Lesson_4/Part_8_Transfer_Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "10ijYmN-BGie"
   },
   "source": [
    "# Transfer Learning\n",
    "\n",
    "In this notebook, you'll learn how to use pre-trained networks to solved challenging problems in computer vision. Specifically, you'll use networks trained on [ImageNet](http://www.image-net.org/) [available from torchvision](http://pytorch.org/docs/0.3.0/torchvision/models.html). \n",
    "\n",
    "ImageNet is a massive dataset with over 1 million labeled images in 1000 categories. It's used to train deep neural networks using an architecture called convolutional layers. I'm not going to get into the details of convolutional networks here, but if you want to learn more about them, please [watch this](https://www.youtube.com/watch?v=2-Ol7ZB0MmU).\n",
    "\n",
    "Once trained, these models work astonishingly well as feature detectors for images they weren't trained on. Using a pre-trained network on images not in the training set is called transfer learning. Here we'll use transfer learning to train a network that can classify our cat and dog photos with near perfect accuracy.\n",
    "\n",
    "With `torchvision.models` you can download these pre-trained networks and use them in your applications. We'll include `models` in our imports now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1261
    },
    "colab_type": "code",
    "id": "ROgkMgk6D5rU",
    "outputId": "8b5f6112-36d8-460a-cc98-72b7ad4e04f1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Collecting Pillow==4.0.0\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/37/e8/b3fbf87b0188d22246678f8cd61e23e31caa1769ebc06f1664e2e5fe8a17/Pillow-4.0.0-cp36-cp36m-manylinux1_x86_64.whl (5.6MB)\n",
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      "Installing collected packages: Pillow\n",
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      "Collecting image\n",
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      "Collecting django (from image)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d1/e5/2676be45ea49cfd09a663f289376b3888accd57ff06c953297bfdee1fb08/Django-2.1.3-py3-none-any.whl (7.3MB)\n",
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      "Installing collected packages: django, image\n",
      "Successfully installed django-2.1.3 image-1.5.27\n"
     ]
    }
   ],
   "source": [
    "#read/write image data\n",
    "!pip install imageio\n",
    "#deep learning library\n",
    "\n",
    "#access kaggle datasets from colab\n",
    "!pip install kaggle\n",
    "#model loading\n",
    "!pip install ipywidgets\n",
    "\n",
    "# Google colab version for Pillow\n",
    "!pip install Pillow==4.0.0\n",
    "!pip install PIL\n",
    "!pip install image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "Pz_zGD6FF6YT",
    "outputId": "0460020b-e337-4e5a-96b0-273940e23229"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'b3fd927620d96ede7e41064b2ff865f2'...\n",
      "remote: Enumerating objects: 3, done.\u001b[K\n",
      "remote: Counting objects:  33% (1/3)   \u001b[K\r",
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      "remote: Counting objects: 100% (3/3)   \u001b[K\r",
      "remote: Counting objects: 100% (3/3), done.\u001b[K\n",
      "remote: Compressing objects:  33% (1/3)   \u001b[K\r",
      "remote: Compressing objects:  66% (2/3)   \u001b[K\r",
      "remote: Compressing objects: 100% (3/3)   \u001b[K\r",
      "remote: Compressing objects: 100% (3/3), done.\u001b[K\n",
      "remote: Total 3 (delta 0), reused 0 (delta 0), pack-reused 0\u001b[K\n",
      "Unpacking objects:  33% (1/3)   \r",
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      "Unpacking objects: 100% (3/3)   \r",
      "Unpacking objects: 100% (3/3), done.\n"
     ]
    }
   ],
   "source": [
    "# for CUDA and pytorch\n",
    "!git clone https://gist.github.com/b3fd927620d96ede7e41064b2ff865f2.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "WDWJ4CHbGZJ2",
    "outputId": "48cc5ce4-8d65-49c5-e89d-5b89ea8e4d6c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/b3fd927620d96ede7e41064b2ff865f2\n"
     ]
    }
   ],
   "source": [
    "# Move to te directory where the file was donwloaded\n",
    "cd b3fd927620d96ede7e41064b2ff865f2/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TpqlAm4cGgYB"
   },
   "outputs": [],
   "source": [
    "# Execute the shell script. \n",
    "# NOTE: This takes sometime and it breaks the connection. Better use the steps after and execute them one by one.\n",
    "\n",
    "#!bash pytorch041_cuda92_colab.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 309
    },
    "colab_type": "code",
    "id": "qb6OeZALGk4m",
    "outputId": "ff733e91-8c61-4dee-8959-f26526b397df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2018-11-18 13:55:20--  https://developer.nvidia.com/compute/cuda/9.2/Prod2/local_installers/cuda-repo-ubuntu1604-9-2-local_9.2.148-1_amd64\n",
      "Resolving developer.nvidia.com (developer.nvidia.com)... 192.229.162.216\n",
      "Connecting to developer.nvidia.com (developer.nvidia.com)|192.229.162.216|:443... connected.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://developer.download.nvidia.com/compute/cuda/9.2/secure/Prod2/local_installers/cuda-repo-ubuntu1604-9-2-local_9.2.148-1_amd64.deb?5rFovfnJUOw7A4dZCdxt7QSMf4WqYoOkjM7S2PNKW6J4g6b9ngFqlLRcfrVcYv18Ya9A-wY2EyMxiq0OldMLxSMW6nalTMJthqEukykHsFoDu8wvm7p1Pm7z4om8L4RgLjahHtVQh7SXhNXGTrBGsebPh4zGKBhrag6X8g7L5zR6LCLyRLPYKY4AB-OHmD3-53kuDgfG_7VdzpzvCj6LTQ [following]\n",
      "--2018-11-18 13:55:20--  https://developer.download.nvidia.com/compute/cuda/9.2/secure/Prod2/local_installers/cuda-repo-ubuntu1604-9-2-local_9.2.148-1_amd64.deb?5rFovfnJUOw7A4dZCdxt7QSMf4WqYoOkjM7S2PNKW6J4g6b9ngFqlLRcfrVcYv18Ya9A-wY2EyMxiq0OldMLxSMW6nalTMJthqEukykHsFoDu8wvm7p1Pm7z4om8L4RgLjahHtVQh7SXhNXGTrBGsebPh4zGKBhrag6X8g7L5zR6LCLyRLPYKY4AB-OHmD3-53kuDgfG_7VdzpzvCj6LTQ\n",
      "Resolving developer.download.nvidia.com (developer.download.nvidia.com)... 192.229.211.70, 2606:2800:21f:3aa:dcf:37b:1ed6:1fb\n",
      "Connecting to developer.download.nvidia.com (developer.download.nvidia.com)|192.229.211.70|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1267151038 (1.2G) [application/x-deb]\n",
      "Saving to: ‘cuda-repo-ubuntu1604-9-2-local_9.2.148-1_amd64’\n",
      "\n",
      "cuda-repo-ubuntu160 100%[===================>]   1.18G   129MB/s    in 8.1s    \n",
      "\n",
      "2018-11-18 13:55:28 (150 MB/s) - ‘cuda-repo-ubuntu1604-9-2-local_9.2.148-1_amd64’ saved [1267151038/1267151038]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://developer.nvidia.com/compute/cuda/9.2/Prod2/local_installers/cuda-repo-ubuntu1604-9-2-local_9.2.148-1_amd64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "sBQwsfAyGqjB",
    "outputId": "1d261708-456c-4d5f-baab-f2b83e959954"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selecting previously unselected package cuda-repo-ubuntu1604-9-2-local.\n",
      "(Reading database ... \r",
      "(Reading database ... 5%\r",
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      "(Reading database ... 95%\r",
      "(Reading database ... 100%\r",
      "(Reading database ... 22280 files and directories currently installed.)\n",
      "Preparing to unpack cuda-repo-ubuntu1604-9-2-local_9.2.148-1_amd64 ...\n",
      "Unpacking cuda-repo-ubuntu1604-9-2-local (9.2.148-1) ...\n",
      "Setting up cuda-repo-ubuntu1604-9-2-local (9.2.148-1) ...\n"
     ]
    }
   ],
   "source": [
    "!dpkg --install cuda-repo-ubuntu1604-9-2-local_9.2.148-1_amd64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "krBfX_gOGvHm",
    "outputId": "68a777d1-b470-4a6d-eaae-b8d05b0ba3d8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK\n"
     ]
    }
   ],
   "source": [
    "!apt-key add /var/cuda-repo-9-2-local/7fa2af80.pub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "colab_type": "code",
    "id": "ay0l8oRmG3oy",
    "outputId": "7d780b33-3013-44f0-a190-15793191ff91"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "0% [Working]\r",
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      "            \r",
      "Ign:1 file:/var/cuda-repo-9-2-local  InRelease\n",
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      "0% [Connecting to archive.ubuntu.com] [Connecting to security.ubuntu.com (91.18\r",
      "                                                                               \r",
      "Get:3 file:/var/cuda-repo-9-2-local  Release.gpg [819 B]\n",
      "\r",
      "0% [3 Release.gpg 0 B/819 B 0%] [Connecting to archive.ubuntu.com] [Connecting \r",
      "0% [Connecting to archive.ubuntu.com] [Connecting to security.ubuntu.com (91.18\r",
      "0% [3 Release.gpg gpgv 574 B] [Connecting to archive.ubuntu.com] [Connecting to\r",
      "                                                                               \r",
      "Ign:4 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1710/x86_64  InRelease\n",
      "\r",
      "0% [3 Release.gpg gpgv 574 B] [Connecting to archive.ubuntu.com (91.189.88.149)\r",
      "                                                                               \r",
      "Hit:5 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic InRelease\n",
      "\r",
      "0% [3 Release.gpg gpgv 574 B] [Waiting for headers] [Waiting for headers] [Wait\r",
      "                                                                               \r",
      "Get:6 http://security.ubuntu.com/ubuntu bionic-security InRelease [83.2 kB]\n",
      "\r",
      "                                                                               \r",
      "Ign:7 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64  InRelease\n",
      "\r",
      "0% [3 Release.gpg gpgv 574 B] [Waiting for headers] [6 InRelease 2,586 B/83.2 k\r",
      "0% [Waiting for headers] [6 InRelease 5,482 B/83.2 kB 7%] [Waiting for headers]\r",
      "                                                                               \r",
      "Hit:8 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1710/x86_64  Release\n",
      "\r",
      "                                                                               \r",
      "0% [Waiting for headers] [6 InRelease 5,482 B/83.2 kB 7%]\r",
      "                                                         \r",
      "Get:9 file:/var/cuda-repo-9-2-local  Packages [18.7 kB]\n",
      "\r",
      "                                                         \r",
      "Hit:10 https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64  Release\n",
      "\r",
      "0% [5 InRelease gpgv 21.3 kB] [Waiting for headers] [6 InRelease 11.3 kB/83.2 k\r",
      "0% [9 Packages store 0 B] [5 InRelease gpgv 21.3 kB] [Waiting for headers] [6 I\r",
      "                                                                               \r",
      "Hit:11 http://archive.ubuntu.com/ubuntu bionic InRelease\n",
      "\r",
      "0% [9 Packages store 0 B] [5 InRelease gpgv 21.3 kB] [6 InRelease 11.3 kB/83.2 \r",
      "0% [5 InRelease gpgv 21.3 kB] [Waiting for headers] [6 InRelease 11.3 kB/83.2 k\r",
      "                                                                               \r",
      "Get:12 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]\n",
      "Get:14 http://archive.ubuntu.com/ubuntu bionic-backports InRelease [74.6 kB]\n",
      "Get:16 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 Packages [567 kB]\n",
      "Get:17 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 Packages [730 kB]\n",
      "Fetched 1,543 kB in 2s (754 kB/s)\n",
      "Reading package lists... Done\n"
     ]
    }
   ],
   "source": [
    "\n",
    "!apt-get update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 18380
    },
    "colab_type": "code",
    "id": "kUIUf9oSG7XP",
    "outputId": "b734268b-d561-4062-881f-1b9e22765f92"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading package lists... Done\n",
      "Building dependency tree       \n",
      "Reading state information... Done\n",
      "The following additional packages will be installed:\n",
      "  acpid adwaita-icon-theme at-spi2-core ca-certificates-java cuda-9-2\n",
      "  cuda-compiler-9-2 cuda-cublas-dev-9-2 cuda-cufft-dev-9-2 cuda-curand-dev-9-2\n",
      "  cuda-cusolver-dev-9-2 cuda-cusparse-dev-9-2 cuda-demo-suite-9-2\n",
      "  cuda-documentation-9-2 cuda-drivers cuda-libraries-dev-9-2 cuda-npp-dev-9-2\n",
      "  cuda-nsight-9-2 cuda-nvgraph-dev-9-2 cuda-nvml-dev-9-2 cuda-nvprune-9-2\n",
      "  cuda-nvrtc-dev-9-2 cuda-nvvp-9-2 cuda-runtime-9-2 cuda-samples-9-2\n",
      "  cuda-toolkit-9-2 cuda-tools-9-2 cuda-visual-tools-9-2 dbus\n",
      "  dconf-gsettings-backend dconf-service default-jre default-jre-headless dkms\n",
      "  dmsetup fakeroot fontconfig fonts-dejavu-core fonts-dejavu-extra freeglut3\n",
      "  freeglut3-dev glib-networking glib-networking-common\n",
      "  glib-networking-services gsettings-desktop-schemas gtk-update-icon-cache\n",
      "  hicolor-icon-theme humanity-icon-theme java-common keyboard-configuration\n",
      "  kmod lib32gcc1 libapparmor1 libargon2-0 libasound2 libasound2-data\n",
      "  libatk-bridge2.0-0 libatk-wrapper-java libatk-wrapper-java-jni libatk1.0-0\n",
      "  libatk1.0-data libatspi2.0-0 libavahi-client3 libavahi-common-data\n",
      "  libavahi-common3 libc6-i386 libcairo-gobject2 libcairo2 libcap2 libcolord2\n",
      "  libcroco3 libcryptsetup12 libcuda1-396 libcups2 libdatrie1 libdconf1\n",
      "  libdevmapper1.02.1 libdrm-amdgpu1 libdrm-dev libdrm-intel1 libdrm-nouveau2\n",
      "  libdrm-radeon1 libepoxy0 libfakeroot libfontenc1 libgdk-pixbuf2.0-0\n",
      "  libgdk-pixbuf2.0-bin libgdk-pixbuf2.0-common libgif7 libgl1 libgl1-mesa-dev\n",
      "  libgl1-mesa-dri libgl1-mesa-glx libgles1 libglu1-mesa libglu1-mesa-dev\n",
      "  libglvnd-core-dev libglvnd-dev libglx-mesa0 libglx0 libgtk-3-0 libgtk-3-bin\n",
      "  libgtk-3-common libice-dev libidn11 libip4tc0 libjansson4 libjbig0\n",
      "  libjson-c3 libjson-glib-1.0-0 libjson-glib-1.0-common libkmod2 liblcms2-2\n",
      "  libllvm6.0 liblocale-gettext-perl libnspr4 libnss-systemd libnss3\n",
      "  libpam-systemd libpango-1.0-0 libpangocairo-1.0-0 libpangoft2-1.0-0\n",
      "  libpciaccess0 libpcsclite1 libpixman-1-0 libpolkit-agent-1-0\n",
      "  libpolkit-backend-1-0 libpolkit-gobject-1-0 libproxy1v5 librest-0.7-0\n",
      "  librsvg2-2 librsvg2-common libsensors4 libsm-dev libsoup-gnome2.4-1\n",
      "  libsoup2.4-1 libsystemd0 libthai-data libthai0 libtiff5 libudev1 libvdpau1\n",
      "  libwayland-cursor0 libwayland-egl1-mesa libx11-xcb-dev libxaw7\n",
      "  libxcb-dri2-0-dev libxcb-dri3-dev libxcb-glx0 libxcb-glx0-dev\n",
      "  libxcb-present-dev libxcb-randr0 libxcb-randr0-dev libxcb-render0\n",
      "  libxcb-render0-dev libxcb-shape0 libxcb-shape0-dev libxcb-shm0\n",
      "  libxcb-sync-dev libxcb-xfixes0-dev libxcomposite1 libxcursor1 libxdamage-dev\n",
      "  libxdamage1 libxext-dev libxfixes-dev libxfixes3 libxfont2 libxi-dev libxi6\n",
      "  libxinerama1 libxkbcommon0 libxkbfile1 libxmu-dev libxmu-headers libxmu6\n",
      "  libxmuu1 libxnvctrl0 libxpm4 libxrandr2 libxshmfence-dev libxt-dev libxt6\n",
      "  libxtst6 libxv1 libxxf86dga1 libxxf86vm-dev libxxf86vm1\n",
      "  linux-headers-4.15.0-39 linux-headers-4.15.0-39-generic\n",
      "  linux-headers-generic mesa-common-dev mesa-vdpau-drivers networkd-dispatcher\n",
      "  nvidia-396 nvidia-396-dev nvidia-modprobe nvidia-opencl-icd-396 nvidia-prime\n",
      "  nvidia-settings ocl-icd-libopencl1 openjdk-11-jre openjdk-11-jre-headless\n",
      "  policykit-1 policykit-1-gnome python3-xkit screen-resolution-extra\n",
      "  shared-mime-info sudo systemd systemd-sysv ubuntu-mono udev vdpau-driver-all\n",
      "  x11-utils x11-xkb-utils x11proto-damage-dev x11proto-dri2-dev\n",
      "  x11proto-fixes-dev x11proto-gl-dev x11proto-input-dev x11proto-xext-dev\n",
      "  x11proto-xf86vidmode-dev xauth xfonts-base xfonts-encodings xfonts-utils\n",
      "  xkb-data xserver-common xserver-xorg-core xserver-xorg-legacy\n",
      "Suggested packages:\n",
      "  default-dbus-session-bus | dbus-session-bus default-java-plugin\n",
      "  python3-apport menu libasound2-plugins alsa-utils colord cups-common gvfs\n",
      "  libice-doc liblcms2-utils pciutils pcscd librsvg2-bin lm-sensors libsm-doc\n",
      "  libxext-doc libxt-doc iw | wireless-tools libnss-mdns fonts-ipafont-gothic\n",
      "  fonts-ipafont-mincho fonts-wqy-microhei | fonts-wqy-zenhei fonts-indic\n",
      "  systemd-container libvdpau-va-gl1 nvidia-vdpau-driver\n",
      "  nvidia-legacy-340xx-vdpau-driver mesa-utils xfs | xserver xfonts-100dpi\n",
      "  | xfonts-75dpi xfonts-scalable\n",
      "The following packages will be REMOVED:\n",
      "  libnvidia-compute-396\n",
      "The following NEW packages will be installed:\n",
      "  acpid adwaita-icon-theme at-spi2-core ca-certificates-java cuda cuda-9-2\n",
      "  cuda-compiler-9-2 cuda-cublas-dev-9-2 cuda-cufft-dev-9-2 cuda-curand-dev-9-2\n",
      "  cuda-cusolver-dev-9-2 cuda-cusparse-dev-9-2 cuda-demo-suite-9-2\n",
      "  cuda-documentation-9-2 cuda-drivers cuda-libraries-dev-9-2 cuda-npp-dev-9-2\n",
      "  cuda-nsight-9-2 cuda-nvgraph-dev-9-2 cuda-nvml-dev-9-2 cuda-nvprune-9-2\n",
      "  cuda-nvrtc-dev-9-2 cuda-nvvp-9-2 cuda-runtime-9-2 cuda-samples-9-2\n",
      "  cuda-toolkit-9-2 cuda-tools-9-2 cuda-visual-tools-9-2 dbus\n",
      "  dconf-gsettings-backend dconf-service default-jre default-jre-headless dkms\n",
      "  dmsetup fakeroot fontconfig fonts-dejavu-core fonts-dejavu-extra freeglut3\n",
      "  freeglut3-dev glib-networking glib-networking-common\n",
      "  glib-networking-services gsettings-desktop-schemas gtk-update-icon-cache\n",
      "  hicolor-icon-theme humanity-icon-theme java-common keyboard-configuration\n",
      "  kmod lib32gcc1 libapparmor1 libargon2-0 libasound2 libasound2-data\n",
      "  libatk-bridge2.0-0 libatk-wrapper-java libatk-wrapper-java-jni libatk1.0-0\n",
      "  libatk1.0-data libatspi2.0-0 libavahi-client3 libavahi-common-data\n",
      "  libavahi-common3 libc6-i386 libcairo-gobject2 libcairo2 libcap2 libcolord2\n",
      "  libcroco3 libcryptsetup12 libcuda1-396 libcups2 libdatrie1 libdconf1\n",
      "  libdevmapper1.02.1 libdrm-amdgpu1 libdrm-dev libdrm-intel1 libdrm-nouveau2\n",
