01_MLP_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Dataset\n",
    "import pandas as pd\n",
    "train_dataset = pd.read_csv(\"../data/MNIST/train.csv\")\n",
    "test_dataset = pd.read_csv(\"../data/MNIST/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check Train Set\n",
    "train_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check Test Set\n",
    "test_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split to Image & Label\n",
    "train_images = (train_dataset.iloc[:, 1:].values).astype(\"float32\")\n",
    "train_labels = train_dataset[\"label\"].values\n",
    "test_images = (test_dataset.values).astype(\"float32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check Train Data's Image \n",
    "train_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, ..., 7, 6, 9], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check Train Data's Label\n",
    "train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check Test Data's Image\n",
    "test_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split into Train, Valid Dataset\n",
    "from sklearn.model_selection import train_test_split\n",
    "train_images, valid_images, train_labels, valid_labels = train_test_split(train_images, \n",
    "                                                                          train_labels, \n",
    "                                                                          stratify = train_labels, \n",
    "                                                                          random_state = 42, \n",
    "                                                                          test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Shape of Train Images:  (33600, 784)\n",
      "The Shape of Valid Images:  (8400, 784)\n",
      "The Shape of Test Images:  (28000, 784)\n"
     ]
    }
   ],
   "source": [
    "# Check Train, Valid, Test Image's Shape\n",
    "print(\"The Shape of Train Images: \", train_images.shape)\n",
    "print(\"The Shape of Valid Images: \", valid_images.shape)\n",
    "print(\"The Shape of Test Images: \", test_images.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Shape of Train Labels:  (33600,)\n",
      "The Shape of Valid Labels:  (8400,)\n"
     ]
    }
   ],
   "source": [
    "# Check Train, Valid Label's Shape\n",
    "print(\"The Shape of Train Labels: \", train_labels.shape)\n",
    "print(\"The Shape of Valid Labels: \", valid_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape image's size to check for ours\n",
    "train_images = train_images.reshape(train_images.shape[0], 28, 28)\n",
    "valid_images = valid_images.reshape(valid_images.shape[0], 28, 28)\n",
    "test_images = test_images.reshape(test_images.shape[0], 28, 28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Shape of Train Images:  (33600, 28, 28)\n",
      "The Shape of Valid Images:  (8400, 28, 28)\n",
      "The Shape of Test Images:  (28000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Check Train, Valid, Test Image's Shape after reshape\n",
    "print(\"The Shape of Train Images: \", train_images.shape)\n",
    "print(\"The Shape of Valid Images: \", valid_images.shape)\n",
    "print(\"The Shape of Test Images: \", test_images.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v7RoK1vSGevu2hj4abpe0rbrNb/q9K/TVl/eNn8sCSfALOiAJwg4kQdiBJAg7kARhB5Ig7EAShB1I4v8BabOHWXVyrg4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nJT0SEU9Vixt/7kbrq1/PWxNhXytplu1TbU+StEjSmgb6+Arbk6s3TmR7sqQFGrypqNdIWlzdXyzp2QZ7+YJBmca71TTjavi5a3z684jo+4+kSzT8jvzvJP1TEz206Os0Sf9T/bzZdG+SVmv4tO7/NHxGdI2kP5P0kqRt1e3UAertXzU8tfcmDQdrRkO9zdfwS8NNkjZWP5c0/dwV+urL88blskASXEEHJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0n8P2pGbTYNeQZVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+to0euvT1FUn/UfztbLs3SY+os1v3v+rsEY1KOlXSFkmvFrenDFFv/6TO1N7b1QnWvJZ6W6LOoeF2SS8Wf1e1/d6V9DWQ942vywJJ8A06IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUji/wA3919e1yPEngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize Train, Valid, Test's Images\n",
