Face_recognition_DeepLearning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "##FACE RECOGNITION\n"
      ],
      "metadata": {
        "id": "fQttv1N0SbXA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Francesco Esposito*  5108223\n",
        "\n",
        "*Alessandro Esposito*  5112139\n",
        "\n",
        "**Face Recognition**\n",
        "\n",
        "The project aims at the recognition of images through the use of new CNN architectures. In order to implement our model we choosed a dataset that distinguishes images representing photographs of real people ('Celebrities'), from drawn faces, sketches, animals, anime and objects.\n",
        "\n",
        "The dataset can be found at this link: https://www.kaggle.com/datasets/pawankumarsahu/multidomain-human-face-classification-dataset?resource=download  "
      ],
      "metadata": {
        "id": "gm5izmQSKd_j"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rgplUu_wlVhw",
        "outputId": "db41b414-a92d-478a-c4c3-940611b06050"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install torch torchvision\n",
        "!pip install d2l==1.0.0a1.post0\n",
        "!pip install split-folders\n",
        "import glob\n",
        "from torch.utils import data \n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "from torchvision import transforms, datasets\n",
        "import torch \n",
        "import torch.nn as nn\n",
        "from torchvision.utils import make_grid\n",
        "from torchvision.utils import save_image\n",
        "from IPython.display import Image \n",
        "import random\n",
        "from d2l import torch as d2l\n",
        "from torch.nn import functional as F\n",
        "d2l.use_svg_display()\n",
        "#Libraries for data exploration\n",
        "%matplotlib inline \n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import plotly.express as px\n",
        "import splitfolders\n",
        "## Libraries for training and validation iterations \n",
        "from torch.utils.data import dataloader,DataLoader"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Connection with drive in order to import the dataset. \n",
        "Images are imported with original colors and converted to tensors, then a resize is done with the aim to have all the images of the same dimension."
      ],
      "metadata": {
        "id": "_d1Pedm8K9HG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ODLrIwJ5vmx",
        "outputId": "71aca0f9-fd22-4a25-efbf-c861b7343dbe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "/content/drive/MyDrive/PROGETTO DEEP LEARNING/Human_Face\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "# ----- Cambiare la directory ------\n",
        "%cd /content/drive/MyDrive/PROGETTO DEEP LEARNING/Human_Face"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kywNY2wWs58t"
      },
      "outputs": [],
      "source": [
        "\n",
        "data_augmentation = transforms.Compose([transforms.Resize((224,224)), #images resize \n",
        "                   transforms.ToTensor(), \n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SOzif58zijXI"
      },
      "outputs": [],
      "source": [
        "\n",
        "my_dataset = datasets.ImageFolder(root='/content/drive/MyDrive/PROGETTO DEEP LEARNING/Human_Face',\n",
        "                                           transform=data_augmentation)\n",
        "some_example_set = data.DataLoader(my_dataset,\n",
        "                                             batch_size=32, shuffle=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##EXAMPLE OF IMAGES AND REBALANCING"
      ],
      "metadata": {
        "id": "2pki5DQ8Sk13"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Definition of the labels of every category and provide a sample of images present in the dataset. "
      ],
      "metadata": {
        "id": "1jMDhAcJPfvI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ir7agAzGvvnW"
      },
      "outputs": [],
      "source": [
        "#dictionary with the names of the labels in the dataset ---cambiato in base alle nuove cartelle----\n",
        "image_label = {0:'HomeObj',1:'NotFace',2:'Animal',3:'Anime',4:'Cartoon',5:'Celeb',\n",
        "               6:'Sketches',7:'VectorArt'}          "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SqWbcBw3pjgW"