      "  libdrm-radeon1 libepoxy0 libfakeroot libfontenc1 libgdk-pixbuf2.0-0\n",
      "  libgdk-pixbuf2.0-bin libgdk-pixbuf2.0-common libgif7 libgl1 libgl1-mesa-dev\n",
      "  libgl1-mesa-dri libgl1-mesa-glx libgles1 libglu1-mesa libglu1-mesa-dev\n",
      "  libglvnd-core-dev libglvnd-dev libglx-mesa0 libglx0 libgtk-3-0 libgtk-3-bin\n",
      "  libgtk-3-common libice-dev libidn11 libip4tc0 libjansson4 libjbig0\n",
      "  libjson-c3 libjson-glib-1.0-0 libjson-glib-1.0-common libkmod2 liblcms2-2\n",
      "  libllvm6.0 liblocale-gettext-perl libnspr4 libnss-systemd libnss3\n",
      "  libpam-systemd libpango-1.0-0 libpangocairo-1.0-0 libpangoft2-1.0-0\n",
      "  libpciaccess0 libpcsclite1 libpixman-1-0 libpolkit-agent-1-0\n",
      "  libpolkit-backend-1-0 libpolkit-gobject-1-0 libproxy1v5 librest-0.7-0\n",
      "  librsvg2-2 librsvg2-common libsensors4 libsm-dev libsoup-gnome2.4-1\n",
      "  libsoup2.4-1 libthai-data libthai0 libtiff5 libvdpau1 libwayland-cursor0\n",
      "  libwayland-egl1-mesa libx11-xcb-dev libxaw7 libxcb-dri2-0-dev\n",
      "  libxcb-dri3-dev libxcb-glx0 libxcb-glx0-dev libxcb-present-dev libxcb-randr0\n",
      "  libxcb-randr0-dev libxcb-render0 libxcb-render0-dev libxcb-shape0\n",
      "  libxcb-shape0-dev libxcb-shm0 libxcb-sync-dev libxcb-xfixes0-dev\n",
      "  libxcomposite1 libxcursor1 libxdamage-dev libxdamage1 libxext-dev\n",
      "  libxfixes-dev libxfixes3 libxfont2 libxi-dev libxi6 libxinerama1\n",
      "  libxkbcommon0 libxkbfile1 libxmu-dev libxmu-headers libxmu6 libxmuu1\n",
      "  libxnvctrl0 libxpm4 libxrandr2 libxshmfence-dev libxt-dev libxt6 libxtst6\n",
      "  libxv1 libxxf86dga1 libxxf86vm-dev libxxf86vm1 linux-headers-4.15.0-39\n",
      "  linux-headers-4.15.0-39-generic linux-headers-generic mesa-common-dev\n",
      "  mesa-vdpau-drivers networkd-dispatcher nvidia-396 nvidia-396-dev\n",
      "  nvidia-modprobe nvidia-opencl-icd-396 nvidia-prime nvidia-settings\n",
      "  ocl-icd-libopencl1 openjdk-11-jre openjdk-11-jre-headless policykit-1\n",
      "  policykit-1-gnome python3-xkit screen-resolution-extra shared-mime-info sudo\n",
      "  systemd systemd-sysv ubuntu-mono udev vdpau-driver-all x11-utils\n",
      "  x11-xkb-utils x11proto-damage-dev x11proto-dri2-dev x11proto-fixes-dev\n",
      "  x11proto-gl-dev x11proto-input-dev x11proto-xext-dev\n",
      "  x11proto-xf86vidmode-dev xauth xfonts-base xfonts-encodings xfonts-utils\n",
      "  xkb-data xserver-common xserver-xorg-core xserver-xorg-legacy\n",
      "The following packages will be upgraded:\n",
      "  libsystemd0 libudev1\n",
      "2 upgraded, 229 newly installed, 1 to remove and 3 not upgraded.\n",
      "Need to get 250 MB/1,059 MB of archives.\n",
      "After this operation, 2,830 MB of additional disk space will be used.\n",
      "Get:1 file:/var/cuda-repo-9-2-local  cuda-nvprune-9-2 9.2.148-1 [36.8 kB]\n",
      "Get:2 file:/var/cuda-repo-9-2-local  cuda-compiler-9-2 9.2.148-1 [2,520 B]\n",
      "Get:3 file:/var/cuda-repo-9-2-local  cuda-nsight-9-2 9.2.148-1 [2,582 B]\n",
      "Get:4 file:/var/cuda-repo-9-2-local  cuda-nvvp-9-2 9.2.148-1 [2,528 B]\n",
      "Get:5 file:/var/cuda-repo-9-2-local  cuda-nvrtc-dev-9-2 9.2.148-1 [9,348 B]\n",
      "Get:6 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1710/x86_64  cuda-cublas-dev-9-2 9.2.148.1-1 [50.4 MB]\n",
      "Get:7 file:/var/cuda-repo-9-2-local  cuda-cusolver-dev-9-2 9.2.148-1 [8,184 kB]\n",
      "Get:8 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libsystemd0 amd64 237-3ubuntu10.6 [204 kB]\n",
      "Get:9 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic/main amd64 libxnvctrl0 amd64 415.13-0ubuntu0~gpu18.04.1 [18.1 kB]\n",
      "Get:10 http://ppa.launchpad.net/graphics-drivers/ppa/ubuntu bionic/main amd64 nvidia-settings amd64 415.13-0ubuntu0~gpu18.04.1 [964 kB]\n",
      "Get:11 http://archive.ubuntu.com/ubuntu bionic/main amd64 liblocale-gettext-perl amd64 1.07-3build2 [16.6 kB]\n",
      "Get:12 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 keyboard-configuration all 1.178ubuntu2.7 [372 kB]\n",
      "Get:13 http://archive.ubuntu.com/ubuntu bionic/main amd64 libcap2 amd64 1:2.25-1.2 [13.0 kB]\n",
      "Get:14 http://archive.ubuntu.com/ubuntu bionic/main amd64 libargon2-0 amd64 0~20161029-1.1 [19.9 kB]\n",
      "Get:15 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libudev1 amd64 237-3ubuntu10.6 [53.7 kB]\n",
      "Get:16 http://archive.ubuntu.com/ubuntu bionic/main amd64 libdevmapper1.02.1 amd64 2:1.02.145-4.1ubuntu3 [127 kB]\n",
      "Get:17 http://archive.ubuntu.com/ubuntu bionic/main amd64 libjson-c3 amd64 0.12.1-1.3 [21.4 kB]\n",
      "Get:18 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libcryptsetup12 amd64 2:2.0.2-1ubuntu1.1 [134 kB]\n",
      "Get:19 http://archive.ubuntu.com/ubuntu bionic/main amd64 libidn11 amd64 1.33-2.1ubuntu1 [45.7 kB]\n",
      "Get:20 http://archive.ubuntu.com/ubuntu bionic/main amd64 libip4tc0 amd64 1.6.1-2ubuntu2 [19.6 kB]\n",
      "Get:21 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libapparmor1 amd64 2.12-4ubuntu5.1 [31.3 kB]\n",
      "Get:22 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libkmod2 amd64 24-1ubuntu3.1 [40.0 kB]\n",
      "Get:23 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 systemd amd64 237-3ubuntu10.6 [2,894 kB]\n",
      "Get:24 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 systemd-sysv amd64 237-3ubuntu10.6 [11.6 kB]\n",
      "Get:25 http://archive.ubuntu.com/ubuntu bionic/main amd64 fontconfig amd64 2.12.6-0ubuntu2 [169 kB]\n",
      "Get:26 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxinerama1 amd64 2:1.1.3-1 [7,908 B]\n",
      "Get:27 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxxf86dga1 amd64 2:1.1.4-1 [13.7 kB]\n",
      "Get:28 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxxf86vm1 amd64 1:1.1.4-1 [10.6 kB]\n",
      "Get:29 http://archive.ubuntu.com/ubuntu bionic/main amd64 dbus amd64 1.12.2-1ubuntu1 [150 kB]\n",
      "Get:30 http://archive.ubuntu.com/ubuntu bionic/main amd64 dmsetup amd64 2:1.02.145-4.1ubuntu3 [74.5 kB]\n",
      "Get:31 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 kmod amd64 24-1ubuntu3.1 [88.7 kB]\n",
      "Get:32 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libnss-systemd amd64 237-3ubuntu10.6 [105 kB]\n",
      "Get:33 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libpam-systemd amd64 237-3ubuntu10.6 [108 kB]\n",
      "Get:34 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 networkd-dispatcher all 1.7-0ubuntu3.3 [13.3 kB]\n",
      "Get:35 http://archive.ubuntu.com/ubuntu bionic/main amd64 shared-mime-info amd64 1.9-2 [426 kB]\n",
      "Get:36 http://archive.ubuntu.com/ubuntu bionic/main amd64 sudo amd64 1.8.21p2-3ubuntu1 [428 kB]\n",
      "Get:37 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 udev amd64 237-3ubuntu10.6 [1,101 kB]\n",
      "Get:38 http://archive.ubuntu.com/ubuntu bionic/main amd64 xkb-data all 2.23.1-1ubuntu1 [325 kB]\n",
      "Get:39 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libpolkit-gobject-1-0 amd64 0.105-20ubuntu0.18.04.1 [36.3 kB]\n",
      "Get:40 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxmuu1 amd64 2:1.1.2-2 [9,674 B]\n",
      "Get:41 http://archive.ubuntu.com/ubuntu bionic/main amd64 xauth amd64 1:1.0.10-1 [24.6 kB]\n",
      "Get:42 http://archive.ubuntu.com/ubuntu bionic/main amd64 acpid amd64 1:2.0.28-1ubuntu1 [36.5 kB]\n",
      "Get:43 http://archive.ubuntu.com/ubuntu bionic/main amd64 hicolor-icon-theme all 0.17-2 [9,976 B]\n",
      "Get:44 http://archive.ubuntu.com/ubuntu bionic/main amd64 libjbig0 amd64 2.1-3.1build1 [26.7 kB]\n",
      "Get:45 http://archive.ubuntu.com/ubuntu bionic/main amd64 libtiff5 amd64 4.0.9-5 [152 kB]\n",
      "Get:46 http://archive.ubuntu.com/ubuntu bionic/main amd64 libgdk-pixbuf2.0-common all 2.36.11-2 [4,536 B]\n",
      "Get:47 http://archive.ubuntu.com/ubuntu bionic/main amd64 libgdk-pixbuf2.0-0 amd64 2.36.11-2 [165 kB]\n",
      "Get:48 http://archive.ubuntu.com/ubuntu bionic/main amd64 gtk-update-icon-cache amd64 3.22.30-1ubuntu1 [28.0 kB]\n",
      "Get:49 http://archive.ubuntu.com/ubuntu bionic/main amd64 libpixman-1-0 amd64 0.34.0-2 [229 kB]\n",
      "Get:50 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxcb-render0 amd64 1.13-1 [14.7 kB]\n",
      "Get:51 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxcb-shm0 amd64 1.13-1 [5,572 B]\n",
      "Get:52 http://archive.ubuntu.com/ubuntu bionic/main amd64 libcairo2 amd64 1.15.10-2 [580 kB]\n",
      "Get:53 http://archive.ubuntu.com/ubuntu bionic/main amd64 libcroco3 amd64 0.6.12-2 [81.3 kB]\n",
      "Get:54 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1710/x86_64  nvidia-396 396.44-0ubuntu1 [80.0 MB]\n",
      "Get:55 http://archive.ubuntu.com/ubuntu bionic/main amd64 libthai-data all 0.1.27-2 [133 kB]\n",
      "Get:56 http://archive.ubuntu.com/ubuntu bionic/main amd64 libdatrie1 amd64 0.2.10-7 [17.8 kB]\n",
      "Get:57 http://archive.ubuntu.com/ubuntu bionic/main amd64 libthai0 amd64 0.1.27-2 [18.0 kB]\n",
      "Get:58 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libpango-1.0-0 amd64 1.40.14-1ubuntu0.1 [153 kB]\n",
