Logistic_Reg_Pytorch.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "# Imports\n",
    "import torch\n",
    "import torchvision\n",
    "from torchvision.datasets import MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Download training dataset\n",
    "dataset = MNIST(root='data/', download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "len(dataset)"
   ]
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   "execution_count": 4,
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   "source": [
    "test_dataset = MNIST(root='data/', train=False)\n",
    "len(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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   "source": [
    "dataset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
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   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "Label: 5\n"
     ]
    },
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     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "image, label = dataset[0]\n",
    "plt.imshow(image, cmap='gray')\n",
    "print('Label:', label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Label: 3\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "image, label = dataset[10]\n",
    "plt.imshow(image, cmap='gray')\n",
    "print('Label:', label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MNIST dataset (images and labels)\n",
    "dataset = MNIST(root='data/', \n",
    "                train=True,\n",
    "                transform=transforms.ToTensor())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "torch.Size([1, 28, 28]) 5\n"
     ]
    }
   ],
   "source": [
    "img_tensor, label = dataset[0]\n",
    "print(img_tensor.shape, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([[0.0039, 0.6039, 0.9922, 0.3529, 0.0000],\n        [0.0000, 0.5451, 0.9922, 0.7451, 0.0078],\n        [0.0000, 0.0431, 0.7451, 0.9922, 0.2745],\n        [0.0000, 0.0000, 0.1373, 0.9451, 0.8824],\n        [0.0000, 0.0000, 0.0000, 0.3176, 0.9412]])\ntensor(1.) tensor(0.)\n"
     ]
    }
   ],
   "source": [
    "print(img_tensor[0,10:15,10:15])\n",
    "print(torch.max(img_tensor), torch.min(img_tensor))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Plot the image by passing in the 28x28 matrix\n",
    "plt.imshow(img_tensor[0,10:15,10:15], cmap='gray');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(50000, 10000)"
      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "from torch.utils.data import random_split\n",
    "\n",
    "train_ds, val_ds = random_split(dataset, [50000, 10000])\n",
    "len(train_ds), len(val_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "\n",
    "batch_size = 128\n",
    "\n",
    "train_loader = DataLoader(train_ds, batch_size, shuffle=True)\n",
    "val_loader = DataLoader(val_ds, batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "\n",
    "input_size = 28*28\n",
    "num_classes = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MnistModel(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.linear = nn.Linear(input_size, num_classes)\n",
    "        \n",
    "    def forward(self, xb):\n",
    "        xb = xb.reshape(-1, 784)\n",
    "        out = self.linear(xb)\n",
    "        return out\n",
    "    \n",
    "model = MnistModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Linear(in_features=784, out_features=10, bias=True)"
      ]
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "model.linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "torch.Size([10, 784]) torch.Size([10])\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[Parameter containing:\n",
       " tensor([[ 0.0204, -0.0300, -0.0327,  ..., -0.0235,  0.0290,  0.0179],\n",
       "         [-0.0166,  0.0042,  0.0080,  ...,  0.0246, -0.0312,  0.0119],\n",
       "         [-0.0088,  0.0345, -0.0164,  ..., -0.0049, -0.0281, -0.0259],\n",
       "         ...,\n",
       "         [ 0.0046,  0.0078, -0.0203,  ...,  0.0187, -0.0225, -0.0123],\n",
       "         [-0.0312,  0.0139,  0.0036,  ...,  0.0273, -0.0020, -0.0199],\n",
       "         [ 0.0012,  0.0054, -0.0299,  ...,  0.0034,  0.0014,  0.0102]],\n",
