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| author |  A. Unique TensorFlower <gardener@tensorflow.org> | 2017-02-01 18:13:33 -0800 |
|---|---|---|
| committer | TensorFlower Gardener <gardener@tensorflow.org> | 2017-02-01 18:33:19 -0800 |
| commit | d1ba01f81d8fa1d0171ba9ce871599063d5c7eb9 (patch) | |
| tree | cd28fd2d32712c59f8452ede903cd592e0dc95bd /tensorflow/examples/udacity | |
| parent | ffc667757c6c328e48d80c14f97e32cf6a9d0f53 (diff) | |
Merge changes from github.
Change: 146316196
Diffstat (limited to 'tensorflow/examples/udacity')
| -rw-r--r-- | tensorflow/examples/udacity/5_word2vec.ipynb | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/tensorflow/examples/udacity/5_word2vec.ipynb b/tensorflow/examples/udacity/5_word2vec.ipynb index ec6413a0a3..9d4243d7ae 100644 --- a/tensorflow/examples/udacity/5_word2vec.ipynb +++ b/tensorflow/examples/udacity/5_word2vec.ipynb @@ -442,8 +442,8 @@ " embed = tf.nn.embedding_lookup(embeddings, train_dataset)\n", " # Compute the softmax loss, using a sample of the negative labels each time.\n", " loss = tf.reduce_mean(\n", - " tf.nn.sampled_softmax_loss(softmax_weights, softmax_biases, embed,\n", - " train_labels, num_sampled, vocabulary_size))\n", + " tf.nn.sampled_softmax_loss(weights=softmax_weights, biases=softmax_biases, inputs=embed,\n", + " labels=train_labels, num_sampled=num_sampled, num_classes=vocabulary_size))\n", "\n", " # Optimizer.\n", " # Note: The optimizer will optimize the softmax_weights AND the embeddings.\n", |