1stPart.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wmLphFIP96LT"
   },
   "source": [
    "# 1. Classifying different pictures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nFDqHjUt-XwT"
   },
   "source": [
    "We first define the libraries needed for this first stage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "oDJ_DMcM9QcE"
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function, division\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import torchvision\n",
    "from torchvision import datasets, models, transforms\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import os\n",
    "import copy\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report\n",
    "\n",
    "plt.ion()   # interactive mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1l2SKtIS-efQ"
   },
   "source": [
    "We import the database from _Google Drive_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jc_90XFEAJ_b",
    "outputId": "59f2b582-efd1-41f0-fe83-5e339b678911"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "#Mount Google Drive as folder\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive', force_remount=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "eXOvj4Z99Ww0"
   },
   "outputs": [],
   "source": [
    "!unzip /content/drive/MyDrive/dataHP.zip -d /content/dataHP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yo8BeOf-_ADW"
   },
   "source": [
    "To approach this first part of the Challenge, I will be using Transfer Learning with a CNN as a fixed feature extractor. To do so, I will first load a pretrained network (in this case, I have chosen the _Resnet18_). Then, I will freeze the weights for all of the network except for the final fully connected layer. This last fully connected layer will be replaced by a new one with random weights and only this layer will be trained."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pcDGgMWWAvgf"
   },
   "source": [
    "We first load the data and perform a normalization of the images of the database with some data augmentation based on center crops. The crops will help the network to capture better the noise and the texture of the different types of documents and it will generalize better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4Ffy5AS0YRfV"
   },
   "outputs": [],
   "source": [
    "data_transforms = {\n",
    "       'train': transforms.Compose([\n",
    "        transforms.Resize(256),       # Resize the short side of the image to 150 keeping aspect ratio\n",
    "        transforms.CenterCrop(256),   # Crop a square in the center of the image\n",
    "        transforms.ToTensor(),        # Convert the image to a tensor with pixels in the range [0, 1]\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "    'val': transforms.Compose([\n",
    "        transforms.Resize(256),       # Resize the short side of the image to 150 keeping aspect ratio\n",
    "        transforms.CenterCrop(256),   # Crop a square in the center of the image\n",
    "        transforms.ToTensor(),        # Convert the image to a tensor with pixels in the range [0, 1]\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ]),\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KSY-JWuLBXbK"
   },
