test_session():
       self.assertAllEqual(
-          math_ops.bincount(np.arange(1000), weights=np.zeros(1000)).eval(),
+          math_ops.bincount(np.arange(1000), np.zeros(1000)).eval(),
           np.zeros(1000))
 
   def test_negative(self):

tensorflow/python/ops/math_ops.py

@@ -2026,9 +2026,9 @@ def tanh(x, name=None):
 
 
 def bincount(arr,
+             weights=None,
              minlength=None,
              maxlength=None,
-             weights=None,
              dtype=dtypes.int32):
   """Counts the number of occurrences of each value in an integer array.
 
@@ -2040,13 +2040,13 @@ def bincount(arr,
 
   Args:
     arr: An int32 tensor of non-negative values.
+    weights: If non-None, must be the same shape as arr. For each value in
+        `arr`, the bin will be incremented by the corresponding weight instead
+        of 1.
     minlength: If given, ensures the output has length at least `minlength`,
         padding with zeros at the end if necessary.
     maxlength: If given, skips values in `arr` that are equal or greater than
         `maxlength`, ensuring that the output has length at most `maxlength`.
-    weights: If non-None, must be the same shape as arr. For each value in
-        `arr`, the bin will be incremented by the corresponding weight instead
-        of 1.
     dtype: If `weights` is None, determines the type of the output bins.
 
   Returns:

tensorflow/tensorboard/dist/tf-tensorboard.html

@@ -4582,7 +4582,7 @@ <h3 id="tooltip-help" class="tooltip-container">
                 // If b===a, we would create an empty range. We instead select the range
                 // [0, 2*a] if a > 0, or [-2*a, 0] if a < 0, plus a little bit of
                 // extra padding on the top and bottom of the plot.
-                padding = Math.abs(a) * 1.1;
+                padding = Math.abs(a) * 1.1 + 1.1;
             }
             else {
                 padding = span * 0.2;
@@ -5332,7 +5332,7 @@ <h3 id="tooltip-help" class="tooltip-container">
                 // If b===a, we would create an empty range. We instead select the range
                 // [0, 2*a] if a > 0, or [-2*a, 0] if a < 0, plus a little bit of
                 // extra padding on the top and bottom of the plot.
-                padding = Math.abs(a) * 1.1;
+                padding = Math.abs(a) * 1.1 + 1.1;
             }
             else {
                 padding = span * 0.2;
@@ -17812,11 +17812,14 @@ <h2>
   properties: {
     _datasets: Object,
     _renderHierarchy: {type: Object, observer: '_renderHierarchyChanged'},
-    backend: {type: Object, observer: '_backendChanged'},
+    backend: Object,
     debuggerDataEnabled: Boolean,
     allStepsModeEnabled: Boolean,
     specificHealthPillStep: {type: Number, value: 0},
     healthPillsToggledOn: {type: Boolean, value: true, observer: '_healthPillsToggledOnChanged'},
+    _isAttached: Boolean,
+    // Whether this dashboard is initialized. This dashboard should only be initialized once.
+    _initialized: Boolean,
     // Whether health pills are currently being loaded, in which case we may want to say show a
     // spinner.
     _areHealthPillsLoading: Boolean,
@@ -17845,7 +17848,14 @@ <h2>
   },
   observers: [
     '_maybeFetchHealthPillsAtSpecificStep(allStepsModeEnabled, specificHealthPillStep)',
+    '_maybeInitializeDashboard(backend, _isAttached)',
   ],
+  attached: function() {
+    this.set('_isAttached', true);
+  },
+  detached: function() {
+    this.set('_isAttached', false);
+  },
   reload: function() {
     if (!this.debuggerDataEnabled ||
         !this.healthPillsToggledOn ||
@@ -17861,7 +17871,14 @@ <h2>
     // would not change across reloads.
     this._requestHealthPills();
   },
-  _backendChanged: function(backend) {
+  _maybeInitializeDashboard: function(backend, isAttached) {
+    if (this._initialized || !backend || !isAttached) {
+      // Either this dashboard is already initialized ... or we are not yet ready to initialize.
+      return;
+    }
+
+    // Set this to true so we only initialize once.
+    this._initialized = true;
     Promise.all([backend.graphRuns(), backend.runMetadataRuns()])
       .then(function(result) {
         var runsWithGraph = result[0].sort(VZ.Sorting.compareTagNames);
@@ -18846,6 +18863,7 @@ <h4><span class="step-label">Step 1:</span> Make data public</h4>
           </p>
           <p>
             One option is using a <a target="_blank" href="https://gist.github.com/">github gist</a>.
+            If you choose this approach, make sure to link directly to the raw file.
           </p>
         </div>
         <div>
@@ -19822,23 +19840,25 @@ <h3>2D controls</h3>
   },
   properties: {
     dataNotFound: Boolean,
-    routePrefix: String
+    routePrefix: String,
+    // Whether this dashboard is initialized. This dashboard should only be initialized once.
+    _initialized: Boolean,
   },
   behaviors: [
     TF.Dashboard.DashboardBehavior("embeddings"),
   ],
-  observers: [
-    "_routePrefixSet(routePrefix)",
-  ],
   reload: function() {
     // Do not reload the embedding projector. Reloading could take a long time.
   },
-  _routePrefixSet: function(routePrefix) {
-    if (routePrefix === undefined) {
-      // The route prefix has not been set yet.
+  attached: function() {
+    if (this._initialized) {
       return;
     }
-    d3.json(routePrefix + '/runs', (err, runs) => {
+
+    // Set this to true so we only initialize once.
+    this._initialized = true;
+
+    d3.json(this.routePrefix + '/runs', (err, runs) => {
       this.set('dataNotFound', runs.length === 0);
     });
   },
@@ -25619,8 +25639,8 @@ <h2>Settings</h2>
             embeddings: [{
                     tensorName: 'My tensor',
                     tensorShape: [1000, 50],
-                    tensorPath: 'https://gist.github.com/.../tensors.tsv',
-                    metadataPath: 'https://gist.github.com/.../optional.metadata.tsv',
+                    tensorPath: 'https://raw.githubusercontent.com/.../tensors.tsv',
+                    metadataPath: 'https://raw.githubusercontent.com/.../optional.metadata.tsv',
                 }],
         };
         this.setProjectorConfigTemplateJson(projectorConfigTemplate, projectorConfigTemplateJson);
@@ -25642,7 +25662,7 @@ <h2>Settings</h2>
         bookmarksFieldCheckbox.addEventListener('change', function () {
             if (bookmarksFieldCheckbox.checked) {
                 projectorConfigTemplateJson.embeddings[0].bookmarksPath =
-                    'https://gist.github.com/.../bookmarks.txt';
+                    'https://raw.githubusercontent.com/.../bookmarks.txt';
             }
             else {
                 delete projectorConfigTemplateJson.embeddings[0].bookmarksPath;
@@ -25653,7 +25673,7 @@ <h2>Settings</h2>
         metadataFieldCheckbox.addEventListener('change', function () {
             if (metadataFieldCheckbox.checked) {
                 projectorConfigTemplateJson.embeddings[0].metadataPath =
-                    'https://gist.github.com/.../optional.metadata.tsv';
+                    'https://raw.githubusercontent.com/.../optional.metadata.tsv';
             }
             else {
                 delete projectorConfigTemplateJson.embeddings[0].metadataPath;