add final eval

justheuristic · justheuristic · commit 84be9c74decb · 2018-04-18T19:46:01.000+03:00
diff --git a/homework04/part1_salary_prediction.ipynb b/homework04/part1_salary_prediction.ipynb
@@ -6,7 +6,9 @@
    "source": [
     "# Natural Language Processing with Deep Learning\n",
     "\n",
-    "__This is exactly the same notebook as in ../week10_textconv/. Feel free submit the seminar notebook, just make sure you read the assignments at the end.\n",
+    "__This is exactly the same notebook as in `../week10_textconv/.`__\n",
+    "\n",
+    "__Feel free submit the seminar notebook, just make sure you read the assignments at the end.__\n",
     "\n",
     "Today we're gonna apply the newly learned DL tools for sequence processing to the task of predicting job salary.\n",
     "\n",
@@ -769,6 +771,33 @@
     "    print('\\n\\n')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "print(\"Final eval:\")\n",
+    "for batch in iterate_minibatches(data_val, shuffle=False):\n",
+    "    title_ix = Variable(torch.LongTensor(batch[\"Title\"]), volatile=True)\n",
+    "    desc_ix = Variable(torch.LongTensor(batch[\"FullDescription\"]), volatile=True)\n",
+    "    cat_features = Variable(torch.FloatTensor(batch[\"Categorical\"]), volatile=True)\n",
+    "    reference = Variable(torch.FloatTensor(batch[target_column]), volatile=True)\n",
+    "\n",
+    "    prediction = model(title_ix, desc_ix, cat_features)\n",
+    "    loss = compute_loss(reference, prediction)\n",
+    "\n",
+    "    val_loss += loss.data.numpy()[0]\n",
+    "    val_mae += compute_mae(reference, prediction).data.numpy()[0]\n",
+    "    val_batches += 1\n",
+    "\n",
+    "print(\"\\tLoss:\\t%.5f\" % (val_loss / val_batches))\n",
+    "print(\"\\tMAE:\\t%.5f\" % (val_mae / val_batches))\n",
+    "print('\\n\\n')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