   "source": [
    "We split the data in the _training_ and _testing_ subsets and we visualize the different classes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-vE0KL8mCxTd",
    "outputId": "f2bf5bd4-88a6-42cd-b52b-63307f51f8fe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Documents', 'Plans', 'Tickets']\n"
     ]
    }
   ],
   "source": [
    "data_dir = '/content/dataHP'\n",
    "\n",
    "image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n",
    "                                          data_transforms[x])\n",
    "                  for x in ['train', 'val']}\n",
    "dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,\n",
    "                                             shuffle=True, num_workers=2)\n",
    "              for x in ['train', 'val']}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n",
    "class_names = image_datasets['train'].classes\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(class_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zt8IkLoxBjoR"
   },
   "source": [
    "We define the following function to show some of the images used to train this last layer of the network, as well as their corresponding classes (Ground Truth)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 148
    },
    "id": "H8viBIbYg4-g",
    "outputId": "d3123a6e-cde2-4fb1-9514-c58dae85b51a"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def imshow(inp, title=None):\n",
    "    \"\"\"Imshow for Tensor.\"\"\"\n",
    "    inp = inp.numpy().transpose((1, 2, 0))\n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    inp = std * inp + mean\n",
    "    inp = np.clip(inp, 0, 1)\n",
    "    plt.imshow(inp)\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
    "\n",
    "\n",
    "# Get a batch of training data\n",
    "inputs, classes = next(iter(dataloaders['train']))\n",
    "\n",
    "# Make a grid from batch\n",
    "out = torchvision.utils.make_grid(inputs)\n",
    "\n",
    "imshow(out, title=[class_names[x] for x in classes])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-JzBt2qTBy6a"
   },
   "source": [
    "We also define a function to train the model. In this case, we will train the model for a fixed number of epochs storing at the end of each epoch both the training and the testing accuracy. The training will stop either when all the epochs have been computed (and then we will pick the weigths of the epoch with higher accuracy) or if the desired accuracy has been reached. We define the desired accuracy at 0.99999 $\\sim$ 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zcgjJveKhE3x"
   },
   "outputs": [],
   "source": [
    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
    "    since = time.time()\n",
    "\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_acc = 0.0\n",
    "\n",
    "    terminator = 0\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
    "        print('-' * 10)\n",
    "\n",
    "        # Each epoch has a training and validation phase\n",
    "        for phase in ['train', 'val']:\n",
    "            if phase == 'train':\n",
    "                scheduler.step()\n",
    "                model.train()  # Set model to training mode\n",
    "            else:\n",
