scripts/Ensemble_2_class.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Ensemble_2_class.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "oodpmuve50yi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Connect to Google Drive\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V0-lEzTQ57y4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Copy the dataset from Google Drive to local\n",
        "\n",
        "!cp \"/content/gdrive/My Drive/CBIS_DDSM.zip\" .\n",
        "!unzip -qq CBIS_DDSM.zip\n",
        "!rm CBIS_DDSM.zip\n",
        "cbis_path = 'CBIS_DDSM'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n2zXzT9i6G4h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import libraries\n",
        "\n",
        "%tensorflow_version 1.x\n",
        "import os\n",
        "import numpy as np\n",
        "import cv2\n",
        "import statistics\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import models\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, Callback\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.optimizers import RMSprop, SGD, Adam, Adadelta, Adagrad, Adamax, Nadam\n",
        "from tensorflow.keras.applications import VGG16\n",
        "from tensorflow.keras.applications.vgg16 import preprocess_input"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K8wchvzg6JJS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_testing():\n",
        "    \"\"\"\n",
        "    Load test set (abnormalities patches and labels, no baseline)\n",
        "    \"\"\"\n",
        "    images = np.load(os.path.join(cbis_path, 'numpy data', 'public_test_tensor.npy'))[1::2]\n",
        "    labels = np.load(os.path.join(cbis_path, 'numpy data', 'public_test_labels.npy'))[1::2]\n",
        "    return images,labels\n",
        "\n",
        "def remap_label(l):\n",
        "    \"\"\"\n",
        "    Remap the labels to 0->mass 1->calcification\n",
        "    \"\"\"\n",
        "    if l == 1 or l == 2:\n",
        "        return 0\n",
        "    elif l == 3 or l == 4:\n",
        "        return 1\n",
        "    else:\n",
        "        print(\"Unrecognized label (%d)\" % l)\n",
        "        return None\n",
        "\n",
        "# Load test images\n",
        "test_images, test_labels= load_testing()\n",
        "test_images_vgg, _ = load_testing()\n",
        "\n",
        "# Remap test labels\n",
        "test_labels = np.array([remap_label(l) for l in test_labels])\n",
        "\n",
        "# Number of images\n",
        "n_test_img = test_images.shape[0]\n",
        "\n",
        "# Compute width and height of images\n",
        "img_w = test_images.shape[1]\n",
        "img_h = test_images.shape[2]\n",
        "\n",
        "# Create a new dimension for color in the images arrays\n",
        "test_images = test_images.reshape((n_test_img, img_w, img_h, 1))\n",
        "test_images_vgg = test_images_vgg.reshape((n_test_img, img_w, img_h, 1))\n",
        "\n",
        "# Rescale values to (0-1), or (0-255) for VGG\n",
        "test_images = test_images.astype('uint16') / 65535\n",
        "test_images_vgg = test_images_vgg.astype('uint16') / 256\n",
        "\n",
        "# Replicate the only channel 3 times for VGG16\n",
        "test_images_vgg = np.repeat(test_images_vgg, 3, axis=3)\n",
        "\n",
        "# Normalize VGG images\n",
        "preprocess_input(test_images_vgg);"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PopXkdA78otR",
        "colab_type": "code",
        "outputId": "16d17ea8-cc53-490b-9a0f-f4f96a6c7c3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        }
      },
      "source": [
        "# Make sure images are still ok\n",
        "\n",
        "idx = 0\n",
        "\n",
        "plt.imshow(test_images[idx][:,:,0], cmap='gray')\n",
        "plt.figure()\n",
        "\n",
        "plt.imshow(test_images_vgg[idx][:,:,0]/256, cmap='gray')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RnUcd-7BABV5",
        "colab_type": "code",
        "outputId": "b7c48e68-05e8-4853-8c56-ba3398360be9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "!cp \"/content/gdrive/My Drive/models/model_5_2cl_best.h5\" .\n",
        "!cp \"/content/gdrive/My Drive/models/model_6_2cl_best.h5\" .\n",
        "!cp \"/content/gdrive/My Drive/models/model_9_2cl_best.h5\" .\n",
        "!cp \"/content/gdrive/My Drive/models/vgg16_ft_2layer_2cl_best.h5\" .\n",
        "\n",
        "model5 = models.load_model('model_5_2cl_best.h5');\n",
        "model6 = models.load_model('model_6_2cl_best.h5');\n",
        "model9 = models.load_model('model_9_2cl_best.h5');\n",