    "import matplotlib.pyplot as plt\n",
    "for idx in range(0, 5):\n",
    "    plt.imshow(train_images[idx], cmap = plt.get_cmap('gray'))\n",
    "    plt.title(train_labels[idx])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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V/SWSnm2xlxOMl2m866YZV8ufXevTn0fEwP8kXa/hb+T/Q9KdbfRQ09fvSPpZ9ber7d4kPaHh3br/0fAe0VJJvyVps6S3q9tp46i3RzU8tfcbGg7W9JZ6+6qGDw3fkLS9+ru+7c+u0NdAPjd+LgskwS/ogCQIO5AEYQeSIOxAEoQdSIKwA0kQdiCJ/wWmAn95MXmpgAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rX69bE2F/QdJs29+wPSTpYkmPNNDHl9g+pvrgRLaPkfQdDd5U1I9IWlw9Xyzp4QZ7+YJBmca71TTjavi1a3z684jo+4+khRr9RP6XkpY30UOLvv5Q0v9UPy833Zuk9Ro9rftco2dEfyPp9yQNS9pRPU4eoN7u1ejU3i9qNFjTGurtLI2+NXxR0tbqZ2HTr12hr768bnxdFkiCb9ABSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBL/B+hCkW9OD2JKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Darw2yXGdMiUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for idx in range(0, 5):\n",
    "    plt.imshow(valid_images[idx], cmap = plt.get_cmap('gray'))\n",
    "    plt.title(valid_labels[idx])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for idx in range(0, 5):\n",
    "    plt.imshow(test_images[idx], cmap = plt.get_cmap('gray'))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make Dataloader to feed on Multi Layer Perceptron Model\n",
    "import torch\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "train_images_tensor = torch.tensor(train_images)\n",
    "train_labels_tensor = torch.tensor(train_labels)\n",
    "train_tensor = TensorDataset(train_images_tensor, train_labels_tensor)\n",
    "train_loader = DataLoader(train_tensor, batch_size = 64, num_workers = 0, shuffle = True)\n",
    "\n",
    "valid_images_tensor = torch.tensor(valid_images)\n",
    "valid_labels_tensor = torch.tensor(valid_labels)\n",
    "valid_tensor = TensorDataset(valid_images_tensor, valid_labels_tensor)\n",
    "valid_loader = DataLoader(valid_tensor, batch_size = 64, num_workers = 0, shuffle = True)\n",
    "\n",
    "test_images_tensor = torch.tensor(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model:  MLP(\n",
      "  (input_layer): Linear(in_features=784, out_features=128, bias=True)\n",
      "  (hidden_layer): Linear(in_features=128, out_features=128, bias=True)\n",
      "  (output_layer): Linear(in_features=128, out_features=10, bias=True)\n",
      ")\n",
      "Device:  cuda\n"
     ]
    }
   ],
   "source": [
    "# Create Multi Layer Perceptron Model\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "\n",
    "class MLP(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(MLP, self).__init__()\n",
    "        self.input_layer = nn.Linear(28 * 28, 128)\n",
    "        self.hidden_layer = nn.Linear(128, 128)\n",
    "        self.output_layer = nn.Linear(128, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.view(-1, 28 * 28)\n",
    "        x = F.relu(self.input_layer(x))\n",
    "        x = F.relu(self.hidden_layer(x))\n",
    "        x = self.output_layer(x)\n",
    "        x = F.log_softmax(x, dim = 1)\n",
    "        return x\n",
    "\n",
    "USE_CUDA = torch.cuda.is_available()\n",
    "DEVICE = torch.device(\"cuda\" if USE_CUDA else \"cpu\")\n",
    "\n",
    "model = MLP().to(DEVICE)\n",
    "optimizer = optim.Adam(model.parameters(), lr = 0.001)\n",
    "\n",
    "print(\"Model: \", model)\n",
    "print(\"Device: \", DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definite Train & Evaluate\n",
    "def train(model, train_loader, optimizer):\n",
    "    model.train()\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        data, target = data.to(DEVICE), target.to(DEVICE)\n",
    "        optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "        loss = F.cross_entropy(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        if batch_idx % 100 == 0:\n",
    "            print(\"Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}\".format(epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item()))\n",
    "\n",
    "def evaluate(model, valid_loader):\n",
    "    model.eval()\n",
    "    valid_loss = 0\n",
    "    correct = 0\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for data, target in valid_loader:\n",
    "            data, target = data.to(DEVICE), target.to(DEVICE)\n",
    "            output = model(data)\n",
    "            valid_loss += F.cross_entropy(output, target, reduction = \"sum\").item()\n",
    "            prediction = output.max(1, keepdim = True)[1]\n",
    "            correct += prediction.eq(target.view_as(prediction)).sum().item()\n",
    "\n",
    "    valid_loss /= len(valid_loader.dataset)\n",
    "    valid_accuracy = 100. * correct / len(valid_loader.dataset)\n",
    "    return valid_loss, valid_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 1 [0/33600 (0%)]\tLoss: 8.973553\n",
      "Train Epoch: 1 [6400/33600 (19%)]\tLoss: 0.529814\n",
      "Train Epoch: 1 [12800/33600 (38%)]\tLoss: 0.314720\n",
      "Train Epoch: 1 [19200/33600 (57%)]\tLoss: 0.096157\n",
      "Train Epoch: 1 [25600/33600 (76%)]\tLoss: 0.055420\n",
      "Train Epoch: 1 [32000/33600 (95%)]\tLoss: 0.336188\n",
      "[EPOCH: 1], \tValidation Loss: 0.1872, \tValidation Accuracy: 94.61 %\n",
      "\n",
      "Train Epoch: 2 [0/33600 (0%)]\tLoss: 0.206463\n",
      "Train Epoch: 2 [6400/33600 (19%)]\tLoss: 0.195046\n",
      "Train Epoch: 2 [12800/33600 (38%)]\tLoss: 0.116956\n",
      "Train Epoch: 2 [19200/33600 (57%)]\tLoss: 0.233528\n",
      "Train Epoch: 2 [25600/33600 (76%)]\tLoss: 0.109654\n",
      "Train Epoch: 2 [32000/33600 (95%)]\tLoss: 0.095079\n",
      "[EPOCH: 2], \tValidation Loss: 0.1891, \tValidation Accuracy: 94.43 %\n",
      "\n",
      "Train Epoch: 3 [0/33600 (0%)]\tLoss: 0.047581\n",
      "Train Epoch: 3 [6400/33600 (19%)]\tLoss: 0.129415\n",
      "Train Epoch: 3 [12800/33600 (38%)]\tLoss: 0.182251\n",
      "Train Epoch: 3 [19200/33600 (57%)]\tLoss: 0.122384\n",
      "Train Epoch: 3 [25600/33600 (76%)]\tLoss: 0.135859\n",
      "Train Epoch: 3 [32000/33600 (95%)]\tLoss: 0.056076\n",
      "[EPOCH: 3], \tValidation Loss: 0.1888, \tValidation Accuracy: 95.15 %\n",
      "\n",
      "Train Epoch: 4 [0/33600 (0%)]\tLoss: 0.062849\n",
      "Train Epoch: 4 [6400/33600 (19%)]\tLoss: 0.076050\n",
      "Train Epoch: 4 [12800/33600 (38%)]\tLoss: 0.096259\n",
      "Train Epoch: 4 [19200/33600 (57%)]\tLoss: 0.086055\n",
      "Train Epoch: 4 [25600/33600 (76%)]\tLoss: 0.033660\n",
      "Train Epoch: 4 [32000/33600 (95%)]\tLoss: 0.017292\n",
      "[EPOCH: 4], \tValidation Loss: 0.1682, \tValidation Accuracy: 95.87 %\n",
      "\n",
      "Train Epoch: 5 [0/33600 (0%)]\tLoss: 0.046166\n",
      "Train Epoch: 5 [6400/33600 (19%)]\tLoss: 0.005271\n",
      "Train Epoch: 5 [12800/33600 (38%)]\tLoss: 0.190780\n",
      "Train Epoch: 5 [19200/33600 (57%)]\tLoss: 0.180615\n",
      "Train Epoch: 5 [25600/33600 (76%)]\tLoss: 0.069891\n",
      "Train Epoch: 5 [32000/33600 (95%)]\tLoss: 0.142133\n",
      "[EPOCH: 5], \tValidation Loss: 0.1636, \tValidation Accuracy: 95.55 %\n",
      "\n",
      "Train Epoch: 6 [0/33600 (0%)]\tLoss: 0.120385\n",
      "Train Epoch: 6 [6400/33600 (19%)]\tLoss: 0.009609\n",
      "Train Epoch: 6 [12800/33600 (38%)]\tLoss: 0.121912\n",
      "Train Epoch: 6 [19200/33600 (57%)]\tLoss: 0.068227\n",
      "Train Epoch: 6 [25600/33600 (76%)]\tLoss: 0.085843\n",
