10. Computer Vision/06. Residual Network - MXNet.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Residual Network - MXNet.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "HO-7HJPVHXCH"
      },
      "source": [
        "# __Residual Network - MXNet__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "t0sXvPG-ytVs"
      },
      "outputs": [],
      "source": [
        "import mxnet as mx\n",
        "from mxnet import nd, autograd, gluon\n",
        "from mxnet.gluon import nn\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "ctx = mx.gpu()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Wt-gqScHHfou"
      },
      "source": [
        "## __Getting the dataset__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "colab_type": "code",
        "id": "FMnHwtmKyvo0",
        "outputId": "636c0278-7524-4e3f-ec6a-5408272598c9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading /root/.mxnet/datasets/cifar10/cifar-10-binary.tar.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/cifar10/cifar-10-binary.tar.gz...\n"
          ]
        }
      ],
      "source": [
        "transform = gluon.data.vision.transforms.ToTensor()\n",
        "\n",
        "train_mnist = gluon.data.vision.CIFAR10(train = True).transform_first(transform)\n",
        "test_mnist = gluon.data.vision.CIFAR10(train = False).transform_first(transform)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "colab_type": "code",
        "id": "fru8__gRywil",
        "outputId": "7c318462-ff57-47a3-f7c1-d05ed6b26260"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(3, 32, 32)\n",
            "9\n"
          ]
        }
      ],
      "source": [
        "image, label = train_mnist[1]\n",
        "print(image.shape)\n",
        "print(label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "colab_type": "code",
        "id": "GOFMgaltywsT",
        "outputId": "22f83c25-3f3a-478c-cd20-ff67c1de5feb"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(image.transpose((1, 2, 0)).asnumpy())\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rcZT9dvzHjLK"
      },
      "source": [
        "## __Defining data loaders__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "68qO-LTnywvY"
      },
      "outputs": [],
      "source": [
        "batch_size = 64\n",
        "train_data = gluon.data.DataLoader(train_mnist, batch_size = batch_size, shuffle = True)\n",
        "test_data = gluon.data.DataLoader(train_mnist, batch_size = batch_size, shuffle = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "6PHbpC6nHmLs"
      },
      "source": [
        "## __Defining Residual Network Model Architecture__ "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "CDyCqHMIHq-R"
      },
      "source": [
        "### First we will define single residual connection block"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "SKImYbq3ywxu"
      },
      "outputs": [],
      "source": [
        "class residualblock(nn.HybridBlock):\n",
        "    def __init__(self, channels, strides):\n",
        "        super(residualblock, self).__init__()\n",
        "        \n",
        "        self.conv1 = nn.HybridSequential()\n",
        "        self.conv1.add(nn.Conv2D(channels, kernel_size = 3, padding = 1, strides = strides))\n",
        "        self.conv1.add(nn.BatchNorm())\n",
        "        self.conv1.add(nn.Activation('relu'))\n",
        "\n",
        "        self.conv2 = nn.HybridSequential()\n",
        "        self.conv2.add(nn.Conv2D(channels, kernel_size = 3, padding = 1))\n",
        "        self.conv2.add(nn.BatchNorm())\n",
        "\n",
        "        self.residual = nn.Conv2D(channels, kernel_size = 1, strides = strides)\n",
        "        # doing network in network convolution(1X1) above for residual connection\n",
        "\n",
        "        self.activation = nn.Activation('relu')\n",
        "\n",
        "    def hybrid_forward(self, F, x):\n",
        "        layer = self.conv1(x)\n",
        "        layer = self.conv2(layer)\n",
        "        residual = self.residual(x)\n",
        "        return self.activation(layer + residual)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "SYhagDrdHwoU"
      },
      "source": [
        "### Visualizing single block of residual block"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 986
        },
        "colab_type": "code",
        "id": "Jevnp5cKywqc",
        "outputId": "e53e3869-6ef6-40e4-e26d-55eb0a965e62"
      },
      "outputs": [
        {
          "data": {