        "modelvgg = models.load_model('vgg16_ft_2layer_2cl_best.h5');"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/init_ops.py:97: calling GlorotUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/init_ops.py:97: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dPvCqa4Q6Smw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "_, acc5 = model5.evaluate(test_images, test_labels, verbose=False)\n",
        "_, acc6 = model6.evaluate(test_images, test_labels, verbose=False)\n",
        "_, acc9 = model9.evaluate(test_images, test_labels, verbose=False)\n",
        "_, accvgg = modelvgg.evaluate(test_images_vgg, test_labels, verbose=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yIFL7itZ3TwI",
        "colab_type": "text"
      },
      "source": [
        "## Ensemble 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2A5XVdvI3Z9e",
        "colab_type": "text"
      },
      "source": [
        "Three models: M5, M6 and M9"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zPxIafMEBZEJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ensemble1_cl = np.array([\n",
        "        np.squeeze(model5.predict_classes(test_images)).tolist(), \n",
        "        np.squeeze(model6.predict_classes(test_images)).tolist(), \n",
        "        np.squeeze(model9.predict_classes(test_images)).tolist() \n",
        "        ]\n",
        ")\n",
        "\n",
        "ensemble1_pred = np.array([\n",
        "        np.squeeze(model5.predict(test_images)).tolist(), \n",
        "        np.squeeze(model6.predict(test_images)).tolist(), \n",
        "        np.squeeze(model9.predict(test_images)).tolist()\n",
        "        ]\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o6vwngD-B5rI",
        "colab_type": "code",
        "outputId": "09c044a4-2b18-431a-fecb-3f2fb866d6cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 918
        }
      },
      "source": [
        "# Accuracy by voting\n",
        "ensemble1_voting_pred = np.array([statistics.mode([ensemble1_cl[m][e] for m in range(len(ensemble1_cl))]) for e in range(n_test_img)])\n",
        "ensemble1_voting_right = sum([p == l for p, l in zip(ensemble1_voting_pred, test_labels)])\n",
        "ensemble1_voting_acc = ensemble1_voting_right / n_test_img\n",
        "\n",
        "# Accuracy by averaging\n",
        "ensemble1_avg_pred = np.array([statistics.mean([ensemble1_pred[m][e] for m in range(len(ensemble1_pred))]) for e in range(n_test_img)])\n",
        "ensemble1_avg_right = sum([(abs(p-l) < 0.5) for p, l in zip(ensemble1_avg_pred, test_labels)])\n",
        "ensemble1_avg_acc = ensemble1_avg_right / n_test_img\n",
        "\n",
        "print(\"\\nM5\")\n",
        "print(ensemble1_cl[0])\n",
        "print(\"Acc: %.4f\" % acc5)\n",
        "print(\"\\nM6\")\n",
        "print(ensemble1_cl[1])\n",
        "print(\"Acc: %.4f\" % acc6)\n",
        "print(\"\\nM9\")\n",
        "print(ensemble1_cl[2])\n",
        "print(\"Acc: %.4f\" % acc9)\n",
        "print(\"\\nEnsemble\")\n",
        "print(ensemble1_voting_pred)\n",
        "\n",
        "print(\"Voting accuracy: %.4f\" % ensemble1_voting_acc)\n",
        "print(\"Averaging accuracy: %.4f\" % ensemble1_avg_acc)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "M5\n",
            "[0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1\n",
            " 0 0 1 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1\n",
            " 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 0 1 1\n",
            " 1 0 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1\n",
            " 1 1 1]\n",
            "Acc: 0.8869\n",
            "\n",
            "M6\n",
            "[0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1\n",
            " 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1\n",
            " 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 0 0 0 0 1\n",
            " 1 1 1]\n",
            "Acc: 0.8780\n",
            "\n",
            "M9\n",
            "[0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1\n",
            " 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1\n",
            " 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1\n",
            " 0 0 1 0 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1\n",
            " 1 1 1]\n",
            "Acc: 0.8810\n",
            "\n",
            "Ensemble\n",
            "[0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1\n",
            " 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1\n",
            " 0 0 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1\n",
            " 1 1 1]\n",
            "Voting accuracy: 0.8958\n",
