VGG11.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "VGG11.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOX3GRUf50Um/lzzGlSXQs8",
      "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/theAkhileshRai/ComputerVision/blob/master/VGG11.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U0LL1vUt8msi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5af1c2f1-23c7-4190-e220-4141b7ccf078"
      },
      "source": [
        "#VGGNet\n",
        "\n",
        "!pip install -U --pre mxnet-cu101mkl  # updating mxnet to at least v1.6\n",
        "!pip install git+https://github.com/d2l-ai/d2l-en # installing d2l"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: mxnet-cu101mkl in /usr/local/lib/python3.6/dist-packages (1.6.0)\n",
            "Requirement already satisfied, skipping upgrade: requests<3,>=2.20.0 in /usr/local/lib/python3.6/dist-packages (from mxnet-cu101mkl) (2.21.0)\n",
            "Requirement already satisfied, skipping upgrade: numpy<2.0.0,>1.16.0 in /usr/local/lib/python3.6/dist-packages (from mxnet-cu101mkl) (1.17.5)\n",
            "Requirement already satisfied, skipping upgrade: graphviz<0.9.0,>=0.8.1 in /usr/local/lib/python3.6/dist-packages (from mxnet-cu101mkl) (0.8.4)\n",
            "Requirement already satisfied, skipping upgrade: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.20.0->mxnet-cu101mkl) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.20.0->mxnet-cu101mkl) (2.8)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.20.0->mxnet-cu101mkl) (2019.11.28)\n",
            "Requirement already satisfied, skipping upgrade: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.20.0->mxnet-cu101mkl) (3.0.4)\n",
            "Collecting git+https://github.com/d2l-ai/d2l-en\n",
            "  Cloning https://github.com/d2l-ai/d2l-en to /tmp/pip-req-build-2sua4x77\n",
            "  Running command git clone -q https://github.com/d2l-ai/d2l-en /tmp/pip-req-build-2sua4x77\n",
            "Requirement already satisfied (use --upgrade to upgrade): d2l==0.11.4 from git+https://github.com/d2l-ai/d2l-en in /usr/local/lib/python3.6/dist-packages\n",
            "Requirement already satisfied: jupyter in /usr/local/lib/python3.6/dist-packages (from d2l==0.11.4) (1.0.0)\n",
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            "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /usr/local/lib/python3.6/dist-packages (from nbformat>=4.2.0->ipywidgets->jupyter->d2l==0.11.4) (2.6.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.6/dist-packages (from bleach->nbconvert->jupyter->d2l==0.11.4) (0.5.1)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from jinja2>=2.4->nbconvert->jupyter->d2l==0.11.4) (1.1.1)\n",
            "Requirement already satisfied: ptyprocess; os_name != \"nt\" in /usr/local/lib/python3.6/dist-packages (from terminado>=0.3.3; sys_platform != \"win32\"->notebook->jupyter->d2l==0.11.4) (0.6.0)\n",
            "Building wheels for collected packages: d2l\n",
            "  Building wheel for d2l (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for d2l: filename=d2l-0.11.4-cp36-none-any.whl size=31756 sha256=16246cce31e1d6a9f9568ad8b70399eb925e21d44631f00f74c7fc4a7d181e20\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-ussg_78z/wheels/82/6f/b8/8422c7ed664272685e3d139b127f89df39d934704a14b2156c\n",
            "Successfully built d2l\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IW3Gb94dWpxh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import d2l\n",
        "from mxnet import np, npx\n",
        "from mxnet.gluon import nn\n",
        "npx.set_np()\n",
        "\n",
        "def VGGBlock(numConv,numChannels):\n",
        "  blk = nn.Sequential()\n",
        "  for _ in range(numConv):\n",
        "    blk.add(nn.Conv2D(numChannels,kernel_size=3,padding = 1,activation='relu'))\n",
        "  blk.add(nn.MaxPool2D(pool_size = 2,strides=2))\n",
        "  return blk\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gLBvIU-tZsDG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "0d951b8f-faee-4729-eea8-cb90fafa324b"
      },
      "source": [
        "VGGBlock(2,64)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): Conv2D(-1 -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))\n",
              "  (1): Conv2D(-1 -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))\n",
