Details for a competitive SST RNN

sst_bakeoff.ipynb

@@ -175,6 +175,36 @@
     "    class_func=sst.binary_class_func)  # Fixed."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By the way, with some informal hyperparameter search on a GPU machine, I found this model\n",
+    "```\n",
+    "tf_rnn_glove = TfRNNClassifier(\n",
+    "    sst_glove_vocab,\n",
+    "    embedding=glove_embedding, ## 100d version\n",
+    "    hidden_dim=300,\n",
+    "    max_length=52,\n",
+    "    hidden_activation=tf.nn.relu,\n",
+    "    cell_class=tf.nn.rnn_cell.LSTMCell,\n",
+    "    train_embedding=True,\n",
+    "    max_iter=5000,\n",
+    "    batch_size=1028,\n",
+    "    eta=0.001)\n",
+    "```\n",
+    "which finished with almost identical performance to the above:\n",
+    "    \n",
+    "```\n",
+    "             precision    recall  f1-score   support\n",
+    "\n",
+    "   negative       0.78      0.75      0.76       428\n",
+    "   positive       0.77      0.80      0.78       444\n",
+    "\n",
+    "avg / total       0.77      0.77      0.77       872\n",
+    "```"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},