10. Computer Vision/05. Inception Network - MXNet.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Inception Network - MXNet.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# __Inception 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",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "## __Getting the dataset__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "FMnHwtmKyvo0"
      },
      "outputs": [],
      "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": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "colab_type": "code",
        "id": "fru8__gRywil",
        "outputId": "8dfb8ee7-0b48-4c86-a02a-f052ac958dd4"
      },
      "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": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "colab_type": "code",
        "id": "GOFMgaltywsT",
        "outputId": "0b5ac086-c3ac-4e91-ad2e-a10486759c25"
      },
      "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",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "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",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "## __Defining Inception Network Model Architecture__"
      ]
    },
    {
      "cell_type": "markdown",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "### First we will define the inception block"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "SKImYbq3ywxu"
      },
      "outputs": [],
      "source": [
        "class inceptionblock(nn.HybridBlock):\n",
        "    def __init__(self, channels1, channels2, channels3, channels4):\n",
        "        super(inceptionblock, self).__init__()\n",
        "        self.conv1 = nn.Conv2D(channels1, kernel_size = 1, activation = 'relu')\n",
        "\n",
        "        self.conv2 = nn.HybridSequential()\n",
        "        self.conv2.add(nn.Conv2D(channels2[0], kernel_size = 1, activation = 'relu'))\n",
        "        self.conv2.add(nn.Conv2D(channels2[1], kernel_size = 3, padding = 1,activation = 'relu'))\n",
        "        self.conv2.add(nn.BatchNorm())\n",
        "\n",
        "        self.conv3 = nn.HybridSequential()\n",
        "        self.conv3.add(nn.Conv2D(channels3[0], kernel_size = 1, activation = 'relu'))\n",
        "        self.conv3.add(nn.Conv2D(channels3[1], kernel_size = 5, padding = 2,activation = 'relu'))\n",
        "        self.conv3.add(nn.BatchNorm())\n",
        "\n",
        "        self.conv4 = nn.HybridSequential()\n",
        "        self.conv4.add(nn.MaxPool2D(pool_size = 3, strides = 1, padding = 1))\n",
        "        self.conv4.add(nn.Conv2D(channels4, kernel_size = 1, activation = 'relu'))\n",
        "        self.conv4.add(nn.BatchNorm())\n",
        "\n",
        "    def hybrid_forward(self, F, x):\n",
        "        conv1 = self.conv1(x)\n",
        "        conv2 = self.conv2(x)\n",
        "        conv3 = self.conv3(x)\n",
        "        conv4 = self.conv4(x)\n",
        "        return F.concat(conv1, conv2, conv3, conv4, dim = 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "### Let's visualize single block of inception network"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 861
        },
        "colab_type": "code",
        "id": "Jevnp5cKywqc",
        "outputId": "df22aef4-298a-4d1f-cf8a-db66a1fcee66"
      },
      "outputs": [
        {
          "data": {
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font-size=\"14.00\" fill=\"#000000\">Activation</text>\n<text text-anchor=\"middle\" x=\"159\" y=\"-205.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">relu</text>\n</g>\n<!-- conv1_relu_fwd&#45;&gt;conv1_fwd -->\n<g id=\"edge4\" class=\"edge\">\n<title>conv1_relu_fwd&#45;&gt;conv1_fwd</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M159,-177.6321C159,-169.1148 159,-160.2539 159,-152.2088\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"159,-187.7731 154.5001,-177.773 159,-182.7731 159.0001,-177.7731 159.0001,-177.7731 159.0001,-177.7731 159,-182.7731 163.5001,-177.7731 159,-187.7731 159,-187.7731\"/>\n</g>\n<!-- conv2_fwd -->\n<g id=\"node6\" class=\"node\">\n<title>conv2_fwd</title>\n<polygon fill=\"#fb8072\" stroke=\"#000000\" points=\"206.5,-340 111.5,-340 111.5,-282 206.5,-282 206.5,-340\"/>\n<text text-anchor=\"middle\" x=\"159\" y=\"-314.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Convolution</text>\n<text text-anchor=\"middle\" x=\"159\" y=\"-299.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">3x3/1x1, 128</text>\n</g>\n<!-- conv2_fwd&#45;&gt;conv1_relu_fwd -->\n<g id=\"edge5\" class=\"edge\">\n<title>conv2_fwd&#45;&gt;conv1_relu_fwd</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M159,-271.6321C159,-263.1148 159,-254.2539 159,-246.2088\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"159,-281.7731 154.5001,-271.773 159,-276.7731 159.0001,-271.7731 159.0001,-271.7731 159.0001,-271.7731 159,-276.7731 163.5001,-271.7731 159,-281.7731 159,-281.7731\"/>\n</g>\n<!-- conv2_relu_fwd -->\n<g id=\"node7\" class=\"node\">\n<title>conv2_relu_fwd</title>\n<polygon fill=\"#ffffb3\" stroke=\"#000000\" points=\"206,-434 112,-434 112,-376 206,-376 206,-434\"/>\n<text text-anchor=\"middle\" x=\"159\" y=\"-408.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Activation</text>\n<text text-anchor=\"middle\" x=\"159\" y=\"-393.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">relu</text>\n</g>\n<!