Run notebook completely

Author: ottonemo

notebooks/MNIST-torchvision.ipynb

@@ -674,7 +674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -683,20 +683,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
     "params = {\n",
     "    'module__dropout': [0, 0.5, 0.8],\n",
-    "    'max_epochs': [1],\n",
+    "    'max_epochs': [2],\n",
     "    'verbose': [False],\n",
+    "    'train_split': [False],\n",
     "}"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The parameter we are interested in here is the dropout rate. We want to see which of the values (no dropout, 50%, 80%) is the best choice for our network.\n",
+    "\n",
+    "Additionally:\n",
+    "\n",
+    "- We use only two epochs (`max_epochs: [2]`) for each `.fit` (only to reduce execution time, normally we wouldn't change this and possibly add an `EarlyStopping` callback).\n",
+    "- Disable the network output (`verbose: [False]`)\n",
+    "- Disable the internal train/validation split (`train_split: [False]`) since the grid search will do k-fold validation anyway"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -714,7 +728,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -723,7 +737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -732,7 +746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 55,
    "metadata": {
     "scrolled": false
    },
@@ -748,16 +762,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/marian/anaconda3/envs/skorch/lib/python3.6/site-packages/sklearn/model_selection/_split.py:1943: FutureWarning: You should specify a value for 'cv' instead of relying on the default value. The default value will change from 3 to 5 in version 0.22.\n",
-      "  warnings.warn(CV_WARNING, FutureWarning)\n",
       "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
-      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:  2.1min finished\n"
+      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:  7.0min finished\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "GridSearchCV(cv='warn', error_score='raise-deprecating',\n",
+       "GridSearchCV(cv=3, error_score='raise-deprecating',\n",
        "       estimator=<class 'skorch.classifier.NeuralNetClassifier'>[initialized](\n",
        "  module_=Cnn(\n",
        "    (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n",
@@ -769,12 +781,12 @@
        "  ),\n",
        "),\n",
        "       fit_params=None, iid='warn', n_jobs=None,\n",
-       "       param_grid={'module__dropout': [0, 0.5, 0.8], 'max_epochs': [1], 'verbose': [False]},\n",
+       "       param_grid={'module__dropout': [0, 0.5, 0.8], 'max_epochs': [2], 'verbose': [False], 'train_split': [False]},\n",
        "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
        "       scoring='accuracy', verbose=1)"
       ]
      },
-     "execution_count": 149,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -792,16 +804,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'max_epochs': 1, 'module__dropout': 0, 'verbose': False}"
+       "{'max_epochs': 2, 'module__dropout': 0, 'train_split': False, 'verbose': False}"
       ]
      },
-     "execution_count": 150,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }