Copy_of_EVA4_Session_2.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jagatabhay/DANN/blob/master/Copy_of_EVA4_Session_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0m2JWFliFfKT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import print_function\n",
        "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"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h_Cx9q2QFgM7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        #self.conv1 = nn.Conv2d(1, 32, 3, padding=1) #input -? OUtput? RF\n",
        "        #self.conv2 = nn.Conv2d(32, 64, 3, padding=1)\n",
        "        #self.pool1 = nn.MaxPool2d(2, 2)\n",
        "        #self.conv3 = nn.Conv2d(64, 128, 3, padding=1)\n",
        "        #self.conv4 = nn.Conv2d(128, 256, 3, padding=1)\n",
        "        #self.pool2 = nn.MaxPool2d(2, 2)\n",
        "        #self.conv5 = nn.Conv2d(256, 512, 3)\n",
        "        #self.conv6 = nn.Conv2d(512, 1024, 3)\n",
        "        #self.conv7 = nn.Conv2d(1024, 10, 3)\n",
        "\n",
        "        self.conv1 = nn.Conv2d(1, 8, 3, padding=1)\n",
        "        self.conv2 = nn.Conv2d(8, 16, 3, padding=1)\n",
        "        self.pool1 = nn.MaxPool2d(2, 2)\n",
        "        self.drop1 = nn.Dropout2d(0.1)\n",
        "\n",
        "        self.bat1  = nn.BatchNorm2d(16)\n",
        "\n",
        "        self.conv3 = nn.Conv2d(16, 16, 3, padding=1)\n",
        "        self.conv4 = nn.Conv2d(16, 32, 3, padding=1)\n",
        "        self.pool2 = nn.MaxPool2d(2, 2)\n",
        "\n",
        "        self.bat3  = nn.BatchNorm2d(32)\n",
        "\n",
        "        self.conv5 = nn.Conv2d(32 ,8 , 1 )\n",
        "        self.bat4  = nn.BatchNorm2d(8)\n",
        "        self.conv6 = nn.Conv2d(8, 16, 3)\n",
        "        self.bat5  = nn.BatchNorm2d(16)\n",
        "        self.conv7 = nn.Conv2d(16, 32, 3) \n",
        "        self.conv8 = nn.Conv2d(32, 10, 3)\n",
        "        #self.conv9 = nn.Conv2d(64, 10, 3)\n",
        "\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool1(F.relu(self.conv2(F.relu(self.conv1(x)))))\n",
        "        x = self.drop1(x)\n",
        "        x = F.relu(self.conv3(F.relu(self.bat1(x))))\n",
        "        x = self.pool2(F.relu(self.conv4(x)))\n",
        "        x = F.relu(self.conv5(F.relu(self.bat3(x))))\n",
        "        x = F.relu(self.conv6(F.relu(self.bat4(x))))\n",
        "        x = F.relu(self.conv7(F.relu(self.bat5(x))))\n",
        "        #x = self.conv8(F.relu(self.conv7(x)))\n",
        "        x = self.conv8(x)\n",
        "        x = x.view(-1, 10)\n",
        "        return F.log_softmax(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "xdydjYTZFyi3",
        "outputId": "2b51bdcb-719f-4921-bd38-a69ffbc2cea3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        }
      },
      "source": [
        "!pip install torchsummary\n",
        "from torchsummary import summary\n",
        "use_cuda = torch.cuda.is_available()\n",
        "device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
        "model = Net().to(device)\n",
        "summary(model, input_size=(1, 28, 28))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torchsummary in /usr/local/lib/python3.6/dist-packages (1.5.1)\n",
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1            [-1, 8, 28, 28]              80\n",
            "            Conv2d-2           [-1, 16, 28, 28]           1,168\n",
            "         MaxPool2d-3           [-1, 16, 14, 14]               0\n",
            "         Dropout2d-4           [-1, 16, 14, 14]               0\n",
            "       BatchNorm2d-5           [-1, 16, 14, 14]              32\n",
            "            Conv2d-6           [-1, 16, 14, 14]           2,320\n",
            "            Conv2d-7           [-1, 32, 14, 14]           4,640\n",
            "         MaxPool2d-8             [-1, 32, 7, 7]               0\n",
            "       BatchNorm2d-9             [-1, 32, 7, 7]              64\n",
            "           Conv2d-10              [-1, 8, 7, 7]             264\n",
            "      BatchNorm2d-11              [-1, 8, 7, 7]              16\n",
            "           Conv2d-12             [-1, 16, 5, 5]           1,168\n",
            "      BatchNorm2d-13             [-1, 16, 5, 5]              32\n",
            "           Conv2d-14             [-1, 32, 3, 3]           4,640\n",
            "           Conv2d-15             [-1, 10, 1, 1]           2,890\n",
            "================================================================\n",
            "Total params: 17,314\n",
            "Trainable params: 17,314\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 0.33\n",
            "Params size (MB): 0.07\n",
            "Estimated Total Size (MB): 0.39\n",
            "----------------------------------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:47: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n"
          ],
          "name": "stderr"
        }
      ]
    },
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      "source": [
        "\n",
        "\n",
        "torch.manual_seed(1)\n",
        "batch_size = 30\n",
        "\n",
        "kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}\n",
        "train_loader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST('../data', train=True, download=True,\n",
        "                    transform=transforms.Compose([\n",
        "                        transforms.ToTensor(),\n",
        "                        transforms.Normalize((0.1307,), (0.3081,))\n",
        "                    ])),\n",
        "    batch_size=batch_size, shuffle=True, **kwargs)\n",
        "test_loader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST('../data', train=False, transform=transforms.Compose([\n",
        "                        transforms.ToTensor(),\n",
        "                        transforms.Normalize((0.1307,), (0.3081,))\n",
        "                    ])),\n",
        "    batch_size=batch_size, shuffle=True, **kwargs)\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ../data/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
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        },
        {
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          "text": [
            "Extracting ../data/MNIST/raw/train-images-idx3-ubyte.gz to ../data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ../data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