      "Get:59 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libpangoft2-1.0-0 amd64 1.40.14-1ubuntu0.1 [33.2 kB]\n",
      "Get:60 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libpangocairo-1.0-0 amd64 1.40.14-1ubuntu0.1 [20.8 kB]\n",
      "Get:61 http://archive.ubuntu.com/ubuntu bionic/main amd64 librsvg2-2 amd64 2.40.20-2 [98.6 kB]\n",
      "Get:62 http://archive.ubuntu.com/ubuntu bionic/main amd64 librsvg2-common amd64 2.40.20-2 [5,124 B]\n",
      "Get:63 http://archive.ubuntu.com/ubuntu bionic/main amd64 humanity-icon-theme all 0.6.15 [1,250 kB]\n",
      "Get:64 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 ubuntu-mono all 16.10+18.04.20181005-0ubuntu1 [149 kB]\n",
      "Get:65 http://archive.ubuntu.com/ubuntu bionic/main amd64 adwaita-icon-theme all 3.28.0-1ubuntu1 [3,306 kB]\n",
      "Get:66 http://archive.ubuntu.com/ubuntu bionic/main amd64 libatspi2.0-0 amd64 2.28.0-1 [59.6 kB]\n",
      "Get:67 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxtst6 amd64 2:1.2.3-1 [12.8 kB]\n",
      "Get:68 http://archive.ubuntu.com/ubuntu bionic/main amd64 at-spi2-core amd64 2.28.0-1 [47.9 kB]\n",
      "Get:69 http://archive.ubuntu.com/ubuntu bionic/main amd64 java-common all 0.63ubuntu1~02 [7,032 B]\n",
      "Get:70 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libavahi-common-data amd64 0.7-3.1ubuntu1.1 [22.1 kB]\n",
      "Get:71 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libavahi-common3 amd64 0.7-3.1ubuntu1.1 [21.7 kB]\n",
      "Get:72 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libavahi-client3 amd64 0.7-3.1ubuntu1.1 [25.3 kB]\n",
      "Get:73 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libcups2 amd64 2.2.7-1ubuntu2.1 [211 kB]\n",
      "Get:74 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 liblcms2-2 amd64 2.9-1ubuntu0.1 [139 kB]\n",
      "Get:75 http://archive.ubuntu.com/ubuntu bionic/main amd64 libnspr4 amd64 2:4.18-1ubuntu1 [112 kB]\n",
      "Get:76 http://archive.ubuntu.com/ubuntu bionic/main amd64 libnss3 amd64 2:3.35-2ubuntu2 [1,135 kB]\n",
      "Get:77 http://archive.ubuntu.com/ubuntu bionic/main amd64 libpcsclite1 amd64 1.8.23-1 [21.3 kB]\n",
      "Get:78 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxi6 amd64 2:1.7.9-1 [29.2 kB]\n",
      "Get:79 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 openjdk-11-jre-headless amd64 10.0.2+13-1ubuntu0.18.04.3 [39.5 MB]\n",
      "Get:80 http://archive.ubuntu.com/ubuntu bionic/main amd64 default-jre-headless amd64 2:1.10-63ubuntu1~02 [3,412 B]\n",
      "Get:81 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 ca-certificates-java all 20180516ubuntu1~18.04.1 [12.2 kB]\n",
      "Get:82 http://archive.ubuntu.com/ubuntu bionic/main amd64 libdconf1 amd64 0.26.0-2ubuntu3 [33.1 kB]\n",
      "Get:83 http://archive.ubuntu.com/ubuntu bionic/main amd64 dconf-service amd64 0.26.0-2ubuntu3 [28.4 kB]\n",
      "Get:84 http://archive.ubuntu.com/ubuntu bionic/main amd64 dconf-gsettings-backend amd64 0.26.0-2ubuntu3 [20.0 kB]\n",
      "Get:85 http://archive.ubuntu.com/ubuntu bionic/main amd64 libgtk-3-common all 3.22.30-1ubuntu1 [228 kB]\n",
      "Get:86 http://archive.ubuntu.com/ubuntu bionic/main amd64 libatk1.0-data all 2.28.1-1 [2,992 B]\n",
      "Get:87 http://archive.ubuntu.com/ubuntu bionic/main amd64 libatk1.0-0 amd64 2.28.1-1 [43.9 kB]\n",
      "Get:88 http://archive.ubuntu.com/ubuntu bionic/main amd64 libatk-bridge2.0-0 amd64 2.26.2-1 [57.3 kB]\n",
      "Get:89 http://archive.ubuntu.com/ubuntu bionic/main amd64 libcairo-gobject2 amd64 1.15.10-2 [17.2 kB]\n",
      "Get:90 file:/var/cuda-repo-9-2-local  cuda-cufft-dev-9-2 9.2.148-1 [106 MB]\n",
      "Get:91 http://archive.ubuntu.com/ubuntu bionic/main amd64 libcolord2 amd64 1.3.3-2build1 [107 kB]\n",
      "Get:92 http://archive.ubuntu.com/ubuntu bionic/main amd64 libepoxy0 amd64 1.4.3-1 [181 kB]\n",
      "Get:93 http://archive.ubuntu.com/ubuntu bionic/main amd64 libjson-glib-1.0-common all 1.4.2-3 [3,464 B]\n",
      "Get:94 http://archive.ubuntu.com/ubuntu bionic/main amd64 libjson-glib-1.0-0 amd64 1.4.2-3 [58.5 kB]\n",
      "Get:95 http://archive.ubuntu.com/ubuntu bionic/main amd64 libproxy1v5 amd64 0.4.15-1 [49.5 kB]\n",
      "Get:96 http://archive.ubuntu.com/ubuntu bionic/main amd64 glib-networking-common all 2.56.0-1 [3,324 B]\n",
      "Get:97 http://archive.ubuntu.com/ubuntu bionic/main amd64 glib-networking-services amd64 2.56.0-1 [8,488 B]\n",
      "Get:98 http://archive.ubuntu.com/ubuntu bionic/main amd64 gsettings-desktop-schemas all 3.28.0-1ubuntu1 [27.8 kB]\n",
      "Get:99 http://archive.ubuntu.com/ubuntu bionic/main amd64 glib-networking amd64 2.56.0-1 [56.7 kB]\n",
      "Get:100 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libsoup2.4-1 amd64 2.62.1-1ubuntu0.1 [292 kB]\n",
      "Get:101 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libsoup-gnome2.4-1 amd64 2.62.1-1ubuntu0.1 [5,092 B]\n",
      "Get:102 http://archive.ubuntu.com/ubuntu bionic/main amd64 librest-0.7-0 amd64 0.8.0-2 [31.8 kB]\n",
      "Get:103 http://archive.ubuntu.com/ubuntu bionic/main amd64 libwayland-cursor0 amd64 1.14.0-2 [9,976 B]\n",
      "Get:104 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libwayland-egl1-mesa amd64 18.0.5-0ubuntu0~18.04.1 [7,148 B]\n",
      "Get:105 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxcomposite1 amd64 1:0.4.4-2 [6,988 B]\n",
      "Get:106 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxfixes3 amd64 1:5.0.3-1 [10.8 kB]\n",
      "Get:107 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxcursor1 amd64 1:1.1.15-1 [19.8 kB]\n",
      "Get:108 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxdamage1 amd64 1:1.1.4-3 [6,934 B]\n",
      "Get:109 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libxkbcommon0 amd64 0.8.0-1ubuntu0.1 [97.9 kB]\n",
      "Get:110 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxrandr2 amd64 2:1.5.1-1 [18.1 kB]\n",
      "Get:111 http://archive.ubuntu.com/ubuntu bionic/main amd64 libgtk-3-0 amd64 3.22.30-1ubuntu1 [2,505 kB]\n",
      "Get:112 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxcb-glx0 amd64 1.13-1 [22.0 kB]\n",
      "Get:113 http://archive.ubuntu.com/ubuntu bionic/main amd64 libdrm-amdgpu1 amd64 2.4.91-2 [19.0 kB]\n",
      "Get:114 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1710/x86_64  nvidia-396-dev 396.44-0ubuntu1 [81.7 kB]\n",
      "Get:115 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1710/x86_64  libcuda1-396 396.44-0ubuntu1 [3,457 kB]\n",
      "Get:116 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1710/x86_64  nvidia-modprobe 396.44-0ubuntu1 [18.9 kB]\n",
      "Get:117 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1710/x86_64  nvidia-opencl-icd-396 396.44-0ubuntu1 [3,581 kB]\n",
      "Get:118 http://archive.ubuntu.com/ubuntu bionic/main amd64 libpciaccess0 amd64 0.14-1 [17.9 kB]\n",
      "Get:119 http://archive.ubuntu.com/ubuntu bionic/main amd64 libdrm-intel1 amd64 2.4.91-2 [59.8 kB]\n",
      "Get:120 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1710/x86_64  cuda-drivers 396.44-1 [2,386 B]\n",
      "Get:121 http://archive.ubuntu.com/ubuntu bionic/main amd64 libdrm-nouveau2 amd64 2.4.91-2 [16.5 kB]\n",
      "Get:122 http://archive.ubuntu.com/ubuntu bionic/main amd64 libdrm-radeon1 amd64 2.4.91-2 [21.7 kB]\n",
      "Get:123 http://archive.ubuntu.com/ubuntu bionic/main amd64 libllvm6.0 amd64 1:6.0-1ubuntu2 [14.5 MB]\n",
      "Get:124 file:/var/cuda-repo-9-2-local  cuda-curand-dev-9-2 9.2.148-1 [57.8 MB]\n",
      "Get:125 http://archive.ubuntu.com/ubuntu bionic/main amd64 libsensors4 amd64 1:3.4.0-4 [28.8 kB]\n",
      "Get:126 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libgl1-mesa-dri amd64 18.0.5-0ubuntu0~18.04.1 [6,011 kB]\n",
      "Get:127 file:/var/cuda-repo-9-2-local  cuda-cusparse-dev-9-2 9.2.148-1 [27.8 MB]\n",
      "Get:128 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libglx-mesa0 amd64 18.0.5-0ubuntu0~18.04.1 [132 kB]\n",
      "Get:129 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libglx0 amd64 1.0.0-2ubuntu2.2 [28.1 kB]\n",
      "Get:130 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libgl1 amd64 1.0.0-2ubuntu2.2 [84.8 kB]\n",
      "Get:131 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libgl1-mesa-glx amd64 18.0.5-0ubuntu0~18.04.1 [3,780 B]\n",
      "Get:132 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libasound2-data all 1.1.3-5ubuntu0.1 [36.3 kB]\n",
      "Get:133 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libasound2 amd64 1.1.3-5ubuntu0.1 [359 kB]\n",
      "Get:134 http://archive.ubuntu.com/ubuntu bionic/main amd64 libgif7 amd64 5.1.4-2 [30.6 kB]\n",
      "Get:135 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 openjdk-11-jre amd64 10.0.2+13-1ubuntu0.18.04.3 [53.2 kB]\n",
      "Get:136 http://archive.ubuntu.com/ubuntu bionic/main amd64 default-jre amd64 2:1.10-63ubuntu1~02 [1,092 B]\n",
      "Get:137 http://archive.ubuntu.com/ubuntu bionic/universe amd64 freeglut3 amd64 2.8.1-3 [73.6 kB]\n",
      "Get:138 http://archive.ubuntu.com/ubuntu bionic/main amd64 libdrm-dev amd64 2.4.91-2 [238 kB]\n",
      "Get:139 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 mesa-common-dev amd64 18.0.5-0ubuntu0~18.04.1 [536 kB]\n",
