S4_Assignment-Acc99.41.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Copy of EVA4 - Session 2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "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/S4_Assignment-Acc99.41.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",
        "\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 = 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": "1abe08a1-f212-4ab9-96a5-00039b9fdaa9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        }
      },
      "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": 64,
      "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",
            "       BatchNorm2d-4           [-1, 16, 14, 14]              32\n",
            "            Conv2d-5           [-1, 16, 14, 14]           2,320\n",
            "            Conv2d-6           [-1, 32, 14, 14]           4,640\n",
            "         MaxPool2d-7             [-1, 32, 7, 7]               0\n",
            "       BatchNorm2d-8             [-1, 32, 7, 7]              64\n",
            "            Conv2d-9              [-1, 8, 7, 7]             264\n",
            "      BatchNorm2d-10              [-1, 8, 7, 7]              16\n",
            "           Conv2d-11             [-1, 16, 5, 5]           1,168\n",
            "      BatchNorm2d-12             [-1, 16, 5, 5]              32\n",
            "           Conv2d-13             [-1, 32, 3, 3]           4,640\n",
            "           Conv2d-14             [-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): 0.30\n",
            "Params size (MB): 0.07\n",
            "Estimated Total Size (MB): 0.37\n",
            "----------------------------------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:45: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DqTWLaM5GHgH",
        "colab_type": "code",
        "colab": {}
      },
      "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": 0,
      "outputs": []
    },
    {
      "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: {}/{} ({:.2f}%)\\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": "70e6054d-be7c-438d-dcc6-11a7a2845419",
        "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": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  0%|          | 0/2000 [00:00<?, ?it/s]/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:45: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "loss=0.002336136531084776 batch_id=1999: 100%|██████████| 2000/2000 [00:32<00:00, 61.89it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0487, Accuracy: 9850/10000 (98.50%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.02389480359852314 batch_id=1999: 100%|██████████| 2000/2000 [00:32<00:00, 61.97it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0426, Accuracy: 9861/10000 (98.61%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.08748911321163177 batch_id=1999: 100%|██████████| 2000/2000 [00:31<00:00, 62.51it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0313, Accuracy: 9902/10000 (99.02%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.070439912378788 batch_id=1999: 100%|██████████| 2000/2000 [00:32<00:00, 61.98it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0236, Accuracy: 9925/10000 (99.25%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.0005906422738917172 batch_id=1999: 100%|██████████| 2000/2000 [00:32<00:00, 62.12it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0246, Accuracy: 9923/10000 (99.23%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.007328574080020189 batch_id=1999: 100%|██████████| 2000/2000 [00:31<00:00, 62.58it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0241, Accuracy: 9918/10000 (99.18%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.008172797970473766 batch_id=1999: 100%|██████████| 2000/2000 [00:32<00:00, 62.12it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0259, Accuracy: 9924/10000 (99.24%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=7.108052523108199e-05 batch_id=1999: 100%|██████████| 2000/2000 [00:32<00:00, 62.26it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0189, Accuracy: 9941/10000 (99.41%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.015522162429988384 batch_id=1999: 100%|██████████| 2000/2000 [00:32<00:00, 61.46it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0247, Accuracy: 9919/10000 (99.19%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.010663255117833614 batch_id=1999: 100%|██████████| 2000/2000 [00:32<00:00, 62.05it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0263, Accuracy: 9920/10000 (99.20%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.017826557159423828 batch_id=1999: 100%|██████████| 2000/2000 [00:32<00:00, 62.27it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0249, Accuracy: 9935/10000 (99.35%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.007225958630442619 batch_id=1999: 100%|██████████| 2000/2000 [00:32<00:00, 61.48it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0292, Accuracy: 9917/10000 (99.17%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=8.776982576819137e-05 batch_id=1999: 100%|██████████| 2000/2000 [00:32<00:00, 61.62it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0255, Accuracy: 9929/10000 (99.29%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.006936359219253063 batch_id=1999: 100%|██████████| 2000/2000 [00:32<00:00, 62.43it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0269, Accuracy: 9933/10000 (99.33%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=8.455912393401377e-06 batch_id=1999: 100%|██████████| 2000/2000 [00:32<00:00, 62.37it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0272, Accuracy: 9939/10000 (99.39%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=4.56810012110509e-05 batch_id=1999: 100%|██████████| 2000/2000 [00:32<00:00, 61.93it/s]\n",
            "  0%|          | 0/2000 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0249, Accuracy: 9938/10000 (99.38%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.003375275991857052 batch_id=1999: 100%|██████████| 2000/2000 [00:32<00:00, 61.63it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0249, Accuracy: 9933/10000 (99.33%)\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": []
    }
  ]
}