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id=\"edge5\" class=\"edge\">\n<title>batchnorm11_fwd&#45;&gt;conv16_fwd</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M56.3931,-459.6321C55.3964,-451.1148 54.3595,-442.2539 53.4181,-434.2088\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"57.5798,-469.7731 51.948,-460.3639 56.9986,-464.807 56.4175,-459.8409 56.4175,-459.8409 56.4175,-459.8409 56.9986,-464.807 60.887,-459.3178 57.5798,-469.7731 57.5798,-469.7731\"/>\n</g>\n<!-- conv17_fwd -->\n<g id=\"node7\" class=\"node\">\n<title>conv17_fwd</title>\n<polygon fill=\"#fb8072\" stroke=\"#000000\" points=\"209,-434 115,-434 115,-376 209,-376 209,-434\"/>\n<text text-anchor=\"middle\" x=\"162\" y=\"-408.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Convolution</text>\n<text text-anchor=\"middle\" x=\"162\" y=\"-393.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">1x1/1x1, 32</text>\n</g>\n<!-- conv17_fwd&#45;&gt;data -->\n<g id=\"edge6\" class=\"edge\">\n<title>conv17_fwd&#45;&gt;data</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M160.7366,-365.5083C158.2933,-306.1163 151.4224,-190.476 133,-94 130.6846,-81.8743 127.0722,-68.7606 123.6163,-57.5093\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"161.1303,-375.5816 156.2431,-365.765 160.935,-370.5854 160.7397,-365.5892 160.7397,-365.5892 160.7397,-365.5892 160.935,-370.5854 165.2362,-365.4134 161.1303,-375.5816 161.1303,-375.5816\"/>\n</g>\n<!-- residualblock1__plus0 -->\n<g id=\"node8\" class=\"node\">\n<title>residualblock1__plus0</title>\n<polygon fill=\"#fccde5\" stroke=\"#000000\" points=\"178.5,-622 31.5,-622 31.5,-564 178.5,-564 178.5,-622\"/>\n<text text-anchor=\"middle\" x=\"105\" y=\"-589.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">residualblock1__plus0</text>\n</g>\n<!-- residualblock1__plus0&#45;&gt;batchnorm11_fwd -->\n<g id=\"edge7\" class=\"edge\">\n<title>residualblock1__plus0&#45;&gt;batchnorm11_fwd</title>\n<path 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class=\"node\">\n<title>relu3_fwd</title>\n<polygon fill=\"#ffffb3\" stroke=\"#000000\" points=\"152,-716 58,-716 58,-658 152,-658 152,-716\"/>\n<text text-anchor=\"middle\" x=\"105\" y=\"-690.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Activation</text>\n<text text-anchor=\"middle\" x=\"105\" y=\"-675.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">relu</text>\n</g>\n<!-- relu3_fwd&#45;&gt;residualblock1__plus0 -->\n<g id=\"edge9\" class=\"edge\">\n<title>relu3_fwd&#45;&gt;residualblock1__plus0</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M105,-647.6321C105,-639.1148 105,-630.2539 105,-622.2088\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"105,-657.7731 100.5001,-647.773 105,-652.7731 105.0001,-647.7731 105.0001,-647.7731 105.0001,-647.7731 105,-652.7731 109.5001,-647.7731 105,-657.7731 105,-657.7731\"/>\n</g>\n</g>\n</svg>\n",
            "text/plain": [
              "<graphviz.dot.Digraph at 0x7f36b4196d68>"
            ]
          },
          "execution_count": 19,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "block = residualblock(32, 1)\n",
        "block.initialize(ctx = ctx)\n",
        "mx.viz.plot_network(block(mx.sym.var(name = 'data')), node_attrs={\"fixedsize\":\"false\"})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "6LOc1aY8Ihlu"
      },
      "source": [
        "## __Defining the whole model__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "colab_type": "code",
        "id": "wlp_psO_ywof",
        "outputId": "8a3495a9-f24d-428f-c153-921dbb57c94a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "HybridSequential(\n",
              "  (0): Conv2D(None -> 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3))\n",
              "  (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "  (2): MaxPool2D(size=(3, 3), stride=(2, 2), padding=(1, 1), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)\n",
              "  (3): residualblock(\n",
              "    (conv1): HybridSequential(\n",
              "      (0): Conv2D(None -> 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
              "      (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "      (2): Activation(relu)\n",
              "    )\n",
              "    (conv2): HybridSequential(\n",
              "      (0): Conv2D(None -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (residual): Conv2D(None -> 64, kernel_size=(1, 1), stride=(2, 2))\n",
              "    (activation): Activation(relu)\n",
              "  )\n",
              "  (4): residualblock(\n",
              "    (conv1): HybridSequential(\n",
              "      (0): Conv2D(None -> 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
              "      (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "      (2): Activation(relu)\n",
              "    )\n",
              "    (conv2): HybridSequential(\n",
              "      (0): Conv2D(None -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (residual): Conv2D(None -> 64, kernel_size=(1, 1), stride=(2, 2))\n",
              "    (activation): Activation(relu)\n",
              "  )\n",
              "  (5): residualblock(\n",
              "    (conv1): HybridSequential(\n",
              "      (0): Conv2D(None -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "      (2): Activation(relu)\n",
              "    )\n",
              "    (conv2): HybridSequential(\n",
              "      (0): Conv2D(None -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (residual): Conv2D(None -> 128, kernel_size=(1, 1), stride=(1, 1))\n",
              "    (activation): Activation(relu)\n",
              "  )\n",
              "  (6): residualblock(\n",
              "    (conv1): HybridSequential(\n",
              "      (0): Conv2D(None -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "      (2): Activation(relu)\n",
              "    )\n",
              "    (conv2): HybridSequential(\n",