-- conv2_relu_fwd&#45;&gt;conv2_fwd -->\n<g id=\"edge6\" class=\"edge\">\n<title>conv2_relu_fwd&#45;&gt;conv2_fwd</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M159,-365.6321C159,-357.1148 159,-348.2539 159,-340.2088\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"159,-375.7731 154.5001,-365.773 159,-370.7731 159.0001,-365.7731 159.0001,-365.7731 159.0001,-365.7731 159,-370.7731 163.5001,-365.7731 159,-375.7731 159,-375.7731\"/>\n</g>\n<!-- batchnorm0_fwd -->\n<g id=\"node8\" class=\"node\">\n<title>batchnorm0_fwd</title>\n<polygon fill=\"#bebada\" stroke=\"#000000\" points=\"194,-528 78,-528 78,-470 194,-470 194,-528\"/>\n<text text-anchor=\"middle\" x=\"136\" y=\"-495.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">batchnorm0_fwd</text>\n</g>\n<!-- batchnorm0_fwd&#45;&gt;conv2_relu_fwd -->\n<g id=\"edge7\" 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            "text/plain": [
              "<graphviz.dot.Digraph at 0x7f3bc916bcc0>"
            ]
          },
          "execution_count": 7,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "block = inceptionblock(32, (64, 128), (16, 32), 32)\n",
        "block.initialize(ctx = ctx)\n",
        "mx.viz.plot_network(block(mx.sym.var(name = 'data')), node_attrs={\"fixedsize\":\"false\"})"
      ]
    },
    {
      "cell_type": "markdown",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "## __Time to build the whole model__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "colab_type": "code",
        "id": "wlp_psO_ywof",
        "outputId": "3d527cd6-7fd0-4baf-9963-6adfcdd5d4a2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "HybridSequential(\n",
              "  (0): Conv2D(None -> 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), Activation(relu))\n",
              "  (1): MaxPool2D(size=(3, 3), stride=(2, 2), padding=(1, 1), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)\n",
              "  (2): Conv2D(None -> 64, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "  (3): Conv2D(None -> 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))\n",
              "  (4): MaxPool2D(size=(3, 3), stride=(2, 2), padding=(1, 1), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)\n",
              "  (5): inceptionblock(\n",
              "    (conv1): Conv2D(None -> 64, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "    (conv2): HybridSequential(\n",
              "      (0): Conv2D(None -> 96, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (1): Conv2D(None -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (conv3): HybridSequential(\n",
              "      (0): Conv2D(None -> 16, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (1): Conv2D(None -> 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (conv4): HybridSequential(\n",
              "      (0): MaxPool2D(size=(3, 3), stride=(1, 1), padding=(1, 1), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)\n",
              "      (1): Conv2D(None -> 32, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "  )\n",
              "  (6): inceptionblock(\n",
              "    (conv1): Conv2D(None -> 128, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "    (conv2): HybridSequential(\n",
              "      (0): Conv2D(None -> 128, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (1): Conv2D(None -> 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (conv3): HybridSequential(\n",
              "      (0): Conv2D(None -> 32, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (1): Conv2D(None -> 96, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (conv4): HybridSequential(\n",
              "      (0): MaxPool2D(size=(3, 3), stride=(1, 1), padding=(1, 1), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)\n",
              "      (1): Conv2D(None -> 64, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "  )\n",
              "  (7): MaxPool2D(size=(3, 3), stride=(2, 2), padding=(1, 1), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)\n",
              "  (8): inceptionblock(\n",
              "    (conv1): Conv2D(None -> 192, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "    (conv2): HybridSequential(\n",
              "      (0): Conv2D(None -> 96, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (1): Conv2D(None -> 208, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (conv3): HybridSequential(\n",
              "      (0): Conv2D(None -> 16, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (1): Conv2D(None -> 48, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (conv4): HybridSequential(\n",
              "      (0): MaxPool2D(size=(3, 3), stride=(1, 1), padding=(1, 1), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)\n",
              "      (1): Conv2D(None -> 64, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "  )\n",
              "  (9): inceptionblock(\n",
              "    (conv1): Conv2D(None -> 160, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "    (conv2): HybridSequential(\n",
              "      (0): Conv2D(None -> 112, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (1): Conv2D(None -> 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (conv3): HybridSequential(\n",
              "      (0): Conv2D(None -> 24, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (1): Conv2D(None -> 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (conv4): HybridSequential(\n",
              "      (0): MaxPool2D(size=(3, 3), stride=(1, 1), padding=(1, 1), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)\n",
