Three_class_classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Three_class_classification.ipynb",
      "provenance": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8kwDtY85NdE5"
      },
      "source": [
        "# Three-class DeepShadows\n",
        "\n",
        "In this notebook we consider two classes of artifacts and one class of LSBGs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "92u1Bq3mNgUZ"
      },
      "source": [
        "# Import basic packages\n",
        "import numpy as np\n",
        "import scipy as sp\n",
        "import pandas as pd\n",
        "from scipy import interpolate\n",
        "import random\n",
        "\n",
        "# Colab in order to download files\n",
        "from google.colab import files\n",
        "\n",
        "# =========================================\n",
        "# =========================================\n",
        "# scikit-learn for Machine Learn\n",
        "\n",
        "# Preprocessing\n",
        "from sklearn.preprocessing import StandardScaler #Standar scaler for standardization\n",
        "from sklearn.manifold import TSNE # Dimensionality reduction technique\n",
        "from sklearn.model_selection import GridSearchCV #Cross-Validation\n",
        "from sklearn.utils import resample #Resampling\n",
        "# Classifiers\n",
        "from sklearn.svm import SVC #Support Vector Classifier\n",
        "from sklearn.ensemble import RandomForestClassifier as RF #Random Forests\n",
        "# Metrics\n",
        "from sklearn.metrics import accuracy_score \n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import roc_curve\n",
        "\n",
        "# =======================================\n",
        "# =======================================\n",
        "# Tensorflow and Keras\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import regularizers\n",
        "from tensorflow.keras import optimizers\n",
        "# Keras Layers\n",
        "from keras.layers import InputLayer\n",
        "from keras.layers import Conv2D\n",
        "from keras.layers import BatchNormalization\n",
        "from keras.layers import Dropout\n",
        "from keras.layers import MaxPool2D\n",
        "from keras.layers import Flatten\n",
        "from keras.layers import Dense\n",
        "# For one-hot encoding\n",
        "from keras.utils.np_utils import to_categorical\n",
        "\n",
        "# ==============================================\n",
        "# ==============================================\n",
        "# Matplotlib, seaborn and plot pretty \n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.gridspec as gridspec\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "from matplotlib import rcParams\n",
        "rcParams['font.family'] = 'serif'\n",
        "\n",
        "# Adjust rc parameters to make plots pretty\n",
        "def plot_pretty(dpi=200, fontsize=9):\n",
        "    \n",
        "\n",
        "    plt.rc(\"savefig\", dpi=dpi)       # dpi resolution of saved image files\n",
        "    plt.rc('text', usetex=False)      # use LaTeX to process labels\n",
        "    plt.rc('font', size=fontsize)    # fontsize\n",
        "    plt.rc('xtick', direction='in')  # make axes ticks point inward\n",
        "    plt.rc('ytick', direction='in')\n",
        "    plt.rc('xtick.major', pad=10) \n",
        "    plt.rc('xtick.minor', pad=5)\n",
        "    plt.rc('ytick.major', pad=10) \n",
        "    plt.rc('ytick.minor', pad=5)\n",
        "    plt.rc('lines', dotted_pattern = [0.5, 1.1]) # fix dotted lines\n",
        "\n",
        "    return\n",
        "\n",
        "plot_pretty()\n",
        "\n",
        "# Pillow\n",
        "from PIL import Image"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8rHljx8vNgcM"
      },
      "source": [
        "### Import datasets\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lmTiXrMkR-cq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "790e2390-03e2-4549-8fe3-83df2b36f715"