              "      (0): Conv2D(None -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (residual): Conv2D(None -> 128, kernel_size=(1, 1), stride=(1, 1))\n",
              "    (activation): Activation(relu)\n",
              "  )\n",
              "  (7): residualblock(\n",
              "    (conv1): HybridSequential(\n",
              "      (0): Conv2D(None -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "      (2): Activation(relu)\n",
              "    )\n",
              "    (conv2): HybridSequential(\n",
              "      (0): Conv2D(None -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (residual): Conv2D(None -> 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "    (activation): Activation(relu)\n",
              "  )\n",
              "  (8): residualblock(\n",
              "    (conv1): HybridSequential(\n",
              "      (0): Conv2D(None -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "      (2): Activation(relu)\n",
              "    )\n",
              "    (conv2): HybridSequential(\n",
              "      (0): Conv2D(None -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (residual): Conv2D(None -> 256, kernel_size=(1, 1), stride=(1, 1))\n",
              "    (activation): Activation(relu)\n",
              "  )\n",
              "  (9): residualblock(\n",
              "    (conv1): HybridSequential(\n",
              "      (0): Conv2D(None -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "      (2): Activation(relu)\n",
              "    )\n",
              "    (conv2): HybridSequential(\n",
              "      (0): Conv2D(None -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (residual): Conv2D(None -> 512, kernel_size=(1, 1), stride=(1, 1))\n",
              "    (activation): Activation(relu)\n",
              "  )\n",
              "  (10): residualblock(\n",
              "    (conv1): HybridSequential(\n",
              "      (0): Conv2D(None -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "      (2): Activation(relu)\n",
              "    )\n",
              "    (conv2): HybridSequential(\n",
              "      (0): Conv2D(None -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
              "      (1): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (residual): Conv2D(None -> 512, kernel_size=(1, 1), stride=(1, 1))\n",
              "    (activation): Activation(relu)\n",
              "  )\n",
              "  (11): GlobalAvgPool2D(size=(1, 1), stride=(1, 1), padding=(0, 0), ceil_mode=True, global_pool=True, pool_type=avg, layout=NCHW)\n",
              "  (12): Dense(None -> 10, linear)\n",
              ")"
            ]
          },
          "execution_count": 20,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = nn.HybridSequential()\n",
        "\n",
        "model.add(nn.Conv2D(64, kernel_size = 7, strides = 2, padding = 3))\n",
        "model.add(nn.BatchNorm())\n",
        "model.add(nn.MaxPool2D(pool_size = 3, strides = 2, padding = 1))\n",
        "\n",
        "model.add(residualblock(channels = 64, strides = 2))\n",
        "model.add(residualblock(channels = 64, strides = 2))\n",
        "\n",
        "model.add(residualblock(channels = 128, strides = 1))\n",
        "model.add(residualblock(channels = 128, strides = 1))\n",
        "\n",
        "model.add(residualblock(channels = 256, strides = 1))\n",
        "model.add(residualblock(channels = 256, strides = 1))\n",
        "\n",
        "model.add(residualblock(channels = 512, strides = 1))\n",
        "model.add(residualblock(channels = 512, strides = 1))\n",
        "\n",
        "model.add(nn.GlobalAvgPool2D())\n",
        "model.add(nn.Dense(10))\n",
        "\n",
        "model.initialize(mx.init.Xavier(), ctx)\n",
        "model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "YGW5do2DInrM"
      },
      "source": [
        "## __Summarizing the model__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "colab_type": "code",
        "id": "fWwau-Uxywmi",
        "outputId": "ff76a079-7ff4-48fd-f60c-56c2bba03b54"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--------------------------------------------------------------------------------\n",
            "        Layer (type)                                Output Shape         Param #\n",
            "================================================================================\n",
            "               Input                              (1, 3, 32, 32)               0\n",
            "            Conv2D-1                             (1, 64, 16, 16)            9472\n",
            "         BatchNorm-2                             (1, 64, 16, 16)             256\n",
            "         MaxPool2D-3                               (1, 64, 8, 8)               0\n",
            "            Conv2D-4                               (1, 64, 4, 4)           36928\n",
            "         BatchNorm-5                               (1, 64, 4, 4)             256\n",
            "        Activation-6                               (1, 64, 4, 4)               0\n",
            "            Conv2D-7                               (1, 64, 4, 4)           36928\n",
            "         BatchNorm-8                               (1, 64, 4, 4)             256\n",
            "            Conv2D-9                               (1, 64, 4, 4)            4160\n",
