Add a Kaggle tutorial

1 files changed, 220 insertions, 87 deletions

diff --git a/quickstart_tutorial.ipynb b/quickstart_tutorial.ipynb
index 55d4021..4e847d9 100644
--- a/quickstart_tutorial.ipynb
+++ b/quickstart_tutorial.ipynb
@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -138,13 +138,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Shape of X [N, C, H, W]:  torch.Size([64, 1, 28, 28])\n",
-      "Shape of y:  torch.Size([64]) torch.int64\n"
+      "Shape of X [N, C, H, W]:  torch.Size([200, 1, 28, 28])\n",
+      "Shape of y:  torch.Size([200]) torch.int64\n"
      ]
     }
    ],
    "source": [
-    "batch_size = 64\n",
+    "batch_size = 200\n",
     "\n",
     "# Create data loaders.\n",
     "train_dataloader = DataLoader(training_data, batch_size=batch_size)\n",
@@ -189,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -205,11 +205,11 @@
       "NeuralNetwork(\n",
       "  (flatten): Flatten(start_dim=1, end_dim=-1)\n",
       "  (linear_relu_stack): Sequential(\n",
-      "    (0): Linear(in_features=784, out_features=512, bias=True)\n",
+      "    (0): Linear(in_features=784, out_features=1024, bias=True)\n",
       "    (1): ReLU()\n",
-      "    (2): Linear(in_features=512, out_features=512, bias=True)\n",
+      "    (2): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "    (3): ReLU()\n",
-      "    (4): Linear(in_features=512, out_features=10, bias=True)\n",
+      "    (4): Linear(in_features=1024, out_features=10, bias=True)\n",
       "    (5): ReLU()\n",
       "  )\n",
       ")\n"
@@ -227,11 +227,11 @@
     "        super(NeuralNetwork, self).__init__()\n",
     "        self.flatten = nn.Flatten()\n",
     "        self.linear_relu_stack = nn.Sequential(\n",
-    "            nn.Linear(28*28, 512),\n",
+    "            nn.Linear(28*28, 1024),\n",
     "            nn.ReLU(),\n",
-    "            nn.Linear(512, 512),\n",
+    "            nn.Linear(1024, 1024),\n",
     "            nn.ReLU(),\n",
-    "            nn.Linear(512, 10),\n",
+    "            nn.Linear(1024, 10),\n",
     "            nn.ReLU()\n",
     "        )\n",
     "\n",
@@ -275,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -299,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -337,7 +337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -374,7 +374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -388,85 +388,170 @@
      "text": [
       "Epoch 1\n",
       "-------------------------------\n",
-      "loss: 2.300270  [    0/60000]\n",
-      "loss: 2.290948  [ 6400/60000]\n",
-      "loss: 2.280627  [12800/60000]\n",
-      "loss: 2.283042  [19200/60000]\n",
-      "loss: 2.268817  [25600/60000]\n",
-      "loss: 2.262104  [32000/60000]\n",
-      "loss: 2.248093  [38400/60000]\n",
-      "loss: 2.233517  [44800/60000]\n",
-      "loss: 2.234299  [51200/60000]\n",
-      "loss: 2.234841  [57600/60000]\n",
+      "loss: 2.301592  [    0/60000]\n",
+      "loss: 2.289894  [20000/60000]\n",
+      "loss: 2.280160  [40000/60000]\n",
       "Test Error: \n",
-      " Accuracy: 51.1%, Avg loss: 2.221716 \n",
+      " Accuracy: 30.9%, Avg loss: 2.269258 \n",
       "\n",
       "Epoch 2\n",
       "-------------------------------\n",
-      "loss: 2.203925  [    0/60000]\n",
-      "loss: 2.200477  [ 6400/60000]\n",
-      "loss: 2.180246  [12800/60000]\n",
-      "loss: 2.213840  [19200/60000]\n",
-      "loss: 2.155768  [25600/60000]\n",
-      "loss: 2.154045  [32000/60000]\n",
-      "loss: 2.130811  [38400/60000]\n",
-      "loss: 2.101326  [44800/60000]\n",
-      "loss: 2.118105  [51200/60000]\n",
-      "loss: 2.116674  [57600/60000]\n",
