01_CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "\n",
    "import torch \n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "from IPython.display import Image\n",
    "from IPython import display\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Tesla P40'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.get_device_name(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Processing...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# prepare batch_loader \n",
    "batch_size=64\n",
    "trn_dataset = datasets.MNIST('../mnist_data/',\n",
    "                             download=True,\n",
    "                             train=True,\n",
    "                             transform=transforms.Compose([\n",
    "                                 transforms.ToTensor(), # image to Tensor\n",
    "                                 transforms.Normalize((0.1307,), (0.3081,)) # image, label\n",
    "                             ])) \n",
    "\n",
    "trn_loader = torch.utils.data.DataLoader(trn_dataset,\n",
    "                                         batch_size=batch_size,\n",
    "                                         shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_dataset = datasets.MNIST(\"../mnist_data/\", \n",
    "                             download=False,\n",
    "                             train=False,\n",
    "                             transform= transforms.Compose([\n",
    "                               transforms.ToTensor(),\n",
    "                               transforms.Normalize((0.1307, ),(0.3081, ))\n",
    "                           ]))\n",
    "\n",
    "val_loader = torch.utils.data.DataLoader(val_dataset,\n",
    "                                         batch_size=batch_size,\n",
    "                                         shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_batches = len(trn_loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 학습시키려는 MNIST 이미지를 확인해보자"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = next(iter(trn_loader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "img, label = data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 1, 28, 28])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.size() # batch_size, num_channel, x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label.size() # 0~9까지의 숫자가 batch_size 만큼"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create figure for plotting\n",
    "row_num = 5\n",
    "col_num = 4\n",
    "fig, ax = plt.subplots(row_num, col_num, figsize=(6,6))\n",
    "for i, j in itertools.product(range(row_num), range(col_num)):\n",
    "    ax[i,j].get_xaxis().set_visible(False)\n",
    "    ax[i,j].get_yaxis().set_visible(False)\n",
    "    \n",
    "    \n",
    "for k in range(row_num*col_num):\n",
    "    i = k//col_num\n",
    "    j = k%col_num\n",
    "    ax[i,j].cla() # clear the current axis\n",
    "    ax[i,j].imshow(img[k,:].data.cpu().numpy().reshape(28,28), cmap='Greys')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://9bow.github.io/PyTorch-tutorials-kr/_images/mnist.png\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url= \"https://9bow.github.io/PyTorch-tutorials-kr/_images/mnist.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# construct model on cuda if available\n",
    "\n",
    "use_cuda = torch.cuda.is_available()\n",
    "\n",
    "class CNNClassifier(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        # 항상 torch.nn.Module을 상속받고 시작\n",
    "        super(CNNClassifier, self).__init__()\n",
    "        conv1 = nn.Conv2d(1, 6, 5, 1) # 6@24*24\n",
    "        # activation ReLU\n",
    "        pool1 = nn.MaxPool2d(2) # 6@12*12\n",
    "        conv2 = nn.Conv2d(6, 16, 5, 1) # 16@8*8\n",
    "        # activation ReLU\n",
    "        pool2 = nn.MaxPool2d(2) # 16@4*4\n",
    "        \n",
    "        self.conv_module = nn.Sequential(\n",
    "            conv1,\n",
    "            nn.ReLU(),\n",
    "            pool1,\n",
    "            conv2,\n",
    "            nn.ReLU(),\n",
    "            pool2\n",
    "        )\n",
    "        \n",
    "        fc1 = nn.Linear(16*4*4, 120)\n",
    "        # activation ReLU\n",
    "        fc2 = nn.Linear(120, 84)\n",
    "        # activation ReLU\n",
    "        fc3 = nn.Linear(84, 10)\n",
    "\n",
    "        self.fc_module = nn.Sequential(\n",
    "            fc1,\n",
    "            nn.ReLU(),\n",
    "            fc2,\n",
    "            nn.ReLU(),\n",
    "            fc3\n",
    "        )\n",
    "        \n",
    "        # gpu로 할당\n",
    "        if use_cuda:\n",
    "            self.conv_module = self.conv_module.cuda()\n",
    "            self.fc_module = self.fc_module.cuda()\n",
    "        \n",
    "    def forward(self, x):\n",
    "        out = self.conv_module(x) # @16*4*4\n",
    "        # make linear\n",
    "        dim = 1\n",
    "        for d in out.size()[1:]: #16, 4, 4\n",
    "            dim = dim * d\n",
    "        out = out.view(-1, dim)\n",
    "        out = self.fc_module(out)\n",
    "        return F.softmax(out, dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn = CNNClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training model\n",