            "       Activation-10                               (1, 64, 4, 4)               0\n",
            "    residualblock-11                               (1, 64, 4, 4)               0\n",
            "           Conv2D-12                               (1, 64, 2, 2)           36928\n",
            "        BatchNorm-13                               (1, 64, 2, 2)             256\n",
            "       Activation-14                               (1, 64, 2, 2)               0\n",
            "           Conv2D-15                               (1, 64, 2, 2)           36928\n",
            "        BatchNorm-16                               (1, 64, 2, 2)             256\n",
            "           Conv2D-17                               (1, 64, 2, 2)            4160\n",
            "       Activation-18                               (1, 64, 2, 2)               0\n",
            "    residualblock-19                               (1, 64, 2, 2)               0\n",
            "           Conv2D-20                              (1, 128, 2, 2)           73856\n",
            "        BatchNorm-21                              (1, 128, 2, 2)             512\n",
            "       Activation-22                              (1, 128, 2, 2)               0\n",
            "           Conv2D-23                              (1, 128, 2, 2)          147584\n",
            "        BatchNorm-24                              (1, 128, 2, 2)             512\n",
            "           Conv2D-25                              (1, 128, 2, 2)            8320\n",
            "       Activation-26                              (1, 128, 2, 2)               0\n",
            "    residualblock-27                              (1, 128, 2, 2)               0\n",
            "           Conv2D-28                              (1, 128, 2, 2)          147584\n",
            "        BatchNorm-29                              (1, 128, 2, 2)             512\n",
            "       Activation-30                              (1, 128, 2, 2)               0\n",
            "           Conv2D-31                              (1, 128, 2, 2)          147584\n",
            "        BatchNorm-32                              (1, 128, 2, 2)             512\n",
            "           Conv2D-33                              (1, 128, 2, 2)           16512\n",
            "       Activation-34                              (1, 128, 2, 2)               0\n",
            "    residualblock-35                              (1, 128, 2, 2)               0\n",
            "           Conv2D-36                              (1, 256, 2, 2)          295168\n",
            "        BatchNorm-37                              (1, 256, 2, 2)            1024\n",
            "       Activation-38                              (1, 256, 2, 2)               0\n",
            "           Conv2D-39                              (1, 256, 2, 2)          590080\n",
            "        BatchNorm-40                              (1, 256, 2, 2)            1024\n",
            "           Conv2D-41                              (1, 256, 2, 2)           33024\n",
            "       Activation-42                              (1, 256, 2, 2)               0\n",
            "    residualblock-43                              (1, 256, 2, 2)               0\n",
            "           Conv2D-44                              (1, 256, 2, 2)          590080\n",
            "        BatchNorm-45                              (1, 256, 2, 2)            1024\n",
            "       Activation-46                              (1, 256, 2, 2)               0\n",
            "           Conv2D-47                              (1, 256, 2, 2)          590080\n",
            "        BatchNorm-48                              (1, 256, 2, 2)            1024\n",
            "           Conv2D-49                              (1, 256, 2, 2)           65792\n",
            "       Activation-50                              (1, 256, 2, 2)               0\n",
            "    residualblock-51                              (1, 256, 2, 2)               0\n",
            "           Conv2D-52                              (1, 512, 2, 2)         1180160\n",
            "        BatchNorm-53                              (1, 512, 2, 2)            2048\n",
            "       Activation-54                              (1, 512, 2, 2)               0\n",
            "           Conv2D-55                              (1, 512, 2, 2)         2359808\n",
            "        BatchNorm-56                              (1, 512, 2, 2)            2048\n",
            "           Conv2D-57                              (1, 512, 2, 2)          131584\n",
            "       Activation-58                              (1, 512, 2, 2)               0\n",
            "    residualblock-59                              (1, 512, 2, 2)               0\n",
            "           Conv2D-60                              (1, 512, 2, 2)         2359808\n",
            "        BatchNorm-61                              (1, 512, 2, 2)            2048\n",
            "       Activation-62                              (1, 512, 2, 2)               0\n",