      "Get:140 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libglvnd-core-dev amd64 1.0.0-2ubuntu2.2 [12.9 kB]\n",
      "Get:141 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libgles1 amd64 1.0.0-2ubuntu2.2 [11.2 kB]\n",
      "Get:142 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libglvnd-dev amd64 1.0.0-2ubuntu2.2 [3,408 B]\n",
      "Get:143 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libx11-xcb-dev amd64 2:1.6.4-3ubuntu0.1 [9,764 B]\n",
      "Get:144 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxcb-dri3-dev amd64 1.13-1 [7,368 B]\n",
      "Get:145 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxcb-randr0 amd64 1.13-1 [16.4 kB]\n",
      "Get:146 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxcb-render0-dev amd64 1.13-1 [18.4 kB]\n",
      "Get:147 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxcb-randr0-dev amd64 1.13-1 [20.4 kB]\n",
      "Get:148 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxcb-shape0 amd64 1.13-1 [5,964 B]\n",
      "Get:149 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxcb-shape0-dev amd64 1.13-1 [7,144 B]\n",
      "Get:150 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxcb-xfixes0-dev amd64 1.13-1 [11.7 kB]\n",
      "Get:151 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxcb-sync-dev amd64 1.13-1 [10.6 kB]\n",
      "Get:152 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxcb-present-dev amd64 1.13-1 [6,968 B]\n",
      "Get:153 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxshmfence-dev amd64 1.3-1 [3,692 B]\n",
      "Get:154 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxcb-dri2-0-dev amd64 1.13-1 [8,476 B]\n",
      "Get:155 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxcb-glx0-dev amd64 1.13-1 [27.9 kB]\n",
      "Get:156 http://archive.ubuntu.com/ubuntu bionic/main amd64 x11proto-fixes-dev all 1:2018.4-4 [2,620 B]\n",
      "Get:157 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxfixes-dev amd64 1:5.0.3-1 [11.0 kB]\n",
      "Get:158 http://archive.ubuntu.com/ubuntu bionic/main amd64 x11proto-damage-dev all 1:2018.4-4 [2,620 B]\n",
      "Get:159 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxdamage-dev amd64 1:1.1.4-3 [5,028 B]\n",
      "Get:160 http://archive.ubuntu.com/ubuntu bionic/main amd64 x11proto-xext-dev all 2018.4-4 [2,620 B]\n",
      "Get:161 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxext-dev amd64 2:1.3.3-1 [82.1 kB]\n",
      "Get:162 http://archive.ubuntu.com/ubuntu bionic/main amd64 x11proto-xf86vidmode-dev all 2018.4-4 [2,632 B]\n",
      "Get:163 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxxf86vm-dev amd64 1:1.1.4-1 [13.3 kB]\n",
      "Get:164 http://archive.ubuntu.com/ubuntu bionic/main amd64 x11proto-dri2-dev all 2018.4-4 [2,620 B]\n",
      "Get:165 http://archive.ubuntu.com/ubuntu bionic/main amd64 x11proto-gl-dev all 2018.4-4 [2,612 B]\n",
      "Get:166 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libgl1-mesa-dev amd64 18.0.5-0ubuntu0~18.04.1 [4,444 B]\n",
      "Get:167 http://archive.ubuntu.com/ubuntu bionic/main amd64 libglu1-mesa amd64 9.0.0-2.1build1 [168 kB]\n",
      "Get:168 file:/var/cuda-repo-9-2-local  cuda-npp-dev-9-2 9.2.148-1 [52.0 MB]\n",
      "Get:169 file:/var/cuda-repo-9-2-local  cuda-nvml-dev-9-2 9.2.148-1 [49.7 kB]\n",
      "Get:170 http://archive.ubuntu.com/ubuntu bionic/main amd64 libglu1-mesa-dev amd64 9.0.0-2.1build1 [206 kB]\n",
      "Get:171 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxt6 amd64 1:1.1.5-1 [160 kB]\n",
      "Get:172 http://archive.ubuntu.com/ubuntu bionic/main amd64 libice-dev amd64 2:1.0.9-2 [46.8 kB]\n",
      "Get:173 http://archive.ubuntu.com/ubuntu bionic/main amd64 libsm-dev amd64 2:1.2.2-1 [16.2 kB]\n",
      "Get:174 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxt-dev amd64 1:1.1.5-1 [395 kB]\n",
      "Get:175 http://archive.ubuntu.com/ubuntu bionic/universe amd64 freeglut3-dev amd64 2.8.1-3 [124 kB]\n",
      "Get:176 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxmu6 amd64 2:1.1.2-2 [46.0 kB]\n",
      "Get:177 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxmu-headers all 2:1.1.2-2 [54.3 kB]\n",
      "Get:178 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxmu-dev amd64 2:1.1.2-2 [49.0 kB]\n",
      "Get:179 http://archive.ubuntu.com/ubuntu bionic/main amd64 x11proto-input-dev all 2018.4-4 [2,620 B]\n",
      "Get:180 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxi-dev amd64 2:1.7.9-1 [186 kB]\n",
      "Get:181 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 dkms all 2.3-3ubuntu9.2 [68.0 kB]\n",
      "Get:182 http://archive.ubuntu.com/ubuntu bionic/main amd64 libc6-i386 amd64 2.27-3ubuntu1 [2,651 kB]\n",
      "Get:183 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 lib32gcc1 amd64 1:8.2.0-1ubuntu2~18.04 [47.9 kB]\n",
      "Get:184 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxpm4 amd64 1:3.5.12-1 [34.0 kB]\n",
      "Get:185 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxaw7 amd64 2:1.0.13-1 [173 kB]\n",
      "Get:186 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxkbfile1 amd64 1:1.0.9-2 [64.6 kB]\n",
      "Get:187 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 x11-xkb-utils amd64 7.7+3ubuntu0.18.04.1 [160 kB]\n",
      "Get:188 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 xserver-common all 2:1.19.6-1ubuntu4.2 [27.3 kB]\n",
      "Get:189 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 xserver-xorg-legacy amd64 2:1.19.6-1ubuntu4.2 [32.9 kB]\n",
      "Get:190 http://archive.ubuntu.com/ubuntu bionic/main amd64 libfontenc1 amd64 1:1.1.3-1 [13.9 kB]\n",
      "Get:191 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxfont2 amd64 1:2.0.3-1 [91.7 kB]\n",
      "Get:192 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 xserver-xorg-core amd64 2:1.19.6-1ubuntu4.2 [1,351 kB]\n",
      "Get:193 http://archive.ubuntu.com/ubuntu bionic/main amd64 python3-xkit all 0.5.0ubuntu2 [18.6 kB]\n",
      "Get:194 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libpolkit-agent-1-0 amd64 0.105-20ubuntu0.18.04.1 [14.9 kB]\n",
      "Get:195 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libpolkit-backend-1-0 amd64 0.105-20ubuntu0.18.04.1 [36.2 kB]\n",
      "Get:196 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 policykit-1 amd64 0.105-20ubuntu0.18.04.1 [53.4 kB]\n",
      "Get:197 http://archive.ubuntu.com/ubuntu bionic/main amd64 policykit-1-gnome amd64 0.105-6ubuntu2 [24.0 kB]\n",
      "Get:198 http://archive.ubuntu.com/ubuntu bionic/main amd64 screen-resolution-extra all 0.17.3 [11.7 kB]\n",
      "Get:199 http://archive.ubuntu.com/ubuntu bionic/main amd64 libvdpau1 amd64 1.1.1-3ubuntu1 [25.5 kB]\n",
      "Get:200 file:/var/cuda-repo-9-2-local  cuda-nvgraph-dev-9-2 9.2.148-1 [30.1 MB]\n",
      "Get:201 http://archive.ubuntu.com/ubuntu bionic/main amd64 libjansson4 amd64 2.11-1 [29.3 kB]\n",
      "Get:202 http://archive.ubuntu.com/ubuntu bionic/main amd64 ocl-icd-libopencl1 amd64 2.2.11-1ubuntu1 [30.3 kB]\n",
      "Get:203 http://archive.ubuntu.com/ubuntu bionic/main amd64 libfakeroot amd64 1.22-2ubuntu1 [25.9 kB]\n",
      "Get:204 http://archive.ubuntu.com/ubuntu bionic/main amd64 fakeroot amd64 1.22-2ubuntu1 [62.3 kB]\n",
      "Get:205 http://archive.ubuntu.com/ubuntu bionic/main amd64 fonts-dejavu-core all 2.37-1 [1,041 kB]\n",
      "Get:206 http://archive.ubuntu.com/ubuntu bionic/main amd64 fonts-dejavu-extra all 2.37-1 [1,953 kB]\n",
      "Get:207 http://archive.ubuntu.com/ubuntu bionic/main amd64 libxv1 amd64 2:1.0.11-1 [10.7 kB]\n",
      "Get:208 http://archive.ubuntu.com/ubuntu bionic/main amd64 x11-utils amd64 7.7+3build1 [196 kB]\n",
      "Get:209 http://archive.ubuntu.com/ubuntu bionic/main amd64 libatk-wrapper-java all 0.33.3-20ubuntu0.1 [34.7 kB]\n",
      "Get:210 http://archive.ubuntu.com/ubuntu bionic/main amd64 libatk-wrapper-java-jni amd64 0.33.3-20ubuntu0.1 [28.3 kB]\n",
      "Get:211 http://archive.ubuntu.com/ubuntu bionic/main amd64 libgdk-pixbuf2.0-bin amd64 2.36.11-2 [7,864 B]\n",
      "Get:212 http://archive.ubuntu.com/ubuntu bionic/main amd64 libgtk-3-bin amd64 3.22.30-1ubuntu1 [57.3 kB]\n",
      "Get:213 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 linux-headers-4.15.0-39 all 4.15.0-39.42 [11.0 MB]\n",
      "Get:214 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 linux-headers-4.15.0-39-generic amd64 4.15.0-39.42 [1,114 kB]\n",
      "Get:215 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 linux-headers-generic amd64 4.15.0.39.41 [2,340 B]\n",
      "Get:216 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 mesa-vdpau-drivers amd64 18.0.5-0ubuntu0~18.04.1 [1,902 kB]\n",
      "Get:217 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 nvidia-prime all 0.8.8.2 [8,412 B]\n",
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      "Created symlink /etc/systemd/system/getty.target.wants/getty@tty1.service → /lib/systemd/system/getty@.service.\n",
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      "dpkg: error processing archive /tmp/apt-dpkg-install-xnTz1f/177-nvidia-396_396.44-0ubuntu1_amd64.deb (--unpack):\n",
