mnist-linear.ipynb

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   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from tqdm.auto import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "from torchsummary import summary\n",
    "from torchvision import datasets\n",
    "from torchvision.transforms import ToTensor\n",
    "from torch.utils.data import DataLoader"
   ]
  },
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   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "\n",
    "train_set = datasets.MNIST(\n",
    "    root='./data',\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=ToTensor()\n",
    ")\n",
    "\n",
    "test_set = datasets.MNIST(\n",
    "    root='./data',\n",
    "    train=False,\n",
    "    download=False,\n",
    "    transform=ToTensor()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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   "source": [
    "batch_size = 32\n",
    "\n",
    "train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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   "source": [
    "class Model(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size):\n",
    "        super(Model, self).__init__()\n",
    "        self.input_size = input_size\n",
    "        self.hidden_size = hidden_size\n",
    "        self.output_size = output_size\n",
    "        \n",
    "        self.layer0 = nn.Linear(input_size, hidden_size, bias=True)\n",
    "        self.layer1 = nn.Linear(hidden_size, hidden_size, bias=True)\n",
    "        self.layer2 = nn.Linear(hidden_size, hidden_size, bias=True)\n",
    "        self.layer3 = nn.Linear(hidden_size, hidden_size, bias=True)\n",
    "        self.layer4 = nn.Linear(hidden_size, hidden_size, bias=True)\n",
    "        self.layer5 = nn.Linear(hidden_size, output_size, bias=True)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # Assuming x is a tensor with size (batch_size, 1, 28, 28)\n",
    "        batch_size, _, height, width = x.size()\n",
    "\n",
    "        # Flatten the input tensor\n",
    "        x = x.view(batch_size, -1)\n",
    "\n",
    "        x = nn.functional.relu(self.layer0(x))\n",
    "        x = nn.functional.relu(self.layer1(x))\n",
    "        x = nn.functional.relu(self.layer2(x))\n",
    "        x = nn.functional.relu(self.layer3(x))\n",
    "        x = nn.functional.relu(self.layer4(x))\n",
    "        x = self.layer4(x)\n",
    "        return x"
   ]
  },
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   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
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   "source": [
    "# Do not change this cell\n",
    "def caluculate_acc(data, model, length):\n",
    "  model.eval()\n",
    "  acc = 0\n",
    "  for images, label in data:\n",
    "    with torch.no_grad():\n",
    "      images = images.to(device)\n",
    "      label = label.to(device)\n",
    "      yp = model(images)\n",
    "      yp = torch.nn.functional.softmax(yp, dim=1)\n",
    "      yp = torch.argmax(yp, 1)\n",
    "      acc += torch.sum(yp == label)\n",
    "  return acc / length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "BHGcupPpiKO1",
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   "source": [
    "# Do not change this cell\n",
    "def train_one_epoch(model, data, optimizer, criterion, length):\n",
    "  model.train()\n",
    "  acc = 0\n",
    "  for images, labels in data:\n",
    "    imgs = torch.autograd.Variable(images).to(device)\n",
    "    label = torch.autograd.Variable(labels).to(device)\n",
    "    optimizer.zero_grad()\n",
    "    yp = model(imgs)\n",
    "    loss = criterion(yp, label)\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    yp = torch.argmax(yp, 1)\n",
    "    acc += torch.sum(yp == label)\n",
    "  return loss.item(), acc / length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "id": "T7q3yT39wWIn",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Linear-1                  [-1, 100]          78,500\n",
      "            Linear-2                  [-1, 100]          10,100\n",
      "            Linear-3                  [-1, 100]          10,100\n",
      "            Linear-4                  [-1, 100]          10,100\n",
      "            Linear-5                  [-1, 100]          10,100\n",
      "            Linear-6                  [-1, 100]          10,100\n",
      "================================================================\n",
      "Total params: 129,000\n",
      "Trainable params: 129,000\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.00\n",
      "Forward/backward pass size (MB): 0.00\n",
      "Params size (MB): 0.49\n",
      "Estimated Total Size (MB): 0.50\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "model = model = Model(input_size=784, hidden_size=100, output_size=10).to(device)\n",
    "\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# model summary\n",
    "summary(model, (1, 28, 28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
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   "outputs": [
    {
     "data": {
      "text/plain": "  0%|          | 0/20 [00:00<?, ?it/s]",
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       "version_major": 2,
       "version_minor": 0,
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   ],
   "source": [
    "# training process\n",
    "val_accs = []\n",
    "accs = []\n",
    "best_acc = 0\n",
    "for e in tqdm(range(20)):\n",
    "  accs.append(train_one_epoch(model, train_loader, optimizer, criterion, len(train_set))[1].item())\n",
    "  val_accs.append(caluculate_acc(test_loader, model, len(test_set)).item())\n",
    "  if best_acc < val_accs[-1]:\n",
    "    torch.save(model, \"model.h5\")\n",
    "    best_Acc = val_accs[-1]\n",
    "  \n",
    "  # just a way to avoid pverfitting\n",
    "  if val_accs[-1] > 0.99 and accs[-1] > 0.99:\n",
    "    break"
   ]
  },
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   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(accs, label='accuracy')\n",
    "plt.plot(val_accs, label = 'val_accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ujZNWCQWyYFO",
    "outputId": "a6ee01d1-73c3-4662-ef84-e66424d56835",
    "ExecuteTime": {
     "end_time": "2023-12-22T12:32:30.874197Z",
     "start_time": "2023-12-22T12:32:29.492303Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(0.9782000184059143, 0.994700014591217)"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "caluculate_acc(test_loader, model, len(test_set)).item(), caluculate_acc(train_loader, model, len(train_set)).item()"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
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