MNIST-Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from sklearn.metrics import f1_score,recall_score,precision_score\n",
    "from tqdm.notebook import tqdm\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model Parameters\n",
    "batch_size = 64\n",
    "learning_rate = 1e-4\n",
    "Num_Epochs= 50\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Shape:  torch.Size([60000, 28, 28])\n",
      "Training Shape:  torch.Size([10000, 28, 28])\n"
     ]
    }
   ],
   "source": [
    "#Download Data and Dataloader\n",
    "transform = transforms.Compose(\n",
    "    [transforms.ToTensor()])\n",
    "\n",
    "Train_Data = torchvision.datasets.MNIST(root='./data', train=True,download=True, transform=transform)\n",
    "Train_Loader = torch.utils.data.DataLoader(Train_Data, batch_size=batch_size,shuffle=True)\n",
    "\n",
    "Test_Data = torchvision.datasets.MNIST(root='./data', train=False,download=True, transform=transform)\n",
    "Test_Loader = torch.utils.data.DataLoader(Test_Data, batch_size=batch_size,shuffle=False)\n",
    "\n",
    "Train_Length = Train_Data.data.shape[0]\n",
    "Test_Length = Test_Data.data.shape[0]\n",
    "# Train_Length = 50\n",
    "# Test_Length = 10\n",
    "print(\"Training Shape: \",Train_Data.data.shape)\n",
    "print(\"Training Shape: \", Test_Data.data.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model Architecture\n",
    "class CNN_Module(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv1=nn.Conv2d(in_channels=1, out_channels=32, kernel_size=(3,3), stride=(1,1))\n",
    "        self.pool1=nn.MaxPool2d(2,2)\n",
    "        self.pool2=nn.MaxPool2d(2,2)\n",
    "        self.conv2=nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(5,5), stride=(1,1))\n",
    "        self.fc1=nn.Linear(1024,1024)\n",
    "        self.fc2=nn.Linear(1024,10)\n",
    "        \n",
    "        self.relu1=nn.ReLU()\n",
    "        self.relu2=nn.ReLU()\n",
    "        self.relu3=nn.ReLU()\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.relu1(x)\n",
    "        x = self.pool1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.relu2(x)\n",
    "        x = self.pool2(x)\n",
    "        x = torch.flatten(x, 1)\n",
    "        x = self.fc1(x)\n",
    "        x = self.relu3(x)\n",
    "        x = self.fc2(x)\n",
    "        x = self.softmax(x)\n",
    "        return x   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "cnn = CNN_Module().to(device)\n",
    "CE_Loss = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(cnn.parameters(), lr = learning_rate)\n",
    "scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [05:33<00:00,  6.68s/it]\n"
     ]
    }
   ],
   "source": [
    "training_loss_per_epoch=[]\n",
    "training_accuracy_per_epoch=[]\n",
    "\n",
    "testing_loss_per_epoch=[]\n",
    "testing_accuracy_per_epoch=[]\n",
    "\n",
    "f1_score_array=[]\n",
    "recall_score_array=[]\n",
    "precision_score_array=[]\n",
    "\n",
    "counter = -1\n",
    "for epoch in tqdm(range(Num_Epochs)):\n",
    "    pool1_list, pool2_list = [], []\n",
    "    train_loss = 0\n",
    "    train_accuracy = 0\n",
    "    \n",
    "    test_loss = 0\n",
    "    test_accuracy = 0\n",
    "\n",
    "    epochtrainloss = []\n",
    "    epochtestloss = []\n",
    "    for i, data in enumerate(Train_Loader):\n",
    "        inputs, labels = data\n",
    "        inputs=inputs.to(device)\n",
    "        labels=labels.to(device)\n",
    "        optimizer.zero_grad()\n",
    "    \n",
    "        outputs = cnn(inputs)\n",
    "\n",
    "        loss = CE_Loss(outputs, labels)\n",
    "        \n",
    "        _, predicted = torch.max(outputs.data,1)\n",
    "        train_accuracy += torch.sum(predicted==labels).item()\n",
    "        \n",
    "        epochtrainloss.append(loss.item())\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "\n",
    "        # print(activation['pool1'])\n",
    "        # print(activation['pool2'])\n",
    "    scheduler.step()\n",
    "    training_loss_per_epoch.append(np.mean(epochtrainloss))\n",
    "    train_accuracy = train_accuracy/Train_Length\n",
    "    training_accuracy_per_epoch.append(train_accuracy)\n",
    "    cnn.eval()\n",
    "    pred = []\n",
    "    tar = []\n",
    "    for i, data in enumerate(Test_Loader):\n",
    "        inputs,labels = data\n",
    "        inputs=inputs.to(device)\n",
    "        labels=labels.to(device)\n",
    "\n",
    "        outputs = cnn(inputs)\n",
    "\n",
    "        Testing_loss= CE_Loss(outputs, labels)\n",
    "        _, predicted = torch.max(outputs.data,1)\n",
    "        test_accuracy += torch.sum(predicted==labels).item()\n",
    "        epochtestloss.append(Testing_loss.item())\n",
    "        pred.extend(predicted.detach().cpu().numpy())\n",
    "        tar.extend(labels.detach().cpu().numpy())\n",
    "\n",
    "    \n",
    "    f1_score_array.append(f1_score(tar,pred,average='weighted'))\n",
    "    recall_score_array.append(recall_score(tar,pred,average='weighted'))\n",
    "    precision_score_array.append(precision_score(tar,pred,average='weighted'))\n",
    "    \n",
    "    testing_loss_per_epoch.append(np.mean(epochtestloss))\n",
    "    test_accuracy = test_accuracy/Test_Length\n",
    "    testing_accuracy_per_epoch.append(test_accuracy)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Train Accuracy:  0.9939666666666667\n",
      "Final Train Loss:  1.4684173887980772\n",
      "Final Test Accuracy:  0.9888\n",
      "Final Test Loss:  1.4729702996600205\n"
     ]
    }
   ],
   "source": [
    "print(\"Final Train Accuracy: \", training_accuracy_per_epoch[-1])\n",
    "print(\"Final Train Loss: \", training_loss_per_epoch[-1])\n",
    "\n",
    "print(\"Final Test Accuracy: \", testing_accuracy_per_epoch[-1])\n",
    "print(\"Final Test Loss: \", testing_loss_per_epoch[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total convolutional layers: 2\n"
     ]
    }
   ],
   "source": [
    "# Number of convolution layers\n",
    "model_weights = []\n",
    "conv_layers = [] \n",
    "model_children = list(cnn.children())\n",
    "counter = 0 \n",
    "for i in range(len(model_children)):\n",
    "    if type(model_children[i]) == nn.Conv2d:\n",
    "        counter += 1\n",
    "        model_weights.append(model_children[i].weight)\n",
    "        conv_layers.append(model_children[i])\n",
    "    elif type(model_children[i]) == nn.Sequential:\n",
    "        for j in range(len(model_children[i])):\n",
    "            for child in model_children[i][j].children():\n",
    "                if type(child) == nn.Conv2d:\n",
    "                    counter += 1\n",
    "                    model_weights.append(child.weight)\n",
    "                    conv_layers.append(child)\n",
    "print(f\"Total convolutional layers: {counter}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CONV: Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1)) ====> SHAPE: torch.Size([32, 1, 3, 3])\n",
      "CONV: Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1)) ====> SHAPE: torch.Size([64, 32, 5, 5])\n"
     ]
    }
   ],
   "source": [
    "for weight, conv in zip(model_weights, conv_layers):\n",
    "    # print(f\"WEIGHT: {weight} \\nSHAPE: {weight.shape}\")\n",
    "    print(f\"CONV: {conv} ====> SHAPE: {weight.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1224 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Kernel filter\n",
    "plt.figure(figsize=(20, 17))\n",
    "for i, filter in enumerate(model_weights[0]):\n",
    "    plt.subplot(4, 16, i+1) # we have 5x5 filters and total of 16 (see printed shapes)\n",
    "    plt.imshow(filter[0, :, :].detach().cpu().numpy(), cmap='binary')\n",
    "    plt.axis('off')\n",
    "    plt.savefig('filter1.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1224 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Kernel filter\n",
    "plt.figure(figsize=(20, 17))\n",
    "for i, filter in enumerate(model_weights[1]):\n",
    "    plt.subplot(6, 16, i+1) # (8, 8) because in conv0 we have 5x5 filters and total of 64 (see printed shapes)\n",
    "    plt.imshow(filter[0, :, :].detach().cpu().numpy(), cmap='binary')\n",
    "    plt.axis('off')\n",
    "    plt.savefig('filter2.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "only one element tensors can be converted to Python scalars",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_14852/947694121.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mh2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister_forward_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgetActivation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'conv2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mli\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrain_Data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mli\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pytorch_acheruvu/lib/python3.9/site-packages/torchvision/datasets/mnist.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0mtuple\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0mwhere\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mindex\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;32mclass\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m         \"\"\"\n\u001b[0;32m--> 127\u001b[0;31m         \u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargets\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m         \u001b[0;31m# doing this so that it is consistent with all other datasets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: only one element tensors can be converted to Python scalars"
     ]
    }
   ],
   "source": [
    "#Activation Map of the second convolution layer\n",
    "activation = {}\n",
    "def getActivation(name):\n",
    "  # the hook signature\n",
    "    def hook(model, input, output):\n",
    "        activation[name] = output.detach()\n",
    "    return hook\n",
    "\n",
    "h1 = cnn.conv1.register_forward_hook(getActivation('conv1'))\n",
    "h2 = cnn.conv2.register_forward_hook(getActivation('conv2'))\n",
    "\n",
    "data, _ = Train_Data[4]\n",
    "data=data.to(device)\n",
    "data.unsqueeze_(0)\n",
    "output = cnn(data)\n",
    "\n",
    "k=0\n",
    "act = activation['conv1'].squeeze()\n",
    "# print(act.size())\n",
    "# print(act.size(0)//8,\" \",act.size(0)//16)\n",
    "fig,ax = plt.subplots(4,4,figsize=(12, 15))\n",
    "\n",
    "# print(act[0].detach().cpu().numpy())\n",
    "\n",
    "for i in range(4):\n",
    "        # print(i)\n",
    "        for j in range(4):\n",
    "          #  print(j)\n",
    "           ax[i,j].imshow(act[k].detach().cpu().numpy(),cmap='binary')\n",
    "           k+=1\n",
    "           plt.savefig('fm1.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x1080 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "k=0\n",
    "act = activation['conv2'].squeeze()\n",
    "# print(act.size())\n",
    "# print(act.size(0)//8,\" \",act.size(0)//16)\n",
    "fig,ax = plt.subplots(4,4,figsize=(12, 15))\n",
    "\n",
    "# print(act[0].detach().cpu().numpy())\n",
    "\n",
    "for i in range(4):\n",
    "        # print(i)\n",
    "        for j in range(4):\n",
    "          #  print(j)\n",
    "           ax[i,j].imshow(act[k].detach().cpu().numpy(),cmap='binary')\n",
    "           k+=1\n",
    "           plt.savefig('fm2.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 0.9939666666666667\n",
      "Test Accuracy: 0.9888\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig1 = plt.figure(figsize=(10,10))\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "plt.title(\"Epoch-wise Train Loss\")\n",
    "plt.plot(training_loss_per_epoch)\n",
    "plt.xlabel('Epochs', fontsize=10)\n",
    "plt.ylabel('Train Loss', fontsize=10)\n",
    "plt.xticks(fontsize=10)\n",
    "plt.yticks(fontsize=10)\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.title(\"Epoch-wise Train Accuracy\")\n",
    "plt.plot(training_accuracy_per_epoch)\n",
    "print(\"Train Accuracy:\",(training_accuracy_per_epoch[-1]) )\n",
    "plt.xlabel('Epochs', fontsize=10)\n",
    "plt.ylabel('Train Accuracy', fontsize=10)\n",
    "plt.xticks(fontsize=10)\n",
    "plt.yticks(fontsize=10)\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "plt.title(\"Epoch-wise Test Loss\")\n",
    "plt.plot(testing_loss_per_epoch)\n",
    "plt.xlabel('Epochs', fontsize=10)\n",
    "plt.ylabel('Test Loss', fontsize=10)\n",
    "\n",
    "plt.xticks(fontsize=10)\n",
    "plt.yticks(fontsize=10)\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "plt.title(\"Epoch-wise Test Accuracy\")\n",
    "plt.plot(testing_accuracy_per_epoch)\n",
    "plt.xlabel('Epochs', fontsize=10)\n",
    "plt.xticks(fontsize=10)\n",
    "plt.yticks(fontsize=10)\n",
    "plt.ylabel('Test Accuracy', fontsize=10)\n",
    "print(\"Test Accuracy:\",(testing_accuracy_per_epoch[-1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 SCORE: 0.9887984007784835\n",
      "Precision: 0.9888340180997857\n",
      "Recall: 0.9888\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Recall')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Epochwise Score Metrics\n",
    "fig1 = plt.figure(figsize=(10,10))\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "plt.plot(f1_score_array)\n",
    "print(\"F1 SCORE:\",(f1_score_array[-1]) )\n",
    "plt.title(\"Epoch-wise F1 SCORE\")\n",
    "plt.xlabel('Epochs', fontsize=10)\n",
    "plt.ylabel('F1 SCORE', fontsize=10)\n",
    "plt.xticks(fontsize=10)\n",
    "plt.yticks(fontsize=10)\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.plot(precision_score_array)\n",
    "print(\"Precision:\",(precision_score_array[-1]) )\n",
    "plt.title(\"Epoch-wise Precision\")\n",
    "plt.xlabel('Epochs', fontsize=10)\n",
    "plt.ylabel('Precision', fontsize=10)\n",
    "plt.xticks(fontsize=10)\n",
    "plt.yticks(fontsize=10)\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "print(\"Recall:\",(recall_score_array[-1]) )\n",
    "plt.title(\"Epoch-wise Recall\")\n",
    "plt.plot(recall_score_array)\n",
    "plt.xlabel('Epochs', fontsize=10)\n",
    "plt.ylabel('Recall', fontsize=10)\n",
    "\n",
    "# plt.xticks(fontsize=10)\n",
    "# plt.yticks(fontsize=10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 0.9939666666666667\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Accuracy')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Accuracy amd Loss comparisions\n",
    "fig1 = plt.figure(figsize=(10,10))\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "plt.title(\"Epoch-wise Train Loss vs Test Loss\")\n",
    "plt.plot(training_loss_per_epoch, color ='red')\n",
    "plt.plot(testing_loss_per_epoch)\n",
    "plt.xlabel('Epochs', fontsize=10)\n",
    "plt.ylabel('Loss', fontsize=10)\n",
    "plt.xticks(fontsize=10)\n",
    "plt.yticks(fontsize=10)\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.title(\"Epoch-wise Train Accuracy vs Test Accuracy\")\n",
    "plt.plot(training_accuracy_per_epoch, color ='red')\n",
    "plt.plot(testing_accuracy_per_epoch)\n",
    "print(\"Train Accuracy:\",(training_accuracy_per_epoch[-1]) )\n",
    "plt.xlabel('Epochs', fontsize=10)\n",
    "plt.ylabel('Accuracy', fontsize=10)\n",
    "# plt.xticks(fontsize=10)\n",
    "# plt.yticks(fontsize=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ten samples with prediction\n",
    "data = next(iter(Train_Loader))\n",
    "inputs, labels = data[0:10]\n",
    "inputs=inputs.to(device)\n",
    "labels=labels.to(device)\n",
    "outputs = cnn(inputs)\n",
    "_, predicted = torch.max(outputs.data,1)\n",
    "# print(predicted)\n",
    "# print(labels)\n",
    "\n",
    "fig, axs = plt.subplots(nrows=3, ncols=4, figsize=(10,10))\n",
    "\n",
    "i = 0\n",
    "for ax in axs.reshape(-1):\n",
    "    ax.set_title(\"Predicted Digit Label: %d \\n Ground truth Label: %d\" % (predicted[i] , labels[i]))\n",
    "    ax.imshow(np.squeeze(inputs.cpu().numpy())[i], cmap=plt.cm.gray, origin='upper', interpolation='nearest')\n",
    "\n",
    "    ax.axis('off')\n",
    "    i += 1\n",
    "plt.tight_layout()   \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "24efd4728f867a1505d4ef0c691c6485ce4d1d48d464278ba8e580775ca59b5e"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 ('pytorch_acheruvu')",
   "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": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}