      },
      "outputs": [],
      "source": [
        "images, labels=next(iter(some_example_set)) #take some images from the dataset and the corresponding labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "EwMSw-v2ptEb",
        "outputId": "3fc6152e-05da-4381-c4c2-38f4af4be282"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
            "needs_background": "light"
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        }
      ],
      "source": [
        "#plot the images with the corresponding labels\n",
        "fig,axes = plt.subplots(3,3,figsize=(8,8)) #we plot the images in 3 rows and 3 columnns, with a 8x8 size \n",
        "for i in range(3): #three rows\n",
        "    for j in range(3): #three columns\n",
        "        plt.sca(axes[i,j])\n",
        "        idx = np.random.randint(0,31) #take random images from a batch of size 32\n",
        "        image = np.moveaxis(images[idx].numpy(),0,2)\n",
        "        plt.title(image_label[labels[idx].item()]) #show the label\n",
        "        plt.imshow(image) #plot the image\n",
        "        plt.axis('off')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a6nS80GvUTXS"
      },
      "source": [
        "Problem: Unbalanced classes, so we rebalance them using Glob and Os library and then we delete some classes that have too few pictures. \n",
        "\n",
        "\n",
        "We resize all the classes with an overall of about 220 images per class. "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## we create our path to select the entire dataset on our Google Drive \n",
        "root_path='/content/drive/MyDrive/PROGETTO DEEP LEARNING/Human_Face'\n",
        "## class_names is a list of all names of labels sorted in alphabetic order \n",
        "class_names_original = sorted(os.listdir(root_path))\n",
        "n_class_original=len(class_names_original) #number of classes\n",
        "## class_dis is a list of all lengths of each labels \n",
        "class_dis_original=[len(os.listdir(root_path + \"/\" + name)) for name in class_names_original]"
      ],
      "metadata": {
        "id": "jePyiq-W1ZNY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.bar(x = class_names_original, y = class_dis_original, text_auto='.2s', color = class_names_original) #create the histogram\n",
        "fig.show() #show results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "D0-p7cuj1Ya_",
        "outputId": "7fa0b2f4-f5d8-4ce6-8b6f-9007f1df5d01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "import glob\n",
        "def ber(p):\n",
        "    if np.random.rand() <= p:\n",
        "        return \"1\"\n",
        "    else:\n",
        "        return \"0\"\n",
        "\n",
        "\n",
        "p=[0.15,0.15,0.045,0.18]\n",
        "folder=[\"anime/*.png\",\"cartoon/*.png\",\"celeb/*.jpg\",\"sketches/*.jpg\"]\n",
        " \n",
        "for i in range(4):\n",
        "    for j in glob.glob(\"/content/drive/MyDrive/PROGETTO DEEP LEARNING/Human_Face/\"+folder[i]):\n",
        "        if ber(p[i]) == \"0\":\n",
        "            os.remove(j)\n",
        "\n"
      ],
      "metadata": {
        "id": "oee7GLXgACEP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F8gNRrmNVZk9"
      },
      "outputs": [],
      "source": [
        "## we create our path to select the entire dataset on our Google Drive \n",
        "root_path='/content/drive/MyDrive/PROGETTO DEEP LEARNING/Human_Face'\n",
        "## class_names is a list of all names of labels sorted in alphabetic order \n",
        "class_names_original = sorted(os.listdir(root_path))\n",
        "n_class_original=len(class_names_original) #number of classes\n",
        "## class_dis is a list of all lengths of each labels \n",
        "class_dis_original=[len(os.listdir(root_path + \"/\" + name)) for name in class_names_original]"
      ]
    },
    {
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        "id": "CKxp2Ez_VVhd",
        "outputId": "bba8ee5f-aecd-497d-f9e4-f49590bd34cd"
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          },
          "metadata": {}
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      ],
      "source": [
        "\n",
        "fig = px.bar(x = class_names_original, y = class_dis_original, text_auto='.2s', color = class_names_original) #create the histogram\n",