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        {
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          "text": [
            "Extracting ../data/MNIST/raw/train-labels-idx1-ubyte.gz to ../data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw/t10k-images-idx3-ubyte.gz\n",
            "\n"
          ],
          "name": "stdout"
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        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "95fd11b7a35240f0891d1413be740e48",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ../data/MNIST/raw/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6fe225e1be8b42478598c89b8d4faa5d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw\n",
            "Processing...\n",
            "Done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8fDefDhaFlwH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tqdm import tqdm\n",
        "def train(model, device, train_loader, optimizer, epoch):\n",
        "    model.train()\n",
        "    pbar = tqdm(train_loader)\n",
        "    for batch_idx, (data, target) in enumerate(pbar):\n",
        "        data, target = data.to(device), target.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        output = model(data)\n",
        "        loss = F.nll_loss(output, target)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        pbar.set_description(desc= f'loss={loss.item()} batch_id={batch_idx}')\n",
        "\n",
        "\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()  # sum up batch loss\n",
        "            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\n",
        "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "\n",
        "    test_loss /= len(test_loader.dataset)\n",
        "\n",
        "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.1f}%)\\n'.format(\n",
        "        test_loss, correct, len(test_loader.dataset),\n",
        "        100. * correct / len(test_loader.dataset)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MMWbLWO6FuHb",
        "colab_type": "code",
        "outputId": "42718008-6023-4680-c9b9-394c5126eaf5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "\n",
        "model = Net().to(device)\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n",
        "\n",
        "for epoch in range(1, 18):\n",
        "    train(model, device, train_loader, optimizer, epoch)\n",
        "    test(model, device, test_loader)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  0%|          | 0/2000 [00:00<?, ?it/s]/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:47: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "loss=0.006600968074053526 batch_id=1999: 100%|██████████| 2000/2000 [00:21<00:00, 95.10it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0523, Accuracy: 9835/10000 (98.3%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.024416828528046608 batch_id=1999: 100%|██████████| 2000/2000 [00:21<00:00, 94.51it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0410, Accuracy: 9863/10000 (98.6%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.008047517389059067 batch_id=1999: 100%|██████████| 2000/2000 [00:21<00:00, 94.79it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0329, Accuracy: 9903/10000 (99.0%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.0378904826939106 batch_id=1999: 100%|██████████| 2000/2000 [00:20<00:00, 95.77it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0289, Accuracy: 9909/10000 (99.1%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.0037826697807759047 batch_id=1999: 100%|██████████| 2000/2000 [00:20<00:00, 95.73it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0249, Accuracy: 9921/10000 (99.2%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.005398591514676809 batch_id=1999: 100%|██████████| 2000/2000 [00:20<00:00, 95.96it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0249, Accuracy: 9923/10000 (99.2%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.03063928335905075 batch_id=1999: 100%|██████████| 2000/2000 [00:20<00:00, 95.93it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0293, Accuracy: 9920/10000 (99.2%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.007105414289981127 batch_id=1999: 100%|██████████| 2000/2000 [00:20<00:00, 96.32it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0251, Accuracy: 9937/10000 (99.4%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.00913691520690918 batch_id=1999: 100%|██████████| 2000/2000 [00:20<00:00, 95.69it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0240, Accuracy: 9925/10000 (99.2%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.0028396924026310444 batch_id=1999: 100%|██████████| 2000/2000 [00:20<00:00, 96.61it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0281, Accuracy: 9915/10000 (99.2%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.007509565446525812 batch_id=1999: 100%|██████████| 2000/2000 [00:20<00:00, 96.46it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0339, Accuracy: 9909/10000 (99.1%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.0054897465743124485 batch_id=1999: 100%|██████████| 2000/2000 [00:20<00:00, 96.38it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0290, Accuracy: 9918/10000 (99.2%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.00016371409583371133 batch_id=1999: 100%|██████████| 2000/2000 [00:20<00:00, 97.27it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0256, Accuracy: 9922/10000 (99.2%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.008080228231847286 batch_id=1999: 100%|██████████| 2000/2000 [00:20<00:00, 97.05it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0270, Accuracy: 9919/10000 (99.2%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.00040928521775640547 batch_id=1999: 100%|██████████| 2000/2000 [00:20<00:00, 97.13it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0323, Accuracy: 9913/10000 (99.1%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=1.5958150470396504e-05 batch_id=1999: 100%|██████████| 2000/2000 [00:20<00:00, 97.05it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0280, Accuracy: 9915/10000 (99.2%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=3.9259593904716894e-05 batch_id=1999: 100%|██████████| 2000/2000 [00:20<00:00, 95.66it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0288, Accuracy: 9921/10000 (99.2%)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oH7ZlXxCxKPt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "So5uk4EkHW6R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}