    "                model.eval()   # Set model to evaluate mode\n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "\n",
    "            # Iterate over data.\n",
    "            for inputs, labels in dataloaders[phase]:\n",
    "                inputs = inputs.to(device)\n",
    "                labels = labels.to(device)\n",
    "\n",
    "                # zero the parameter gradients\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # forward\n",
    "                # track history if only in train\n",
    "                with torch.set_grad_enabled(phase == 'train'):\n",
    "                    outputs = model(inputs)\n",
    "                    _, preds = torch.max(outputs, 1)\n",
    "                    loss = criterion(outputs, labels)\n",
    "\n",
    "                    # backward + optimize only if in training phase\n",
    "                    if phase == 'train':\n",
    "                        loss.backward()\n",
    "                        optimizer.step()\n",
    "\n",
    "                # statistics\n",
    "                running_loss += loss.item() * inputs.size(0)\n",
    "                running_corrects += torch.sum(preds == labels.data)\n",
    "\n",
    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
    "\n",
    "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(\n",
    "                phase, epoch_loss, epoch_acc))\n",
    "\n",
    "            # deep copy the model\n",
    "            if phase == 'val' and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "                if epoch_acc > 0.99999:\n",
    "                  terminator = 1\n",
    "                  break\n",
    "        print()\n",
    "        if terminator == 1:\n",
    "          print(\"Desired accuracy reached!\")\n",
    "          break\n",
    "\n",
    "    time_elapsed = time.time() - since\n",
    "    print('Training complete in {:.0f}m {:.0f}s'.format(\n",
    "        time_elapsed // 60, time_elapsed % 60))\n",
    "    print('Best val Acc: {:4f}'.format(best_acc))\n",
    "\n",
    "    # load best model weights\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Oox2kPScC8R-"
   },
   "source": [
    "Finally, we also define a function for making predictions of concrete images extracted from the testing dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FYypWCSChXsD"
   },
   "outputs": [],
   "source": [
    "def visualize_model(model, num_images=4):\n",
    "    was_training = model.training\n",
    "    model.eval()\n",
    "    images_so_far = 0\n",
    "    fig = plt.figure()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for i, (inputs, labels) in enumerate(dataloaders['val']):\n",
    "            inputs = inputs.to(device)\n",
    "            labels = labels.to(device)\n",
    "\n",
    "            outputs = model(inputs)\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "\n",
    "            for j in range(inputs.size()[0]):\n",
    "                images_so_far += 1\n",
    "                ax = plt.subplot(num_images//2, 2, images_so_far)\n",
    "                ax.axis('off')\n",
    "                ax.set_title('predicted: {}'.format(class_names[preds[j]]))\n",
    "                imshow(inputs.cpu().data[j])\n",
    "\n",