        "fig.show() #show results\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OvVxTzGYWgMF",
        "outputId": "3f3a5593-e576-4ee3-ab15-fe5f6f6bca3b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1926"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "len(my_dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##DIVISION IN: TRAIN, VALIDATION AND TEST"
      ],
      "metadata": {
        "id": "zaD1IKx1SIBV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xp5LzcE2223N"
      },
      "outputs": [],
      "source": [
        "#Update dataset with new amount of images per category\n",
        "my_dataset = datasets.ImageFolder(root='/content/drive/MyDrive/PROGETTO DEEP LEARNING/Human_Face',\n",
        "                                           transform=data_augmentation)\n",
        "\n",
        "## Creating train validation and test sets about 0.7,0.15,0.15 respectively\n",
        " \n",
        "train_set, val_set,test_set = torch.utils.data.random_split(my_dataset, [1348, 289, 289 ])\n",
        "\n",
        "## We make train and test sets iterable in order to apply the neural network training function\n",
        "train_iter=dataloader.DataLoader(train_set,batch_size=128,num_workers=0)\n",
        "val_iter=dataloader.DataLoader(val_set,batch_size=30,num_workers=0)\n",
        "test_iter=dataloader.DataLoader(test_set,batch_size=30,num_workers=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OLJDv2SNMbHS"
      },
      "source": [
        "##FIRST ARCHITECTURE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5Fr52PpTIEZK"
      },
      "outputs": [],
      "source": [
        "def evaluate_accuracy_gpu(net, data_iter, device=None):\n",
        "    \"\"\"Compute the accuracy for a model on a dataset using a GPU.\"\"\"\n",
        "    if isinstance(net, torch.nn.Module):\n",
        "        net.eval()  # Set the model to evaluation mode\n",
        "        if not device:\n",
        "            device = next(iter(net.parameters())).device\n",
        "    # No. of correct predictions, no. of predictions\n",
        "    metric = d2l.Accumulator(2)\n",
        "    for X, y in data_iter:\n",
        "        if isinstance(X, list):\n",
        "            # Required if doing the fine-tuning of BERT (recurrent network)\n",
        "            X = [x.to(device) for x in X]\n",
        "        else:\n",
        "            X = X.to(device)\n",
        "        y = y.to(device)\n",
        "        metric.add(d2l.accuracy(net(X), y), y.numel())\n",
        "    return metric[0] / metric[1]\n",
        "\n",
        "def train(net, train_iter, test_iter, num_epochs, lr,\n",
        "              device=d2l.try_gpu()):\n",
        "    \"\"\"Train a model with a GPU\"\"\"\n",
        "    def init_weights(m):\n",
        "        if type(m) == nn.Linear or type(m) == nn.Conv2d:\n",
        "            nn.init.xavier_uniform_(m.weight) # Xavier parameter initialization: a particular method (see textbook section)\n",
        "    net.apply(init_weights)\n",
        "    print('training on', device)\n",
        "\n",
        "    net.to(device) # MOVE the net to the GPU (if available)\n",
        "    optimizer = torch.optim.SGD(net.parameters(), lr=lr)\n",
        "    loss = nn.CrossEntropyLoss()\n",
        "    animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs],\n",
        "                            legend=['train loss', 'train acc', 'test acc'])\n",
        "    timer, num_batches = d2l.Timer(), len(train_iter)\n",
        "    for epoch in range(num_epochs):\n",
        "        # Sum of training loss, sum of training accuracy, no. of examples\n",
        "        metric = d2l.Accumulator(3)\n",
        "        net.train() # Set the network to training mode (not evaluation mode)\n",
        "\n",
        "        for i, (X, y) in enumerate(train_iter):\n",
        "            timer.start()\n",
        "            optimizer.zero_grad()\n",
        "            X, y = X.to(device), y.to(device) #MOVE each minibatch of data to the GPU (if available)\n",
        "            y_hat = net(X)\n",
        "            l = loss(y_hat, y)\n",
        "            l.backward()\n",
        "            optimizer.step()\n",