+      "loss: 2.268190  [    0/60000]\n",
+      "loss: 2.259592  [20000/60000]\n",
+      "loss: 2.251709  [40000/60000]\n",
       "Test Error: \n",
-      " Accuracy: 51.0%, Avg loss: 2.093179 \n",
+      " Accuracy: 34.5%, Avg loss: 2.238048 \n",
       "\n",
       "Epoch 3\n",
       "-------------------------------\n",
-      "loss: 2.054594  [    0/60000]\n",
-      "loss: 2.047193  [ 6400/60000]\n",
-      "loss: 2.009665  [12800/60000]\n",
-      "loss: 2.093936  [19200/60000]\n",
-      "loss: 1.965194  [25600/60000]\n",
-      "loss: 1.976750  [32000/60000]\n",
-      "loss: 1.938592  [38400/60000]\n",
-      "loss: 1.889513  [44800/60000]\n",
-      "loss: 1.942611  [51200/60000]\n",
-      "loss: 1.936963  [57600/60000]\n",
+      "loss: 2.236049  [    0/60000]\n",
+      "loss: 2.230108  [20000/60000]\n",
+      "loss: 2.223023  [40000/60000]\n",
       "Test Error: \n",
-      " Accuracy: 51.2%, Avg loss: 1.900150 \n",
+      " Accuracy: 34.4%, Avg loss: 2.204923 \n",
       "\n",
       "Epoch 4\n",
       "-------------------------------\n",
-      "loss: 1.837173  [    0/60000]\n",
-      "loss: 1.822518  [ 6400/60000]\n",
-      "loss: 1.775139  [12800/60000]\n",
-      "loss: 1.925843  [19200/60000]\n",
-      "loss: 1.731390  [25600/60000]\n",
-      "loss: 1.778743  [32000/60000]\n",
-      "loss: 1.714922  [38400/60000]\n",
-      "loss: 1.670009  [44800/60000]\n",
-      "loss: 1.755909  [51200/60000]\n",
-      "loss: 1.763030  [57600/60000]\n",
+      "loss: 2.201698  [    0/60000]\n",
+      "loss: 2.199297  [20000/60000]\n",
+      "loss: 2.192280  [40000/60000]\n",
       "Test Error: \n",
-      " Accuracy: 52.5%, Avg loss: 1.711389 \n",
+      " Accuracy: 34.7%, Avg loss: 2.168218 \n",
       "\n",
       "Epoch 5\n",
       "-------------------------------\n",
-      "loss: 1.621799  [    0/60000]\n",
-      "loss: 1.615258  [ 6400/60000]\n",
-      "loss: 1.567131  [12800/60000]\n",
-      "loss: 1.768921  [19200/60000]\n",
-      "loss: 1.539987  [25600/60000]\n",
-      "loss: 1.627408  [32000/60000]\n",
-      "loss: 1.533756  [38400/60000]\n",
-      "loss: 1.510703  [44800/60000]\n",
-      "loss: 1.603583  [51200/60000]\n",
-      "loss: 1.636089  [57600/60000]\n",
+      "loss: 2.163056  [    0/60000]\n",
+      "loss: 2.165860  [20000/60000]\n",
+      "loss: 2.158360  [40000/60000]\n",
       "Test Error: \n",
-      " Accuracy: 53.2%, Avg loss: 1.568043 \n",
+      " Accuracy: 35.1%, Avg loss: 2.126790 \n",
+      "\n",
+      "Epoch 6\n",
+      "-------------------------------\n",
+      "loss: 2.118984  [    0/60000]\n",
+      "loss: 2.128839  [20000/60000]\n",
+      "loss: 2.120741  [40000/60000]\n",
+      "Test Error: \n",
+      " Accuracy: 35.5%, Avg loss: 2.080902 \n",
+      "\n",
+      "Epoch 7\n",
+      "-------------------------------\n",
+      "loss: 2.069693  [    0/60000]\n",
+      "loss: 2.088246  [20000/60000]\n",
+      "loss: 2.080452  [40000/60000]\n",
+      "Test Error: \n",
+      " Accuracy: 35.9%, Avg loss: 2.032795 \n",
+      "\n",
+      "Epoch 8\n",
+      "-------------------------------\n",
+      "loss: 2.017604  [    0/60000]\n",
+      "loss: 2.045965  [20000/60000]\n",
+      "loss: 2.040246  [40000/60000]\n",
+      "Test Error: \n",
+      " Accuracy: 36.2%, Avg loss: 1.986216 \n",
+      "\n",
+      "Epoch 9\n",
+      "-------------------------------\n",
+      "loss: 1.966350  [    0/60000]\n",
+      "loss: 2.004781  [20000/60000]\n",
+      "loss: 2.002753  [40000/60000]\n",
+      "Test Error: \n",
+      " Accuracy: 36.6%, Avg loss: 1.944349 \n",
+      "\n",
+      "Epoch 10\n",
+      "-------------------------------\n",
