Convolutional_Neural_Network.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Convolutional Neural Network"
      ],
      "metadata": {
        "id": "OM43ccB3ueRz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As redes neurais convolucionais se distinguem de outras redes neurais por terem desempenho superior com dados de imagem e áudio. Eles têm 3 camadas, à medida que as formas são revestidas pelos dados da imagem progridem da CNN, ela começa a reconhecer elementos ou maiores até mesmo identificar o objeto identificado ou objeto. A cada camada, a CNN aumenta em sua complexidade, identificando maiores porções da imagem.\n",
        "\n",
        "* convolutional layer\n",
        "\n",
        "* pool layer\n",
        "\n",
        "* Fully connected (FC) layer\n",
        "\n",
        "#### convolutional layer\n",
        "A camada convolucional é a parte central de uma CNN, é onde ocorre a maior parte da computação. Ela precisa de alguns componentes, que são dados de entrada, um filtro e um mapa de recursos. \n",
        "\n",
        "Considerando uma imagem colorida, composta por uma matriz de pixels em 3D. A entrada terá três dimensões — altura, largura e profundidade — que correspondem ao RGB em uma imagem. Também temos um detector de feição, também conhecido como kernel ou filtro, que irá percorrer os campos receptivos da imagem, verificando se a feição está presente. Esse processo é conhecido como convolução.\n",
        "\n",
        "Embora possa variar em tamanho, o tamanho do filtro é normalmente uma matriz 3x3; isso também determina o tamanho do campo receptivo. O filtro é  aplicado a uma área da imagem em que um produto escalar é calculado entre os pixels de entrada e o filtro. Este produto escalar é então alimentado em uma matriz de saída. Depois, o filtro muda um passo, repetindo o processo até que o kernel tenha varrido toda a imagem. A saída final da série de produtos escalares da entrada e do filtro é conhecida como mapa de recursos, mapa de ativação ou recurso convoluído.\n",
        "\n",
        "Na imagem abaixo vemos que , cada valor de saída no mapa de recursos não precisa se conectar a cada valor de pixel na imagem de entrada. Ele só precisa se conectar ao campo receptivo, onde o filtro está sendo aplicado.\n",
        "\n",
        "![image](https://user-images.githubusercontent.com/14276167/179381754-89dfc357-6549-4854-9559-efc1f8070ef1.png)\n",
        "\n",
        "Os pesos no detector de recursos permanecem fixos à medida que ele se move pela imagem, isso é conhecido como compartilhamento de parâmetros. Alguns parâmetros, como os valores de peso, se ajustam durante o treinamento através do processo de retropropagação e gradiente descendente. No entanto, existem três hiperparâmetros que afetam o tamanho do volume da saída que precisam ser definidos antes do início do treinamento da rede neural. \n",
        "\n",
        "Esses incluem:\n",
        "\n",
        "1. O número de filtros -> afeta a profundidade da saída. Por exemplo, três filtros distintos produziriam três mapas de recursos diferentes, criando uma profundidade de três.\n",
        "\n",
        "2. Stride -> é a distância, ou número de pixels, que o kernel se move sobre a matriz de entrada. Embora valores de passada de dois ou mais sejam raros, uma passada maior produz uma saída menor.\n",
        "\n",
        "3. Zero padding ->  geralmente é usado quando os filtros não se ajustam à imagem de entrada. Isso define todos os elementos que estão fora da matriz de entrada para zero, produzindo uma saída maior ou de tamanho igual. \n",
        "\n",
        "Após cada operação de convolução, uma CNN aplica uma transformação de Unidade Linear Retificada (ReLU) ao mapa de características, para introduzir a não linearidade no modelo.\n",
        "\n",
        "#### Pooling Layer\n",
        "Conduz a redução de dimensionalidade, reduzindo o número de parâmetros na entrada. Varre um filtro em toda a entrada, esse filtro não possui pesose sim um kernel que aplica uma função de agregação aos valores dentro do campo receptivo e preenche a matriz de saída.\n",
        "\n",
        "#### Fully-Connected Layer\n",
        "Essa camada realiza a tarefa de classificação com base nas características extraídas das camadas anteriores e seus diferentes filtros. Enquanto as camadas convolucionais e de pooling tendem a usar funções ReLu, as camadas FC geralmente aproveitam uma função de ativação softmax para classificar as entradas adequadamente, produzindo uma probabilidade de 0 a 1.\n",
        "\n",
        "Na camada Fully-Connected Layer, cada nó na camada de saída se conecta diretamente a um nó na camada anterior."