        "            with torch.no_grad(): # detach gradient calculation for avoiding effect on backpropagation calculation\n",
        "                metric.add(l * X.shape[0], d2l.accuracy(y_hat, y), X.shape[0])\n",
        "            timer.stop()\n",
        "            \n",
        "            train_l = metric[0] / metric[2]\n",
        "            train_acc = metric[1] / metric[2]\n",
        "            if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:\n",
        "                animator.add(epoch + (i + 1) / num_batches,\n",
        "                             (train_l, train_acc, None))\n",
        "        test_acc = evaluate_accuracy_gpu(net, test_iter)\n",
        "        animator.add(epoch + 1, (None, None, test_acc))\n",
        "    print(f'loss {train_l:.3f}, train acc {train_acc:.3f}, '\n",
        "          f'test acc {test_acc:.3f}')\n",
        "    print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec '\n",
        "          f'on {str(device)}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lqsdrMNJO9li"
      },
      "outputs": [],
      "source": [
        "#Define the total length of the batches for train and validation set \n",
        "total_batch=len(train_iter) #number of training images\n",
        "def show_losses(model, val_iter):\n",
        "  epochs=10 #number of epochs\n",
        "  total_l_list=[] #initialize empty list\n",
        "  validation_l_list=[] #initialize empty list\n",
        "  validation_batch=len(val_iter) #number of test images. val_iter and test_iter have the same length\n",
        "  optimizer = torch.optim.SGD(model.parameters(), lr=0.01, weight_decay=0.0001) #optimizer \n",
        "  for epoch in range(epochs):\n",
        "    total_loss=0 #set loss to 0 at the start\n",
        "    for num, data in enumerate(train_iter):\n",
        "      X, y=data\n",
        "      optimizer.zero_grad() \n",
        "      yhat=model.forward(X)\n",
        "      loss=F.cross_entropy(yhat,y) #loss function\n",
        "      total_loss+=loss #update total loss\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "    total_l_list.append((total_loss/total_batch).detach().numpy()) #add the updated loss to the list\n",
        "    with torch.no_grad():\n",
        "      val_loss=0 #set loss to 0 at the start\n",
        "      for num, data in enumerate(val_iter):\n",
        "        val_x, val_y=data\n",
        "        validation_out=model.forward(val_x)\n",
        "        v_loss=F.cross_entropy(validation_out,val_y) #loss function\n",
        "        val_loss+=v_loss #update total loss\n",
        "      validation_l_list.append((val_loss/validation_batch).detach().numpy()) #add the updated loss to the list\n",
        "  print(f'train loss: {total_loss/total_batch}')    #print results\n",
        "  print(f'validation loss: {val_loss/validation_batch}')  #print results\n",
        "  #plot results\n",
        "  plt.plot([i for i in range(1,11)], [j for j in total_l_list], label='Train Loss') \n",
        "  plt.plot([i for i in range(1,11)], [j for j in validation_l_list], label='Test Loss') \n",
        "  plt.legend()\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Loss')\n",
        "  plt.title('Test and Train Loss')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LeCGOs60XYPV"
      },
      "source": [
        "First Net:\n",
        "It is the simplest of those analyzed. It provides the *lowest* loss, but at the same time the *widest* distance between Train and Test accuracy \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BXgapKzAQdVk"
      },
      "outputs": [],
      "source": [
        "net1 = nn.Sequential(nn.BatchNorm2d(3),\n",
        "    nn.Conv2d(3, 30, kernel_size=11, stride=4, padding=1), nn.ReLU(), \n",
        "    nn.MaxPool2d(kernel_size=3, stride=2),\n",
        "\n",
        "    nn.Conv2d(30, 96, kernel_size=5, padding=2), nn.ReLU(), \n",
        "    nn.MaxPool2d(kernel_size=3, stride=2),\n",
        "\n",
        "    nn.Conv2d(96, 128, kernel_size=3, padding=1), nn.ReLU(), \n",
        "    nn.Conv2d(128, 96, kernel_size=3, padding=1), nn.ReLU(),\n",
        "    nn.MaxPool2d(kernel_size=3, stride=2),\n",
        "    nn.Flatten(),\n",
        "\n",
        "    nn.Linear(2400, 2400), nn.ReLU(),                                           \n",