+      "loss: 1.919495  [    0/60000]\n",
+      "loss: 1.967277  [20000/60000]\n",
+      "loss: 1.969415  [40000/60000]\n",
+      "Test Error: \n",
+      " Accuracy: 37.1%, Avg loss: 1.908297 \n",
+      "\n",
+      "Epoch 11\n",
+      "-------------------------------\n",
+      "loss: 1.878579  [    0/60000]\n",
+      "loss: 1.934277  [20000/60000]\n",
+      "loss: 1.940233  [40000/60000]\n",
+      "Test Error: \n",
+      " Accuracy: 38.0%, Avg loss: 1.877542 \n",
+      "\n",
+      "Epoch 12\n",
+      "-------------------------------\n",
+      "loss: 1.843474  [    0/60000]\n",
+      "loss: 1.905277  [20000/60000]\n",
+      "loss: 1.914412  [40000/60000]\n",
+      "Test Error: \n",
+      " Accuracy: 38.9%, Avg loss: 1.850760 \n",
+      "\n",
+      "Epoch 13\n",
+      "-------------------------------\n",
+      "loss: 1.812682  [    0/60000]\n",
+      "loss: 1.879232  [20000/60000]\n",
+      "loss: 1.890868  [40000/60000]\n",
+      "Test Error: \n",
+      " Accuracy: 39.6%, Avg loss: 1.826784 \n",
+      "\n",
+      "Epoch 14\n",
+      "-------------------------------\n",
+      "loss: 1.785155  [    0/60000]\n",
+      "loss: 1.855434  [20000/60000]\n",
+      "loss: 1.868896  [40000/60000]\n",
+      "Test Error: \n",
+      " Accuracy: 40.1%, Avg loss: 1.804814 \n",
+      "\n",
+      "Epoch 15\n",
+      "-------------------------------\n",
+      "loss: 1.760130  [    0/60000]\n",
+      "loss: 1.833383  [20000/60000]\n",
+      "loss: 1.848235  [40000/60000]\n",
+      "Test Error: \n",
+      " Accuracy: 40.5%, Avg loss: 1.784439 \n",
+      "\n",
+      "Epoch 16\n",
+      "-------------------------------\n",
+      "loss: 1.737113  [    0/60000]\n",
+      "loss: 1.812643  [20000/60000]\n",
+      "loss: 1.828667  [40000/60000]\n",
+      "Test Error: \n",
+      " Accuracy: 40.9%, Avg loss: 1.765419 \n",
+      "\n",
+      "Epoch 17\n",
+      "-------------------------------\n",
+      "loss: 1.715825  [    0/60000]\n",
+      "loss: 1.792752  [20000/60000]\n",
+      "loss: 1.809863  [40000/60000]\n",
+      "Test Error: \n",
+      " Accuracy: 41.2%, Avg loss: 1.747591 \n",
+      "\n",
+      "Epoch 18\n",
+      "-------------------------------\n",
+      "loss: 1.695961  [    0/60000]\n",
+      "loss: 1.773857  [20000/60000]\n",
+      "loss: 1.791943  [40000/60000]\n",
+      "Test Error: \n",
+      " Accuracy: 41.5%, Avg loss: 1.723384 \n",
+      "\n",
+      "Epoch 19\n",
+      "-------------------------------\n",
+      "loss: 1.669920  [    0/60000]\n",
+      "loss: 1.726300  [20000/60000]\n",
+      "loss: 1.758728  [40000/60000]\n",
+      "Test Error: \n",
+      " Accuracy: 42.2%, Avg loss: 1.677625 \n",
+      "\n",
+      "Epoch 20\n",
+      "-------------------------------\n",
+      "loss: 1.626003  [    0/60000]\n",
+      "loss: 1.674431  [20000/60000]\n",
+      "loss: 1.733464  [40000/60000]\n",
+      "Test Error: \n",
+      " Accuracy: 43.5%, Avg loss: 1.645380 \n",
       "\n",
       "Done!\n"
      ]
     }
    ],
    "source": [
-    "epochs = 5\n",
+    "epochs = 20\n",
     "for t in range(epochs):\n",
     "    print(f\"Epoch {t+1}\\n-------------------------------\")\n",
     "    train(train_dataloader, model, loss_fn, optimizer)\n",
@@ -504,7 +589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -539,7 +624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -553,7 +638,7 @@
        "<All keys matched successfully>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -573,7 +658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -585,7 +670,56 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Predicted: \"Ankle boot\", Actual: \"Ankle boot\"\n"
