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| author |  Yash Katariya <yash.katariya10@gmail.com> | 2018-08-17 17:06:47 -0400 |
|---|---|---|
| committer | Yash Katariya <yash.katariya10@gmail.com> | 2018-08-22 10:15:21 -0400 |
| commit | 4c2f6aeaaf4aeafccc85a289a5a105d52738b410 (patch) | |
| tree | 6e51f61f3a6543c03e0c879c58e7374484378e52 /tensorflow/contrib/eager | |
| parent | ce40173f61c79af05dcd0e0330cdb80bb179585d (diff) | |
Simplyfing the evaluation step by taking argmax of the softmax of the predictions instead of tf.multinomial
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 315d7a4893..e0f7137184 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(tf.nn.softmax(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..b13e5aae9b 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(tf.nn.softmax(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..3e02d9fbb0 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(tf.nn.softmax(predictions[0])).numpy()\n", "\n", " result += targ_lang.idx2word[predicted_id] + ' '\n", "\n", |