            "           Conv2D-63                              (1, 512, 2, 2)         2359808\n",
            "        BatchNorm-64                              (1, 512, 2, 2)            2048\n",
            "           Conv2D-65                              (1, 512, 2, 2)          262656\n",
            "       Activation-66                              (1, 512, 2, 2)               0\n",
            "    residualblock-67                              (1, 512, 2, 2)               0\n",
            "  GlobalAvgPool2D-68                              (1, 512, 1, 1)               0\n",
            "            Dense-69                                     (1, 10)            5130\n",
            "================================================================================\n",
            "Parameters in forward computation graph, duplicate included\n",
            "   Total params: 11545738\n",
            "   Trainable params: 11537930\n",
            "   Non-trainable params: 7808\n",
            "Shared params in forward computation graph: 0\n",
            "Unique parameters in model: 11545738\n",
            "--------------------------------------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "model.summary(image.expand_dims(0).as_in_context(ctx))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "4KlBsabaItQc"
      },
      "source": [
        "## __Visializing model architecture__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "colab_type": "code",
        "id": "_dssSLsM0thA",
        "outputId": "f0bf8b55-c77d-4a85-9311-aea6c13661ed"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<graphviz.dot.Digraph at 0x7f36b4122c18>"
            ]
          },
          "execution_count": 22,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "plot = mx.viz.plot_network(model(mx.sym.var(name = 'data')), node_attrs={\"fixedsize\":\"false\"})\n",
        "plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "riAfI2gDI1aV"
      },
      "outputs": [],
      "source": [
        "model.hybridize()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "bFmcxFtTI5uX"
      },
      "source": [
        "## __Defining our objective loss and optimizer__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "OR_RGJRb18bd"
      },
      "outputs": [],
      "source": [
        "objective = gluon.loss.SoftmaxCrossEntropyLoss()\n",
        "trainer = gluon.Trainer(model.collect_params(), 'adam', {'learning_rate': 0.001})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "GMtZRsrgI7qm"
      },
      "source": [
        "## __Training the model__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        },
        "colab_type": "code",
        "id": "_--4aXog18Yo",
        "outputId": "c62ad0fe-9c1e-4a0e-8423-2c40c68139bd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 1 | Loss: 1.51742 | Training Accuracy: 0.44686\n",
            "Epoch: 2 | Loss: 1.15200 | Training Accuracy: 0.59308\n",
            "Epoch: 3 | Loss: 0.97572 | Training Accuracy: 0.65704\n",
            "Epoch: 4 | Loss: 0.86427 | Training Accuracy: 0.69810\n",
            "Epoch: 5 | Loss: 0.77756 | Training Accuracy: 0.72822\n",
            "Epoch: 6 | Loss: 0.70661 | Training Accuracy: 0.75324\n",
            "Epoch: 7 | Loss: 0.64107 | Training Accuracy: 0.77824\n",
            "Epoch: 8 | Loss: 0.58440 | Training Accuracy: 0.79530\n",
            "Epoch: 9 | Loss: 0.53230 | Training Accuracy: 0.81536\n",
            "Epoch: 10 | Loss: 0.48557 | Training Accuracy: 0.83184\n"
          ]
        }
      ],
      "source": [
        "epochs = 10\n",
        "batches = len(train_data)\n",
        "acc = mx.metric.Accuracy()\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    acc.reset()\n",
        "    cum_loss = 0.0\n",
        "    for features, labels in train_data:\n",
        "        features = features.as_in_context(ctx)\n",
        "        labels = labels.as_in_context(ctx)\n",
        "\n",
        "        with autograd.record():\n",
        "            outputs = model(features)\n",
        "            loss = objective(outputs, labels)\n",
        "        loss.backward()\n",
        "        trainer.step(batch_size)\n",
        "\n",
        "        cum_loss += loss.mean()\n",
        "        acc.update(labels, outputs)\n",
        "    \n",
        "    print(f'Epoch: {epoch + 1} | Loss: {cum_loss.asscalar()/batches:.5f} | Training Accuracy: {acc.get()[1]:.5f}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "8pbbrT7XI9sp"
      },
      "source": [
        "## __Testing the model__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "colab_type": "code",
        "id": "mC2FT6we4vJ3",
        "outputId": "2fdc4b3c-248f-45be-d986-4353d1a1b471"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('accuracy', 0.82788)"
            ]
          },
          "execution_count": 26,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "acc.reset()\n",
        "for features, labels in test_data:\n",
        "    features = features.as_in_context(ctx)\n",
        "    labels = labels.as_in_context(ctx)\n",
        "    outputs = model(features)\n",
        "    acc.update(labels, outputs)\n",
        "acc.get()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "ZR95IT9RVkpb"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ]
}