      ],
      "metadata": {
        "id": "ufpcYleyufbV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Preparando o ambiente"
      ],
      "metadata": {
        "id": "vQZbinHTolT8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "rwIQr_tDohX2"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torchvision import datasets, transforms\n",
        "from tqdm.autonotebook import tqdm\n",
        "from torchsummary import summary\n",
        "from tqdm import tqdm\n",
        "\n",
        "import matplotlib.pyplot as plt \n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Leitura da base de dados\n",
        "\n",
        "\n",
        "*   Criação de diretório\n",
        "*   Conversão da imagem em tensor\n",
        "*   Noemalização dos dados\n",
        "\n"
      ],
      "metadata": {
        "id": "Y5B8j_-AooQ9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train = datasets.MNIST('../data', train=True, download=True, \n",
        "                    transform=transforms.Compose([\n",
        "                        transforms.ToTensor(), \n",
        "                        transforms.Normalize((0.1307,), (0.3081,)) #\n",
        "]))\n",
        "test = datasets.MNIST('../data', train=False,  \n",
        "                   transform=transforms.Compose([ \n",
        "                        transforms.ToTensor(),\n",
        "                        transforms.Normalize((0.1307,), (0.3081,)) \n",
        "]))"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "1GBjVsnpoqd-",
        "outputId": "a90209bb-a736-4cde-f2a4-c8130854fe7d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ../data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ../data/MNIST/raw/train-images-idx3-ubyte.gz to ../data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ../data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
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            "text/plain": [
              "  0%|          | 0/28881 [00:00<?, ?it/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ../data/MNIST/raw/train-labels-idx1-ubyte.gz to ../data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ../data/MNIST/raw/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4542 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "517277e731ee4757a4e097b003ff8c05"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modelo\n",
        "## Configurações do modelo\n",
        "\n",
        "O CUDA executa um cálculo com a GPU, que garante que o compilador seja direcionado para a parte que corresponde ao núcleo da GPU. Torna a unidade de processamento eficiente para a operação a ser realizada. semelhante a um ponteiro."
      ],
      "metadata": {
        "id": "bJ6r46_txNsi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SEED = 1\n",
        "\n",
        "cuda = torch.cuda.is_available()\n",
        "print(\"CUDA:\", cuda)\n",
        "\n",
        "torch.manual_seed(SEED)\n",
        "\n",
        "if cuda:\n",
        "    torch.cuda.manual_seed(SEED)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AmES0VMnxRMz",
        "outputId": "c346a41c-5a59-4517-e35f-8d03e2ad1b55"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CUDA: False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataloader_args = dict(shuffle=True, batch_size=128, num_workers=2, pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)\n",
        "train_loader = torch.utils.data.DataLoader(train, **dataloader_args)\n",
        "test_loader = torch.utils.data.DataLoader(test, **dataloader_args)"
      ],
      "metadata": {
        "id": "z3lq0mOB2Cxy"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cálculo dos parâmetros do modelo\n",
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "MpVcRjvm3Omi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   Input Block\n",
        "\t* output_size = 26    RF:  2\n",
        "* CONVOLUTION BLOCK 1\n",
        " \t* output_size = 24 RF: 5\n",
        " \t* output_size = 12    RF:  6\n",
        "* TRANSITION BLOCK 1\n",
        "  * output_size = 12    RF:  6\n",
        "* CONVOLUTION BLOCK \n",
        "  * output_size = 10    RF:  10\n",
        "  * output_size = 8    RF: 14\n",
        "  * output_size = 6    RF: 18\n",
        "* Global average pooling\n",