      },
      "source": [
        "# Mount drive to upload the datasets\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cIkR4aCFNges"
      },
      "source": [
        "# Features - 3class\n",
        "X_tr = np.float64(np.load(\"/content/drive/My Drive/Galaxies_Data/X_train_3cat.npy\")) #Training\n",
        "X_val = np.float64(np.load(\"/content/drive/My Drive/Galaxies_Data/X_val_3cat.npy\")) #Validation\n",
        "X_test = np.float64(np.load(\"/content/drive/My Drive/Galaxies_Data/X_test_3cat.npy\")) #Test\n",
        "\n",
        "\n",
        "# Labels - 3class\n",
        "y_tr = np.float64(np.load(\"/content/drive/My Drive/Galaxies_Data/y_train_3cat.npy\")) # Training\n",
        "y_val = np.float64(np.load(\"/content/drive/My Drive/Galaxies_Data/y_val_3cat.npy\")) #Validation\n",
        "y_test = np.float64(np.load(\"/content/drive/My Drive/Galaxies_Data/y_test_3cat.npy\")) #Test\n",
        "\n",
        "\n",
        "# Categorical values of the test test\n",
        "y_test_cat = np.float64(np.load(\"/content/drive/My Drive/Galaxies_Data/y_test_cat_3cat.npy\")) #Test"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ALdYOMS-Ngje"
      },
      "source": [
        "#### Build DeepShadows model for 3-level classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uATq33wmNgoJ"
      },
      "source": [
        "# build the  3-class model\n",
        "model = keras.models.Sequential()\n",
        "model.add(InputLayer((64,64,3)))\n",
        "model.add(Conv2D(filters=16, kernel_size=(3,3), padding='same', activation='relu',kernel_regularizer=regularizers.l2(0.13)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPool2D(pool_size=(2, 2), strides=None, padding='valid'))\n",
        "model.add(Dropout(rate=0.4))\n",
        "model.add(Conv2D(filters=2*16, kernel_size=(3,3), padding='same', activation='relu',kernel_regularizer=regularizers.l2(0.13)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPool2D(pool_size=(2, 2), strides=None, padding='valid'))\n",
        "model.add(Dropout(rate=0.4))\n",
        "model.add(Conv2D(filters=2*32, kernel_size=(3,3), padding='same', activation='relu',kernel_regularizer=regularizers.l2(0.13)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPool2D(pool_size=(2, 2), strides=None, padding='valid'))\n",
        "model.add(Dropout(rate=0.4))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(units=1024, activation='relu',kernel_regularizer=regularizers.l2(0.12)))\n",
        "model.add(Dense(units=3, activation='softmax'))\n",
        "model.compile(optimizer=optimizers.Adadelta(0.1),\n",
        "              loss= 'categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cb4XjhdnNgsB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eac169d2-b048-49af-8a67-4d4c9b090f2f"
      },
      "source": [
        "model.fit(x=X_tr, y=y_tr,\n",
        "          epochs=100, batch_size=64,\n",
        "          shuffle=True,\n",
        "          validation_data=(X_val,y_val))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "704/704 [==============================] - 5s 8ms/step - loss: 48.9904 - accuracy: 0.7344 - val_loss: 4.1266 - val_accuracy: 0.7061\n",
            "Epoch 2/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 1.6950 - accuracy: 0.8156 - val_loss: 0.9431 - val_accuracy: 0.8107\n",
            "Epoch 3/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.7265 - accuracy: 0.8382 - val_loss: 0.6264 - val_accuracy: 0.8489\n",
            "Epoch 4/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.5716 - accuracy: 0.8516 - val_loss: 0.5786 - val_accuracy: 0.8353\n",
            "Epoch 5/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.5125 - accuracy: 0.8567 - val_loss: 0.6134 - val_accuracy: 0.8132\n",
            "Epoch 6/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.4827 - accuracy: 0.8601 - val_loss: 0.5453 - val_accuracy: 0.8288\n",
