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| author |  TensorFlower Gardener <gardener@tensorflow.org> | 2018-09-11 08:26:30 -0700 |
|---|---|---|
| committer | TensorFlower Gardener <gardener@tensorflow.org> | 2018-09-11 08:26:30 -0700 |
| commit | e35b461be974d820ad08c61912536ecee9dc3a1f (patch) | |
| tree | 347c78591f7e8dee7c11045e603c73b32c0d4637 /tensorflow/contrib/eager | |
| parent | 87d440506547d5c549261922c268aa55badf0bc4 (diff) | |
| parent | c36ff7ae1d667979fa49899bf97de26cf35321de (diff) | |
Merge pull request #21689 from yashk2810:master
PiperOrigin-RevId: 212455284
Diffstat (limited to 'tensorflow/contrib/eager')
3 files changed, 3 insertions, 3 deletions
diff --git a/tensorflow/contrib/eager/python/examples/generative_examples/image_captioning_with_attention.ipynb b/tensorflow/contrib/eager/python/examples/generative_examples/image_captioning_with_attention.ipynb index 529c99b37c..3acecd283c 100644 --- a/tensorflow/contrib/eager/python/examples/generative_examples/image_captioning_with_attention.ipynb +++ b/tensorflow/contrib/eager/python/examples/generative_examples/image_captioning_with_attention.ipynb @@ -1056,7 +1056,7 @@ "\n", " attention_plot[i] = tf.reshape(attention_weights, (-1, )).numpy()\n", "\n", - " predicted_id = tf.multinomial(predictions, num_samples=1)[0][0].numpy()\n", + " predicted_id = tf.argmax(predictions[0]).numpy()\n", " result.append(index_word[predicted_id])\n", "\n", " if index_word[predicted_id] == '<end>':\n", diff --git a/tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb b/tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb index 40bc098724..e0d5e494d4 100644 --- a/tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb +++ b/tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb @@ -610,7 +610,7 @@ "\n", " # using a multinomial distribution to predict the word returned by the model\n", " predictions = predictions / temperature\n", - " predicted_id = tf.multinomial(predictions, num_samples=1)[0][0].numpy()\n", + " predicted_id = tf.argmax(predictions[0]).numpy()\n", " \n", " # We pass the predicted word as the next input to the model\n", " # along with the previous hidden state\n", diff --git a/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb b/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb index f1e1f99c57..560fc8c5a2 100644 --- a/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb +++ b/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb @@ -677,7 +677,7 @@ " attention_weights = tf.reshape(attention_weights, (-1, ))\n", " attention_plot[t] = attention_weights.numpy()\n", "\n", - " predicted_id = tf.multinomial(predictions, num_samples=1)[0][0].numpy()\n", + " predicted_id = tf.argmax(predictions[0]).numpy()\n", "\n", " result += targ_lang.idx2word[predicted_id] + ' '\n", "\n", |