              "      (1): Conv2D(None -> 64, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "  )\n",
              "  (10): inceptionblock(\n",
              "    (conv1): Conv2D(None -> 128, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "    (conv2): HybridSequential(\n",
              "      (0): Conv2D(None -> 128, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (1): Conv2D(None -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (conv3): HybridSequential(\n",
              "      (0): Conv2D(None -> 24, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (1): Conv2D(None -> 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (conv4): HybridSequential(\n",
              "      (0): MaxPool2D(size=(3, 3), stride=(1, 1), padding=(1, 1), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)\n",
              "      (1): Conv2D(None -> 64, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "  )\n",
              "  (11): inceptionblock(\n",
              "    (conv1): Conv2D(None -> 112, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "    (conv2): HybridSequential(\n",
              "      (0): Conv2D(None -> 144, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (1): Conv2D(None -> 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (conv3): HybridSequential(\n",
              "      (0): Conv2D(None -> 32, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (1): Conv2D(None -> 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (conv4): HybridSequential(\n",
              "      (0): MaxPool2D(size=(3, 3), stride=(1, 1), padding=(1, 1), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)\n",
              "      (1): Conv2D(None -> 64, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "  )\n",
              "  (12): inceptionblock(\n",
              "    (conv1): Conv2D(None -> 256, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "    (conv2): HybridSequential(\n",
              "      (0): Conv2D(None -> 160, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (1): Conv2D(None -> 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (conv3): HybridSequential(\n",
              "      (0): Conv2D(None -> 32, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (1): Conv2D(None -> 128, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (conv4): HybridSequential(\n",
              "      (0): MaxPool2D(size=(3, 3), stride=(1, 1), padding=(1, 1), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)\n",
              "      (1): Conv2D(None -> 64, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "  )\n",
              "  (13): MaxPool2D(size=(3, 3), stride=(2, 2), padding=(1, 1), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)\n",
              "  (14): inceptionblock(\n",
              "    (conv1): Conv2D(None -> 256, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "    (conv2): HybridSequential(\n",
              "      (0): Conv2D(None -> 160, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (1): Conv2D(None -> 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (conv3): HybridSequential(\n",
              "      (0): Conv2D(None -> 32, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (1): Conv2D(None -> 128, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (conv4): HybridSequential(\n",
              "      (0): MaxPool2D(size=(3, 3), stride=(1, 1), padding=(1, 1), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)\n",
              "      (1): Conv2D(None -> 128, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "  )\n",
              "  (15): inceptionblock(\n",
              "    (conv1): Conv2D(None -> 384, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "    (conv2): HybridSequential(\n",
              "      (0): Conv2D(None -> 192, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (1): Conv2D(None -> 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (conv3): HybridSequential(\n",
              "      (0): Conv2D(None -> 48, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (1): Conv2D(None -> 128, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "    (conv4): HybridSequential(\n",
              "      (0): MaxPool2D(size=(3, 3), stride=(1, 1), padding=(1, 1), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)\n",
              "      (1): Conv2D(None -> 128, kernel_size=(1, 1), stride=(1, 1), Activation(relu))\n",
              "      (2): BatchNorm(axis=1, eps=1e-05, momentum=0.9, fix_gamma=False, use_global_stats=False, in_channels=None)\n",
              "    )\n",
              "  )\n",
              "  (16): GlobalAvgPool2D(size=(1, 1), stride=(1, 1), padding=(0, 0), ceil_mode=True, global_pool=True, pool_type=avg, layout=NCHW)\n",
              "  (17): Dense(None -> 10, linear)\n",
              ")"
            ]
          },
          "execution_count": 8,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = nn.HybridSequential()\n",
        "\n",
        "model.add(nn.Conv2D(64, kernel_size = 7, strides = 2, padding = 3, activation = 'relu'))\n",
        "model.add(nn.MaxPool2D(pool_size = 3, strides = 2, padding = 1))\n",
        "\n",
        "model.add(nn.Conv2D(64, kernel_size = 1, activation = 'relu'))\n",
        "model.add(nn.Conv2D(192, kernel_size = 3, padding = 1, activation = 'relu'))\n",
        "model.add(nn.MaxPool2D(pool_size = 3, strides = 2, padding = 1))\n",
        "\n",
        "model.add(inceptionblock(64, (96, 128), (16, 32), 32))\n",