    "                if images_so_far == num_images:\n",
    "                    model.train(mode=was_training)\n",
    "                    return\n",
    "        model.train(mode=was_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tYwoQzwuEDGt"
   },
   "source": [
    "Finally, we also define a function for the evaluation of the model by means of the _Confusion Matrix_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dHnYEz3-VPMm"
   },
   "outputs": [],
   "source": [
    "def compute_confunsion_matrix(model, dataloader, state):\n",
    "  y_pred = []\n",
    "  y_true = []\n",
    "  with torch.no_grad():\n",
    "      for inputs, labels in dataloader:\n",
    "        \n",
    "          inputs, labels = inputs.to(device), labels.to(device)\n",
    "\n",
    "          output = model(inputs)\n",
    "          output = (torch.max(output, 1)[1]).data.cpu().numpy()\n",
    "          y_pred.extend(output) #save prediction\n",
    "\n",
    "          labels =labels.data.cpu().numpy()\n",
    "          y_true.extend(labels)   #save truth\n",
    "\n",
    "  print(\"Train set classification report\\n\")\n",
    "  print(classification_report(y_true, y_pred))\n",
    "\n",
    "  cm = confusion_matrix(y_true,y_pred,normalize='true')\n",
    "  display = ConfusionMatrixDisplay(cm, display_labels=class_names)\n",
    "\n",
    "  fig, ax = plt.subplots(figsize=(3,3))\n",
    "  display.plot(ax=ax, values_format='.2f')\n",
    "  fig.suptitle('Normalized Confusion matrix ' + str(state) + ' set')\n",
    "  return plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SxSSIMk1TeQH"
   },
   "source": [
    "Here we load the pre-trained network and freeze all the layers except for the last one. We define this last fully connected layer as a layer with 3 neurons (one neuron for each of the classes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "m8tbCVB2cDn4"
   },
   "outputs": [],
   "source": [
    "model_conv = torchvision.models.resnet18(pretrained=True)\n",
    "for param in model_conv.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "# Parameters of newly constructed modules have requires_grad=True by default\n",
    "num_ftrs = model_conv.fc.in_features\n",
    "model_conv.fc = nn.Linear(num_ftrs, 3)\n",
    "\n",
    "model_conv = model_conv.to(device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Observe that only parameters of final layer are being optimized as\n",
    "# opoosed to before.\n",
    "optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)\n",
    "\n",
    "# Decay LR by a factor of 0.1 every 7 epochs\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3pKX71RzEard"
   },
   "source": [
    "We start the actual training of the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Orm9R0bLv8oE",
    "outputId": "0002404a-412a-4699-b68e-e2c3f3dc76a2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/24\n",
      "----------\n",
      "train Loss: 0.2585 Acc: 0.9042\n",
      "val Loss: 0.0301 Acc: 0.9944\n",
      "\n",
      "Epoch 1/24\n",
      "----------\n",
      "train Loss: 0.2427 Acc: 0.9056\n",
      "val Loss: 0.0421 Acc: 0.9889\n",
      "\n",
      "Epoch 2/24\n",
      "----------\n",
      "train Loss: 0.2756 Acc: 0.9056\n",
