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| author |  Yifei Feng <yifeif@google.com> | 2018-04-23 21:19:14 -0700 |
|---|---|---|
| committer |  TensorFlower Gardener <gardener@tensorflow.org> | 2018-04-23 21:21:38 -0700 |
| commit | 22f3a97b8b089202f60bb0c7697feb0c8e0713cc (patch) | |
| tree | d16f95826e4be15bbb3b0f22bed0ca25d3eb5897 /tensorflow/examples | |
| parent | 24b7c9a800ab5086d45a7d83ebcd6218424dc9e3 (diff) | |
Merge changes from github.
PiperOrigin-RevId: 194031845
Diffstat (limited to 'tensorflow/examples')
| -rw-r--r-- | tensorflow/examples/tutorials/word2vec/word2vec_basic.py | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py index 14ae7fbf35..b09ee99768 100644 --- a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py +++ b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py @@ -224,7 +224,7 @@ with graph.as_default(): optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss) # Compute the cosine similarity between minibatch examples and all embeddings. - norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True)) + norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keepdims=True)) normalized_embeddings = embeddings / norm valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset) |