        "  * output_size = 1    RF:  28\n",
        "* Fully connected\n",
        "  * output_size = 1    RF: 28\n",
        "  * output  RF: 28\n",
        "        \n"
      ],
      "metadata": {
        "id": "7OPANauZ3r2J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        \n",
        "        self.convblock1 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=1, out_channels=8, kernel_size=(3, 3), padding=0, bias=False),\n",
        "            nn.ReLU(),\n",
        "        )        \n",
        "        self.convblock2 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=8, out_channels=16, kernel_size=(3, 3), padding=0, bias=False),  \n",
        "            nn.ReLU(),\n",
        "        ) \n",
        "        self.pool1 = nn.MaxPool2d(2, 2)        \n",
        "        self.trans1 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=16, out_channels=8, kernel_size=(1, 1), padding=0, bias=False),\n",
        "            nn.ReLU()\n",
        "        )        \n",
        "        self.convblock3 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=8, out_channels=12, kernel_size=(3, 3), padding=0, bias=False), \n",
        "            nn.ReLU(),\n",
        "            nn.Conv2d(in_channels=12, out_channels=16, kernel_size=(3, 3), padding=0, bias=False), \n",
        "            nn.ReLU(),\n",
        "            nn.Conv2d(in_channels=16, out_channels=20, kernel_size=(3, 3), padding=0, bias=False), \n",
        "            nn.ReLU(),\n",
        "        )         \n",
        "        \n",
        "        self.gap = nn.Sequential(\n",
        "            nn.AvgPool2d(6)  \n",
        "        )\n",
        "\n",
        "       \n",
        "        self.convblock5 = nn.Sequential(\n",
        "            nn.Conv2d(in_channels=20, out_channels=16, kernel_size=(1, 1), padding=0, bias=False), \n",
        "            nn.ReLU(),\n",
        "            nn.Conv2d(in_channels=16, out_channels=10, kernel_size=(1, 1), padding=0, bias=False),  \n",
        "            )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.convblock1(x)\n",
        "        x = self.convblock2(x)\n",
        "        x = self.pool1(x)\n",
        "        x = self.trans1(x)\n",
        "        x = self.convblock3(x)\n",
        "        x = self.gap(x)\n",
        "        x = self.convblock5(x)\n",
        "        x = x.view(-1, 10)   # Converte 2D to 1D\n",
        "        \n",
        "        return F.log_softmax(x, dim=-1)"
      ],
      "metadata": {
        "id": "aSOvsAW02QE_"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "use_cuda = torch.cuda.is_available()\n",
        "device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
        "print(device)\n",
        "model = Net().to(device)\n",
        "summary(model, input_size=(1, 28, 28))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O6Qjy3UE2WXZ",
        "outputId": "fe428451-6a23-4242-ba30-be87047b5873"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cpu\n",
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1            [-1, 8, 26, 26]              72\n",
            "              ReLU-2            [-1, 8, 26, 26]               0\n",
            "            Conv2d-3           [-1, 16, 24, 24]           1,152\n",
            "              ReLU-4           [-1, 16, 24, 24]               0\n",
            "         MaxPool2d-5           [-1, 16, 12, 12]               0\n",
            "            Conv2d-6            [-1, 8, 12, 12]             128\n",
            "              ReLU-7            [-1, 8, 12, 12]               0\n",
            "            Conv2d-8           [-1, 12, 10, 10]             864\n",
            "              ReLU-9           [-1, 12, 10, 10]               0\n",
            "           Conv2d-10             [-1, 16, 8, 8]           1,728\n",
            "             ReLU-11             [-1, 16, 8, 8]               0\n",
            "           Conv2d-12             [-1, 20, 6, 6]           2,880\n",
            "             ReLU-13             [-1, 20, 6, 6]               0\n",
            "        AvgPool2d-14             [-1, 20, 1, 1]               0\n",
            "           Conv2d-15             [-1, 16, 1, 1]             320\n",
            "             ReLU-16             [-1, 16, 1, 1]               0\n",