      "val Loss: 0.0196 Acc: 0.9944\n",
      "\n",
      "Epoch 3/24\n",
      "----------\n",
      "train Loss: 0.2281 Acc: 0.9070\n",
      "val Loss: 0.0215 Acc: 0.9963\n",
      "\n",
      "Epoch 4/24\n",
      "----------\n",
      "train Loss: 0.2422 Acc: 0.9085\n",
      "val Loss: 0.0279 Acc: 0.9926\n",
      "\n",
      "Epoch 5/24\n",
      "----------\n",
      "train Loss: 0.2596 Acc: 0.9028\n",
      "val Loss: 0.0610 Acc: 0.9815\n",
      "\n",
      "Epoch 6/24\n",
      "----------\n",
      "train Loss: 0.2615 Acc: 0.9021\n",
      "val Loss: 0.0267 Acc: 0.9963\n",
      "\n",
      "Epoch 7/24\n",
      "----------\n",
      "train Loss: 0.2904 Acc: 0.8993\n",
      "val Loss: 0.0599 Acc: 0.9815\n",
      "\n",
      "Epoch 8/24\n",
      "----------\n",
      "train Loss: 0.2356 Acc: 0.9204\n",
      "val Loss: 0.0391 Acc: 0.9889\n",
      "\n",
      "Epoch 9/24\n",
      "----------\n",
      "train Loss: 0.2745 Acc: 0.8930\n",
      "val Loss: 0.0420 Acc: 0.9889\n",
      "\n",
      "Epoch 10/24\n",
      "----------\n",
      "train Loss: 0.2816 Acc: 0.8965\n",
      "val Loss: 0.0302 Acc: 0.9944\n",
      "\n",
      "Epoch 11/24\n",
      "----------\n",
      "train Loss: 0.2412 Acc: 0.9134\n",
      "val Loss: 0.0423 Acc: 0.9889\n",
      "\n",
      "Epoch 12/24\n",
      "----------\n",
      "train Loss: 0.2397 Acc: 0.9056\n",
      "val Loss: 0.0567 Acc: 0.9796\n",
      "\n",
      "Epoch 13/24\n",
      "----------\n",
      "train Loss: 0.2283 Acc: 0.9218\n",
      "val Loss: 0.0233 Acc: 0.9963\n",
      "\n",
      "Epoch 14/24\n",
      "----------\n",
      "train Loss: 0.2051 Acc: 0.9359\n",
      "val Loss: 0.0240 Acc: 0.9944\n",
      "\n",
      "Epoch 15/24\n",
      "----------\n",
      "train Loss: 0.2904 Acc: 0.8915\n",
      "val Loss: 0.0468 Acc: 0.9852\n",
      "\n",
      "Epoch 16/24\n",
      "----------\n",
      "train Loss: 0.2396 Acc: 0.9113\n",
      "val Loss: 0.0386 Acc: 0.9889\n",
      "\n",
      "Epoch 17/24\n",
      "----------\n",
      "train Loss: 0.2635 Acc: 0.8951\n",
      "val Loss: 0.0223 Acc: 0.9963\n",
      "\n",
      "Epoch 18/24\n",
      "----------\n",
      "train Loss: 0.3043 Acc: 0.8937\n",
      "val Loss: 0.0620 Acc: 0.9815\n",
      "\n",
      "Epoch 19/24\n",
      "----------\n",
      "train Loss: 0.2707 Acc: 0.9007\n",
      "val Loss: 0.0276 Acc: 0.9944\n",
      "\n",
      "Epoch 20/24\n",
      "----------\n",
      "train Loss: 0.2592 Acc: 0.9056\n",
      "val Loss: 0.0196 Acc: 0.9963\n",
      "\n",
      "Epoch 21/24\n",
      "----------\n",
      "train Loss: 0.2609 Acc: 0.8965\n",
      "val Loss: 0.0451 Acc: 0.9852\n",
      "\n",
      "Epoch 22/24\n",
      "----------\n",
      "train Loss: 0.2500 Acc: 0.9141\n",
      "val Loss: 0.0832 Acc: 0.9667\n",
      "\n",
      "Epoch 23/24\n",
      "----------\n",
      "train Loss: 0.2937 Acc: 0.8838\n",
      "val Loss: 0.0503 Acc: 0.9870\n",
      "\n",
      "Epoch 24/24\n",
      "----------\n",
      "train Loss: 0.2859 Acc: 0.8894\n",
      "val Loss: 0.0407 Acc: 0.9907\n",
      "\n",
      "Training complete in 146m 13s\n",
      "Best val Acc: 0.996296\n"
     ]
    }
   ],
   "source": [
