SmallCNN_Val_Acc_Ensembles.ipynb

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        "<a href=\"https://colab.research.google.com/github/ayulockin/LossLandscape/blob/master/SmallCNN_Val_Acc_Ensembles.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kWXU2ICvCDN1",
        "colab_type": "text"
      },
      "source": [
        "# Setups, Imports and Installations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQAggd-btWJK",
        "colab_type": "code",
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        "outputId": "da80eee1-d17b-453d-8a08-ef2f9dcc0ea2"
      },
      "source": [
        "## This is so that I can save my models.\n",
        "from google.colab import drive\n",
        "drive.mount('gdrive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sAZBTFNQq0ys",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%capture\n",
        "!pip install wandb"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9-APP7-6C5uK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.datasets import cifar10\n",
        "from tensorflow.keras.applications import resnet50"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Pr3HrMxq8SF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "3a91080f-820b-4c20-82fc-4cb9dafa3a58"
      },
      "source": [
        "import os\n",
        "os.environ[\"TF_DETERMINISTIC_OPS\"] = \"1\"\n",
        "\n",
        "import numpy as np\n",
        "from numpy.linalg import norm\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "import seaborn as sns\n",
        "sns.set()\n",
        "\n",
        "from tqdm.notebook import tqdm_notebook\n",
        "from sklearn.manifold import TSNE\n",
        "\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "XmVkQbw_q_07",
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          "height": 108
        },
        "outputId": "f59ed69a-96ec-4838-8fb6-54e0da5a0af2"
      },
      "source": [
        "import wandb\n",
        "from wandb.keras import WandbCallback\n",
        "\n",
        "wandb.login()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
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              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
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              "    "
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            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[32m\u001b[41mERROR\u001b[0m Not authenticated.  Copy a key from https://app.wandb.ai/authorize\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "API Key: ··········\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FhhwqSIdCu9V",
        "colab_type": "text"
      },
      "source": [
        "# Get Trained Models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qDLJ-R6jCz1I",
        "colab_type": "text"
      },
      "source": [
        "(For now I am using models trained from SmallCNN.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WWCudbf8CgoH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ROOT_PATH = 'gdrive/My Drive/LossLandscape/'\n",
        "## SmallCNN models with different initialization(total 10 models)\n",
        "MODEL_PATH = ROOT_PATH+'IndependentSolutions/'"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pZjcYJYJDqHA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a93f6dfa-8c1b-4a1b-e084-95fbcd8f4bb2"
      },
      "source": [
        "model_ckpts = os.listdir(MODEL_PATH)\n",
        "\n",
        "print(len(model_ckpts))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fzS6bWLyEwU2",
        "colab_type": "text"
      },
      "source": [
        "# Get Dataset and Prepare"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LMFYLweasySz",
        "colab_type": "text"
      },
      "source": [
        "#### CIFAR-10"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Posjlu7lDJBd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "53cfa52d-8cc1-4cee-f8d6-7b28c0f07e19"
      },
      "source": [
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "\n",
        "y_train = y_train.flatten()\n",
        "y_test = y_test.flatten()\n",
        "\n",
        "CLASS_NAMES = (\"airplane\", \"automobile\", \"bird\", \"cat\", \"deer\", \"dog\", \"frog\", \"horse\", \"ship\", \"truck\")\n",
        "print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 2s 0us/step\n",
            "(50000, 32, 32, 3) (50000,) (10000, 32, 32, 3) (10000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QZzY01xwxS-f",
        "colab_type": "text"
      },
      "source": [
        "#### Dataloader"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6TqcgM67xxDF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "AUTO = tf.data.experimental.AUTOTUNE\n",
        "BATCH_SIZE = 128\n",
        "IMG_SHAPE = 32\n",
        "\n",
        "testloader = tf.data.Dataset.from_tensor_slices((x_test, y_test))\n",
        "\n",
        "def preprocess_image(image, label):\n",
        "  img = tf.cast(image, tf.float32)\n",
        "  img = img/255.\n",
        "\n",
        "  return img, label\n",
        "\n",
        "testloader = (\n",
        "    testloader\n",
        "    .map(preprocess_image, num_parallel_calls=AUTO)\n",
        "    .batch(BATCH_SIZE)\n",
        "    .prefetch(AUTO)\n",
        ")"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IZ1-EmWrLRVa",
        "colab_type": "text"
      },
      "source": [