       "        requires_grad=True),\n",
       " Parameter containing:\n",
       " tensor([ 0.0212,  0.0237,  0.0165, -0.0263,  0.0053,  0.0139,  0.0232, -0.0142,\n",
       "          0.0345, -0.0245], requires_grad=True)]"
      ]
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "print(model.linear.weight.shape, model.linear.bias.shape)\n",
    "list(model.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "torch.Size([128, 1, 28, 28])\noutputs.shape :  torch.Size([128, 10])\nSample outputs :\n tensor([[ 0.1774, -0.2591, -0.2467,  0.1138, -0.0486, -0.0753,  0.1490, -0.2380,\n         -0.0016,  0.0292],\n        [ 0.1101,  0.0703, -0.3353,  0.0044, -0.1202, -0.0731,  0.1149, -0.2527,\n         -0.0447,  0.0316]])\n"
     ]
    }
   ],
   "source": [
    "for images, labels in train_loader:\n",
    "    print(images.shape)\n",
    "    outputs = model(images)\n",
    "    break\n",
    "\n",
    "print('outputs.shape : ', outputs.shape)\n",
    "print('Sample outputs :\\n', outputs[:2].data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "tensor([[ 0.1774, -0.2591, -0.2467,  0.1138, -0.0486, -0.0753,  0.1490, -0.2380,\n",
       "         -0.0016,  0.0292],\n",
       "        [ 0.1101,  0.0703, -0.3353,  0.0044, -0.1202, -0.0731,  0.1149, -0.2527,\n",
       "         -0.0447,  0.0316]], grad_fn=<SliceBackward>)"
      ]
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "outputs[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Sample probabilities:\n tensor([[0.1228, 0.0794, 0.0804, 0.1152, 0.0980, 0.0954, 0.1194, 0.0811, 0.1027,\n         0.1059],\n        [0.1161, 0.1116, 0.0744, 0.1045, 0.0923, 0.0967, 0.1167, 0.0808, 0.0995,\n         0.1074]])\nSum:  1.0\n"
     ]
    }
   ],
   "source": [
    "# Apply softmax for each output row\n",
    "probs = F.softmax(outputs, dim=1)\n",
    "\n",
    "# Look at sample probabilities\n",
    "print(\"Sample probabilities:\\n\", probs[:2].data)\n",
    "\n",
    "# Add up the probabilities of an output row\n",
    "print(\"Sum: \", torch.sum(probs[0]).item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor([0, 6, 0, 5, 9, 8, 0, 6, 0, 6, 3, 6, 0, 0, 6, 4, 5, 6, 5, 0, 5, 2, 0, 6,\n        5, 9, 0, 9, 5, 5, 9, 8, 8, 2, 5, 0, 0, 0, 6, 0, 6, 8, 6, 5, 9, 9, 9, 6,\n        9, 9, 9, 8, 9, 5, 6, 3, 0, 3, 6, 0, 0, 0, 3, 0, 0, 8, 0, 0, 6, 8, 0, 9,\n        9, 5, 5, 0, 0, 6, 0, 4, 6, 6, 0, 8, 2, 0, 6, 6, 3, 6, 5, 6, 3, 0, 6, 5,\n        0, 3, 0, 6, 4, 0, 8, 3, 5, 0, 6, 6, 0, 8, 0, 3, 6, 0, 6, 3, 1, 0, 4, 0,\n        3, 5, 0, 5, 9, 0, 0, 6])\ntensor([0.1228, 0.1167, 0.1355, 0.1519, 0.1241, 0.1220, 0.1809, 0.1095, 0.1357,\n        0.1414, 0.1279, 0.1389, 0.1457, 0.1164, 0.1630, 0.1423, 0.1253, 0.1453,\n        0.1144, 0.1292, 0.1216, 0.1437, 0.1766, 0.1289, 0.1280, 0.1136, 0.1113,\n        0.1253, 0.1238, 0.1237, 0.1325, 0.1277, 0.1198, 0.1326, 0.1480, 0.1297,\n        0.1301, 0.1289, 0.1277, 0.1469, 0.1346, 0.1103, 0.1277, 0.1152, 0.1286,\n        0.1186, 0.1136, 0.1172, 0.1171, 0.1257, 0.1312, 0.1194, 0.1198, 0.1314,\n        0.1152, 0.1140, 0.1285, 0.1302, 0.1190, 0.1268, 0.1230, 0.1401, 0.1176,\n        0.1454, 0.1432, 0.1286, 0.1357, 0.1548, 0.1527, 0.1259, 0.1261, 0.1333,\n        0.1302, 0.1360, 0.1261, 0.1528, 0.1518, 0.1190, 0.1354, 0.1224, 0.1271,\n        0.1158, 0.1239, 0.1195, 0.1101, 0.1261, 0.1127, 0.1152, 0.1253, 0.1339,\n        0.1249, 0.1278, 0.1167, 0.1488, 0.1265, 0.1231, 0.1770, 0.1281, 0.1114,\n        0.1342, 0.1146, 0.1426, 0.1189, 0.1171, 0.1166, 0.1389, 0.1261, 0.1172,\n        0.1634, 0.1237, 0.1364, 0.1200, 0.1510, 0.1273, 0.1311, 0.1276, 0.1229,\n        0.1187, 0.1515, 0.1172, 0.1138, 0.1397, 0.1374, 0.1170, 0.1419, 0.1331,\n        0.1282, 0.1238], grad_fn=<MaxBackward0>)\n"
     ]
    }
   ],
   "source": [
    "max_probs, preds = torch.max(probs, dim=1)\n",
    "print(preds)\n",
    "print(max_probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "tensor([8, 7, 0, 5, 6, 3, 3, 7, 8, 6, 2, 6, 4, 4, 3, 9, 7, 0, 7, 8, 7, 3, 2, 0,\n",
       "        5, 4, 5, 6, 7, 7, 3, 1, 9, 4, 0, 4, 4, 5, 0, 8, 0, 4, 3, 8, 9, 9, 5, 6,\n",