    "model_conv_wout_horizontalflips = train_model(model_conv, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HTz1vTkYEhIe"
   },
   "source": [
    "Once the training is finished, we can compute the _Confunsion Matrix_ for both the training and the testing sets. For the training one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 436
    },
    "id": "G3ec5vae3Q_l",
    "outputId": "2b4435da-96da-42a9-9d5c-a6f201f89484"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set classification report\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.99      0.99       344\n",
      "           1       0.99      0.99      0.99       606\n",
      "           2       0.98      0.99      0.98       470\n",
      "\n",
      "    accuracy                           0.99      1420\n",
      "   macro avg       0.99      0.99      0.99      1420\n",
      "weighted avg       0.99      0.99      0.99      1420\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compute_confunsion_matrix(model_conv_wout_horizontalflips, dataloaders['train'], \"train\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4UOmu6JNErsV"
   },
   "source": [
    "For the testing one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 436
    },
    "id": "2_E1GYW-Q6L0",
    "outputId": "11523e8f-4d3c-4f3b-cb33-0dbd30811630"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set classification report\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       181\n",
      "           1       0.99      1.00      1.00       203\n",
      "           2       1.00      0.99      0.99       156\n",
      "\n",
      "    accuracy                           1.00       540\n",
      "   macro avg       1.00      1.00      1.00       540\n",
      "weighted avg       1.00      1.00      1.00       540\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compute_confunsion_matrix(model_conv_wout_horizontalflips, dataloaders['val'], \"test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OQo9-9cZFAyP"
   },
   "source": [
    "Finally, we can make some predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 581
    },
    "id": "5AlHo9tGhhy4",
    "outputId": "b6754c25-9111-4742-d738-4872d6263e87"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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FYsXulevsXrnOpSsvUAxHRAXnS65cv0VeDtje2WU+W1IUQ6oqMignZOWQrZ09inxABIbDMaGJ7F6+TJ5lLBYLZrM5PisoyiG3X3mN7Z3LPHy4nyLseK6i5b3HOUlVqfgUyd7ZSx3eeU6Pj5jNTqiqJTFisGlTJ/WXVAK1F3LxQ74RnzydTlVafJF+YK1onxg2AMS1PNgliNByWFGjA7WmFTCBkGrAUfEOggiESAiB1WpFORggzkqaqOXNxlvw2Bq5XptbMKVld3QtaWSMkcX8DJ95qzA5T1NXFIVVpQaDYZdFtO0Zov9gAhHzv2Am0KUSoXaCW+dY9ZrknO8qTtCWfQVNtei33/i/ZFnBzqUdnHc4PDgocisX1pX55a2tPfKi6ICLFk/vmSB9nbpPuEl9U5wIk8k2ITZWN8YTsgzvE14tlkU4HFHXJ+zFto0J2XLPRMNpBzNqJ3Rx/YB3x8SYIO3/7Tov59vHSFQe75nNzrh39y2yzHH5ynUWixlNvWI0nrBcrJjPZnzym7+Fsixx/vx9u1x4vb9raBopGBNnfX60f5+6rri0c4mj40NEYDLZtnw4maX2GTaBa25EyC2DsaW4QYqovVFxYjK7PdMxdia0014RwPdaJyBq9ywHY8aTHV648SKLxQzvMsrB2FKqquLy1S3qakUMSl03uGC+0nsz1+sCPufORBC/ptk4Wrg1KixWS1AhxEBVNQmX7ycw2tOdLrJtxCefnZ2pUVl7wXWaDF19GFrDeJ491/K11ltbVtRopUtLUQ0MiWpFDo0QJRqNSK2k6RIoE0JDCA0iQpblXb/W+0liYZ4z6VEJiSQYVS2McBZw5WXZ03pj69v12fHJT08u0x5d40omLUm+rw1s1u1dO9AtMtUVMRIXyyUsm5imiQO0ATFQwom3awVA0AhBw7nCgveOqEoIkSzzhNAYx7vjfCu2KZAVQ7z3CSwJ5Jp3E1NT32QD0fXGhGw8KEkFgbaeDIg3jUiBWFQbxLb4vl74F1n32wBCCA2/8Zlf5dLeHuWg5PTomN29y0zPply9dtO0G4Mb2wKGiJDlBVmRrx1vrUzvS2MInZbb93pCUyGiLOcrRuMxi9WUPB+g9doztQHbZuKuzW/W1kbGbeSp9KiQxSyOGKyq5LwjapPosmIszuS3DZ82IUx2LrFcrrh/9w5VVfHO22+SFwOuXr9lbJJkNjVpLfSTZJ0UaBqd+tL5Uk2TwK47PHhMaCqWizm3br/Gnbfe4dKlHfK8IM9zWuJei4lvwj1uqECRBClr/jid6wKyzmS2plFQ+gCsI8cnDYmBLnB74YWbxBjY3t5GcVTVkrIc4JMFsJRMrHKFa4tinEuh1PrZWo92OUzH1U4T0YlQh0ATIsfHR4wnI+qmQcXbRSm86IK1DaRRGwu82nqvw38JYNCZYATn1MyrswFORtSu0W6k0wcbo8vWNYpYvpqwZqHlY6dlMRrwPjML4fJusrXlRWKqGbfHMXpvR2FI4xbVypYG0Dgi7UoNl3w1a9G13X80Hj8bgVc7cCHUKTBJgmtXHUgSLGBRMz2wpVh6ou0Ct1bTLED73Gd+hdHWFpnP2N6+RF03LOZThqMhi8WKyfYOD+7dZWtrm+npIbdf/RqGw8n5vrne50dtF665TpNtoljZcbmYMzs7ZW/3KgeP9ymHxvQcbe0ka9H2vccBLrJtLrrGSn2t2VV6tmZHjlc95xcF6c110na35utEHCpKXStnJycsV0vu37tHURZMtnY4PjlG8MxnCx7vPwANrJYVi/mM4WjSp22t+2i1OJlZAVr2ifXT/j85OiLGmvnijLqp2H/7PleuvsBwvH0O1LEI+yJH2drmAq8I4HA+lfVUk5BJsz+ZyBAw1qMmlqfrrEDbeu23JSkf//pvsrw3NilNMlQshNru7jy3P/JR6rri4f175EWRzHrK04UEo7amWjsro9rGBQJqVKLtnV0ODx7TNMYXe+H6rbU+9jm14NDzQNqFtA1xvGbd7khdwNW9b1kjrVoZWU5cGlhZXxZjUbFgUXZ7zxhJwEYfIcenQIzOBxt6QagbmqbCOU+W2Vor7zLayaUa8c6jMSQ+dUYXGyC9hq6lcs65VJI0a9Cie4PR8KvfJ2tXXUoPnwIeAxnaJLj1tb4vEqxpR5t4qiaEjN5sr5ZzVKEoiu5a1UioKyMojLaIGvBkVouW0N01JKAjRk3FfgNLYgyoV2JsqFYrymKQeN0B8UY6qpuaLMsNQAmBLAEjPWDXT+qLbBtKofrAq1/Nb8FNR8EVW07TmvCorvtcuyTVAjD7fEvjVY288flfYzSaEGNka3uHo8NDhuMJZVFwenxM09QsFgteuHmD+dmM6zdfZjTeJssLgPPuIJpo2tUaUSNFMTDtFKjmM/IiYzafsX1pl/vvvMPO7i5VXXHlygt0LJOUtm3CKW+ENNBHmAY/2rF2AAz6E8QGOEW3YBMhJmy695d9Uw1ojGT5GJ8NiCrcv3uXw4MDRBzFYEg5GrFYrRAn3L9zh8f7+7Dmd/t7pf+dJFfQ5vJCW9sWhKauePTgAfv371GtKkKInJ2d0jQ1TdOkrCBVsLrE/mLbRnzybDZTCCk67VMVVK1gkIKobvmMdZWncWtzf9JpcFsKbEIDUcBFUElEezP7IUBT1zgvFtQh5OUQ7/tJY669LYH239eabl2rJB0fHhC1YblYMBxNCKEhyzKapmF39zLeZ4bKqS2Ujwrj8bNArp/POrKTbfaia0FRv/qALs2iO3YueFqDRGMyh+vkeuf6yJ20QsM49k+NsUBIa5a992RFYT44GmuzaRpQyHKL1Js6kBclTiCkxfTOZTTB9jZxCVARX+CcgSVtcBYVRqNnAAw5z66JtBWndQG2mm0x2TpDZM3DtLkndAK1j7vkr1uhAiliF4lpMXiPcAlCbBqapmFVLRmJaXlVV5RlwaqqaOqa8WRCDNE4XKEmzzKWyxU+82RZQahXqS+OJgRGkwFgE0FU8VnWxRgX2Taiyaenxy3WkHJgd84sWku4dYsBt6yPbu+PdRCkFW7ru41c3wdl55PTvj5MP8m0XUPVdOiZRjWz3roS54nR6LXtFlQhxLSCwsy4yzxCJMSIc0WCNCOhqfGZw/uCwfAZSKG8c8nMGQldusJ8+7LWFhA01Wxbv9hWgXqgoR+zGCOzs2ki0AfEOZqmoSwGiDNCQB9TufMTSKwQQrq/ZOb3s846RHyWgaptRKOKa/N3DIgJBBBH5vMUcNmEzHxG3dTp+ottG6P/dIvCYS0HTvtdYYPsEh1H2posrSYH6KLdHtJEI6Fp2H9wn0t7l/ncZ36Z8WSHssg5m07ZvrTLa69/nBapbIsc4oxfbcCGoWsibeC1Huz1bsOle3z+859h99Iu88WCGzdu8uTJE4ajgiwbMN7awRYK+A7/ZgOZ8oaEnNiW9KlTS3k1ZmOPMvV+VhPenUwl7WL21mwbndd7o8c+ebzPtRdepK4qQlBuvfK6rWZIVaIeg4Z2hWPf5EsshH1HWy5pAylhsrXN4dETnMs4nZ4QY+Dk+ITJNox0K5n+iDjBbWhHrQ1t8bTSlmLbClqTX0y6TcSWobSL1AE6xkYCgNtlLN35hKRZLh3xyQ1YNtTix/bJ1jg415rU/l7naUVfGo230Tso8/k8rblaGcKmkKWJVpTFuXvFZHGekeg6pFy0PZL8INBuBcFaMGUmnTVAwsxeW2Ls0jFxxBi4f+eLZHlOWZagMJ1OESfkRUloakLT0ITIrZdf4ektl95t0vf0JO2QK028MefMtA/KnKIY0sQaLw6nvb9vc/iODnTBbXOwZldntSAGTSsF26Wj9MWHHvttWRkWkHVMyrbAD4QQqKoK7zPefOMLrOYLmtAwGA1ZzedcuX6d05MpV65c6apd7WQKIZ7T5r6I0Qr/S/cKm53YJmzz5Skvvvgqjx89ZLw9IUZlZ+eyFVScYdnEmAobF9s2RxqgAecsj1V6bpdGUAc+JjOrqDqUAGKIWGvm290D1gGRzOfs7l3FiefjX/8tnE1PGA5HVNWqW1m4u3vNLECCM8EmjhUWktY5BbxtndhhbqaZMUYET9RA3VSoRopiyGq1pBwMWC0rUMPTvdNuUqv0EO2FjvUmfPLBwWMtsgxxls/GGGy/jZQeOfE0ocL53FKPugFsb00wA+6SdphwbJF5jA1ZVhDVdv3LshwlEqoVeG/bO4YKEeNbR8XWQbkWSetptsb8ccl6SJpsdHFDW1KOIXbrlBHQEMEZmOKzzCagxiR0cN49G7zr0DQE7yFE6lWVNCfS1A1Z5nGZspwvyYsA5YDZbIbGwGg87nYQ8M4IeqowGAyp65pqtWAy2QYRZmdnjMYDQlBmsxmD4dAQqsXC8tY8Z75cUhYFeZ4xm87w3jZsW60WCcbMWS7mZJknK0pWiyUhNAxHI5q6MvTKZQgGpZaDkhiUJqQ9wTRQrSq8F1sU1yghKMPR6ELHeyOa/PZbn1OCslgsGIy22NqacHjwmKvXb1plp6nxLiM0FSpG6POZ+WHvHHk57PJiC9JcsgAAtmQlxsCDu2+xd+UaaMR5j0ZwWdpAPURmszOapqJaLhgOx+R5TtMEyuGIra1LyYWkfcYsdrcAkMbCwaiIKtPpEc5nDEdbBoiEmtV8TtBIWQwsqnbOVjkiDAbPAOJ1/949RsMxk+1tyuGAECPL5YyH9+7yaP8heVFQFgWrZcVytQQNrFYVt16+jTjPa69/DUYssAkgrt/q2FiTgWq14M7bX+TR/gPEmZnOs5KogdA0FOWI5WLGlSvXODo6YDKeM5vNGY3GFIMBk0mf47ZeWdeCwDZbRoSqCngfefTwc0wmO0S1hQOz2WnHBhlv7xBDY1yyC24bEfL1Gy+BOsaTId5boX5n7xqOjNF4x8xeCqJUI9PTE7K8sO2NyyJF1/0gQxv99hi1iONrP/nNVGnXvKYJzOdnZD4zEGMysYXp01NefuV1VqsVL778MRaLM5bLWYd3txWw7r5Oup81aNOinZ09qnqB9yXlYMSqmjMaTmyLi7xAEKp6iUhGu3ryIttGzPXhwWO1zdCyLvDymXGfvbNV/pJSDSsutUX9lg7U9tmtpTktzUfTXp3ahu2EdlWkni9qgFLXNXmW27YR0XJ427va9vd8ujDSY+w9qYGOkQkSI00wc57nBYgYAb+xOnPd1EwuuJ68EU1+8vgBp8fHvPrRr+Hh/Xd4eO8Or3/iGzk7OUZj5OTkmFsv3ebytRtWvDjHHmm51k/fdb1gYUJo11JZJB2TVvZVrv0Hd0CV2eyMV177KG9+4Qtce+E6x4dHvPzqR/AurbIQOjAEWuylTfkwuFT6Nc4+yzk8eMh4vI2qslotqaqK3b3LZPngKzu479I2AqbuP3yAiG1uevD4CVGVpmqYL5Y0MVIMxvzGZ3+d48MnNCESQkw77LSrE4T1zcw7mFE1VayeLkNakeB8mVII0bakyIucqm4s8Koqtna2LHLuIq2eGrQOkrSIW7ueyiEgnqZZUVUV07MpZ7MzzqaniGDkgw0UKDa0j9cjVZTMC6cnpyyWC65fv0mIITEpPHXVUKad5Vuo02Tn1wZ4HSZs4c+WPd2iZ+2Ouk9xuGLDdHpGiA2CUBYDVtWScjCkKDKj46r5fJeWy7REhvYuBsismetUMIlEjg8PbP2zs8h/uViwc2kPn2UXjl1vRMjHxwca2woTLi0nTR1KQY4T3y1G7wkB0hUSTItDV+C3z7YbsPb0nySX8wLuzG5fp7Ytl7VjjRhGbRu29Rw0Q9m6tcas97s151Yt61dNtN9JV9Z8JkgDiNhCN9YXZa9PtjR8qUpER8Htx6ZdxdAW5fsCQvtKlZ/YTo7+fW/Ge//d+nMjw0ebfOn/9UANlwCsNarS+QBBQFzH8zr32E42YKw3VU+WDFzSjFbL1v2og6ANDk8Mtk9H33RNsH15sNfAXktjbM21dlrW3aUz/60WsnY+ke/WSpTtd4uQdgF0fZm0zQBCS59v8+r+/q1F2EQZaiNCFkm8KqEjtsVEByIVAbofZhJS6qOdRlvrtbU91AlTe0PaM0G7GKprMRrMEdc2auuWvThBn+ZIS1qOmkxvF5ClWmhnpvG0+431m8PYI2yCyLe5nyeIIUDeNiwAAAJQSURBVAnS9X6vyz8jQWP3IyIiTRcIqQSzBK3QkY4H3WrKet2XJAP726dgbQ6tRMt3xRnHS1xaMqtGRUoTLK5/hz0FvfB0bceiVBjXtvxpE7pdLmPbOF5s24iQz6YnFFlhIUoTGZQFEVsk5jMLgGy7f1votlwsyfI8/XhHg5OMLMtYrZZWPPA5q6pCHJTFgGq1QokU5YCmbqjrmrIs0qpDxfscNBoQkn4ecLVcMhgMrGQZG0ScVbFiJGI/RrJcLCjzHHGOECNZnpM5IyrYxjEZddp91zlBQxsctnzthizPKcryQsd7I0I+3H9AXpaMt3YYlAMePLibuMsOJGNn7wqTyTYalHsP3mI0GFBVCy5fvs7dO/e4tHuZw4NTXnzpNnffeRNV88mj4ZC6LFmtKurKflZoWa8YFAPieMzO7tVkrgMHj+4DnqIsqZZzhkPbZddlGUppPxdYV5ydWY6b5SWZ8yxWc6IGBsWQGJRlHcm8Tdgm1FaMQHC+xDTdYFSNSp67Z0eT58slp/v7jEZDPvqxT7BYLDk+eszLr3yE2XzKzs4uUgirakFRZGTlkIdvvWk4twbe+PxnmZ4a+F/XkQf373Lr1kvUITA7OuLwyRNevv0qJyfH1NWSzGeUQ/vVVcF21V2uKmIMHB8+4uDgCaPxhPF4i9uvvm4hQWY02pPjY6anhxSDId7ljCcjfJYT6ojPcvIiI9TzZKGVwWhkW1aIdHl2h4759aTr4trzn7t/BtrzX0J/BtpzIT8D7bmQn4H2XMjPQHsu5GegPRfyM9D+Hw991L5xGEYCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_model(model_conv, 6)\n",
    "\n",
    "plt.ioff()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UZQpymJ6FLbG"
   },
   "source": [
    "As can observe, the predictions above are correct (we can see it also by printing the Ground Truth labels) and we are obtaining pretty good accuracy for both the training and the testing phases."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "TOP SECRET ROSIES - CHALLENGE HP",
   "provenance": []
  },
  "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}