    "\n",
    "**What do we need?** <br>\n",
    "1. loss ex) MSELoss\n",
    "2. backpropagation method ex) SGD ... <br>\n",
    "    2.1 update할 parameter들 <br>\n",
    "    2.2 learning rate\n",
    "        \n",
    "3. hyperparamters ex) num_epochs ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loss\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "# backpropagation method\n",
    "learning_rate = 1e-3\n",
    "optimizer = optim.Adam(cnn.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hyper-parameters\n",
    "num_epochs = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "938"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_batches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: tensor([ 5,  5,  1,  9,  9,  7,  4,  4], device='cuda:0')\n",
      "prediction: tensor([ 5,  5,  1,  9,  9,  7,  4,  4], device='cuda:0')\n",
      "epoch: 2/2 | step: 900/938 | trn loss: 1.4803 | val loss: 1.4784\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create figure for plotting\n",
    "row_num = 2\n",
    "col_num = 4\n",
    "fig, ax = plt.subplots(row_num, col_num, figsize=(6,6))\n",
    "for i, j in itertools.product(range(row_num), range(col_num)):\n",
    "    ax[i,j].get_xaxis().set_visible(False)\n",
    "    ax[i,j].get_yaxis().set_visible(False) \n",
    "    \n",
    "trn_loss_list = []\n",
    "val_loss_list = []\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    trn_loss = 0.0\n",
    "    for i, data in enumerate(trn_loader):\n",
    "        x, label = data\n",
    "        if use_cuda:\n",
    "            x = x.cuda()\n",
    "            label = label.cuda()\n",
    "        # grad init\n",
    "        optimizer.zero_grad()\n",
    "        # forward propagation\n",
    "        model_output = cnn(x)\n",
    "        # calculate loss\n",
    "        loss = criterion(model_output, label)\n",
    "        # back propagation \n",
    "        loss.backward()\n",
    "        # weight update\n",
    "        optimizer.step()\n",
    "        \n",
    "        # trn_loss summary\n",
    "        trn_loss += loss.item()\n",
    "        # del (memory issue)\n",
    "        del loss\n",
    "        del model_output\n",
    "        \n",
    "        # 학습과정 출력\n",
    "        if (i+1) % 100 == 0: # every 100 mini-batches\n",
    "            with torch.no_grad(): # very very very very important!!!\n",
    "                val_loss = 0.0\n",
    "                for j, val in enumerate(val_loader):\n",
    "                    val_x, val_label = val\n",
    "                    if use_cuda:\n",
    "                        val_x = val_x.cuda()\n",
    "                        val_label =val_label.cuda()\n",
    "                    val_output = cnn(val_x)\n",
    "                    v_loss = criterion(val_output, val_label)\n",
    "                    val_loss += v_loss\n",
    "\n",
    "            # draw last val dataset\n",
    "            for k in range(row_num*col_num):\n",
    "                ii = k//col_num\n",
    "                jj = k%col_num\n",
    "                ax[ii,jj].cla() # clear the current axis\n",
    "                ax[ii,jj].imshow(val_x[k,:].data.cpu().numpy().reshape(28,28), cmap='Greys')\n",
    "            \n",
    "            display.clear_output(wait=True)\n",
    "            display.display(plt.gcf()) # get a reference to a current figure\n",
    "                \n",
    "            print(\"label: {}\".format(val_label[:row_num*col_num]))\n",
    "            print(\"prediction: {}\".format(val_output.argmax(dim=1)[:row_num*col_num]))\n",
    "            del val_output\n",
    "            del v_loss\n",
    "            \n",
    "            print(\"epoch: {}/{} | step: {}/{} | trn loss: {:.4f} | val loss: {:.4f}\".format(\n",
    "                epoch+1, num_epochs, i+1, num_batches, trn_loss / 100, val_loss / len(val_loader)\n",
    "            ))            \n",
    "            \n",
    "            trn_loss_list.append(trn_loss/100)\n",
    "            val_loss_list.append(val_loss/len(val_loader))\n",
    "            trn_loss = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'loss')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "x_range = range(len(trn_loss_list))\n",
    "plt.plot(x_range, trn_loss_list, label=\"trn\")\n",
    "plt.plot(x_range, val_loss_list, label=\"val\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"training steps\")\n",
    "plt.ylabel(\"loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: 98.43\n"
     ]
    }
   ],
   "source": [
    "# val acc\n",
    "with torch.no_grad():\n",
    "    corr_num = 0\n",
    "    total_num = 0\n",
    "    for j, val in enumerate(val_loader):\n",
    "        val_x, val_label = val\n",
    "        if use_cuda:\n",
    "            val_x = val_x.cuda()\n",
    "            val_label =val_label.cuda()\n",
    "        val_output = cnn(val_x)\n",
    "        model_label = val_output.argmax(dim=1)\n",
    "        corr = val_label[val_label == model_label].size(0)\n",
    "        corr_num += corr\n",
    "        total_num += val_label.size(0)\n",
    "\n",
    "print(\"acc: {:.2f}\".format(corr_num / total_num * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}