       "        5, 5, 3, 9, 3, 5, 6, 1, 3, 3, 4, 7, 2, 0, 1, 2, 4, 7, 8, 8, 5, 9, 8, 6,\n",
       "        6, 0, 3, 8, 2, 6, 0, 6, 5, 9, 5, 7, 7, 6, 2, 9, 8, 6, 2, 2, 4, 0, 6, 1,\n",
       "        0, 2, 7, 3, 7, 2, 1, 6, 5, 2, 9, 6, 8, 1, 3, 7, 3, 6, 3, 7, 6, 0, 2, 5,\n",
       "        7, 5, 2, 1, 8, 2, 2, 5])"
      ]
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "tensor([[ 0.1774, -0.2591, -0.2467,  0.1138, -0.0486, -0.0753,  0.1490, -0.2380,\n",
       "         -0.0016,  0.0292],\n",
       "        [ 0.1101,  0.0703, -0.3353,  0.0044, -0.1202, -0.0731,  0.1149, -0.2527,\n",
       "         -0.0447,  0.0316]], grad_fn=<SliceBackward>)"
      ]
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "source": [
    "outputs[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(outputs, labels):\n",
    "    _, preds = torch.max(outputs, dim=1)\n",
    "    return torch.tensor(torch.sum(preds == labels).item() / len(preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "tensor(0.1719)"
      ]
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "source": [
    "accuracy(outputs, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "tensor([[0.1228, 0.0794, 0.0804,  ..., 0.0811, 0.1027, 0.1059],\n",
       "        [0.1161, 0.1116, 0.0744,  ..., 0.0808, 0.0995, 0.1074],\n",
       "        [0.1355, 0.0720, 0.1242,  ..., 0.0714, 0.0617, 0.1048],\n",
       "        ...,\n",
       "        [0.1331, 0.0947, 0.0911,  ..., 0.0799, 0.1105, 0.0967],\n",
       "        [0.1282, 0.0979, 0.0842,  ..., 0.0679, 0.0853, 0.0978],\n",
       "        [0.1100, 0.0917, 0.0796,  ..., 0.1019, 0.0903, 0.0939]],\n",
       "       grad_fn=<SoftmaxBackward>)"
      ]
     },
     "metadata": {},
     "execution_count": 29
    }
   ],
   "source": [
    "probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_fn = F.cross_entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensor(2.3000, grad_fn=<NllLossBackward>)\n"
     ]
    }
   ],
   "source": [
    "# Loss for current batch of data\n",
    "loss = loss_fn(outputs, labels)\n",
    "print(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):\n",
    "    optimizer = opt_func(model.parameters(), lr)\n",
    "    history = [] # for recording epoch-wise results\n",
    "    \n",
    "    for epoch in range(epochs):\n",
    "        \n",
    "        # Training Phase \n",
    "        for batch in train_loader:\n",
    "            loss = model.training_step(batch)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            optimizer.zero_grad()\n",
    "        \n",
    "        # Validation phase\n",
    "        result = evaluate(model, val_loader)\n",
    "        model.epoch_end(epoch, result)\n",
    "        history.append(result)\n",
    "\n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "l1 = [1, 2, 3, 4, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[2, 4, 6, 8, 10]"
      ]
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "l2 = [x*2 for x in l1]\n",
    "l2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, val_loader):\n",
    "    outputs = [model.validation_step(batch) for batch in val_loader]\n",
    "    return model.validation_epoch_end(outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MnistModel(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.linear = nn.Linear(input_size, num_classes)\n",
    "        \n",
    "    def forward(self, xb):\n",
    "        xb = xb.reshape(-1, 784)\n",
    "        out = self.linear(xb)\n",
    "        return out\n",
    "    \n",
    "    def training_step(self, batch):\n",
    "        images, labels = batch \n",
    "        out = self(images)                  # Generate predictions\n",
    "        loss = F.cross_entropy(out, labels) # Calculate loss\n",
    "        return loss\n",
    "    \n",
    "    def validation_step(self, batch):\n",
    "        images, labels = batch \n",
    "        out = self(images)                    # Generate predictions\n",
    "        loss = F.cross_entropy(out, labels)   # Calculate loss\n",
    "        acc = accuracy(out, labels)           # Calculate accuracy\n",
    "        return {'val_loss': loss, 'val_acc': acc}\n",
    "        \n",