            "Averaging accuracy: 0.8899\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "855z0QGq3kxg",
        "colab_type": "text"
      },
      "source": [
        "## Ensemble 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nzr4cAq_z6El",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ensemble2_cl = np.array([\n",
        "        np.squeeze(model5.predict_classes(test_images)).tolist(), \n",
        "        np.squeeze(model6.predict_classes(test_images)).tolist(), \n",
        "        np.squeeze(model9.predict_classes(test_images)).tolist(),\n",
        "        np.squeeze(modelvgg.predict_classes(test_images_vgg)).tolist(),\n",
        "        np.squeeze(modelvgg.predict_classes(test_images_vgg)).tolist()\n",
        "        ]\n",
        ")\n",
        "\n",
        "ensemble2_pred = np.array([\n",
        "        np.squeeze(model5.predict(test_images)).tolist(), \n",
        "        np.squeeze(model6.predict(test_images)).tolist(), \n",
        "        np.squeeze(model9.predict(test_images)).tolist(),\n",
        "        np.squeeze(modelvgg.predict(test_images_vgg)).tolist(),\n",
        "        np.squeeze(modelvgg.predict(test_images_vgg)).tolist()\n",
        "        ]\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MIJ1IaXO4O72",
        "colab_type": "code",
        "outputId": "90c54a47-2590-4c2a-bf25-b8191d958fdc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Accuracy by voting\n",
        "ensemble2_voting_pred = np.array([statistics.mode([ensemble2_cl[m][e] for m in range(len(ensemble2_cl))]) for e in range(n_test_img)])\n",
        "ensemble2_voting_right = sum([p == l for p, l in zip(ensemble2_voting_pred, test_labels)])\n",
        "ensemble2_voting_acc = ensemble2_voting_right / n_test_img\n",
        "\n",
        "# Accuracy by averaging\n",
        "ensemble2_avg_pred = np.array([statistics.mean([ensemble2_pred[m][e] for m in range(len(ensemble2_pred))]) for e in range(n_test_img)])\n",
        "ensemble2_avg_right = sum([(abs(p-l) < 0.5) for p, l in zip(ensemble2_avg_pred, test_labels)])\n",
        "ensemble2_avg_acc = ensemble2_avg_right / n_test_img\n",
        "\n",
        "print(\"\\nM5\")\n",
        "print(ensemble2_cl[0])\n",
        "print(\"Acc: %.4f\" % acc5)\n",
        "\n",
        "print(\"\\nM6\")\n",
        "print(ensemble2_cl[1])\n",
        "print(\"Acc: %.4f\" % acc6)\n",
        "\n",
        "print(\"\\nM9\")\n",
        "print(ensemble2_cl[2])\n",
        "print(\"Acc: %.4f\" % acc9)\n",
        "\n",
        "print(\"\\nVGG\")\n",
        "print(ensemble2_cl[3])\n",
        "print(\"Acc: %.4f\" % accvgg)\n",
        "\n",
        "print(\"\\nEnsemble\")\n",
        "print(ensemble2_voting_pred)\n",
        "\n",
        "print(\"Voting accuracy: %.4f\" % ensemble2_voting_acc)\n",
        "print(\"Averaging accuracy: %.4f\" % ensemble2_avg_acc)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "M5\n",
            "[0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1\n",
            " 0 0 1 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1\n",
            " 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 0 1 1\n",
            " 1 0 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1\n",
            " 1 1 1]\n",
            "Acc: 0.8869\n",
            "\n",
            "M6\n",
            "[0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1\n",
            " 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1\n",
            " 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 0 0 0 0 1\n",
            " 1 1 1]\n",
            "Acc: 0.8780\n",
            "\n",
            "M9\n",
            "[0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1\n",
            " 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1\n",
            " 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1\n",
            " 0 0 1 0 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1\n",
            " 1 1 1]\n",
            "Acc: 0.8810\n",
            "\n",
            "VGG\n",
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0\n",
            " 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1\n",
            " 1 1 1]\n",
            "Acc: 0.9137\n",
            "\n",
            "Ensemble\n",
            "[0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1\n",
            " 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1\n",
            " 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1\n",
            " 1 1 1]\n",
            "Voting accuracy: 0.9107\n",
            "Averaging accuracy: 0.9107\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}