Merge pull request tensorflow#4667 from jhseu/branch_134734156

Branch 134734156

Author: jhseu

tensorflow/c/c_api.h

@@ -740,7 +740,7 @@ extern TF_Operation* TF_GraphOperationByName(TF_Graph* graph,
 extern TF_Operation* TF_GraphNextOperation(TF_Graph* graph, size_t* pos);
 
 // Write out a serialized representation of `graph` (as a GraphDef protocol
-// message) to `output_graph_def`.
+// message) to `output_graph_def` (allocated by TF_NewBuffer()).
 //
 // May fail on very large graphs in the future.
 extern void TF_GraphToGraphDef(TF_Graph* graph, TF_Buffer* output_graph_def,

tensorflow/cc/saved_model/constants.h

@@ -1,4 +1,4 @@
-/* Copyright 2016 Google Inc. All Rights Reserved.
+/* Copyright 2016 The TensorFlow Authors. 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.

tensorflow/cc/saved_model/loader.cc

@@ -1,4 +1,4 @@
-/* Copyright 2016 Google Inc. All Rights Reserved.
+/* Copyright 2016 The TensorFlow Authors. 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.
@@ -37,18 +37,8 @@ Status ReadSavedModel(const string& export_dir, SavedModel* saved_model_proto) {
   const string saved_model_pbtxt_path =
       io::JoinPath(export_dir, kSavedModelFilenamePbTxt);
   if (Env::Default()->FileExists(saved_model_pbtxt_path)) {
-    string pbtxt_data;
-    Status status =
-        ReadFileToString(Env::Default(), saved_model_pbtxt_path, &pbtxt_data);
-    if (status != Status::OK()) {
-      return status;
-    }
-    if (!protobuf::TextFormat::ParseFromString(pbtxt_data, saved_model_proto)) {
-      return Status(error::Code::UNKNOWN,
-                    "Could not parse SavedModel proto from .pbtxt format: " +
-                        saved_model_pbtxt_path);
-    }
-    return Status::OK();
+    return ReadTextProto(Env::Default(), saved_model_pbtxt_path,
+                         saved_model_proto);
   }
   return Status(error::Code::NOT_FOUND,
                 "Could not find SavedModel .pb or .pbtxt at supplied export "

tensorflow/cc/saved_model/loader.h

@@ -1,4 +1,4 @@
-/* Copyright 2016 Google Inc. All Rights Reserved.
+/* Copyright 2016 The TensorFlow Authors. 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.

tensorflow/cc/saved_model/loader_test.cc

@@ -1,7 +1,6 @@
-/*Copyright 2016 Google Inc.All Rights Reserved.
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
 
- Licensed under the Apache License,
-    Version 2.0(the "License");
+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
 

tensorflow/contrib/__init__.py

@@ -1,4 +1,4 @@
-# Copyright 2015 Google Inc. All Rights Reserved.
+# Copyright 2015 The TensorFlow Authors. 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.

tensorflow/contrib/bayesflow/python/ops/entropy.py

@@ -25,7 +25,7 @@
 
 Example of fitting a variational posterior with the ELBO.
 
-```
+```python
 # We start by assuming knowledge of the log of a joint density p(z, x) over
 # latent variable z and fixed measurement x.  Since x is fixed, the Python
 # function does not take x as an argument.
@@ -384,7 +384,7 @@ def renyi_alpha(step,
   Args:
     step:  Non-negative scalar `Tensor`.  Typically the global step or an
       offset version thereof.
-    decay_time:  Postive scalar `Tensor`.
+    decay_time:  Positive scalar `Tensor`.
     alpha_min:  `float` or `double` `Tensor`.
       The minimal, final value of `alpha`, achieved when `step >= decay_time`
     alpha_max:  `Tensor` of same `dtype` as `alpha_min`.

tensorflow/contrib/bayesflow/python/ops/monte_carlo.py

@@ -30,7 +30,7 @@
 numbers.  If `E_p[f(Z)^2] < infinity`, then `S_n` is asymptotically normal with
 variance `Var[f(Z)] / n`.
 
-Practicioners of Bayesian statistics often find themselves wanting to estimate
+Practitioners of Bayesian statistics often find themselves wanting to estimate
 `E_p[f(Z)]` when the distribution `p` is known only up to a constant.  For
 example, the joint distribution `p(z, x)` may be known, but the evidence
 `p(x) = \int p(z, x) dz` may be intractable.  In that case, a parameterized

tensorflow/contrib/crf/python/ops/crf.py

@@ -128,13 +128,13 @@ def crf_log_likelihood(inputs,
                        tag_indices,
                        sequence_lengths,
                        transition_params=None):
-  """Computes the log-likehood of tag sequences in a CRF.
+  """Computes the log-likelihood of tag sequences in a CRF.
 
   Args:
     inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials
         to use as input to the CRF layer.
     tag_indices: A [batch_size, max_seq_len] matrix of tag indices for which we
-        compute the log-likehood.
+        compute the log-likelihood.
     sequence_lengths: A [batch_size] vector of true sequence lengths.
     transition_params: A [num_tags, num_tags] transition matrix, if available.
   Returns:
@@ -291,7 +291,7 @@ def viterbi_decode(score, transition_params):
   Returns:
     viterbi: A [seq_len] list of integers containing the highest scoring tag
         indicies.
-    viterbi_score: A float containing the score for the viterbi sequence.
+    viterbi_score: A float containing the score for the Viterbi sequence.
   """
   trellis = np.zeros_like(score)
   backpointers = np.zeros_like(score, dtype=np.int32)

tensorflow/contrib/distributions/__init__.py

@@ -73,7 +73,7 @@
 ### Normal likelihood with conjugate prior.
 
 @@normal_conjugates_known_sigma_posterior
-@@normal_congugates_known_sigma_predictive
+@@normal_conjugates_known_sigma_predictive
 
 ## Kullback Leibler Divergence