      "Train Epoch: 6 [32000/33600 (95%)]\tLoss: 0.275753\n",
      "[EPOCH: 6], \tValidation Loss: 0.1747, \tValidation Accuracy: 95.87 %\n",
      "\n",
      "Train Epoch: 7 [0/33600 (0%)]\tLoss: 0.012210\n",
      "Train Epoch: 7 [6400/33600 (19%)]\tLoss: 0.009179\n",
      "Train Epoch: 7 [12800/33600 (38%)]\tLoss: 0.063687\n",
      "Train Epoch: 7 [19200/33600 (57%)]\tLoss: 0.095253\n",
      "Train Epoch: 7 [25600/33600 (76%)]\tLoss: 0.032581\n",
      "Train Epoch: 7 [32000/33600 (95%)]\tLoss: 0.332259\n",
      "[EPOCH: 7], \tValidation Loss: 0.1795, \tValidation Accuracy: 95.69 %\n",
      "\n",
      "Train Epoch: 8 [0/33600 (0%)]\tLoss: 0.124395\n",
      "Train Epoch: 8 [6400/33600 (19%)]\tLoss: 0.040737\n",
      "Train Epoch: 8 [12800/33600 (38%)]\tLoss: 0.102835\n",
      "Train Epoch: 8 [19200/33600 (57%)]\tLoss: 0.028803\n",
      "Train Epoch: 8 [25600/33600 (76%)]\tLoss: 0.138287\n",
      "Train Epoch: 8 [32000/33600 (95%)]\tLoss: 0.008582\n",
      "[EPOCH: 8], \tValidation Loss: 0.1796, \tValidation Accuracy: 96.06 %\n",
      "\n",
      "Train Epoch: 9 [0/33600 (0%)]\tLoss: 0.045058\n",
      "Train Epoch: 9 [6400/33600 (19%)]\tLoss: 0.080057\n",
      "Train Epoch: 9 [12800/33600 (38%)]\tLoss: 0.084229\n",
      "Train Epoch: 9 [19200/33600 (57%)]\tLoss: 0.092566\n",
      "Train Epoch: 9 [25600/33600 (76%)]\tLoss: 0.113861\n",
      "Train Epoch: 9 [32000/33600 (95%)]\tLoss: 0.288750\n",
      "[EPOCH: 9], \tValidation Loss: 0.1562, \tValidation Accuracy: 96.50 %\n",
      "\n",
      "Train Epoch: 10 [0/33600 (0%)]\tLoss: 0.035417\n",
      "Train Epoch: 10 [6400/33600 (19%)]\tLoss: 0.013922\n",
      "Train Epoch: 10 [12800/33600 (38%)]\tLoss: 0.011579\n",
      "Train Epoch: 10 [19200/33600 (57%)]\tLoss: 0.061219\n",
      "Train Epoch: 10 [25600/33600 (76%)]\tLoss: 0.211030\n",
      "Train Epoch: 10 [32000/33600 (95%)]\tLoss: 0.094472\n",
      "[EPOCH: 10], \tValidation Loss: 0.2299, \tValidation Accuracy: 95.39 %\n",
      "\n"
     ]
    }
   ],
   "source": [
    "''' Training'''\n",
    "EPOCHS = 10\n",
    "for epoch in range(1, EPOCHS + 1):\n",
    "    train(model, train_loader, optimizer)\n",
    "    valid_loss, valid_accuracy = evaluate(model, valid_loader)\n",
    "    print(\"[EPOCH: {}], \\tValidation Loss: {:.4f}, \\tValidation Accuracy: {:.2f} %\\n\".format(epoch, valid_loss, valid_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict Test Dataset\n",
    "def testset_prediction(model, test_images_tensor):\n",
    "    model.eval()\n",
    "    result = []\n",
    "    with torch.no_grad():\n",
    "        for data in test_images_tensor:\n",
    "            data = data.to(DEVICE)\n",
    "            output = model(data)\n",
    "            prediction = output.max(1, keepdim = True)[1]\n",
    "            result.append(prediction.tolist())\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[[2]], [[0]], [[9]], [[9]], [[3]]]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predict_result = testset_prediction(model, test_images_tensor)\n",
    "test_predict_result[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 3203),\n",
       " (2, 2958),\n",
       " (7, 2887),\n",
       " (5, 2867),\n",
       " (3, 2842),\n",
       " (9, 2681),\n",
       " (4, 2671),\n",
       " (6, 2668),\n",
       " (0, 2654),\n",
       " (8, 2569)]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from collections import Counter\n",
    "Counter(np.squeeze(test_predict_result)).most_common()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for idx in range(0, 10):\n",
    "    plt.imshow(test_images[idx], cmap = plt.get_cmap('gray'))\n",
    "    plt.title(\"Predict: \" + str(test_predict_result[idx]))\n",
    "    plt.show()"
   ]
  },
  {
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}