        "model.add(inceptionblock(128, (128, 192), (32, 96), 64))\n",
        "model.add(nn.MaxPool2D(pool_size = 3, strides = 2, padding = 1))\n",
        "\n",
        "model.add(inceptionblock(192, (96, 208), (16, 48), 64))\n",
        "model.add(inceptionblock(160, (112, 224), (24, 64), 64))\n",
        "model.add(inceptionblock(128, (128, 256), (24, 64), 64))\n",
        "model.add(inceptionblock(112, (144, 288), (32, 64), 64))\n",
        "model.add(inceptionblock(256, (160, 320), (32, 128), 64))\n",
        "model.add(nn.MaxPool2D(pool_size = 3, strides = 2, padding = 1))\n",
        "\n",
        "model.add(inceptionblock(256, (160, 320), (32, 128), 128))\n",
        "model.add(inceptionblock(384, (192, 384), (48, 128), 128))\n",
        "model.add(nn.GlobalAvgPool2D())\n",
        "model.add(nn.Dense(10))\n",
        "\n",
        "model.initialize(mx.init.Xavier(), ctx)\n",
        "model"
      ]
    },
    {
      "cell_type": "markdown",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "## __Summarizing the model__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "colab_type": "code",
        "id": "fWwau-Uxywmi",
        "outputId": "62f00a54-16a7-483f-f0ba-756587d1885e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--------------------------------------------------------------------------------\n",
            "        Layer (type)                                Output Shape         Param #\n",
            "================================================================================\n",
            "               Input                              (1, 3, 32, 32)               0\n",
            "        Activation-1                     <Symbol conv6_relu_fwd>               0\n",
            "        Activation-2                             (1, 64, 16, 16)               0\n",
            "            Conv2D-3                             (1, 64, 16, 16)            9472\n",
            "         MaxPool2D-4                               (1, 64, 8, 8)               0\n",
            "        Activation-5                     <Symbol conv7_relu_fwd>               0\n",
            "        Activation-6                               (1, 64, 8, 8)               0\n",
            "            Conv2D-7                               (1, 64, 8, 8)            4160\n",
            "        Activation-8                     <Symbol conv8_relu_fwd>               0\n",
            "        Activation-9                              (1, 192, 8, 8)               0\n",
            "           Conv2D-10                              (1, 192, 8, 8)          110784\n",
            "        MaxPool2D-11                              (1, 192, 4, 4)               0\n",
            "       Activation-12                     <Symbol conv9_relu_fwd>               0\n",
            "       Activation-13                               (1, 64, 4, 4)               0\n",
            "           Conv2D-14                               (1, 64, 4, 4)           12352\n",
            "       Activation-15                    <Symbol conv10_relu_fwd>               0\n",
            "       Activation-16                               (1, 96, 4, 4)               0\n",
            "           Conv2D-17                               (1, 96, 4, 4)           18528\n",
            "       Activation-18                    <Symbol conv11_relu_fwd>               0\n",
            "       Activation-19                              (1, 128, 4, 4)               0\n",
            "           Conv2D-20                              (1, 128, 4, 4)          110720\n",
            "        BatchNorm-21                              (1, 128, 4, 4)             512\n",
            "       Activation-22                    <Symbol conv12_relu_fwd>               0\n",
            "       Activation-23                               (1, 16, 4, 4)               0\n",
            "           Conv2D-24                               (1, 16, 4, 4)            3088\n",
            "       Activation-25                    <Symbol conv13_relu_fwd>               0\n",
            "       Activation-26                               (1, 32, 4, 4)               0\n",
            "           Conv2D-27                               (1, 32, 4, 4)           12832\n",
            "        BatchNorm-28                               (1, 32, 4, 4)             128\n",
            "        MaxPool2D-29                              (1, 192, 4, 4)               0\n",
            "       Activation-30                    <Symbol conv14_relu_fwd>               0\n",
            "       Activation-31                               (1, 32, 4, 4)               0\n",
            "           Conv2D-32                               (1, 32, 4, 4)            6176\n",
            "        BatchNorm-33                               (1, 32, 4, 4)             128\n",
            "   inceptionblock-34                              (1, 256, 4, 4)               0\n",
            "       Activation-35                    <Symbol conv15_relu_fwd>               0\n",
            "       Activation-36                              (1, 128, 4, 4)               0\n",
            "           Conv2D-37                              (1, 128, 4, 4)           32896\n",
            "       Activation-38                    <Symbol conv16_relu_fwd>               0\n",
            "       Activation-39                              (1, 128, 4, 4)               0\n",
