Merge pull request #2128 from metric-space:word2vec-redundant-ops

PiperOrigin-RevId: 484066361

Author: copybara-github

site/en/tutorials/text/word2vec.ipynb

@@ -526,19 +526,15 @@
       },
       "outputs": [],
       "source": [
-        "# Add a dimension so you can use concatenation (in the next step).\n",
-        "negative_sampling_candidates = tf.expand_dims(negative_sampling_candidates, 1)\n",
+        "# Reduce a dimension so you can use concatenation (in the next step).\n",
+        "squeezed_context_class = tf.squeeze(context_class, 1)\n",
         "\n",
         "# Concatenate a positive context word with negative sampled words.\n",
-        "context = tf.concat([context_class, negative_sampling_candidates], 0)\n",
+        "context = tf.concat([squeezed_context_class, negative_sampling_candidates], 0)\n",
         "\n",
         "# Label the first context word as `1` (positive) followed by `num_ns` `0`s (negative).\n",
         "label = tf.constant([1] + [0]*num_ns, dtype=\"int64\")\n",
-        "\n",
-        "# Reshape the target to shape `(1,)` and context and label to `(num_ns+1,)`.\n",
-        "target = tf.squeeze(target_word)\n",
-        "context = tf.squeeze(context)\n",
-        "label = tf.squeeze(label)"
+        "target = target_word\n"
       ]
     },
     {
@@ -751,10 +747,7 @@
         "          name=\"negative_sampling\")\n",
         "\n",
         "      # Build context and label vectors (for one target word)\n",
-        "      negative_sampling_candidates = tf.expand_dims(\n",
-        "          negative_sampling_candidates, 1)\n",
-        "\n",
-        "      context = tf.concat([context_class, negative_sampling_candidates], 0)\n",
+        "      context = tf.concat([tf.squeeze(context_class,1), negative_sampling_candidates], 0)\n",
         "      label = tf.constant([1] + [0]*num_ns, dtype=\"int64\")\n",
         "\n",
         "      # Append each element from the training example to global lists.\n",
@@ -1053,7 +1046,7 @@
         "    seed=SEED)\n",
         "\n",
         "targets = np.array(targets)\n",
-        "contexts = np.array(contexts)[:,:,0]\n",
+        "contexts = np.array(contexts)\n",
         "labels = np.array(labels)\n",
         "\n",
         "print('\\n')\n",