    "    def validation_epoch_end(self, outputs):\n",
    "        batch_losses = [x['val_loss'] for x in outputs]\n",
    "        epoch_loss = torch.stack(batch_losses).mean()   # Combine losses\n",
    "        batch_accs = [x['val_acc'] for x in outputs]\n",
    "        epoch_acc = torch.stack(batch_accs).mean()      # Combine accuracies\n",
    "        return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}\n",
    "    \n",
    "    def epoch_end(self, epoch, result):\n",
    "        print(\"Epoch [{}], val_loss: {:.4f}, val_acc: {:.4f}\".format(epoch, result['val_loss'], result['val_acc']))\n",
    "    \n",
    "model = MnistModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'val_loss': 2.3550493717193604, 'val_acc': 0.07446598261594772}"
      ]
     },
     "metadata": {},
     "execution_count": 37
    }
   ],
   "source": [
    "result0 = evaluate(model, val_loader)\n",
    "result0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch [0], val_loss: 1.9782, val_acc: 0.5836\n",
      "Epoch [1], val_loss: 1.7041, val_acc: 0.7009\n",
      "Epoch [2], val_loss: 1.4993, val_acc: 0.7417\n",
      "Epoch [3], val_loss: 1.3448, val_acc: 0.7668\n",
      "Epoch [4], val_loss: 1.2261, val_acc: 0.7831\n"
     ]
    }
   ],
   "source": [
    "history1 = fit(5, 0.001, model, train_loader, val_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch [0], val_loss: 1.1329, val_acc: 0.7970\n",
      "Epoch [1], val_loss: 1.0582, val_acc: 0.8046\n",
      "Epoch [2], val_loss: 0.9971, val_acc: 0.8132\n",
      "Epoch [3], val_loss: 0.9464, val_acc: 0.8175\n",
      "Epoch [4], val_loss: 0.9036, val_acc: 0.8219\n"
     ]
    }
   ],
   "source": [
    "history2 = fit(5, 0.001, model, train_loader, val_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch [0], val_loss: 0.8669, val_acc: 0.8258\n",
      "Epoch [1], val_loss: 0.8351, val_acc: 0.8292\n",
      "Epoch [2], val_loss: 0.8073, val_acc: 0.8323\n",
      "Epoch [3], val_loss: 0.7828, val_acc: 0.8346\n",
      "Epoch [4], val_loss: 0.7609, val_acc: 0.8368\n"
     ]
    }
   ],
   "source": [
    "history3 = fit(5, 0.001, model, train_loader, val_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch [0], val_loss: 0.7413, val_acc: 0.8394\n",
      "Epoch [1], val_loss: 0.7237, val_acc: 0.8414\n",
      "Epoch [2], val_loss: 0.7076, val_acc: 0.8429\n",
      "Epoch [3], val_loss: 0.6930, val_acc: 0.8445\n",
      "Epoch [4], val_loss: 0.6796, val_acc: 0.8464\n"
     ]
    }
   ],
   "source": [
    "history4 = fit(5, 0.001, model, train_loader, val_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "history = [result0] + history1 + history2 + history3 + history4\n",
    "accuracies = [result['val_acc'] for result in history]\n",
    "plt.plot(accuracies, '-x')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.title('Accuracy vs. No. of epochs');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define test dataset\n",
    "test_dataset = MNIST(root='data/', \n",
    "                     train=False,\n",
    "                     transform=transforms.ToTensor())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_image(img, model):\n",
    "    xb = img.unsqueeze(0)\n",
    "    yb = model(xb)\n",
    "    _, preds = torch.max(yb, dim=1)\n",
    "    return preds[0].item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Label: 7 , Predicted: 7\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "img, label = test_dataset[0]\n",
    "plt.imshow(img[0], cmap='gray')\n",
    "print('Label:', label, ', Predicted:', predict_image(img, model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Label: 9 , Predicted: 9\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "img, label = test_dataset[193]\n",
    "plt.imshow(img[0], cmap='gray')\n",
    "print('Label:', label, ', Predicted:', predict_image(img, model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Label: 2 , Predicted: 8\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "img, label = test_dataset[1839]\n",
    "plt.imshow(img[0], cmap='gray')\n",