            "           Conv2D-40                              (1, 128, 4, 4)           32896\n",
            "       Activation-41                    <Symbol conv17_relu_fwd>               0\n",
            "       Activation-42                              (1, 192, 4, 4)               0\n",
            "           Conv2D-43                              (1, 192, 4, 4)          221376\n",
            "        BatchNorm-44                              (1, 192, 4, 4)             768\n",
            "       Activation-45                    <Symbol conv18_relu_fwd>               0\n",
            "       Activation-46                               (1, 32, 4, 4)               0\n",
            "           Conv2D-47                               (1, 32, 4, 4)            8224\n",
            "       Activation-48                    <Symbol conv19_relu_fwd>               0\n",
            "       Activation-49                               (1, 96, 4, 4)               0\n",
            "           Conv2D-50                               (1, 96, 4, 4)           76896\n",
            "        BatchNorm-51                               (1, 96, 4, 4)             384\n",
            "        MaxPool2D-52                              (1, 256, 4, 4)               0\n",
            "       Activation-53                    <Symbol conv20_relu_fwd>               0\n",
            "       Activation-54                               (1, 64, 4, 4)               0\n",
            "           Conv2D-55                               (1, 64, 4, 4)           16448\n",
            "        BatchNorm-56                               (1, 64, 4, 4)             256\n",
            "   inceptionblock-57                              (1, 480, 4, 4)               0\n",
            "        MaxPool2D-58                              (1, 480, 2, 2)               0\n",
            "       Activation-59                    <Symbol conv21_relu_fwd>               0\n",
            "       Activation-60                              (1, 192, 2, 2)               0\n",
            "           Conv2D-61                              (1, 192, 2, 2)           92352\n",
            "       Activation-62                    <Symbol conv22_relu_fwd>               0\n",
            "       Activation-63                               (1, 96, 2, 2)               0\n",
            "           Conv2D-64                               (1, 96, 2, 2)           46176\n",
            "       Activation-65                    <Symbol conv23_relu_fwd>               0\n",
            "       Activation-66                              (1, 208, 2, 2)               0\n",
            "           Conv2D-67                              (1, 208, 2, 2)          179920\n",
            "        BatchNorm-68                              (1, 208, 2, 2)             832\n",
            "       Activation-69                    <Symbol conv24_relu_fwd>               0\n",
            "       Activation-70                               (1, 16, 2, 2)               0\n",
            "           Conv2D-71                               (1, 16, 2, 2)            7696\n",
            "       Activation-72                    <Symbol conv25_relu_fwd>               0\n",
            "       Activation-73                               (1, 48, 2, 2)               0\n",
            "           Conv2D-74                               (1, 48, 2, 2)           19248\n",
            "        BatchNorm-75                               (1, 48, 2, 2)             192\n",
            "        MaxPool2D-76                              (1, 480, 2, 2)               0\n",
            "       Activation-77                    <Symbol conv26_relu_fwd>               0\n",
            "       Activation-78                               (1, 64, 2, 2)               0\n",
            "           Conv2D-79                               (1, 64, 2, 2)           30784\n",
            "        BatchNorm-80                               (1, 64, 2, 2)             256\n",
            "   inceptionblock-81                              (1, 512, 2, 2)               0\n",
            "       Activation-82                    <Symbol conv27_relu_fwd>               0\n",
            "       Activation-83                              (1, 160, 2, 2)               0\n",
            "           Conv2D-84                              (1, 160, 2, 2)           82080\n",
            "       Activation-85                    <Symbol conv28_relu_fwd>               0\n",
            "       Activation-86                              (1, 112, 2, 2)               0\n",
            "           Conv2D-87                              (1, 112, 2, 2)           57456\n",
            "       Activation-88                    <Symbol conv29_relu_fwd>               0\n",
            "       Activation-89                              (1, 224, 2, 2)               0\n",
            "           Conv2D-90                              (1, 224, 2, 2)          226016\n",
            "        BatchNorm-91                              (1, 224, 2, 2)             896\n",
            "       Activation-92                    <Symbol conv30_relu_fwd>               0\n",
            "       Activation-93                               (1, 24, 2, 2)               0\n",
            "           Conv2D-94                               (1, 24, 2, 2)           12312\n",
