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| author |  A. Unique TensorFlower <gardener@tensorflow.org> | 2017-02-06 02:19:45 -0800 |
|---|---|---|
| committer | TensorFlower Gardener <gardener@tensorflow.org> | 2017-02-06 02:31:09 -0800 |
| commit | 6bf199906ab90cc418666d020dfe5161cd5c61df (patch) | |
| tree | 5131c54b280b0504cee625e71d754836dfe3c13c | |
| parent | 6bb3d98fe7218fac10eff360f242fde7e389c391 (diff) | |
Update generated Python Op docs.
Change: 146642486
| -rw-r--r-- | tensorflow/g3doc/api_docs/python/contrib.learn.md | 2 | ||||
| -rw-r--r-- | tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.KMeansClustering.md | 2 |
2 files changed, 2 insertions, 2 deletions
diff --git a/tensorflow/g3doc/api_docs/python/contrib.learn.md b/tensorflow/g3doc/api_docs/python/contrib.learn.md index f618b14644..d90b6b1a53 100644 --- a/tensorflow/g3doc/api_docs/python/contrib.learn.md +++ b/tensorflow/g3doc/api_docs/python/contrib.learn.md @@ -857,7 +857,7 @@ Returns a path in which the eval process will look for checkpoints. ### `class tf.contrib.learn.KMeansClustering` {#KMeansClustering} -An Estimator fo rK-Means clustering. +An Estimator for K-Means clustering. - - - #### `tf.contrib.learn.KMeansClustering.__init__(num_clusters, model_dir=None, initial_clusters='random', distance_metric='squared_euclidean', random_seed=0, use_mini_batch=True, kmeans_plus_plus_num_retries=2, relative_tolerance=None, config=None)` {#KMeansClustering.__init__} diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.KMeansClustering.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.KMeansClustering.md index 422ca984bf..4eb8750685 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.KMeansClustering.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.KMeansClustering.md @@ -1,4 +1,4 @@ -An Estimator fo rK-Means clustering. +An Estimator for K-Means clustering. - - - #### `tf.contrib.learn.KMeansClustering.__init__(num_clusters, model_dir=None, initial_clusters='random', distance_metric='squared_euclidean', random_seed=0, use_mini_batch=True, kmeans_plus_plus_num_retries=2, relative_tolerance=None, config=None)` {#KMeansClustering.__init__} |