        "    nn.Dropout(p=0.5), \n",
        "    \n",
        "    nn.Linear(2400, 8))   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "RzWaeFDnQgOZ",
        "outputId": "534efbd8-31ea-4384-a438-40e3edb04195"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loss 0.483, train acc 0.815, test acc 0.758\n",
            "1470.2 examples/sec on cuda:0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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      "source": [
        "lr, num_epochs = 0.01,20                                                                 \n",
        "train(net1, train_iter, val_iter, num_epochs, lr, device=d2l.try_gpu())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "net1.to('cpu')\n",
        "show_losses(net1, test_iter)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "S8IPPTMQJIIU",
        "outputId": "f73f2052-9291-4353-beaf-5e99f6d62f43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/PIL/Image.py:959: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train loss: 0.33387959003448486\n",
            "validation loss: 0.6581077575683594\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
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    {
      "cell_type": "markdown",
      "source": [
        "Second Net: \n",
        "Before arriving at this net, we performed several steps to improve it. \n",
        "\n",
        "We initially added a *convolution* layer, then changed the pooling from MaxPool to AvgPool. \n",
        "At this point we noticed that the network was not providing significantly better values, because it decreased the distance of accuracy but at the cost of having a very high loss function. \n",
        "\n",
        "At this point we reduced the step of the last *linear transformation* from (2400, 8) to (1028, 8). As we can see, we get a loss function slightly larger than the first network, but by greatly reducing the distance between the two accuracies. "
      ],
      "metadata": {
        "id": "-7eWyZYsLvod"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "net3 = nn.Sequential(nn.BatchNorm2d(3),\n",
        "    nn.Conv2d(3, 30, kernel_size=11, stride=4, padding=1), nn.ReLU(), #96==> 30\n",
        "    nn.MaxPool2d(kernel_size=3, stride=2),\n",
        "\n",
        "    nn.Conv2d(30, 96, kernel_size=5, padding=2), nn.ReLU(), #96==> 30, 256=>96\n",
        "    nn.AvgPool2d(kernel_size=3, stride=2),\n",
        "\n",
        "    nn.Conv2d(96, 128, kernel_size=3, padding=1), nn.ReLU(), #reduce the 3 layers to 2\n",
        "    nn.Conv2d(128, 256, kernel_size=3, padding=1), nn.ReLU(),\n",
        "    nn.Conv2d(256, 96, kernel_size=3, padding=1), nn.ReLU(),\n",
        "    nn.AvgPool2d(kernel_size=3, stride=2),\n",
        "    nn.Flatten(),                                                                           \n",
        "\n",
        "    nn.Linear(2400, 1028), nn.ReLU(),      #riduzione output\n",
        "    nn.Dropout(p=0.5), \n",
        "    nn.Linear(1028, 8))"
      ],
      "metadata": {
        "id": "BnUZoOgmg9cY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#First, execute on validation set\n",
        "lr, num_epochs = 0.01, 25\n",
        "train(net3, train_iter, val_iter, num_epochs, lr, device=d2l.try_gpu())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "Lhywlx1JhBwE",
        "outputId": "7cc7aa31-2ba8-4941-ac16-b7b16cba4b25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loss 0.697, train acc 0.723, test acc 0.720\n",
            "1375.8 examples/sec on cuda:0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 252x180 with 1 Axes>"
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            "train loss: 0.4361506700515747\n",
            "validation loss: 0.7211195230484009\n"
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          "metadata": {
            "needs_background": "light"
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        }
      ],
      "source": [
        "net3.to('cpu')\n",
        "show_losses(net3, test_iter)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Below we can find the probabilities of correctly predicting the image class (''classification report'')"
      ],
      "metadata": {