            "       Activation-95                    <Symbol conv31_relu_fwd>               0\n",
            "       Activation-96                               (1, 64, 2, 2)               0\n",
            "           Conv2D-97                               (1, 64, 2, 2)           38464\n",
            "        BatchNorm-98                               (1, 64, 2, 2)             256\n",
            "        MaxPool2D-99                              (1, 512, 2, 2)               0\n",
            "      Activation-100                    <Symbol conv32_relu_fwd>               0\n",
            "      Activation-101                               (1, 64, 2, 2)               0\n",
            "          Conv2D-102                               (1, 64, 2, 2)           32832\n",
            "       BatchNorm-103                               (1, 64, 2, 2)             256\n",
            "  inceptionblock-104                              (1, 512, 2, 2)               0\n",
            "      Activation-105                    <Symbol conv33_relu_fwd>               0\n",
            "      Activation-106                              (1, 128, 2, 2)               0\n",
            "          Conv2D-107                              (1, 128, 2, 2)           65664\n",
            "      Activation-108                    <Symbol conv34_relu_fwd>               0\n",
            "      Activation-109                              (1, 128, 2, 2)               0\n",
            "          Conv2D-110                              (1, 128, 2, 2)           65664\n",
            "      Activation-111                    <Symbol conv35_relu_fwd>               0\n",
            "      Activation-112                              (1, 256, 2, 2)               0\n",
            "          Conv2D-113                              (1, 256, 2, 2)          295168\n",
            "       BatchNorm-114                              (1, 256, 2, 2)            1024\n",
            "      Activation-115                    <Symbol conv36_relu_fwd>               0\n",
            "      Activation-116                               (1, 24, 2, 2)               0\n",
            "          Conv2D-117                               (1, 24, 2, 2)           12312\n",
            "      Activation-118                    <Symbol conv37_relu_fwd>               0\n",
            "      Activation-119                               (1, 64, 2, 2)               0\n",
            "          Conv2D-120                               (1, 64, 2, 2)           38464\n",
            "       BatchNorm-121                               (1, 64, 2, 2)             256\n",
            "       MaxPool2D-122                              (1, 512, 2, 2)               0\n",
            "      Activation-123                    <Symbol conv38_relu_fwd>               0\n",
            "      Activation-124                               (1, 64, 2, 2)               0\n",
            "          Conv2D-125                               (1, 64, 2, 2)           32832\n",
            "       BatchNorm-126                               (1, 64, 2, 2)             256\n",
            "  inceptionblock-127                              (1, 512, 2, 2)               0\n",
            "      Activation-128                    <Symbol conv39_relu_fwd>               0\n",
            "      Activation-129                              (1, 112, 2, 2)               0\n",
            "          Conv2D-130                              (1, 112, 2, 2)           57456\n",
            "      Activation-131                    <Symbol conv40_relu_fwd>               0\n",
            "      Activation-132                              (1, 144, 2, 2)               0\n",
            "          Conv2D-133                              (1, 144, 2, 2)           73872\n",
            "      Activation-134                    <Symbol conv41_relu_fwd>               0\n",
            "      Activation-135                              (1, 288, 2, 2)               0\n",
            "          Conv2D-136                              (1, 288, 2, 2)          373536\n",
            "       BatchNorm-137                              (1, 288, 2, 2)            1152\n",
            "      Activation-138                    <Symbol conv42_relu_fwd>               0\n",
            "      Activation-139                               (1, 32, 2, 2)               0\n",
            "          Conv2D-140                               (1, 32, 2, 2)           16416\n",
            "      Activation-141                    <Symbol conv43_relu_fwd>               0\n",
            "      Activation-142                               (1, 64, 2, 2)               0\n",
            "          Conv2D-143                               (1, 64, 2, 2)           51264\n",
            "       BatchNorm-144                               (1, 64, 2, 2)             256\n",
            "       MaxPool2D-145                              (1, 512, 2, 2)               0\n",
            "      Activation-146                    <Symbol conv44_relu_fwd>               0\n",
            "      Activation-147                               (1, 64, 2, 2)               0\n",
            "          Conv2D-148                               (1, 64, 2, 2)           32832\n",
            "       BatchNorm-149                               (1, 64, 2, 2)             256\n",
            "  inceptionblock-150                              (1, 528, 2, 2)               0\n",