            "Epoch 7/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.4658 - accuracy: 0.8622 - val_loss: 0.4703 - val_accuracy: 0.8484\n",
            "Epoch 8/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.4543 - accuracy: 0.8611 - val_loss: 0.4704 - val_accuracy: 0.8633\n",
            "Epoch 9/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.4459 - accuracy: 0.8637 - val_loss: 0.4769 - val_accuracy: 0.8428\n",
            "Epoch 10/100\n",
            "704/704 [==============================] - 5s 6ms/step - loss: 0.4395 - accuracy: 0.8647 - val_loss: 0.4558 - val_accuracy: 0.8515\n",
            "Epoch 11/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.4301 - accuracy: 0.8664 - val_loss: 0.4672 - val_accuracy: 0.8440\n",
            "Epoch 12/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.4278 - accuracy: 0.8662 - val_loss: 0.4760 - val_accuracy: 0.8459\n",
            "Epoch 13/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.4224 - accuracy: 0.8678 - val_loss: 0.4050 - val_accuracy: 0.8683\n",
            "Epoch 14/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.4212 - accuracy: 0.8679 - val_loss: 0.7690 - val_accuracy: 0.6684\n",
            "Epoch 15/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.4183 - accuracy: 0.8673 - val_loss: 0.4774 - val_accuracy: 0.8367\n",
            "Epoch 16/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.4135 - accuracy: 0.8711 - val_loss: 0.4299 - val_accuracy: 0.8589\n",
            "Epoch 17/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.4108 - accuracy: 0.8703 - val_loss: 0.4456 - val_accuracy: 0.8499\n",
            "Epoch 18/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.4095 - accuracy: 0.8693 - val_loss: 0.4498 - val_accuracy: 0.8559\n",
            "Epoch 19/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.4065 - accuracy: 0.8696 - val_loss: 0.4282 - val_accuracy: 0.8644\n",
            "Epoch 20/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.4056 - accuracy: 0.8706 - val_loss: 0.4666 - val_accuracy: 0.8456\n",
            "Epoch 21/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.4041 - accuracy: 0.8708 - val_loss: 0.4820 - val_accuracy: 0.8315\n",
            "Epoch 22/100\n",
            "704/704 [==============================] - 5s 6ms/step - loss: 0.4013 - accuracy: 0.8703 - val_loss: 0.4283 - val_accuracy: 0.8523\n",
            "Epoch 23/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.4007 - accuracy: 0.8706 - val_loss: 0.4452 - val_accuracy: 0.8465\n",
            "Epoch 24/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3991 - accuracy: 0.8717 - val_loss: 0.3948 - val_accuracy: 0.8719\n",
            "Epoch 25/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3987 - accuracy: 0.8726 - val_loss: 0.4304 - val_accuracy: 0.8565\n",
            "Epoch 26/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3969 - accuracy: 0.8731 - val_loss: 0.4618 - val_accuracy: 0.8440\n",
            "Epoch 27/100\n",
            "704/704 [==============================] - 5s 6ms/step - loss: 0.3959 - accuracy: 0.8734 - val_loss: 0.4960 - val_accuracy: 0.8392\n",
            "Epoch 28/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3963 - accuracy: 0.8703 - val_loss: 0.4850 - val_accuracy: 0.8332\n",
            "Epoch 29/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3920 - accuracy: 0.8731 - val_loss: 0.3960 - val_accuracy: 0.8628\n",
            "Epoch 30/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3944 - accuracy: 0.8731 - val_loss: 0.4410 - val_accuracy: 0.8355\n",
            "Epoch 31/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3915 - accuracy: 0.8727 - val_loss: 0.4299 - val_accuracy: 0.8501\n",
            "Epoch 32/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3932 - accuracy: 0.8731 - val_loss: 0.4312 - val_accuracy: 0.8533\n",