      " trying to overwrite '/usr/share/egl/egl_external_platform.d/10_nvidia_wayland.json', which is also in package libnvidia-gl-396:amd64 396.54-0ubuntu0~gpu18.04.1\n",
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      "Errors were encountered while processing:\n",
      " /tmp/apt-dpkg-install-xnTz1f/177-nvidia-396_396.44-0ubuntu1_amd64.deb\n",
      "E: Sub-process /usr/bin/dpkg returned an error code (1)\n"
     ]
    }
   ],
   "source": [
    "# NOTE: This might take some time..\n",
    "!apt-get install cuda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "B3ymdnEPHBs-",
    "outputId": "1a76090d-cc73-4624-a7cc-3fb68c79cf71"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CUDA Version 9.2.148\n"
     ]
    }
   ],
   "source": [
    "# Check the version of CUDA on the system\n",
    "!cat /usr/local/cuda/version.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "3auYdTraIt6w",
    "outputId": "ee3fe78a-a768-4c7f-b60b-37591f66fd65"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: torch==0.4.1 from http://download.pytorch.org/whl/cu92/torch-0.4.1-cp36-cp36m-linux_x86_64.whl in /usr/local/lib/python3.6/dist-packages (0.4.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install http://download.pytorch.org/whl/cu92/torch-0.4.1-cp36-cp36m-linux_x86_64.whl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 275
    },
    "colab_type": "code",
    "id": "9x54_P8lIwva",
    "outputId": "ffb98c73-82d8-4965-f579-4f048d7d5f5c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting torchvision\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ca/0d/f00b2885711e08bd71242ebe7b96561e6f6d01fdb4b9dcf4d37e2e13c5e1/torchvision-0.2.1-py2.py3-none-any.whl (54kB)\n",
      "\u001b[K    100% |████████████████████████████████| 61kB 2.6MB/s \n",
      "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (from torchvision) (0.4.1)\n",
      "Collecting pillow>=4.1.1 (from torchvision)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/62/94/5430ebaa83f91cc7a9f687ff5238e26164a779cca2ef9903232268b0a318/Pillow-5.3.0-cp36-cp36m-manylinux1_x86_64.whl (2.0MB)\n",
      "\u001b[K    100% |████████████████████████████████| 2.0MB 7.9MB/s \n",
      "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.14.6)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.11.0)\n",
      "Installing collected packages: pillow, torchvision\n",
      "  Found existing installation: Pillow 4.0.0\n",
      "    Uninstalling Pillow-4.0.0:\n",
      "      Successfully uninstalled Pillow-4.0.0\n",
      "Successfully installed pillow-5.3.0 torchvision-0.2.1\n"
     ]
    }
   ],
   "source": [
    "!pip install torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "Rqlkk28nI03N",
    "outputId": "416dbeaa-5a9e-4ad9-8b29-26f67f5db429"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyTorch version: \n",
      "0.4.1\n",
      "CUDA Version: \n",
      "9.0.176\n",
      "cuDNN version is: \n",
      "7102\n"
     ]
    }
   ],
   "source": [
    "# Use PyTorch to check versions, CUDA version and cuDNN\n",
    "\n",
    "import torch\n",
    "\n",
    "print(\"PyTorch version: \")\n",
    "print(torch.__version__)\n",
    "print(\"CUDA Version: \")\n",
    "print(torch.version.cuda)\n",
    "print(\"cuDNN version is: \")\n",
    "print(torch.backends.cudnn.version())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306
    },
    "colab_type": "code",
    "id": "ZNGpIVSdI48L",
    "outputId": "0e82342b-f5b9-45b7-9707-ca556be988a5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sun Nov 18 14:05:32 2018       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 396.44                 Driver Version: 396.44                    |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
      "| N/A   29C    P8    29W / 149W |      0MiB / 11441MiB |      0%      Default |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                       GPU Memory |\n",
      "|  GPU       PID   Type   Process name                             Usage      |\n",
      "|=============================================================================|\n",
      "|  No running processes found                                                 |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "V3Kh8n5tBGig"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from torch import nn\n",
    "from torch import optim\n",
    "import torch.nn.functional as F\n",
    "from torchvision import datasets, transforms, models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 89,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": "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",
       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "colab_type": "code",
    "id": "rvyGmeTmC4KM",
    "outputId": "3973fff9-14d6-4bd4-d681-684b8ca8b413"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-73e0fa98-e5ae-4113-b98e-2145ed5e82ee\" name=\"files[]\" multiple disabled />\n",
       "     <output id=\"result-73e0fa98-e5ae-4113-b98e-2145ed5e82ee\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving kaggle.json to kaggle.json\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'kaggle.json': b'{\"username\":\"souvikb07\",\"key\":\"25065a0f8df4c23b753ef64d1fd4f303\"}'}"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# upload the kaggle data\n",
    "from google.colab import files\n",
    "files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ldwYowqJINzy",
    "outputId": "f2f1ac05-c420-4338-f1d6-4c5e57823992"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kaggle.json\n"
     ]
    }
   ],
   "source": [
    "# is it there?\n",
    "ls -1ha kaggle.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uiZaqxw_LhbX"
   },
   "outputs": [],
   "source": [
    "# file configuration\n",
    "!mkdir -p ~/.kaggle\n",
    "!cp kaggle.json ~/.kaggle/\n",
    "!chmod 600 ~/.kaggle/kaggle.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "Xtj89uWcMQu7",
    "outputId": "b351b0d8-64b7-4feb-c729-371b09981f9c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading cat-and-dog.zip to /content/b3fd927620d96ede7e41064b2ff865f2\n",
      " 89% 193M/217M [00:01<00:00, 102MB/s] \n",
      "100% 217M/217M [00:02<00:00, 110MB/s]\n"
     ]
    }
   ],
   "source": [
    "!kaggle datasets download -d tongpython/cat-and-dog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 136255
    },
    "colab_type": "code",
    "id": "zfuJ10EQMZjz",
    "outputId": "accdb000-452f-4d77-f540-9f8266c9e29e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  cat-and-dog.zip\n",
      "  inflating: training_set.zip        \n",
      "  inflating: test_set.zip            \n",
      "Archive:  training_set.zip\n",
      "   creating: training_set/\n",
      "   creating: training_set/cats/\n",
      "  inflating: training_set/cats/_DS_Store  \n",
      "  inflating: training_set/cats/cat.1.jpg  \n",
      "  inflating: training_set/cats/cat.10.jpg  \n",
      "  inflating: training_set/cats/cat.100.jpg  \n",
      "  inflating: training_set/cats/cat.1000.jpg  \n",
      "  inflating: training_set/cats/cat.1001.jpg  \n",
      "  inflating: training_set/cats/cat.1002.jpg  \n",
      "  inflating: training_set/cats/cat.1003.jpg  \n",
      "  inflating: training_set/cats/cat.1004.jpg  \n",
      "  inflating: training_set/cats/cat.1005.jpg  \n",
      "  inflating: training_set/cats/cat.1006.jpg  \n",
      "  inflating: training_set/cats/cat.1007.jpg  \n",
      "  inflating: training_set/cats/cat.1008.jpg  \n",
      "  inflating: training_set/cats/cat.1009.jpg  \n",
      "  inflating: training_set/cats/cat.101.jpg  \n",
      "  inflating: training_set/cats/cat.1010.jpg  \n",
      "  inflating: training_set/cats/cat.1011.jpg  \n",
      "  inflating: training_set/cats/cat.1012.jpg  \n",
      "  inflating: training_set/cats/cat.1013.jpg  \n",
      "  inflating: training_set/cats/cat.1014.jpg  \n",
      "  inflating: training_set/cats/cat.1015.jpg  \n",
      "  inflating: training_set/cats/cat.1016.jpg  \n",
      "  inflating: training_set/cats/cat.1017.jpg  \n",
      "  inflating: training_set/cats/cat.1018.jpg  \n",
      "  inflating: training_set/cats/cat.1019.jpg  \n",
      "  inflating: training_set/cats/cat.102.jpg  \n",
      "  inflating: training_set/cats/cat.1020.jpg  \n",
      "  inflating: training_set/cats/cat.1021.jpg  \n",
      "  inflating: training_set/cats/cat.1022.jpg  \n",
      "  inflating: training_set/cats/cat.1023.jpg  \n",
      "  inflating: training_set/cats/cat.1024.jpg  \n",
      "  inflating: training_set/cats/cat.1025.jpg  \n",
      "  inflating: training_set/cats/cat.1026.jpg  \n",
      "  inflating: training_set/cats/cat.1027.jpg  \n",
      "  inflating: training_set/cats/cat.1028.jpg  \n",
      "  inflating: training_set/cats/cat.1029.jpg  \n",
      "  inflating: training_set/cats/cat.103.jpg  \n",
      "  inflating: training_set/cats/cat.1030.jpg  \n",
      "  inflating: training_set/cats/cat.1031.jpg  \n",
      "  inflating: training_set/cats/cat.1032.jpg  \n",
      "  inflating: training_set/cats/cat.1033.jpg  \n",
      "  inflating: training_set/cats/cat.1034.jpg  \n",
      "  inflating: training_set/cats/cat.1035.jpg  \n",
      "  inflating: training_set/cats/cat.1036.jpg  \n",
      "  inflating: training_set/cats/cat.1037.jpg  \n",