            "      Activation-151                    <Symbol conv45_relu_fwd>               0\n",
            "      Activation-152                              (1, 256, 2, 2)               0\n",
            "          Conv2D-153                              (1, 256, 2, 2)          135424\n",
            "      Activation-154                    <Symbol conv46_relu_fwd>               0\n",
            "      Activation-155                              (1, 160, 2, 2)               0\n",
            "          Conv2D-156                              (1, 160, 2, 2)           84640\n",
            "      Activation-157                    <Symbol conv47_relu_fwd>               0\n",
            "      Activation-158                              (1, 320, 2, 2)               0\n",
            "          Conv2D-159                              (1, 320, 2, 2)          461120\n",
            "       BatchNorm-160                              (1, 320, 2, 2)            1280\n",
            "      Activation-161                    <Symbol conv48_relu_fwd>               0\n",
            "      Activation-162                               (1, 32, 2, 2)               0\n",
            "          Conv2D-163                               (1, 32, 2, 2)           16928\n",
            "      Activation-164                    <Symbol conv49_relu_fwd>               0\n",
            "      Activation-165                              (1, 128, 2, 2)               0\n",
            "          Conv2D-166                              (1, 128, 2, 2)          102528\n",
            "       BatchNorm-167                              (1, 128, 2, 2)             512\n",
            "       MaxPool2D-168                              (1, 528, 2, 2)               0\n",
            "      Activation-169                    <Symbol conv50_relu_fwd>               0\n",
            "      Activation-170                               (1, 64, 2, 2)               0\n",
            "          Conv2D-171                               (1, 64, 2, 2)           33856\n",
            "       BatchNorm-172                               (1, 64, 2, 2)             256\n",
            "  inceptionblock-173                              (1, 768, 2, 2)               0\n",
            "       MaxPool2D-174                              (1, 768, 1, 1)               0\n",
            "      Activation-175                    <Symbol conv51_relu_fwd>               0\n",
            "      Activation-176                              (1, 256, 1, 1)               0\n",
            "          Conv2D-177                              (1, 256, 1, 1)          196864\n",
            "      Activation-178                    <Symbol conv52_relu_fwd>               0\n",
            "      Activation-179                              (1, 160, 1, 1)               0\n",
            "          Conv2D-180                              (1, 160, 1, 1)          123040\n",
            "      Activation-181                    <Symbol conv53_relu_fwd>               0\n",
            "      Activation-182                              (1, 320, 1, 1)               0\n",
            "          Conv2D-183                              (1, 320, 1, 1)          461120\n",
            "       BatchNorm-184                              (1, 320, 1, 1)            1280\n",
            "      Activation-185                    <Symbol conv54_relu_fwd>               0\n",
            "      Activation-186                               (1, 32, 1, 1)               0\n",
            "          Conv2D-187                               (1, 32, 1, 1)           24608\n",
            "      Activation-188                    <Symbol conv55_relu_fwd>               0\n",
            "      Activation-189                              (1, 128, 1, 1)               0\n",
            "          Conv2D-190                              (1, 128, 1, 1)          102528\n",
            "       BatchNorm-191                              (1, 128, 1, 1)             512\n",
            "       MaxPool2D-192                              (1, 768, 1, 1)               0\n",
            "      Activation-193                    <Symbol conv56_relu_fwd>               0\n",
            "      Activation-194                              (1, 128, 1, 1)               0\n",
            "          Conv2D-195                              (1, 128, 1, 1)           98432\n",
            "       BatchNorm-196                              (1, 128, 1, 1)             512\n",
            "  inceptionblock-197                              (1, 832, 1, 1)               0\n",
            "      Activation-198                    <Symbol conv57_relu_fwd>               0\n",
            "      Activation-199                              (1, 384, 1, 1)               0\n",
            "          Conv2D-200                              (1, 384, 1, 1)          319872\n",
            "      Activation-201                    <Symbol conv58_relu_fwd>               0\n",
            "      Activation-202                              (1, 192, 1, 1)               0\n",
            "          Conv2D-203                              (1, 192, 1, 1)          159936\n",
            "      Activation-204                    <Symbol conv59_relu_fwd>               0\n",
            "      Activation-205                              (1, 384, 1, 1)               0\n",
            "          Conv2D-206                              (1, 384, 1, 1)          663936\n",