            "Epoch 33/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3918 - accuracy: 0.8729 - val_loss: 0.3862 - val_accuracy: 0.8748\n",
            "Epoch 34/100\n",
            "704/704 [==============================] - 5s 6ms/step - loss: 0.3921 - accuracy: 0.8721 - val_loss: 0.4174 - val_accuracy: 0.8588\n",
            "Epoch 35/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3889 - accuracy: 0.8747 - val_loss: 0.3959 - val_accuracy: 0.8680\n",
            "Epoch 36/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3897 - accuracy: 0.8730 - val_loss: 0.4550 - val_accuracy: 0.8304\n",
            "Epoch 37/100\n",
            "704/704 [==============================] - 5s 6ms/step - loss: 0.3883 - accuracy: 0.8734 - val_loss: 0.4697 - val_accuracy: 0.8411\n",
            "Epoch 38/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3884 - accuracy: 0.8728 - val_loss: 0.4413 - val_accuracy: 0.8435\n",
            "Epoch 39/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3872 - accuracy: 0.8731 - val_loss: 0.4747 - val_accuracy: 0.8367\n",
            "Epoch 40/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3855 - accuracy: 0.8751 - val_loss: 0.5033 - val_accuracy: 0.8168\n",
            "Epoch 41/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3843 - accuracy: 0.8752 - val_loss: 0.4104 - val_accuracy: 0.8576\n",
            "Epoch 42/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3848 - accuracy: 0.8736 - val_loss: 0.4130 - val_accuracy: 0.8528\n",
            "Epoch 43/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3840 - accuracy: 0.8750 - val_loss: 0.5020 - val_accuracy: 0.8219\n",
            "Epoch 44/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3848 - accuracy: 0.8736 - val_loss: 0.4074 - val_accuracy: 0.8707\n",
            "Epoch 45/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3839 - accuracy: 0.8748 - val_loss: 0.4402 - val_accuracy: 0.8415\n",
            "Epoch 46/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3815 - accuracy: 0.8749 - val_loss: 0.4170 - val_accuracy: 0.8493\n",
            "Epoch 47/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3807 - accuracy: 0.8744 - val_loss: 0.3793 - val_accuracy: 0.8727\n",
            "Epoch 48/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3818 - accuracy: 0.8752 - val_loss: 0.5284 - val_accuracy: 0.7944\n",
            "Epoch 49/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3801 - accuracy: 0.8755 - val_loss: 0.4973 - val_accuracy: 0.8209\n",
            "Epoch 50/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3808 - accuracy: 0.8766 - val_loss: 0.4310 - val_accuracy: 0.8453\n",
            "Epoch 51/100\n",
            "704/704 [==============================] - 5s 6ms/step - loss: 0.3815 - accuracy: 0.8743 - val_loss: 0.3832 - val_accuracy: 0.8748\n",
            "Epoch 52/100\n",
            "704/704 [==============================] - 5s 6ms/step - loss: 0.3797 - accuracy: 0.8763 - val_loss: 0.4292 - val_accuracy: 0.8541\n",
            "Epoch 53/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3774 - accuracy: 0.8760 - val_loss: 0.4363 - val_accuracy: 0.8571\n",
            "Epoch 54/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3802 - accuracy: 0.8751 - val_loss: 0.3948 - val_accuracy: 0.8671\n",
            "Epoch 55/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3780 - accuracy: 0.8766 - val_loss: 0.3912 - val_accuracy: 0.8656\n",
            "Epoch 56/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3790 - accuracy: 0.8756 - val_loss: 0.3982 - val_accuracy: 0.8656\n",
            "Epoch 57/100\n",
            "704/704 [==============================] - 5s 6ms/step - loss: 0.3785 - accuracy: 0.8750 - val_loss: 0.5363 - val_accuracy: 0.8087\n",
            "Epoch 58/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3774 - accuracy: 0.8747 - val_loss: 0.4069 - val_accuracy: 0.8680\n",