            "           Conv2d-17             [-1, 10, 1, 1]             160\n",
            "================================================================\n",
            "Total params: 7,304\n",
            "Trainable params: 7,304\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 0.30\n",
            "Params size (MB): 0.03\n",
            "Estimated Total Size (MB): 0.33\n",
            "----------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "1-JB05F1EBi-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_losses = []\n",
        "test_losses = []\n",
        "train_acc = []\n",
        "test_acc = []\n"
      ],
      "metadata": {
        "id": "a_YblGZ-KTeE"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train(model, device, train_loader, optimizer, epoch):\n",
        "  model.train()\n",
        "  pbar = tqdm(train_loader)\n",
        "  correct = 0\n",
        "  processed = 0\n",
        "  for batch_idx, (data, target) in enumerate(pbar):\n",
        "   \n",
        "    data, target = data.to(device), target.to(device)\n",
        "\n",
        "    optimizer.zero_grad()\n",
        "    y_pred = model(data)\n",
        "\n",
        "    # Calcula a perda\n",
        "    loss = F.nll_loss(y_pred, target)\n",
        "    train_losses.append(loss)\n",
        "\n",
        "    # Backpropagation\n",
        "    loss.backward()\n",
        "    optimizer.step()    \n",
        "    pred = y_pred.argmax(dim=1, keepdim=True) \n",
        "    correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "    processed += len(data)\n",
        "\n",
        "    pbar.set_description(desc= f'Batch_id={batch_idx} Loss={loss.item():.5f} Accuracy={100*correct/processed:0.2f}')\n",
        "    train_acc.append(100*correct/processed)\n",
        "def test(model, device, test_loader):\n",
        "    model.eval()\n",
        "    test_loss = 0\n",
        "    correct = 0\n",
        "    with torch.no_grad():\n",
        "        for data, target in test_loader:\n",
        "            data, target = data.to(device), target.to(device)\n",
        "            output = model(data)\n",
        "            test_loss += F.nll_loss(output, target, reduction='sum').item() \n",
        "            pred = output.argmax(dim=1, keepdim=True)  \n",
        "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "\n",
        "    test_loss /= len(test_loader.dataset)\n",
        "    test_losses.append(test_loss)\n",
        "\n",
        "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\\n'.format(\n",
        "        test_loss, correct, len(test_loader.dataset),\n",
        "        100. * correct / len(test_loader.dataset)))\n",
        "    \n",
        "    test_acc.append(100. * correct / len(test_loader.dataset))"
      ],
      "metadata": {
        "id": "EojDEt__EBsz"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Execução"
      ],
      "metadata": {
        "id": "DnmJH3gyKndI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model =  Net().to(device)\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n",
        "NUM_EPOCHS = 15\n",
        "for epoch in range(1,NUM_EPOCHS+1):\n",
        "    print(\"EPOCH:\", epoch)\n",
        "    train(model, device, train_loader, optimizer, epoch)\n",
        "    test(model, device, test_loader)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ovr_kh4Kpyp",
        "outputId": "c8853207-a88a-4d9c-a679-97f1a050279c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EPOCH: 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Batch_id=937 Loss=2.30204 Accuracy=9.86: 100%|██████████| 938/938 [00:41<00:00, 22.79it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 2.3024, Accuracy: 958/10000 (9.58%)\n",
            "\n",
            "EPOCH: 2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Batch_id=937 Loss=1.14968 Accuracy=15.12: 100%|██████████| 938/938 [00:40<00:00, 23.39it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 1.0244, Accuracy: 6526/10000 (65.26%)\n",
            "\n",
            "EPOCH: 3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Batch_id=937 Loss=0.04412 Accuracy=90.40: 100%|██████████| 938/938 [00:39<00:00, 23.59it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.1219, Accuracy: 9632/10000 (96.32%)\n",
