updated

mnist_classifier.ipynb

@@ -180,7 +180,24 @@
    "cell_type": "code",
    "execution_count": 109,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train, epoch:1, loss: 0.1077, accuracy: 0.9104\n",
+      "eval, epoch:1, loss: 0.0382, accuracy: 0.9791\n",
+      "train, epoch:2, loss: 0.0410, accuracy: 0.9832\n",
+      "eval, epoch:2, loss: 0.0221, accuracy: 0.9866\n",
+      "train, epoch:3, loss: 0.0538, accuracy: 0.9871\n",
+      "eval, epoch:3, loss: 0.0141, accuracy: 0.9887\n",
+      "train, epoch:4, loss: 0.0343, accuracy: 0.9858\n",
+      "eval, epoch:4, loss: 0.0163, accuracy: 0.9871\n",
+      "train, epoch:5, loss: 0.0390, accuracy: 0.9865\n",
+      "eval, epoch:5, loss: 0.0169, accuracy: 0.9871\n"
+     ]
+    }
+   ],
    "source": [
     "model = cnn_classifier()\n",
     "model.apply(kaiming_init)\n",
@@ -210,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 110,
    "metadata": {
     "tags": [
      "exclude"
@@ -218,8 +235,8 @@
    },
    "outputs": [],
    "source": [
-    "# with open('./classifier.pkl', 'wb') as model_file:\n",
-    "#     pickle.dump(model, model_file)"
+    "with open('./classifier.pkl', 'wb') as model_file:\n",
+    "    pickle.dump(model, model_file)"
    ]
   },
   {
@@ -235,7 +252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 111,
    "metadata": {
     "tags": [
      "exclude"
@@ -247,7 +264,7 @@
      "output_type": "stream",
      "text": [
       "[NbConvertApp] Converting notebook mnist_classifier.ipynb to script\n",
-      "[NbConvertApp] Writing 5934 bytes to mnist_classifier.py\n"
+      "[NbConvertApp] Writing 3691 bytes to mnist_classifier.py\n"
      ]
     }
    ],