            "Epoch 59/100\n",
            "704/704 [==============================] - 5s 6ms/step - loss: 0.3801 - accuracy: 0.8742 - val_loss: 0.4482 - val_accuracy: 0.8483\n",
            "Epoch 60/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3770 - accuracy: 0.8758 - val_loss: 0.4019 - val_accuracy: 0.8603\n",
            "Epoch 61/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3763 - accuracy: 0.8761 - val_loss: 0.3839 - val_accuracy: 0.8709\n",
            "Epoch 62/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3779 - accuracy: 0.8746 - val_loss: 0.4104 - val_accuracy: 0.8560\n",
            "Epoch 63/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3789 - accuracy: 0.8754 - val_loss: 0.3998 - val_accuracy: 0.8599\n",
            "Epoch 64/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3793 - accuracy: 0.8737 - val_loss: 0.4054 - val_accuracy: 0.8583\n",
            "Epoch 65/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3740 - accuracy: 0.8762 - val_loss: 0.4285 - val_accuracy: 0.8375\n",
            "Epoch 66/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3762 - accuracy: 0.8756 - val_loss: 0.4158 - val_accuracy: 0.8597\n",
            "Epoch 67/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3765 - accuracy: 0.8758 - val_loss: 0.4359 - val_accuracy: 0.8536\n",
            "Epoch 68/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3751 - accuracy: 0.8769 - val_loss: 0.4119 - val_accuracy: 0.8576\n",
            "Epoch 69/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3746 - accuracy: 0.8763 - val_loss: 0.4165 - val_accuracy: 0.8587\n",
            "Epoch 70/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3745 - accuracy: 0.8765 - val_loss: 0.6890 - val_accuracy: 0.7225\n",
            "Epoch 71/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3746 - accuracy: 0.8771 - val_loss: 0.4114 - val_accuracy: 0.8608\n",
            "Epoch 72/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3736 - accuracy: 0.8776 - val_loss: 0.4057 - val_accuracy: 0.8641\n",
            "Epoch 73/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3742 - accuracy: 0.8765 - val_loss: 0.4533 - val_accuracy: 0.8447\n",
            "Epoch 74/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3720 - accuracy: 0.8774 - val_loss: 0.4207 - val_accuracy: 0.8491\n",
            "Epoch 75/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3737 - accuracy: 0.8774 - val_loss: 0.4076 - val_accuracy: 0.8569\n",
            "Epoch 76/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3745 - accuracy: 0.8758 - val_loss: 0.4704 - val_accuracy: 0.8449\n",
            "Epoch 77/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3728 - accuracy: 0.8768 - val_loss: 0.3875 - val_accuracy: 0.8663\n",
            "Epoch 78/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3717 - accuracy: 0.8768 - val_loss: 0.3646 - val_accuracy: 0.8748\n",
            "Epoch 79/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3709 - accuracy: 0.8776 - val_loss: 0.3715 - val_accuracy: 0.8719\n",
            "Epoch 80/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3716 - accuracy: 0.8772 - val_loss: 0.3748 - val_accuracy: 0.8744\n",
            "Epoch 81/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3730 - accuracy: 0.8764 - val_loss: 0.3775 - val_accuracy: 0.8723\n",
            "Epoch 82/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3723 - accuracy: 0.8769 - val_loss: 0.3706 - val_accuracy: 0.8761\n",
            "Epoch 83/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3719 - accuracy: 0.8770 - val_loss: 0.5034 - val_accuracy: 0.8252\n",
            "Epoch 84/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3699 - accuracy: 0.8772 - val_loss: 0.3928 - val_accuracy: 0.8617\n",
            "Epoch 85/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3715 - accuracy: 0.8771 - val_loss: 0.4460 - val_accuracy: 0.8413\n",