        "id": "nLI-r3QTacsV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class_correct = list(0. for i in range(8))\n",
        "class_total = list(0. for i in range(8))\n",
        "with torch.no_grad():\n",
        "     for data in test_iter:\n",
        "         images, labels = data     \n",
        "         outputs = net3(images)\n",
        "         _, predicted = torch.max(outputs, 1)\n",
        "         c = (predicted == labels).squeeze()\n",
        "         for i in range(4):\n",
        "              label = labels[i]\n",
        "              class_correct[label] += c[i].item()\n",
        "              class_total[label] += 1\n",
        "for i in range(8):\n",
        "     print('Accuracy of %5s : %2d %%' % (\n",
        "            image_label[i], 100 * class_correct[i] / class_total[i]))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zv9WQ54AT8sj",
        "outputId": "0c49fc1f-5e1e-4f10-df82-d40bdf9885b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/PIL/Image.py:959: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of HomeObj : 60 %\n",
            "Accuracy of NotFace : 50 %\n",
            "Accuracy of Animal : 40 %\n",
            "Accuracy of Anime : 85 %\n",
            "Accuracy of Cartoon : 100 %\n",
            "Accuracy of Celeb : 60 %\n",
            "Accuracy of Sketches : 100 %\n",
            "Accuracy of VectorArt : 50 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MEuY42Mm6daJ"
      },
      "source": [
        "##SECOND ARCHITECTURE \n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The second architecture was then implemented.\n",
        "As can be seen from the accuracy grades and the corresponding graph, although convolution layers were added and the inception class was defined, there is no evidence of improvement.\n",
        "Through a process of \"Trial and Error\" we then sought the choice of parameters that would produce the best result.\n"
      ],
      "metadata": {
        "id": "Xx4B-nyt8Lrl"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2sCgoMdtBk2o"
      },
      "outputs": [],
      "source": [
        "## Our Inception blocks consists in 5 paths concatenated. \n",
        "class Inception(nn.Module):\n",
        "    ## `c1`--`c5` are the number of output channels for each path\n",
        "    def __init__(self, in_channels, c1, c2, c3, c4, c5, **kwargs): \n",
        "        super(Inception, self).__init__(**kwargs)\n",
        "        ## Path 1 is a single 1 x 1 convolutional layer\n",
        "        self.p1_1 = nn.Conv2d(in_channels, c1, kernel_size=1)\n",
        "        ## Path 2 is a 1 x 1 convolutional layer followed by a 3 x 3\n",
        "        ## convolutional layer\n",
        "        self.p2_1 = nn.Conv2d(in_channels, c2[0], kernel_size=1)\n",
        "        self.p2_2 = nn.Conv2d(c2[0], c2[1], kernel_size=3, padding=1)\n",
        "        ## Path 3 is a 1 x 1 convolutional layer followed by a 5 x 5\n",
        "        ## convolutional layer\n",
        "        self.p3_1 = nn.Conv2d(in_channels, c3[0], kernel_size=1)\n",
        "        self.p3_2 = nn.Conv2d(c3[0], c3[1], kernel_size=5, padding=2)\n",
        "        ## Path 4 is a 3 x 3 convolutional layer followed by a 1 x 1\n",
        "        ## convolutional layer\n",
        "        self.p4_1 = nn.Conv2d(in_channels,c4[0],kernel_size=3, stride=1, padding=1)\n",
        "        self.p4_2 = nn.Conv2d(c4[0], c4[1], kernel_size=1)\n",
        "        ## Path 5 is a 3x3 maximum pooling layer followed by a 1 x 1\n",
        "        ## convolutional layer\n",
        "        self.p5_1=nn.MaxPool2d(kernel_size=3,stride=1,padding=1)\n",
        "        self.p5_2=nn.Conv2d(in_channels,c5,kernel_size=1)\n",
        "\n",
        "    def forward(self, x):\n",
        "        p1 = F.relu(self.p1_1(x))\n",
        "        p2 = F.relu(self.p2_2(F.relu(self.p2_1(x))))\n",
        "        p3 = F.relu(self.p3_2(F.relu(self.p3_1(x))))\n",
        "        p4 = F.relu(self.p4_2(F.relu(self.p4_1(x))))\n",
        "        p5=F.relu(self.p5_2(self.p5_1(x)))\n",
        "        ## Concatenate the outputs on the channel dimension\n",
        "        return torch.cat((p1, p2, p3, p4,p5), dim=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "skN8IOsAioZE"
      },
      "outputs": [],
      "source": [
