From 2dc7575123ffa0e6413fc3d2700968ef25f049de Mon Sep 17 00:00:00 2001 From: Sergii Khomenko Date: Fri, 4 May 2018 04:22:09 +0200 Subject: [PATCH] Fix minor typos (#19070) --- tensorflow/python/estimator/training.py | 2 +- tensorflow/python/feature_column/feature_column.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/python/estimator/training.py b/tensorflow/python/estimator/training.py index 534c357..9536613 100644 --- a/tensorflow/python/estimator/training.py +++ b/tensorflow/python/estimator/training.py @@ -588,7 +588,7 @@ class _TrainingExecutor(object): # max_steps, the evaluator will send the final export signal. There is a # small chance that the Estimator.train stopping logic sees a different # global_step value (due to global step race condition and the fact the - # saver sees a larger value for checkpoing saving), which does not end + # saver sees a larger value for checkpoint saving), which does not end # the training. When the training ends, a new checkpoint is generated, which # triggers the listener again. So, it could be the case the final export is # triggered twice. diff --git a/tensorflow/python/feature_column/feature_column.py b/tensorflow/python/feature_column/feature_column.py index 9e6429e..40386ae 100644 --- a/tensorflow/python/feature_column/feature_column.py +++ b/tensorflow/python/feature_column/feature_column.py @@ -280,7 +280,7 @@ def input_layer(features, # TODO(akshayka): InputLayer should be a subclass of Layer, and it # should implement the logic in input_layer using Layer's build-and-call # paradigm; input_layer should create an instance of InputLayer and -# return the result of inovking its apply method, just as functional layers do. +# return the result of invoking its apply method, just as functional layers do. class InputLayer(object): """An object-oriented version of `input_layer` that reuses variables.""" @@ -834,7 +834,7 @@ def shared_embedding_columns( tensor_name_in_ckpt=None, max_norm=None, trainable=True): """List of dense columns that convert from sparse, categorical input. - This is similar to `embedding_column`, except that that it produces a list of + This is similar to `embedding_column`, except that it produces a list of embedding columns that share the same embedding weights. Use this when your inputs are sparse and of the same type (e.g. watched and -- 2.7.4