            "Epoch 86/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3714 - accuracy: 0.8770 - val_loss: 0.3898 - val_accuracy: 0.8651\n",
            "Epoch 87/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3681 - accuracy: 0.8796 - val_loss: 0.4054 - val_accuracy: 0.8580\n",
            "Epoch 88/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3688 - accuracy: 0.8782 - val_loss: 0.4066 - val_accuracy: 0.8673\n",
            "Epoch 89/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3700 - accuracy: 0.8770 - val_loss: 0.3835 - val_accuracy: 0.8672\n",
            "Epoch 90/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3677 - accuracy: 0.8778 - val_loss: 0.3761 - val_accuracy: 0.8696\n",
            "Epoch 91/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3679 - accuracy: 0.8776 - val_loss: 0.5004 - val_accuracy: 0.8221\n",
            "Epoch 92/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3702 - accuracy: 0.8768 - val_loss: 0.3969 - val_accuracy: 0.8645\n",
            "Epoch 93/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3671 - accuracy: 0.8772 - val_loss: 0.3828 - val_accuracy: 0.8703\n",
            "Epoch 94/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3694 - accuracy: 0.8776 - val_loss: 0.3758 - val_accuracy: 0.8675\n",
            "Epoch 95/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3683 - accuracy: 0.8770 - val_loss: 0.3856 - val_accuracy: 0.8648\n",
            "Epoch 96/100\n",
            "704/704 [==============================] - 5s 6ms/step - loss: 0.3670 - accuracy: 0.8777 - val_loss: 0.3563 - val_accuracy: 0.8780\n",
            "Epoch 97/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3673 - accuracy: 0.8763 - val_loss: 0.3889 - val_accuracy: 0.8644\n",
            "Epoch 98/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3661 - accuracy: 0.8782 - val_loss: 0.3699 - val_accuracy: 0.8755\n",
            "Epoch 99/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3676 - accuracy: 0.8788 - val_loss: 0.4107 - val_accuracy: 0.8489\n",
            "Epoch 100/100\n",
            "704/704 [==============================] - 5s 7ms/step - loss: 0.3668 - accuracy: 0.8792 - val_loss: 0.3761 - val_accuracy: 0.8695\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f80c00996a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FLThXrnNcvsg"
      },
      "source": [
        "\n",
        "Plot History\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "luJcTcgONgm_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "db5e0863-cc06-4acc-a273-827f653db4c9"
      },
      "source": [
        "Epochs = np.arange(1,101,1)\n",
        "history_dict = model.history.history\n",
        "\n",
        "train_acc = history_dict['accuracy']\n",
        "val_acc = history_dict['val_accuracy']\n",
        "train_loss = history_dict['loss']\n",
        "val_loss = history_dict['val_loss']\n",
        "train_err = 1.0 - np.asarray(train_acc)\n",
        "val_err = 1.0 - np.asarray(val_acc)\n",
        "\n",
        "# =====================================================================\n",
        "# =====================================================================\n",
        "plt.figure(figsize=(7.0, 6.0))\n",
        "\n",
        "# Plot Accuracy\n",
        "plt.plot(Epochs, train_acc, c = 'darkred',linewidth=2.5,label='Training accuracy')\n",
        "plt.plot(Epochs, val_acc , c = 'darkorange',linewidth=2.5,label='Validation accuracy')\n",
        "# Plot Loss\n",
        "plt.plot(Epochs, train_loss, c = 'mediumblue',linewidth=2.5,label='Training loss')\n",
        "plt.plot(Epochs, val_loss , c = 'dodgerblue',linewidth=2.5,label='Validation loss')\n",
        "\n",
        "# =====================================================================\n",
        "plt.grid(ls='--',alpha=0.6)\n",
        "plt.xlabel('Epochs', fontsize=15.5);plt.ylabel('Accuracy/Loss',fontsize=15.5)\n",