        "# Deep Ensemble "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4EGLJG5tLRBi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "9422d45f-6334-4f01-e981-4eae0adc0b34"
      },
      "source": [
        "# Choose a single model randomly and load it up\n",
        "model_ckpt = model_ckpts[int(np.random.choice(len(model_ckpts), 1))]\n",
        "single_model = tf.keras.models.load_model(MODEL_PATH + model_ckpt)\n",
        "\n",
        "# Evaluate it\n",
        "accuracy_single_model = single_model.evaluate(testloader)[1] * 100\n",
        "accuracy_single_model"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "79/79 [==============================] - 3s 42ms/step - loss: 1.0363 - accuracy: 0.6266\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
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            "text/plain": [
              "62.66000270843506"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "avosm4PfLQ3M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8a8cd55f-52d4-4bd3-d1ac-d54a27a3070d"
      },
      "source": [
        "# Create list to models with different solutions\n",
        "members = [tf.keras.models.load_model(MODEL_PATH + model_ckpts[i]) \n",
        "                            for i in range(len(model_ckpts))]\n",
        "len(members)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8TzKOLvMzwCO",
        "colab_type": "text"
      },
      "source": [
        "Reference: https://machinelearningmastery.com/model-averaging-ensemble-for-deep-learning-neural-networks/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "coGl-4WMxBGH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# make an ensemble prediction for multi-class classification\n",
        "def ensemble_predictions(members):\n",
        "\t# make predictions\n",
        "\tyhats = [model.predict(x_test/255.) for model in members]\n",
        "\tyhats = np.array(yhats)\n",
        "\t# sum across ensemble members\n",
        "\tsummed = np.sum(yhats, axis=0)\n",
        "\t# argmax across classes\n",
        "\tresult = np.argmax(summed, axis=1)\n",
        "\treturn result\n",
        " \n",
        "# evaluate a specific number of members in an ensemble\n",
        "def evaluate_n_members(members):\n",
        "    accuracy_list = []\n",
        "    for n_members in tqdm_notebook(range(2, 12, 2)):\n",
        "        # select a subset of members\n",
        "        subset = members[:n_members]\n",
        "        print(len(subset))\n",
        "        # make prediction\n",
        "        yhat = ensemble_predictions(subset)\n",
        "        # calculate accuracy\n",
        "        accuracy_list.append(100*accuracy_score(y_test, yhat))\n",
        "\n",
        "    return accuracy_list"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nP3sUVfO1F72",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159,
          "referenced_widgets": [
            "7f829fe2beca4454a1db9004507e3d03",
            "314988cbaaea44b69c8fd28f63e44c08",
            "a63e380744c849d2b6f5aa3827e8345f",
            "2e4a3e172798451c93b2e248859f023c",
            "f6353766bf254f69817487ca43ea1555",
            "bbcc38388b8a4269bbf4cfad3e1c2b16",
            "4cc994120d404153ac78e30915e89aeb",
            "e454c62bca444cf3907e5c125d5a7350"
          ]
        },
        "outputId": "a6436b10-3b06-48fc-96a5-07ddede8a0a6"
      },
      "source": [
        "# Run the ensembles\n",
        "accuracy_list = evaluate_n_members(members)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7f829fe2beca4454a1db9004507e3d03",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=5.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "4\n",
            "6\n",
            "8\n",
            "10\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R7men6lu1ZoW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "accuracy_list.insert(0, accuracy_single_model)\n",
        "rng = [i for i in range(0, 12, 2)]"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c4ZxtCgUKoxt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "outputId": "bdb7fabb-cb45-4388-f47a-18f86965ff3d"
      },
      "source": [
        "plt.figure(figsize=(9,8))\n",
        "plt.plot(rng, accuracy_list, label='deep ensemble')\n",
        "plt.plot(rng, [accuracy_single_model]*len(rng), '--', label='single model')\n",
        "plt.title(\"Test accuracy as a function of ensemble size\")\n",
        "plt.xlabel(\"Ensemble size\")\n",
        "plt.ylabel(\"Test accuracy\")\n",
        "plt.legend();\n",
        "plt.savefig('ensemble_func.png')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s21FDW4qBbA1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "db58de96-5019-45f5-81c1-3a796bba46d4"
      },
      "source": [
        "wandb.init(entity='authors', project='loss-landscape', id='small_cnn_ensemble')\n",
        "wandb.log({'ensemble_function': wandb.Image('ensemble_func.png')})"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/authors/loss-landscape\" target=\"_blank\">https://app.wandb.ai/authors/loss-landscape</a><br/>\n",
              "                Run page: <a href=\"https://app.wandb.ai/authors/loss-landscape/runs/small_cnn_ensemble\" target=\"_blank\">https://app.wandb.ai/authors/loss-landscape/runs/small_cnn_ensemble</a><br/>\n",
              "            "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}