174_MNIST_CNN.ipynb

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  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from torchsummary import summary\n",
    "from matplotlib_inline.backend_inline import set_matplotlib_formats\n",
    "set_matplotlib_formats('svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000, 1, 28, 28)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.loadtxt(open('sample_data/mnist_train_small.csv', 'rb'), delimiter=',')\n",
    "\n",
    "label = data[:,0]\n",
    "data = data[:, 1:]\n",
    "data = data/np.max(data)\n",
    "\n",
    "img_data = data.reshape(data.shape[0], 1, 28,28)\n",
    "img_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(torch.tensor(img_data).float(),\n",
    "                                                    torch.tensor(label).long(),\n",
    "                                                    test_size=0.15)\n",
    "\n",
    "train_loader = TensorDataset(x_train, y_train)\n",
    "test_loader = TensorDataset(x_test, y_test)\n",
    "\n",
    "train_loader = DataLoader(train_loader, batch_size=32, shuffle=True, drop_last=True)\n",
    "test_loader = DataLoader(test_loader, batch_size=test_loader.tensors[0].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([17000, 784])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_loader.dataset.tensors[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_MNIST_CNN():\n",
    "    class mnistNet(nn.Module):\n",
    "        def __init__(self, img_width=28):\n",
    "            super().__init__()\n",
    "            \n",
    "            self.cv1 = nn.Conv2d(1,10,kernel_size=5, stride=1,padding=1)\n",
    "            out_width = np.floor( (img_width + 2*1 - 5)/1 ) + 1  # After conv\n",
    "            out_width = int(out_width/2)                         # After Max Pool\n",
    "            self.cv2 = nn.Conv2d(10, 20, kernel_size=5, stride=1, padding=1)\n",
    "            out_width = np.floor( (out_width + 2*1 - 5)/1 ) + 1  # After conv\n",
    "            out_width = int(out_width/2)                         # After Max Pool\n",
    "            \n",
    "            out_height = out_width                               # Since same size of img\n",
    "            input_for_fc1 = 20*out_width*out_height\n",
    "            \n",
    "            self.fc1 = nn.Linear(input_for_fc1, 50)\n",
    "            self.out = nn.Linear(50, 10)\n",
    "            \n",
    "        def forward(self, x):\n",
    "            x = F.relu(F.max_pool2d( self.cv1(x), 2 )) # kernal size of max pool = 2 ->> This is why we devided by 2 while calculationg out_width in __init__\n",
    "            x = F.relu(F.max_pool2d( self.cv2(x), 2 ))\n",
    "            \n",
    "            one_img_length = x.shape.numel()/x.shape[0] # x.shape.numel() = no_of_img * channel_per_img * width * height || x.shape[0] = no_of_img\n",
    "            x = x.view((-1, int(one_img_length)) )             # -1 ->> you can try x.shape[0] it shall work\n",
    "            \n",
    "            x = F.relu( self.fc1(x) )\n",
    "            x = self.out(x)\n",
    "            return x\n",
    "    \n",
    "    net = mnistNet()\n",
    "    loss_fxn = nn.CrossEntropyLoss()\n",
    "    opt = torch.optim.Adam(net.parameters(), lr=0.001)\n",
    "    \n",
    "    return net, loss_fxn, opt\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 10])\n"
     ]
    }
   ],
   "source": [
    "net = create_MNIST_CNN()[0]\n",
    "\n",
    "x,y = next(iter(train_loader))\n",
    "y_ = net(x)\n",
    "print(y_.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1           [-1, 10, 26, 26]             260\n",
      "            Conv2d-2           [-1, 20, 11, 11]           5,020\n",
      "            Linear-3                   [-1, 50]          25,050\n",
      "            Linear-4                   [-1, 10]             510\n",
      "================================================================\n",
      "Total params: 30,840\n",
      "Trainable params: 30,840\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.00\n",
      "Forward/backward pass size (MB): 0.07\n",
      "Params size (MB): 0.12\n",
      "Estimated Total Size (MB): 0.19\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "summary(net, (1,28,28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(y_, y):\n",
    "    return 100*torch.mean((torch.argmax(y_, axis=1) == y).float()).item()\n",
    "\n",
    "\n",
    "def trainModel(model : nn.Module,\n",
    "               train : DataLoader,\n",
    "               test : DataLoader,\n",
    "               optmiser,\n",
    "               loss_fxn,\n",
    "               epoches : int =1000\n",
    "              ):\n",
    "    \n",
    "    loss_list = torch.zeros(epoches)\n",
    "    train_accuracy = []\n",
    "    test_accuracy = []\n",
    "    \n",
    "    for epoch in range(epoches):\n",
    "        batch_accuracy = []\n",
    "        batch_loss =[]\n",
    "        print(epoch)\n",
    "        \n",
    "        for x, y in train:\n",
    "            y_ = torch.squeeze(model(x))\n",
    "#             print(\"Y_ = \", torch.squeeze(y_), \"shape = \", y_.shape, \"y = \", y.shape)\n",
    "            loss = loss_fxn(y_, y.long())\n",
    "            \n",
    "            optmiser.zero_grad()\n",
    "            loss.backward()\n",
    "            optmiser.step()\n",
    "            \n",
    "            batch_loss.append(loss.item())\n",
    "            batch_accuracy.append(accuracy(y_, y))\n",
    "        \n",
    "        train_accuracy.append(np.mean(batch_accuracy))\n",
    "        loss_list[epoch] = np.mean(batch_loss)\n",
    "        \n",
    "        model.eval()\n",
    "        x, y = next(iter(test))\n",
    "        with torch.no_grad():\n",
    "            y_ = model(x)\n",
    "        test_accuracy.append(accuracy(y_, y))\n",
    "        model.train()\n",
    "        \n",
    "    return train_accuracy, test_accuracy, loss_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "net, loss_fxn, optmiser = create_MNIST_CNN()\n",
    "ta, tea, l = trainModel(net,\n",
    "           train_loader,\n",
    "           test_loader,\n",
    "           optmiser,\n",
    "           loss_fxn,\n",
    "           epoches=10)"
   ]
  },
  {
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   "execution_count": 37,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
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   "source": [
    "fig,ax = plt.subplots(1,2,figsize=(12, 4))\n",
    "\n",
    "ax[0].plot(l)\n",
    "ax[0].legend(['loss'])\n",
    "ax[0].grid()\n",
    "ax[0].set_title(f\"Loss ->> {l[-1] : .4f}\")\n",
    "\n",
    "ax[1].plot(ta)\n",
    "ax[1].plot(tea)\n",
    "ax[1].legend(['training accuracy', 'test accuracy'])\n",
    "ax[1].grid()\n",
    "ax[1].set_title(f\"Test acc ->> {tea[-1] : .2f}%\")\n",
    "plt.show()"
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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