        "plt.xlim(1,100)\n",
        "plt.ylim(0.2,1)\n",
        "plt.legend(frameon=True, loc='center right', fontsize=17)\n",
        "plt.tick_params(axis='both', labelsize=14)\n",
        "\n",
        "plt.tight_layout()\n",
        "#plt.savefig(\"/content/drive/My Drive/Plots/Acc_Loss_3Class.pdf\")\n",
        "#files.download(\"/content/drive/My Drive/Plots/Acc_Loss_3Class.pdf\")\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sQfDbT4JNghi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66236f91-dc0b-4215-b822-25991c768afd"
      },
      "source": [
        "# Predict on test data \n",
        "y_pred_DL = model.predict_classes(X_test)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-12-7b3808abc198>:2: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
            "Instructions for updating:\n",
            "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q-Y7uZUGci4N"
      },
      "source": [
        "Make confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JQxy2EORcjGB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "outputId": "8f9ea952-527d-4b6d-8ca2-e6865caf7a90"
      },
      "source": [
        "# Get the confusion matrix for the Deep Learning\n",
        "con_mat_DL = confusion_matrix(y_test_cat, y_pred_DL)\n",
        "class_names = ['Artifacts 1','Artifacts 2','LSBG']\n",
        "\n",
        "# Get annotations and normalized confusion matrix\n",
        "a_three = con_mat_DL[0]/np.sum(con_mat_DL[0])\n",
        "b_three = con_mat_DL[1]/np.sum(con_mat_DL[1])\n",
        "c_three = con_mat_DL[2]/np.sum(con_mat_DL[2])\n",
        "\n",
        "con_mat_norm_three = np.zeros([3,3])\n",
        "con_mat_norm_three[0] = a_three\n",
        "con_mat_norm_three[1] = b_three\n",
        "con_mat_norm_three[2] = c_three\n",
        "\n",
        "\n",
        "# Annotations\n",
        "# First line\n",
        "an_1_three = \"(\"+\"{:.3f}\".format(a_three[0])+\")\"\n",
        "an_2_three = \"(\"+\"{:.3f}\".format(a_three[1])+\")\"\n",
        "an_3_three = \"(\"+\"{:.3f}\".format(a_three[2])+\")\"\n",
        "# Second line \n",
        "an_4_three = \"(\"+\"{:.3f}\".format(b_three[0])+\")\"\n",
        "an_5_three = \"(\"+\"{:.3f}\".format(b_three[1])+\")\"\n",
        "an_6_three = \"(\"+\"{:.3f}\".format(b_three[2])+\")\"\n",
        "# Third line\n",
        "an_7_three = \"(\"+\"{:.3f}\".format(c_three[0])+\")\"\n",
        "an_8_three = \"(\"+\"{:.3f}\".format(c_three[1])+\")\"\n",
        "an_9_three = \"(\"+\"{:.3f}\".format(c_three[2])+\")\"\n",
        "\n",
        "annots_three = np.zeros([3,3],dtype='U7')\n",
        "\n",
        "annots_three[0,0] = an_1_three;annots_three[0,1] = an_2_three;annots_three[0,2] =  an_3_three\n",
        "annots_three[1,0] = an_4_three;annots_three[1,1] = an_5_three;annots_three[1,2] =  an_6_three\n",
        "annots_three[2,0] = an_7_three;annots_three[2,1] = an_8_three;annots_three[2,2] =  an_9_three\n",
        "\n",
        "# =======================================================\n",
        "# =======================================================\n",
        "plt.figure(figsize = (7.0,6.2))\n",
        "g = sns.heatmap(con_mat_DL.T, square = True, annot=True,annot_kws={'va':'bottom',\"size\": 22},fmt='.0f', cmap='Blues', cbar=False, \n",
        "           xticklabels= class_names,\n",
        "           yticklabels= class_names)\n",
        "sns.heatmap(con_mat_norm_three.T, square=True, annot=annots_three.T, annot_kws={'va':'top',\"size\": 20},cmap='Blues',fmt='', cbar=True,\n",
        "            cbar_kws={'shrink':0.94})\n",
        "g.set_xticklabels(class_names, fontsize = 18)\n",
        "g.set_yticklabels(class_names, fontsize = 18)\n",
        "\n",
        "\n",
        "\n",
        "# Ticks for colorbar\n",
        "cax = plt.gcf().axes[-1]\n",
        "cax.tick_params(labelsize=18)\n",
        "\n",