            "       BatchNorm-207                              (1, 384, 1, 1)            1536\n",
            "      Activation-208                    <Symbol conv60_relu_fwd>               0\n",
            "      Activation-209                               (1, 48, 1, 1)               0\n",
            "          Conv2D-210                               (1, 48, 1, 1)           39984\n",
            "      Activation-211                    <Symbol conv61_relu_fwd>               0\n",
            "      Activation-212                              (1, 128, 1, 1)               0\n",
            "          Conv2D-213                              (1, 128, 1, 1)          153728\n",
            "       BatchNorm-214                              (1, 128, 1, 1)             512\n",
            "       MaxPool2D-215                              (1, 832, 1, 1)               0\n",
            "      Activation-216                    <Symbol conv62_relu_fwd>               0\n",
            "      Activation-217                              (1, 128, 1, 1)               0\n",
            "          Conv2D-218                              (1, 128, 1, 1)          106624\n",
            "       BatchNorm-219                              (1, 128, 1, 1)             512\n",
            "  inceptionblock-220                             (1, 1024, 1, 1)               0\n",
            " GlobalAvgPool2D-221                             (1, 1024, 1, 1)               0\n",
            "           Dense-222                                     (1, 10)           10250\n",
            "================================================================================\n",
            "Parameters in forward computation graph, duplicate included\n",
            "   Total params: 5928058\n",
            "   Trainable params: 5920570\n",
            "   Non-trainable params: 7488\n",
            "Shared params in forward computation graph: 0\n",
            "Unique parameters in model: 5928058\n",
            "--------------------------------------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "model.summary(image.expand_dims(0).as_in_context(ctx))"
      ]
    },
    {
      "cell_type": "markdown",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "## __Visualizing model architecture__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "colab_type": "code",
        "id": "_dssSLsM0thA",
        "outputId": "dc254483-23c0-48a6-90aa-d4371585f66f"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<graphviz.dot.Digraph at 0x7f3bc9d8c6d8>"
            ]
          },
          "execution_count": 10,
          "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": "markdown",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "By hybridizing the model becomes almost 1.5x fast, as it converts dynamically defined model in to static graph. Its a really cool feature that MXNet provides"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "model.hybridize()"
      ]
    },
    {
      "cell_type": "markdown",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "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",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "## __Training the model__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "colab_type": "code",
        "id": "_--4aXog18Yo",
        "outputId": "038ccd28-48ec-414f-e6f2-0afd026ee8e3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 1 | Loss: 1.83370 | Training Accuracy: 0.33006\n",
            "Epoch: 2 | Loss: 1.39600 | Training Accuracy: 0.49104\n",
            "Epoch: 3 | Loss: 1.21781 | Training Accuracy: 0.56460\n",
            "Epoch: 4 | Loss: 1.08757 | Training Accuracy: 0.61250\n",
            "Epoch: 5 | Loss: 1.01173 | Training Accuracy: 0.64276\n",
            "Epoch: 6 | Loss: 0.92592 | Training Accuracy: 0.67560\n",
            "Epoch: 7 | Loss: 0.85912 | Training Accuracy: 0.69682\n",
            "Epoch: 8 | Loss: 0.80191 | Training Accuracy: 0.72408\n",
            "Epoch: 9 | Loss: 0.74436 | Training Accuracy: 0.74444\n",
            "Epoch: 10 | Loss: 0.70236 | Training Accuracy: 0.75838\n",
            "Epoch: 11 | Loss: 0.65505 | Training Accuracy: 0.77270\n",
            "Epoch: 12 | Loss: 0.60975 | Training Accuracy: 0.79024\n",
            "Epoch: 13 | Loss: 0.57003 | Training Accuracy: 0.80252\n",
            "Epoch: 14 | Loss: 0.51526 | Training Accuracy: 0.82128\n",
            "Epoch: 15 | Loss: 0.47354 | Training Accuracy: 0.83484\n",
            "Epoch: 16 | Loss: 0.43601 | Training Accuracy: 0.84842\n",
            "Epoch: 17 | Loss: 0.39993 | Training Accuracy: 0.85914\n",
            "Epoch: 18 | Loss: 0.35675 | Training Accuracy: 0.87560\n",
            "Epoch: 19 | Loss: 0.32684 | Training Accuracy: 0.88626\n",
            "Epoch: 20 | Loss: 0.28719 | Training Accuracy: 0.89996\n"
          ]
        }
      ],
      "source": [
        "epochs = 20\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",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "## __Testing the model__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "colab_type": "code",
        "id": "mC2FT6we4vJ3",
        "outputId": "6305a2d9-e1e2-471e-e0a1-733413010166"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('accuracy', 0.89852)"
            ]
          },
          "execution_count": 13,
          "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()"
      ]
    }
  ]
}