              "  (2): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pwmJteroaOdc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "conv_arch = ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-mAA_nd5bFg0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#VGGNet\n",
        "def vgg(conv_arch):\n",
        "    net = nn.Sequential()\n",
        "    # The convolutional layer part\n",
        "    for (num_convs, num_channels) in conv_arch:\n",
        "        net.add(VGGBlock(num_convs, num_channels))\n",
        "    # The fully connected layer part\n",
        "    net.add(nn.Dense(4096, activation='relu'), nn.Dropout(0.5),\n",
        "            nn.Dense(4096, activation='relu'), nn.Dropout(0.5),\n",
        "            nn.Dense(10))\n",
        "    return net\n",
        "\n",
        "net = vgg(conv_arch)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZsRRW8MSap3w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "outputId": "9e582c07-ab13-4b51-9015-1cdfa0471322"
      },
      "source": [
        "net\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): Sequential(\n",
              "    (0): Conv2D(-1 -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))\n",
              "    (1): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)\n",
              "  )\n",
              "  (1): Sequential(\n",
              "    (0): Conv2D(-1 -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))\n",
              "    (1): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)\n",
              "  )\n",
              "  (2): Sequential(\n",
              "    (0): Conv2D(-1 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))\n",
              "    (1): Conv2D(-1 -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))\n",
              "    (2): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)\n",
              "  )\n",
              "  (3): Sequential(\n",
              "    (0): Conv2D(-1 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))\n",
              "    (1): Conv2D(-1 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))\n",
              "    (2): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)\n",
              "  )\n",
              "  (4): Sequential(\n",
              "    (0): Conv2D(-1 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))\n",
              "    (1): Conv2D(-1 -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))\n",
              "    (2): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)\n",
              "  )\n",
              "  (5): Dense(-1 -> 4096, Activation(relu))\n",
              "  (6): Dropout(p = 0.5, axes=())\n",
              "  (7): Dense(-1 -> 4096, Activation(relu))\n",
              "  (8): Dropout(p = 0.5, axes=())\n",
              "  (9): Dense(-1 -> 10, linear)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RFFCsK8HgbBH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        },
        "outputId": "ec35c2c1-b46c-490e-f313-72b7776f4f88"
      },
      "source": [
        "net.initialize()\n",
        "X = np.random.uniform(size=(1, 1, 224, 224))\n",
        "for blk in net:\n",
        "    X = blk(X)\n",
        "    print(blk.name, 'output shape:\\t', X.shape)\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sequential15 output shape:\t (1, 64, 112, 112)\n",
            "sequential16 output shape:\t (1, 128, 56, 56)\n",
            "sequential17 output shape:\t (1, 256, 28, 28)\n",
            "sequential18 output shape:\t (1, 512, 14, 14)\n",
            "sequential19 output shape:\t (1, 512, 7, 7)\n",
            "dense6 output shape:\t (1, 4096)\n",
            "dropout4 output shape:\t (1, 4096)\n",
            "dense7 output shape:\t (1, 4096)\n",
            "dropout5 output shape:\t (1, 4096)\n",
            "dense8 output shape:\t (1, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SVJ8S8mehVEs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ratio = 4\n",
        "small_conv_arch = [(pair[0], pair[1] // ratio) for pair in conv_arch]\n",
        "net = vgg(small_conv_arch)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cMe6saPQhYU4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "outputId": "cd7c6950-917f-4090-9219-4de72432b8ec"
      },
      "source": [
        "lr, num_epochs, batch_size = 0.05, 10, 128,\n",
        "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)\n",
        "d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 252x180 with 1 Axes>"
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