        "plt.xlabel('True label',fontsize=20)\n",
        "plt.ylabel('Predicted label',fontsize=20)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"/content/drive/My Drive/Plots/Conf_matrix_3cat_DL.pdf\")\n",
        "files.download(\"/content/drive/My Drive/Plots/Conf_matrix_3cat_DL.pdf\")\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_873c8d88-a6b7-41e8-b004-ab1ff2c21f0e\", \"Conf_matrix_3cat_DL.pdf\", 16196)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 504x446.4 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JoYFxC2lcIBd"
      },
      "source": [
        "### Reduced confusion matrix\n",
        "\n",
        "Ceate a confusion matrix "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9F8nh4RWcIH7"
      },
      "source": [
        "y_pred_reduced = np.copy(y_pred_DL)\n",
        "y_pred_reduced[y_pred_reduced==1.0] = 0.0\n",
        "y_pred_reduced[y_pred_reduced==2.0] = 1.0\n",
        "\n",
        "y_test_reduced = np.copy(y_test_cat)\n",
        "y_test_reduced[y_test_reduced==1.0] = 0.0\n",
        "y_test_reduced[y_test_reduced==2.0] = 1.0"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KcwjMmu_cIE-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "outputId": "ede0cf98-f327-4e77-d176-d477a8824086"
      },
      "source": [
        "# Get the confusion matrix for the Deep Learning\n",
        "con_mat_DL_red = confusion_matrix(y_test_reduced, y_pred_reduced)\n",
        "class_names_red = ['Artifacts','LSBG']\n",
        "\n",
        "\n",
        "# Get annotations and normalized confusion matrix\n",
        "a_red = con_mat_DL_red[0]/np.sum(con_mat_DL_red[0])\n",
        "b_red = con_mat_DL_red[1]/np.sum(con_mat_DL_red[1])\n",
        "\n",
        "con_mat_norm_red = np.zeros([2,2])\n",
        "con_mat_norm_red[0] = a_red\n",
        "con_mat_norm_red[1] = b_red\n",
        "\n",
        "\n",
        "# Annotations\n",
        "# First line\n",
        "an_1_red = \"(\"+\"{:.2f}\".format(a_red[0])+\")\"\n",
        "an_2_red = \"(\"+\"{:.2f}\".format(a_red[1])+\")\"\n",
        "# Second line\n",
        "an_3_red = \"(\"+\"{:.2f}\".format(b_red[0])+\")\"\n",
        "an_4_red = \"(\"+\"{:.2f}\".format(b_red[1])+\")\"\n",
        "\n",
        "annots_red = np.zeros([2,2],dtype='U6')\n",
        "\n",
        "annots_red[0,0] = an_1_red;annots_red[0,1] = an_2_red\n",
        "annots_red[1,0] = an_3_red;annots_red[1,1] = an_4_red\n",
        "\n",
        "\n",
        "plt.figure(figsize = (7.0,6.2))\n",
        "g = sns.heatmap(con_mat_DL_red.T, square = True, annot=True,annot_kws={'va':'bottom',\"size\": 22},fmt='.0f', cmap='Blues', cbar=False, \n",
        "           xticklabels= class_names_red,\n",
        "           yticklabels= class_names_red)\n",
        "sns.heatmap(con_mat_norm_red.T, square=True, annot=annots_red.T, annot_kws={'va':'top',\"size\": 20},cmap='Blues',fmt='', cbar=True,\n",
        "            cbar_kws={'shrink':0.94})\n",
        "\n",
        "g.set_xticklabels(class_names_red, fontsize = 19)\n",
        "g.set_yticklabels(class_names_red, fontsize = 19)\n",
        "\n",
        "# Ticks for colorbar\n",
        "cax = plt.gcf().axes[-1]\n",
        "cax.tick_params(labelsize=18)\n",
        "\n",
        "plt.xlabel('True label',fontsize=20)\n",
        "plt.ylabel('Predicted label',fontsize=20)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig(\"/content/drive/My Drive/Plots/Conf_matrix_3cat_red_DL.pdf\")\n",
        "files.download(\"/content/drive/My Drive/Plots/Conf_matrix_3cat_red_DL.pdf\")\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_ff7defbb-9c32-4007-8c9c-49285bd0de09\", \"Conf_matrix_3cat_red_DL.pdf\", 15581)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 504x446.4 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FjEUPDSLfUS_"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p2dCzEL8a9Ao"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}