+      "Predicted: \"Sandal\", Actual: \"Ankle boot\"\n",
+      "Predicted: \"Shirt\", Actual: \"Pullover\"\n",
+      "Predicted: \"Trouser\", Actual: \"Trouser\"\n",
+      "Predicted: \"Trouser\", Actual: \"Trouser\"\n",
+      "Predicted: \"Shirt\", Actual: \"Shirt\"\n",
+      "Predicted: \"Trouser\", Actual: \"Trouser\"\n",
+      "Predicted: \"Shirt\", Actual: \"Coat\"\n",
+      "Predicted: \"Shirt\", Actual: \"Shirt\"\n",
+      "Predicted: \"Sneaker\", Actual: \"Sandal\"\n",
+      "Predicted: \"Sneaker\", Actual: \"Sneaker\"\n",
+      "Predicted: \"T-shirt/top\", Actual: \"Coat\"\n",
+      "Predicted: \"Sandal\", Actual: \"Sandal\"\n",
+      "Predicted: \"Sandal\", Actual: \"Sneaker\"\n",
+      "Predicted: \"Dress\", Actual: \"Dress\"\n",
+      "Predicted: \"Shirt\", Actual: \"Coat\"\n",
+      "Predicted: \"Trouser\", Actual: \"Trouser\"\n",
+      "Predicted: \"Shirt\", Actual: \"Pullover\"\n",
+      "Predicted: \"Shirt\", Actual: \"Coat\"\n",
+      "Predicted: \"Bag\", Actual: \"Bag\"\n",
+      "Predicted: \"T-shirt/top\", Actual: \"T-shirt/top\"\n",
+      "Predicted: \"Shirt\", Actual: \"Pullover\"\n",
+      "Predicted: \"Sneaker\", Actual: \"Sandal\"\n",
+      "Predicted: \"Sneaker\", Actual: \"Sneaker\"\n",
+      "Predicted: \"Sneaker\", Actual: \"Ankle boot\"\n",
+      "Predicted: \"Trouser\", Actual: \"Trouser\"\n",
+      "Predicted: \"Shirt\", Actual: \"Coat\"\n",
+      "Predicted: \"Shirt\", Actual: \"Shirt\"\n",
+      "Predicted: \"Dress\", Actual: \"T-shirt/top\"\n",
+      "Predicted: \"Sandal\", Actual: \"Ankle boot\"\n",
+      "Predicted: \"Dress\", Actual: \"Dress\"\n",
+      "Predicted: \"Bag\", Actual: \"Bag\"\n",
+      "Predicted: \"Bag\", Actual: \"Bag\"\n",
+      "Predicted: \"Dress\", Actual: \"Dress\"\n",
+      "Predicted: \"Dress\", Actual: \"Dress\"\n",
+      "Predicted: \"Bag\", Actual: \"Bag\"\n",
+      "Predicted: \"T-shirt/top\", Actual: \"T-shirt/top\"\n",
+      "Predicted: \"Sneaker\", Actual: \"Sneaker\"\n",
+      "Predicted: \"Sandal\", Actual: \"Sandal\"\n",
+      "Predicted: \"Sneaker\", Actual: \"Sneaker\"\n",
+      "Predicted: \"T-shirt/top\", Actual: \"Ankle boot\"\n",
+      "Predicted: \"T-shirt/top\", Actual: \"Shirt\"\n",
+      "Predicted: \"Trouser\", Actual: \"Trouser\"\n",
+      "Predicted: \"Dress\", Actual: \"Dress\"\n",
+      "Predicted: \"Sneaker\", Actual: \"Sneaker\"\n",
+      "Predicted: \"Shirt\", Actual: \"Shirt\"\n",
+      "Predicted: \"Sandal\", Actual: \"Sneaker\"\n",
+      "Predicted: \"Shirt\", Actual: \"Pullover\"\n",
+      "Predicted: \"Trouser\", Actual: \"Trouser\"\n",
+      "Predicted: \"Trouser\", Actual: \"Pullover\"\n",
+      "Predicted: \"Shirt\", Actual: \"Pullover\"\n"
      ]
     }
    ],
@@ -604,21 +738,20 @@
     "]\n",
     "\n",
     "model.eval()\n",
-    "x, y = test_data[0][0], test_data[0][1]\n",
-    "with torch.no_grad():\n",
-    "    pred = model(x)\n",
-    "    predicted, actual = classes[pred[0].argmax(0)], classes[y]\n",
-    "    print(f'Predicted: \"{predicted}\", Actual: \"{actual}\"')"
+    "for i in range(0, 50):\n",
+    "    x, y = test_data[i][0], test_data[i][1]\n",
+    "    with torch.no_grad():\n",
+    "        pred = model(x)\n",
+    "        predicted, actual = classes[pred[0].argmax(0)], classes[y]\n",
+    "        print(f'Predicted: \"{predicted}\", Actual: \"{actual}\"')"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
-   "source": [
-    "Read more about `Saving & Loading your model <saveloadrun_tutorial.html>`_.\n",
-    "\n",
-    "\n"
-   ]
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {