FMNIST_MLP_Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torchvision import datasets,transforms\n",
    "from torch import nn,optim,no_grad\n",
    "from torch.utils.data import DataLoader\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import classification_report\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import pickle\n",
    "%matplotlib inline\n",
    "np.set_printoptions(suppress=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset FashionMNIST\n",
      "    Number of datapoints: 60000\n",
      "    Split: train\n",
      "    Root Location: FMNIST/\n",
      "    Transforms (if any): Compose(\n",
      "                             ToTensor()\n",
      "                             Normalize(mean=(0.5,), std=(0.5,))\n",
      "                         )\n",
      "    Target Transforms (if any): None\n",
      "Dataset FashionMNIST\n",
      "    Number of datapoints: 10000\n",
      "    Split: test\n",
      "    Root Location: FMNIST/\n",
      "    Transforms (if any): Compose(\n",
      "                             ToTensor()\n",
      "                             Normalize(mean=(0.5,), std=(0.5,))\n",
      "                         )\n",
      "    Target Transforms (if any): None\n"
     ]
    }
   ],
   "source": [
    "train_dataset=datasets.FashionMNIST('FMNIST/',\n",
    "                              train=True,\n",
    "                              download=True,\n",
    "                              transform=transforms.Compose([\n",
    "                                  transforms.ToTensor(),\n",
    "                                  transforms.Normalize((0.5,),(0.5,))\n",
    "                              ]))\n",
    "print(train_dataset)\n",
    "\n",
    "test_dataset=datasets.FashionMNIST('FMNIST/',\n",
    "                              train=False,\n",
    "                              download=True,\n",
    "                              transform=transforms.Compose([\n",
    "                                  transforms.ToTensor(),\n",
    "                                  transforms.Normalize((0.5,),(0.5,))\n",
    "                              ]))\n",
    "\n",
    "print(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx2lbl={0:'T-shirt/top',1:'Trouser',2:'Pullover',3:'Dress',4:'Coat',5:'Sandal',6:'Shirt',7:'Sneaker',8:'Bag',9:'Ankle boot'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>Label</th>\n",
    "<th>Description</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody>\n",
    "<tr>\n",
    "<td>0</td>\n",
    "<td>T-shirt/top</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>1</td>\n",
    "<td>Trouser</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>2</td>\n",
    "<td>Pullover</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>3</td>\n",
    "<td>Dress</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>4</td>\n",
    "<td>Coat</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>5</td>\n",
    "<td>Sandal</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>6</td>\n",
    "<td>Shirt</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>7</td>\n",
    "<td>Sneaker</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>8</td>\n",
    "<td>Bag</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>9</td>\n",
    "<td>Ankle boot</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataLoader=DataLoader(train_dataset,batch_size=64,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_dataloader=DataLoader(test_dataset,batch_size=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x288 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "images,labels=next(iter(train_dataLoader))\n",
    "fig=plt.figure(figsize=(25,4))\n",
    "for i in range(1,21):\n",
    "    ax=fig.add_subplot(2,10,i,xticks=[],yticks=[])\n",
    "    ax.imshow(images[i].view(28,28),cmap='gray')\n",
    "    ax.set_title(str(idx2lbl[labels[i].item()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_test_data=list(iter(val_dataloader))\n",
    "val_images,val_labels=val_test_data[0]\n",
    "test_images,test_labels=val_test_data[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_images=test_images.reshape(-1,784)\n",
    "val_images=val_images.reshape(-1,784)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Linear(in_features=784, out_features=128, bias=True)\n",
       "  (1): ReLU()\n",
       "  (2): Dropout(p=0.2)\n",
       "  (3): Linear(in_features=128, out_features=64, bias=True)\n",
       "  (4): ReLU()\n",
       "  (5): Dropout(p=0.2)\n",
       "  (6): Linear(in_features=64, out_features=10, bias=True)\n",
       "  (7): LogSoftmax()\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=nn.Sequential(\n",
    "    nn.Linear(784,128),\n",
    "    nn.ReLU(),\n",
    "    nn.Dropout(p=0.2),\n",
    "    nn.Linear(128,64),\n",
    "    nn.ReLU(),\n",
    "    nn.Dropout(p=0.2),\n",
    "    nn.Linear(64,10),\n",
    "    nn.LogSoftmax(dim=1)\n",
    ")\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion=nn.NLLLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer=optim.Adam(model.parameters(),lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load data and model to gpu\n",
    "\n",
    "model.to('cuda')\n",
    "val_images,val_labels=val_images.cuda(),val_labels.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPOCH: 1 Training Loss: 0.717 Val Loss: 0.589 Train Accuracy: 74.88% Val Accuracy: 78.70% \n",
      "EPOCH: 2 Training Loss: 0.641 Val Loss: 0.520 Train Accuracy: 78.02% Val Accuracy: 81.34% \n",
      "EPOCH: 3 Training Loss: 0.615 Val Loss: 0.520 Train Accuracy: 79.03% Val Accuracy: 82.24% \n",
      "EPOCH: 4 Training Loss: 0.614 Val Loss: 0.572 Train Accuracy: 79.44% Val Accuracy: 80.28% \n",
      "EPOCH: 5 Training Loss: 0.603 Val Loss: 0.516 Train Accuracy: 79.68% Val Accuracy: 82.34% \n",
      "EPOCH: 6 Training Loss: 0.599 Val Loss: 0.539 Train Accuracy: 79.80% Val Accuracy: 81.98% \n",
      "EPOCH: 7 Training Loss: 0.587 Val Loss: 0.504 Train Accuracy: 79.98% Val Accuracy: 82.38% \n",
      "EPOCH: 8 Training Loss: 0.578 Val Loss: 0.511 Train Accuracy: 80.28% Val Accuracy: 82.06% \n",
      "EPOCH: 9 Training Loss: 0.594 Val Loss: 0.513 Train Accuracy: 79.84% Val Accuracy: 82.42% \n",
      "EPOCH: 10 Training Loss: 0.573 Val Loss: 0.494 Train Accuracy: 80.38% Val Accuracy: 83.50% \n",
      "EPOCH: 11 Training Loss: 0.587 Val Loss: 0.489 Train Accuracy: 80.23% Val Accuracy: 83.06% \n",
      "EPOCH: 12 Training Loss: 0.565 Val Loss: 0.472 Train Accuracy: 80.75% Val Accuracy: 83.10% \n",
      "EPOCH: 13 Training Loss: 0.590 Val Loss: 0.561 Train Accuracy: 80.09% Val Accuracy: 81.26% \n",
      "EPOCH: 14 Training Loss: 0.572 Val Loss: 0.597 Train Accuracy: 80.30% Val Accuracy: 82.36% \n",
      "EPOCH: 15 Training Loss: 0.567 Val Loss: 0.522 Train Accuracy: 80.88% Val Accuracy: 81.88% \n",
      "EPOCH: 16 Training Loss: 0.561 Val Loss: 0.488 Train Accuracy: 80.97% Val Accuracy: 83.08% \n",
      "EPOCH: 17 Training Loss: 0.566 Val Loss: 0.523 Train Accuracy: 80.79% Val Accuracy: 82.20% \n",
      "EPOCH: 18 Training Loss: 0.546 Val Loss: 0.467 Train Accuracy: 81.43% Val Accuracy: 83.34% \n",
      "EPOCH: 19 Training Loss: 0.545 Val Loss: 0.500 Train Accuracy: 81.46% Val Accuracy: 82.82% \n",
      "EPOCH: 20 Training Loss: 0.556 Val Loss: 0.464 Train Accuracy: 80.89% Val Accuracy: 83.90% \n",
      "EPOCH: 21 Training Loss: 0.553 Val Loss: 0.523 Train Accuracy: 81.20% Val Accuracy: 82.36% \n",
      "EPOCH: 22 Training Loss: 0.555 Val Loss: 0.475 Train Accuracy: 81.36% Val Accuracy: 84.54% \n",
      "EPOCH: 23 Training Loss: 0.553 Val Loss: 0.459 Train Accuracy: 81.22% Val Accuracy: 84.68% \n",
      "EPOCH: 24 Training Loss: 0.561 Val Loss: 0.486 Train Accuracy: 80.86% Val Accuracy: 83.84% \n",
      "EPOCH: 25 Training Loss: 0.567 Val Loss: 0.492 Train Accuracy: 80.64% Val Accuracy: 83.98% \n",
      "EPOCH: 26 Training Loss: 0.548 Val Loss: 0.463 Train Accuracy: 81.29% Val Accuracy: 84.04% \n",
      "EPOCH: 27 Training Loss: 0.559 Val Loss: 0.474 Train Accuracy: 80.86% Val Accuracy: 84.28% \n",
      "EPOCH: 28 Training Loss: 0.537 Val Loss: 0.492 Train Accuracy: 81.66% Val Accuracy: 83.82% \n",
      "EPOCH: 29 Training Loss: 0.528 Val Loss: 0.486 Train Accuracy: 81.97% Val Accuracy: 83.40% \n",
      "EPOCH: 30 Training Loss: 0.549 Val Loss: 0.484 Train Accuracy: 81.14% Val Accuracy: 83.72% \n"
     ]
    }
   ],
   "source": [
    "epochs=30\n",
    "\n",
    "train_loss_pe=list()\n",
    "val_loss_pe=list()\n",
    "\n",
    "train_acc_pe=list()\n",
    "val_acc_pe=list()\n",
    "\n",
    "for epoch in range(1,epochs+1):\n",
    "    print(\"EPOCH:\",epoch,end=\" \")\n",
    "    running_loss=0\n",
    "    running_acc=0\n",
    "    \n",
    "    for images,labels in train_dataLoader:\n",
    "        images,labels=images.cuda(),labels.cuda()   \n",
    "        X=images.view(-1,784)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        output=model.forward(X)\n",
    "        loss=criterion(output,labels)\n",
    "        \n",
    "        result=torch.argmax(output,dim=1)\n",
    "        running_loss+=loss.item()\n",
    "        running_acc+=torch.mean((result==labels).type(torch.float))\n",
    "        \n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    else:\n",
    "        train_loss=running_loss/len(train_dataLoader)\n",
    "        train_acc=running_acc/len(train_dataLoader)\n",
    "        \n",
    "        with no_grad():\n",
    "            model.eval()\n",
    "            output=model.forward(val_images)\n",
    "            val_loss=criterion(output,val_labels)\n",
    "            model.train()\n",
    "        \n",
    "        result_labels=torch.argmax(output,dim=1)\n",
    "        accuracy=torch.mean((result_labels==val_labels).type(torch.float))\n",
    "        \n",
    "        train_loss_pe.append(train_loss)\n",
    "        val_loss_pe.append(val_loss.item())\n",
    "        \n",
    "        train_acc_pe.append(train_acc.item())\n",
    "        val_acc_pe.append(accuracy.item())\n",
    "        \n",
    "        print(\"Training Loss: {:.3f}\".format(train_loss),end=\" \") \n",
    "        print(\"Val Loss: {:.3f}\".format(val_loss),end=\" \")\n",
    "        print(\"Train Accuracy: {:.2f}%\".format(train_acc.item()*100),end=\" \")\n",
    "        print(\"Val Accuracy: {:.2f}%\".format(accuracy.item()*100),end=\" \\n\")\n",
    "        \n",
    "        with open(\"Models/mlp_fmnist_model_{}.pth\".format(epoch),\"wb\") as f:\n",
    "            model.eval()\n",
    "            pickle.dump(model,f)\n",
    "            model.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots=[(train_loss_pe,val_loss_pe),(train_acc_pe,val_acc_pe)]\n",
    "plt_labels=[(\"Training Loss\",\"Validation Loss\"),(\"Training Accuracy\",\"Validation Accuracy\")]\n",
    "plt_titles=[\"Loss\",\"Accuracy\"]\n",
    "plt.figure(figsize=(20,7))\n",
    "for i in range(0,2):\n",
    "    ax=plt.subplot(1,2,i+1)\n",
    "    ax.plot(plots[i][0],label=plt_labels[i][0])\n",
    "    ax.plot(plots[i][1],label=plt_labels[i][1])\n",
    "    ax.set_title(plt_titles[i])\n",
    "    ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#selecting the best model\n",
    "with open(\"Models/mlp_fmnist_model_23.pth\",\"rb\") as f:\n",
    "    loaded_model=pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(model,images):\n",
    "    model.cpu()\n",
    "    with no_grad():\n",
    "        model.eval()\n",
    "        output=loaded_model.forward(test_images)\n",
    "        test_loss=criterion(output,test_labels)\n",
    "        result=torch.argmax(output,dim=1)\n",
    "        result_labels=[idx2lbl[x] for x in result.numpy()]\n",
    "    return (test_loss,result,result_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_loss,result,result_labels=predict(loaded_model,test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy=torch.mean((result==test_labels).type(torch.float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.48\n",
      "Test Accuracy: 84.36%\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Loss: {:.2f}\".format(test_loss))\n",
    "print(\"Test Accuracy: {:.2f}%\".format(accuracy*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "report=pd.DataFrame.from_dict(classification_report(result,test_labels,output_dict=True)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "report['Label']=[idx2lbl.get(int(x)) if x.isdigit() else \" \" for x in report.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "report=report[['Label','f1-score','precision','recall','support']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Label</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>T-shirt/top</td>\n",
       "      <td>0.800395</td>\n",
       "      <td>0.821501</td>\n",
       "      <td>0.780347</td>\n",
       "      <td>519.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Trouser</td>\n",
       "      <td>0.966732</td>\n",
       "      <td>0.951830</td>\n",
       "      <td>0.982107</td>\n",
       "      <td>503.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pullover</td>\n",
       "      <td>0.743383</td>\n",
       "      <td>0.674322</td>\n",
       "      <td>0.828205</td>\n",
       "      <td>390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dress</td>\n",
       "      <td>0.830612</td>\n",
       "      <td>0.814000</td>\n",
       "      <td>0.847917</td>\n",
       "      <td>480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Coat</td>\n",
       "      <td>0.751342</td>\n",
       "      <td>0.876827</td>\n",
       "      <td>0.657277</td>\n",
       "      <td>639.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sandal</td>\n",
       "      <td>0.931206</td>\n",
       "      <td>0.906796</td>\n",
       "      <td>0.956967</td>\n",
       "      <td>488.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Shirt</td>\n",
       "      <td>0.576375</td>\n",
       "      <td>0.546332</td>\n",
       "      <td>0.609914</td>\n",
       "      <td>464.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sneaker</td>\n",
       "      <td>0.921105</td>\n",
       "      <td>0.934000</td>\n",
       "      <td>0.908560</td>\n",
       "      <td>514.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bag</td>\n",
       "      <td>0.959402</td>\n",
       "      <td>0.947257</td>\n",
       "      <td>0.971861</td>\n",
       "      <td>462.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ankle boot</td>\n",
       "      <td>0.945489</td>\n",
       "      <td>0.961759</td>\n",
       "      <td>0.929760</td>\n",
       "      <td>541.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>micro avg</th>\n",
       "      <td></td>\n",
       "      <td>0.843600</td>\n",
       "      <td>0.843600</td>\n",
       "      <td>0.843600</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td></td>\n",
       "      <td>0.842604</td>\n",
       "      <td>0.843462</td>\n",
       "      <td>0.847292</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td></td>\n",
       "      <td>0.844092</td>\n",
       "      <td>0.850633</td>\n",
       "      <td>0.843600</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Label  f1-score  precision    recall  support\n",
       "0             T-shirt/top  0.800395   0.821501  0.780347    519.0\n",
       "1                 Trouser  0.966732   0.951830  0.982107    503.0\n",
       "2                Pullover  0.743383   0.674322  0.828205    390.0\n",
       "3                   Dress  0.830612   0.814000  0.847917    480.0\n",
       "4                    Coat  0.751342   0.876827  0.657277    639.0\n",
       "5                  Sandal  0.931206   0.906796  0.956967    488.0\n",
       "6                   Shirt  0.576375   0.546332  0.609914    464.0\n",
       "7                 Sneaker  0.921105   0.934000  0.908560    514.0\n",
       "8                     Bag  0.959402   0.947257  0.971861    462.0\n",
       "9              Ankle boot  0.945489   0.961759  0.929760    541.0\n",
       "micro avg                  0.843600   0.843600  0.843600   5000.0\n",
       "macro avg                  0.842604   0.843462  0.847292   5000.0\n",
       "weighted avg               0.844092   0.850633  0.843600   5000.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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