Assign4.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/shritigupta/EVA/blob/master/Assign4.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, 8, 3, padding=1) #input -? OUtput? RF\n",
        "        self.conv2 = nn.Sequential(nn.Conv2d(8, 8, 3, padding=1),nn.BatchNorm2d(8))\n",
        "        self.pool1 = nn.MaxPool2d(2, 2)\n",
        "        self.conv3 = nn.Conv2d(8, 8, 3, padding=1)\n",
        "        self.conv4 = nn.Sequential(nn.Conv2d(8, 16, 3, padding=1),nn.BatchNorm2d(16),nn.Dropout(0.10))\n",
        "        self.pool2 = nn.MaxPool2d(2, 2)\n",
        "        self.conv5 = nn.Conv2d(16, 32, 3)\n",
        "        self.conv6 = nn.Conv2d(32, 32, 3)\n",
        "        self.conv7 = nn.Conv2d(32, 10, 3)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool1(F.relu(self.conv2(F.relu(self.conv1(x)))))\n",
        "        x = self.pool2(F.relu(self.conv4(F.relu(self.conv3(x)))))\n",
        "        x = F.relu(self.conv6(F.relu(self.conv5(x))))\n",
        "        x = self.conv7(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": "2a51ac54-f5df-4c5c-d1ce-42755fc4cf53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        }
      },
      "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": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1            [-1, 8, 28, 28]              80\n",
            "            Conv2d-2            [-1, 8, 28, 28]             584\n",
            "       BatchNorm2d-3            [-1, 8, 28, 28]              16\n",
            "         MaxPool2d-4            [-1, 8, 14, 14]               0\n",
            "            Conv2d-5            [-1, 8, 14, 14]             584\n",
            "            Conv2d-6           [-1, 16, 14, 14]           1,168\n",
            "       BatchNorm2d-7           [-1, 16, 14, 14]              32\n",
            "           Dropout-8           [-1, 16, 14, 14]               0\n",
            "         MaxPool2d-9             [-1, 16, 7, 7]               0\n",
            "           Conv2d-10             [-1, 32, 5, 5]           4,640\n",
            "           Conv2d-11             [-1, 32, 3, 3]           9,248\n",
            "           Conv2d-12             [-1, 10, 1, 1]           2,890\n",
            "================================================================\n",
            "Total params: 19,242\n",
            "Trainable params: 19,242\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 0.25\n",
            "Params size (MB): 0.07\n",
            "Estimated Total Size (MB): 0.33\n",
            "----------------------------------------------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:20: 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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            "a08f72ca3da2415ca406fae306dc4fdd"
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      },
      "source": [
        "torch.manual_seed(1)\n",
        "batch_size = 128\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": [
        {
          "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"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "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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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "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"
          ],
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          "text": [
            "\n",
            "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"
          ],
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          "text": [
            "\n",
            "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: {}/{} ({:.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": "97f3113b-b07c-4f75-955e-fc26504c6f12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "\n",
        "model = Net().to(device)\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.009, momentum=0.9)\n",
        "\n",
        "for epoch in range(1, 20):\n",
        "    train(model, device, train_loader, optimizer, epoch)\n",
        "    test(model, device, test_loader)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  0%|          | 0/469 [00:00<?, ?it/s]/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:20: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "loss=0.10134577751159668 batch_id=468: 100%|██████████| 469/469 [00:12<00:00, 36.75it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0874, Accuracy: 9738/10000 (97.38%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.05334269627928734 batch_id=468: 100%|██████████| 469/469 [00:12<00:00, 38.19it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0472, Accuracy: 9861/10000 (98.61%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.009639720432460308 batch_id=468: 100%|██████████| 469/469 [00:12<00:00, 37.34it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0493, Accuracy: 9861/10000 (98.61%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.017367057502269745 batch_id=468: 100%|██████████| 469/469 [00:12<00:00, 38.49it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0319, Accuracy: 9911/10000 (99.11%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.007785449270159006 batch_id=468: 100%|██████████| 469/469 [00:12<00:00, 35.67it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0372, Accuracy: 9894/10000 (98.94%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.012144923210144043 batch_id=468: 100%|██████████| 469/469 [00:12<00:00, 37.74it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0277, Accuracy: 9914/10000 (99.14%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.013582895509898663 batch_id=468: 100%|██████████| 469/469 [00:12<00:00, 37.14it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0336, Accuracy: 9905/10000 (99.05%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.09544902294874191 batch_id=468: 100%|██████████| 469/469 [00:12<00:00, 37.08it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0252, Accuracy: 9921/10000 (99.21%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.020992809906601906 batch_id=468: 100%|██████████| 469/469 [00:12<00:00, 37.60it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0231, Accuracy: 9933/10000 (99.33%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.013559852726757526 batch_id=468: 100%|██████████| 469/469 [00:12<00:00, 37.47it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0243, Accuracy: 9924/10000 (99.24%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.003363370895385742 batch_id=468: 100%|██████████| 469/469 [00:12<00:00, 38.70it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0231, Accuracy: 9925/10000 (99.25%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.04148498550057411 batch_id=468: 100%|██████████| 469/469 [00:12<00:00, 37.79it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0269, Accuracy: 9921/10000 (99.21%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.02008223533630371 batch_id=468: 100%|██████████| 469/469 [00:12<00:00, 38.15it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0216, Accuracy: 9931/10000 (99.31%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.031293775886297226 batch_id=468: 100%|██████████| 469/469 [00:12<00:00, 38.32it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0246, Accuracy: 9921/10000 (99.21%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.0010272959480062127 batch_id=468: 100%|██████████| 469/469 [00:12<00:00, 36.82it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0247, Accuracy: 9923/10000 (99.23%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.05963079631328583 batch_id=468: 100%|██████████| 469/469 [00:12<00:00, 37.31it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0210, Accuracy: 9939/10000 (99.39%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.0021605391521006823 batch_id=468: 100%|██████████| 469/469 [00:12<00:00, 37.14it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0202, Accuracy: 9943/10000 (99.43%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.07204557210206985 batch_id=468: 100%|██████████| 469/469 [00:12<00:00, 41.05it/s]\n",
            "  0%|          | 0/469 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0212, Accuracy: 9932/10000 (99.32%)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "loss=0.023050108924508095 batch_id=468: 100%|██████████| 469/469 [00:12<00:00, 36.91it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Test set: Average loss: 0.0208, Accuracy: 9935/10000 (99.35%)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "So5uk4EkHW6R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}