            "\n",
            "EPOCH: 4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Batch_id=937 Loss=0.06429 Accuracy=96.06: 100%|██████████| 938/938 [00:40<00:00, 23.00it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0790, Accuracy: 9767/10000 (97.67%)\n",
            "\n",
            "EPOCH: 5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Batch_id=937 Loss=0.02071 Accuracy=97.14: 100%|██████████| 938/938 [00:39<00:00, 23.58it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0763, Accuracy: 9768/10000 (97.68%)\n",
            "\n",
            "EPOCH: 6\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Batch_id=937 Loss=0.02585 Accuracy=97.71: 100%|██████████| 938/938 [00:40<00:00, 22.99it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0690, Accuracy: 9783/10000 (97.83%)\n",
            "\n",
            "EPOCH: 7\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Batch_id=937 Loss=0.02186 Accuracy=97.97: 100%|██████████| 938/938 [00:39<00:00, 23.72it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0559, Accuracy: 9820/10000 (98.20%)\n",
            "\n",
            "EPOCH: 8\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Batch_id=937 Loss=0.02479 Accuracy=98.25: 100%|██████████| 938/938 [00:41<00:00, 22.68it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0550, Accuracy: 9830/10000 (98.30%)\n",
            "\n",
            "EPOCH: 9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Batch_id=937 Loss=0.06839 Accuracy=98.42: 100%|██████████| 938/938 [00:39<00:00, 23.65it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0738, Accuracy: 9767/10000 (97.67%)\n",
            "\n",
            "EPOCH: 10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Batch_id=937 Loss=0.03581 Accuracy=98.54: 100%|██████████| 938/938 [00:40<00:00, 23.12it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0548, Accuracy: 9825/10000 (98.25%)\n",
            "\n",
            "EPOCH: 11\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Batch_id=937 Loss=0.01509 Accuracy=98.68: 100%|██████████| 938/938 [00:39<00:00, 23.75it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0415, Accuracy: 9879/10000 (98.79%)\n",
            "\n",
            "EPOCH: 12\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Batch_id=937 Loss=0.00425 Accuracy=98.70: 100%|██████████| 938/938 [00:40<00:00, 23.16it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0430, Accuracy: 9865/10000 (98.65%)\n",
            "\n",
            "EPOCH: 13\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Batch_id=937 Loss=0.03061 Accuracy=98.86: 100%|██████████| 938/938 [00:41<00:00, 22.83it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0365, Accuracy: 9892/10000 (98.92%)\n",
            "\n",
            "EPOCH: 14\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Batch_id=937 Loss=0.04339 Accuracy=98.89: 100%|██████████| 938/938 [00:40<00:00, 23.38it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0447, Accuracy: 9854/10000 (98.54%)\n",
            "\n",
            "EPOCH: 15\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Batch_id=937 Loss=0.10634 Accuracy=98.94: 100%|██████████| 938/938 [00:40<00:00, 23.06it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test set: Average loss: 0.0388, Accuracy: 9879/10000 (98.79%)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from fastai.basics import *\n",
        "import fastai.basics"
      ],
      "metadata": {
        "id": "Dxon0nZx19ep"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with torch.no_grad():\n",
        "      fig, axs = plt.subplots(2,2,figsize=(15,10))\n",
        "      axs[0, 0].plot(train_losses)\n",
        "      axs[0, 0].set_title(\"Treino Loss\")\n",
        "      axs[1, 0].plot(train_acc)\n",
        "      axs[1, 0].set_title(\"Treino Acurácia\")\n",
        "      axs[0, 1].plot(test_losses)\n",
        "      axs[0, 1].set_title(\"Teste Loss\")\n",
        "      axs[1, 1].plot(test_acc)\n",
        "      axs[1, 1].set_title(\"Teste Acurácia\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "cQRLBErtUoLh",
        "outputId": "2ba20f51-63e3-4f8d-f213-9c849fa1c818"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "xwgCgH04Lw6J"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}