      "  inflating: training_set/cats/cat.1038.jpg  \n",
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     ]
    }
   ],
   "source": [
    "#unzip\n",
    "! unzip cat-and-dog.zip\n",
    "! unzip training_set.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34510
    },
    "colab_type": "code",
    "id": "lyA8Kt4aOddL",
    "outputId": "89260bb4-7095-4b8f-9f13-acf32bbbf0b4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  test_set.zip\n",
      "   creating: test_set/\n",
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     ]
    }
   ],
   "source": [
    "! unzip test_set.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "nC_2j2-2Oe74",
    "outputId": "61cd459e-d301-4ce6-d146-1ec42de46007"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cat-and-dog.zip\t\t\t\t\ttest_set\n",
      "cuda-repo-ubuntu1604-9-2-local_9.2.148-1_amd64\ttest_set.zip\n",
      "kaggle.json\t\t\t\t\ttraining_set\n",
      "pytorch041_cuda92_colab.sh\t\t\ttraining_set.zip\n"
     ]
    }
   ],
   "source": [
    "! ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lZT70gVDBGio"
   },
   "source": [
    "Most of the pretrained models require the input to be 224x224 images. Also, we'll need to match the normalization used when the models were trained. Each color channel was normalized separately, the means are `[0.485, 0.456, 0.406]` and the standard deviations are `[0.229, 0.224, 0.225]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-b1-wua5BGip"
   },
   "outputs": [],
   "source": [
    "# TODO: Define transforms for the training data and testing data\n",
    "train_transforms = transforms.Compose([transforms.RandomRotation(30),\n",
    "                                       transforms.RandomResizedCrop(224),\n",
    "                                       transforms.RandomHorizontalFlip(),\n",
    "                                       transforms.ToTensor(),\n",
    "                                       transforms.Normalize([0.485, 0.456, 0.406],\n",
    "                                                            [0.229, 0.224, 0.225])])\n",
    "\n",
    "test_transforms = transforms.Compose([transforms.Resize(255),\n",
    "                                      transforms.CenterCrop(224),\n",
    "                                      transforms.ToTensor(),\n",
    "                                      transforms.Normalize([0.485, 0.456, 0.406],\n",
    "                                                           [0.229, 0.224, 0.225])])\n",
    "\n",
    "# Pass transforms in here, then run the next cell to see how the transforms look\n",
    "train_data = datasets.ImageFolder('training_set', transform=train_transforms)\n",
    "test_data = datasets.ImageFolder('test_set', transform=test_transforms)\n",
    "\n",
    "trainloader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True)\n",
    "testloader = torch.utils.data.DataLoader(test_data, batch_size=64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_0CIx8ANBGiv"
   },
   "source": [
    "We can load in a model such as [DenseNet](http://pytorch.org/docs/0.3.0/torchvision/models.html#id5). Let's print out the model architecture so we can see what's going on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 8605
    },
    "colab_type": "code",
    "id": "LPLwsPuUBGix",
    "outputId": "ba029676-8159-4e95-fe4e-13de5e44c5ef",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/torchvision/models/densenet.py:212: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n",
      "  nn.init.kaiming_normal(m.weight.data)\n",
      "Downloading: \"https://download.pytorch.org/models/densenet121-a639ec97.pth\" to /root/.torch/models/densenet121-a639ec97.pth\n",
      "100%|██████████| 32342954/32342954 [00:01<00:00, 29529003.13it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DenseNet(\n",
       "  (features): Sequential(\n",
       "    (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "    (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (relu0): ReLU(inplace)\n",
       "    (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    (denseblock1): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition1): _Transition(\n",
       "      (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock2): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition2): _Transition(\n",
       "      (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock3): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer13): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer14): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer15): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer16): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer17): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer18): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer19): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer20): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer21): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer22): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer23): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer24): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition3): _Transition(\n",
       "      (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock4): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer13): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer14): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer15): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer16): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (classifier): Linear(in_features=1024, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = models.densenet121(pretrained=True)\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cC9bcQYsBGi2"
   },
   "source": [
    "This model is built out of two main parts, the features and the classifier. The features part is a stack of convolutional layers and overall works as a feature detector that can be fed into a classifier. The classifier part is a single fully-connected layer `(classifier): Linear(in_features=1024, out_features=1000)`. This layer was trained on the ImageNet dataset, so it won't work for our specific problem. That means we need to replace the classifier, but the features will work perfectly on their own. In general, I think about pre-trained networks as amazingly good feature detectors that can be used as the input for simple feed-forward classifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OYkEsU-wBGi4"
   },
   "outputs": [],
   "source": [
    "# Freeze parameters so we don't backprop through them\n",
    "for param in model.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "from collections import OrderedDict\n",
    "classifier = nn.Sequential(OrderedDict([\n",
    "                          ('fc1', nn.Linear(1024, 500)),\n",
    "                          ('relu', nn.ReLU()),\n",
    "                          ('fc2', nn.Linear(500, 2)),\n",
    "                          ('output', nn.LogSoftmax(dim=1))\n",
    "                          ]))\n",
    "    \n",
    "model.classifier = classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 8602
    },
    "colab_type": "code",
    "id": "Q5fVArl9NG2Z",
    "outputId": "d5f7c938-a2c4-4e5f-b68e-80e29e22f0f7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DenseNet(\n",
       "  (features): Sequential(\n",
       "    (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "    (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (relu0): ReLU(inplace)\n",
       "    (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    (denseblock1): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition1): _Transition(\n",
       "      (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock2): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition2): _Transition(\n",
       "      (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock3): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer13): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer14): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer15): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer16): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer17): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer18): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer19): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer20): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer21): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer22): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer23): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer24): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition3): _Transition(\n",
       "      (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock4): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer13): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer14): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer15): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer16): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (classifier): Sequential(\n",
       "    (fc1): Linear(in_features=1024, out_features=500, bias=True)\n",
       "    (relu): ReLU()\n",