        "b1 = nn.Sequential(nn.Conv2d(3, 96, kernel_size=7, stride=2, padding=3),\n",
        "                   nn.ReLU(),\n",
        "                   nn.Conv2d(96,128,kernel_size=7,stride=2,padding=3),\n",
        "                   nn.ReLU(),\n",
        "                   nn.BatchNorm2d(128),\n",
        "                   nn.Conv2d(128,100,kernel_size=7,stride=2,padding=3),\n",
        "                   nn.MaxPool2d(kernel_size=3, stride=2, padding=1))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "b2 = nn.Sequential(nn.Conv2d(100,128,kernel_size=3,padding=1),\n",
        "                   nn.ReLU(),\n",
        "                   nn.Conv2d(128, 192, kernel_size=3, padding=1),\n",
        "                   nn.ReLU(),\n",
        "                   nn.BatchNorm2d(192),\n",
        "                   nn.Dropout(0.5),\n",
        "                   nn.Conv2d(192,256,kernel_size=3,padding=1),\n",
        "                   nn.MaxPool2d(kernel_size=3, stride=2, padding=1))"
      ],
      "metadata": {
        "id": "0riL1O4lqlPv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nRXmOdSyCW0Q"
      },
      "outputs": [],
      "source": [
        "b3 = nn.Sequential(Inception(256, 128, (96, 128), (16, 32), (32,64),64),\n",
        "                   Inception(416, 128, (128, 192), (32, 96),(64,96),64),\n",
        "                   nn.MaxPool2d(kernel_size=3, stride=2, padding=1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SkOzqT-MCYvt"
      },
      "outputs": [],
      "source": [
        "b4 = nn.Sequential(Inception(576, 128, (96, 208), (16, 48), (64,78),64),\n",
        "                   Inception(526, 128, (112, 224), (24, 64), (64,120),64),\n",
        "                   Inception(600, 128, (128, 352), (24, 64), (64,96),64),\n",
        "                   nn.MaxPool2d(kernel_size=3, stride=2, padding=1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "96l1OnXTCZgF"
      },
      "outputs": [],
      "source": [
        "\n",
        "b5 = nn.Sequential(Inception(704, 256, (160, 320), (32, 128), (128,222),128),\n",
        "                   Inception(1054, 384, (192, 384), (48, 128), (128,352),128),\n",
        "                   nn.AdaptiveAvgPool2d((1,1)), \n",
        "                   nn.Flatten())\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-xNV35ugllm1"
      },
      "outputs": [],
      "source": [
        "net5 = nn.Sequential(b1, b2, b3, b4, b5, nn.Linear(1376, 8))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PmP6pvHICe26"
      },
      "source": [
        "Training\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "Jt-XWVjlCdc7",
        "outputId": "74531d2e-115a-46b5-9b39-8a69366a6e00"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loss 0.614, train acc 0.757, test acc 0.709\n",
            "1610.5 examples/sec on cuda:0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
            "needs_background": "light"
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        }
      ],
      "source": [
        "lr, num_epochs = 0.01,20\n",
        "d2l.train_ch6(net5, train_iter, val_iter, num_epochs, lr, device=d2l.try_gpu())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "net5.to('cpu')\n",
        "show_losses(net5, test_iter)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "8uEZ6WQaqier",
        "outputId": "79d083b4-3f03-4d33-d0fa-28a9505b93da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train loss: 0.23767980933189392\n",
            "validation loss: 0.5142125487327576\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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        "CONCLUSION:\n",
        "By making a comparison between the first and second architecture the following information is highlighted:\n",
        "The first architecture appears to be more adequate, both in terms of accuracy and graphically.\n",
        "A constraint common to both architectures is the poor sampling of the dataset, as we have seen our dataset contained about 2000 photos, thus forcing a very limited division of train/validation and test.\n",
        "This leads us to infer that the dataset, not having a large number of \"fetures,\" does not need overly complicated architectures."
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