    "print('Label:', label, ', Predicted:', predict_image(img, model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'val_loss': 0.6435826420783997, 'val_acc': 0.8589843511581421}"
      ]
     },
     "metadata": {},
     "execution_count": 48
    }
   ],
   "source": [
    "test_loader = DataLoader(test_dataset, batch_size=256)\n",
    "result = evaluate(model, test_loader)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), 'mnist-logistic.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "OrderedDict([('linear.weight',\n",
       "              tensor([[-0.0216, -0.0353, -0.0142,  ..., -0.0071,  0.0292,  0.0211],\n",
       "                      [ 0.0229, -0.0264,  0.0045,  ...,  0.0336, -0.0193, -0.0288],\n",
       "                      [-0.0300,  0.0017, -0.0287,  ...,  0.0201, -0.0014,  0.0327],\n",
       "                      ...,\n",
       "                      [-0.0211,  0.0251, -0.0188,  ...,  0.0204,  0.0280, -0.0177],\n",
       "                      [-0.0150, -0.0202,  0.0104,  ..., -0.0197,  0.0349, -0.0333],\n",
       "                      [ 0.0277, -0.0173, -0.0169,  ...,  0.0130,  0.0135, -0.0356]])),\n",
       "             ('linear.bias',\n",
       "              tensor([-0.0659,  0.1084, -0.0066,  0.0030,  0.0545,  0.0302,  0.0200,  0.0385,\n",
       "                      -0.0683,  0.0009]))])"
      ]
     },
     "metadata": {},
     "execution_count": 50
    }
   ],
   "source": [
    "model.state_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = MnistModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "OrderedDict([('linear.weight',\n",
       "              tensor([[ 0.0196, -0.0173,  0.0199,  ..., -0.0314,  0.0296,  0.0328],\n",
       "                      [ 0.0209, -0.0285,  0.0312,  ...,  0.0097, -0.0289,  0.0049],\n",
       "                      [ 0.0192, -0.0011,  0.0101,  ...,  0.0188,  0.0075,  0.0080],\n",
       "                      ...,\n",
       "                      [-0.0026, -0.0157, -0.0104,  ..., -0.0229, -0.0015, -0.0136],\n",
       "                      [ 0.0335,  0.0243,  0.0089,  ...,  0.0235, -0.0231, -0.0286],\n",
       "                      [-0.0102, -0.0270,  0.0082,  ..., -0.0172, -0.0268, -0.0283]])),\n",
       "             ('linear.bias',\n",
       "              tensor([-0.0063,  0.0030,  0.0232,  0.0160,  0.0034,  0.0069, -0.0027,  0.0061,\n",
       "                      -0.0332,  0.0123]))])"
      ]
     },
     "metadata": {},
     "execution_count": 52
    }
   ],
   "source": [
    "model2.state_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'val_loss': 2.321748733520508, 'val_acc': 0.12314452975988388}"
      ]
     },
     "metadata": {},
     "execution_count": 53
    }
   ],
   "source": [
    "evaluate(model2, test_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "OrderedDict([('linear.weight',\n",
       "              tensor([[-0.0216, -0.0353, -0.0142,  ..., -0.0071,  0.0292,  0.0211],\n",
       "                      [ 0.0229, -0.0264,  0.0045,  ...,  0.0336, -0.0193, -0.0288],\n",
       "                      [-0.0300,  0.0017, -0.0287,  ...,  0.0201, -0.0014,  0.0327],\n",
       "                      ...,\n",
       "                      [-0.0211,  0.0251, -0.0188,  ...,  0.0204,  0.0280, -0.0177],\n",
       "                      [-0.0150, -0.0202,  0.0104,  ..., -0.0197,  0.0349, -0.0333],\n",
       "                      [ 0.0277, -0.0173, -0.0169,  ...,  0.0130,  0.0135, -0.0356]])),\n",
       "             ('linear.bias',\n",
       "              tensor([-0.0659,  0.1084, -0.0066,  0.0030,  0.0545,  0.0302,  0.0200,  0.0385,\n",
       "                      -0.0683,  0.0009]))])"
      ]
     },
     "metadata": {},
     "execution_count": 54
    }
   ],
   "source": [
    "model2.load_state_dict(torch.load('mnist-logistic.pth'))\n",
    "model2.state_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'val_loss': 0.6435826420783997, 'val_acc': 0.8589843511581421}"
      ]
     },
     "metadata": {},
     "execution_count": 55
    }
   ],
   "source": [
    "test_loader = DataLoader(test_dataset, batch_size=256)\n",
    "result = evaluate(model2, test_loader)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}