       "    (fc2): Linear(in_features=500, out_features=2, bias=True)\n",
       "    (output): LogSoftmax()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NVUAcY1HBGi-"
   },
   "source": [
    "With our model built, we need to train the classifier. However, now we're using a **really deep** neural network. If you try to train this on a CPU like normal, it will take a long, long time. Instead, we're going to use the GPU to do the calculations. The linear algebra computations are done in parallel on the GPU leading to 100x increased training speeds. It's also possible to train on multiple GPUs, further decreasing training time.\n",
    "\n",
    "PyTorch, along with pretty much every other deep learning framework, uses [CUDA](https://developer.nvidia.com/cuda-zone) to efficiently compute the forward and backwards passes on the GPU. In PyTorch, you move your model parameters and other tensors to the GPU memory using `model.to('cuda')`. You can move them back from the GPU with `model.to('cpu')` which you'll commonly do when you need to operate on the network output outside of PyTorch. As a demonstration of the increased speed, I'll compare how long it takes to perform a forward and backward pass with and without a GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0BYYqHQxBGi_"
   },
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "Y6MD3R6dSeEo",
    "outputId": "465fea69-933d-4f77-c01b-9c92007284b8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device = cpu; Time per batch: 6.891 seconds\n",
      "Device = cuda; Time per batch: 0.021 seconds\n"
     ]
    }
   ],
   "source": [
    "for device in ['cpu', 'cuda']:\n",
    "\n",
    "    criterion = nn.NLLLoss()\n",
    "    # Only train the classifier parameters, feature parameters are frozen\n",
    "    optimizer = optim.Adam(model.classifier.parameters(), lr=0.001)\n",
    "\n",
    "    model.to(device)\n",
    "\n",
    "    for ii, (inputs, labels) in enumerate(trainloader):\n",
    "\n",
    "        # Move input and label tensors to the GPU\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "\n",
    "        start = time.time()\n",
    "\n",
    "        outputs = model.forward(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        if ii==3:\n",
    "            break\n",
    "        \n",
    "    print(f\"Device = {device}; Time per batch: {(time.time() - start)/3:.3f} seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vFfDwfUaBGjJ"
   },
   "source": [
    "You can write device agnostic code which will automatically use CUDA if it's enabled like so:\n",
    "```python\n",
    "# at beginning of the script\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "...\n",
    "\n",
    "# then whenever you get a new Tensor or Module\n",
    "# this won't copy if they are already on the desired device\n",
    "input = data.to(device)\n",
    "model = MyModule(...).to(device)\n",
    "```\n",
    "\n",
    "From here, I'll let you finish training the model. The process is the same as before except now your model is much more powerful. You should get better than 95% accuracy easily.\n",
    "\n",
    ">**Exercise:** Train a pretrained models to classify the cat and dog images. Continue with the DenseNet model, or try ResNet, it's also a good model to try out first. Make sure you are only training the classifier and the parameters for the features part are frozen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qinLRRx8Z3uY"
   },
   "source": [
    "## Pretrained Model to classify the cat and dog images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "id": "GQM2VMsBBGjM",
    "outputId": "4614b5ce-b465-4b9b-c5f6-0a9035ba10ae"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/torchvision/models/densenet.py:212: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n",
      "  nn.init.kaiming_normal(m.weight.data)\n"
     ]
    }
   ],
   "source": [
    "## TODO: Use a pretrained model to classify the cat and dog images\n",
    "# Use GPU if it's available\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "model = models.densenet121(pretrained=True)\n",
    "\n",
    "# Freeze parameters so we don't backprop through them\n",
    "for param in model.parameters():\n",
    "    #Stop gradient Descent\n",
    "    param.requires_grad = False\n",
    "    \n",
    "model.classifier = nn.Sequential(nn.Linear(1024, 256),\n",
    "                                 nn.ReLU(),\n",
    "                                 nn.Dropout(0.2),\n",
    "                                 nn.Linear(256, 2),\n",
    "                                 nn.LogSoftmax(dim=1))\n",
    "\n",
    "criterion = nn.NLLLoss()\n",
    "\n",
    "# Only train the classifier parameters, feature parameters are frozen\n",
    "optimizer = optim.Adam(model.classifier.parameters(), lr=0.003)\n",
    "\n",
    "model.to(device);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3lyxUOYLT_4q"
   },
   "source": [
    "## The Transfer Learning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "UOPThCQaNqt0",
    "outputId": "77f865ec-d79e-4492-93fa-abc077f84590"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1.. Train loss: 1.053.. Test loss: 0.390.. Test accuracy: 0.844\n",
      "Epoch 1/1.. Train loss: 0.494.. Test loss: 0.233.. Test accuracy: 0.960\n",
      "Epoch 1/1.. Train loss: 0.314.. Test loss: 0.133.. Test accuracy: 0.980\n",
      "Epoch 1/1.. Train loss: 0.264.. Test loss: 0.108.. Test accuracy: 0.970\n",
      "Epoch 1/1.. Train loss: 0.230.. Test loss: 0.073.. Test accuracy: 0.983\n",
      "Epoch 1/1.. Train loss: 0.224.. Test loss: 0.064.. Test accuracy: 0.981\n",
      "Epoch 1/1.. Train loss: 0.237.. Test loss: 0.057.. Test accuracy: 0.986\n",
      "Epoch 1/1.. Train loss: 0.286.. Test loss: 0.083.. Test accuracy: 0.969\n",
      "Epoch 1/1.. Train loss: 0.184.. Test loss: 0.068.. Test accuracy: 0.976\n",
      "Epoch 1/1.. Train loss: 0.177.. Test loss: 0.063.. Test accuracy: 0.978\n",
      "Epoch 1/1.. Train loss: 0.178.. Test loss: 0.047.. Test accuracy: 0.988\n",
      "Epoch 1/1.. Train loss: 0.143.. Test loss: 0.046.. Test accuracy: 0.987\n",
      "Epoch 1/1.. Train loss: 0.147.. Test loss: 0.053.. Test accuracy: 0.982\n",
      "Epoch 1/1.. Train loss: 0.124.. Test loss: 0.050.. Test accuracy: 0.981\n",
      "Epoch 1/1.. Train loss: 0.136.. Test loss: 0.048.. Test accuracy: 0.983\n",
      "Epoch 1/1.. Train loss: 0.196.. Test loss: 0.047.. Test accuracy: 0.984\n",
      "Epoch 1/1.. Train loss: 0.211.. Test loss: 0.043.. Test accuracy: 0.987\n",
      "Epoch 1/1.. Train loss: 0.156.. Test loss: 0.042.. Test accuracy: 0.988\n",
      "Epoch 1/1.. Train loss: 0.225.. Test loss: 0.043.. Test accuracy: 0.989\n",
      "Epoch 1/1.. Train loss: 0.143.. Test loss: 0.040.. Test accuracy: 0.991\n",
      "Epoch 1/1.. Train loss: 0.141.. Test loss: 0.039.. Test accuracy: 0.988\n",
      "Epoch 1/1.. Train loss: 0.151.. Test loss: 0.040.. Test accuracy: 0.989\n",
      "Epoch 1/1.. Train loss: 0.158.. Test loss: 0.036.. Test accuracy: 0.990\n",
      "Epoch 1/1.. Train loss: 0.153.. Test loss: 0.041.. Test accuracy: 0.988\n",
      "Epoch 1/1.. Train loss: 0.177.. Test loss: 0.045.. Test accuracy: 0.984\n"
     ]
    }
   ],
   "source": [
    "epochs = 1\n",
    "steps = 0\n",
    "running_loss = 0\n",
    "print_every = 5\n",
    "for epoch in range(epochs):\n",
    "    for inputs, labels in trainloader:\n",
    "        steps += 1\n",
    "        # Move input and label tensors to the default device\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        logps = model.forward(inputs)\n",
    "        loss = criterion(logps, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss += loss.item()\n",
    "        \n",
    "        if steps % print_every == 0:\n",
    "            test_loss = 0\n",
    "            accuracy = 0\n",
    "            model.eval()\n",
    "            with torch.no_grad():\n",
    "                for inputs, labels in testloader:\n",
    "                    inputs, labels = inputs.to(device), labels.to(device)\n",
    "                    logps = model.forward(inputs)\n",
    "                    batch_loss = criterion(logps, labels)\n",
    "                    \n",
    "                    test_loss += batch_loss.item()\n",
    "                    \n",
    "                    # Calculate accuracy\n",
    "                    ps = torch.exp(logps)\n",
    "                    top_p, top_class = ps.topk(1, dim=1)\n",
    "                    equals = top_class == labels.view(*top_class.shape)\n",
    "                    accuracy += torch.mean(equals.type(torch.FloatTensor)).item()\n",
    "                    \n",
    "            print(f\"Epoch {epoch+1}/{epochs}.. \"\n",
    "                  f\"Train loss: {running_loss/print_every:.3f}.. \"\n",
    "                  f\"Test loss: {test_loss/len(testloader):.3f}.. \"\n",
    "                  f\"Test accuracy: {accuracy/len(testloader):.3f}\")\n",
    "            running_loss = 0\n",
    "            model.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RH4fYSXTP0Tw"
   },
   "outputs": [],
   "source": []
  }
 ],
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