autoencoder.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\n",
            "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
            " 81 2509k   81 2052k    0     0  3014k      0 --:--:-- --:--:-- --:--:-- 3023k\n",
            "100 2509k  100 2509k    0     0  3562k      0 --:--:-- --:--:-- --:--:-- 3569k\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting pip\n",
            "  Using cached pip-22.3-py3-none-any.whl (2.1 MB)\n",
            "Installing collected packages: pip\n",
            "  Attempting uninstall: pip\n",
            "    Found existing installation: pip 22.3\n",
            "    Uninstalling pip-22.3:\n",
            "      Successfully uninstalled pip-22.3\n",
            "Successfully installed pip-22.3\n"
          ]
        }
      ],
      "source": [
        "!curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py\n",
        "!python get-pip.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "MKaYXJf_3Gsj",
        "outputId": "c9d5d90c-021e-4aa9-9566-72a8a4e043f9"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Source IP</th>\n",
              "      <th>Source Port</th>\n",
              "      <th>Destination IP</th>\n",
              "      <th>Destination Port</th>\n",
              "      <th>Protocol</th>\n",
              "      <th>Flow Duration</th>\n",
              "      <th>Total Fwd Packets</th>\n",
              "      <th>Total Backward Packets</th>\n",
              "      <th>Total Length of Fwd Packets</th>\n",
              "      <th>Total Length of Bwd Packets</th>\n",
              "      <th>...</th>\n",
              "      <th>min_seg_size_forward</th>\n",
              "      <th>Active Mean</th>\n",
              "      <th>Active Std</th>\n",
              "      <th>Active Max</th>\n",
              "      <th>Active Min</th>\n",
              "      <th>Idle Mean</th>\n",
              "      <th>Idle Std</th>\n",
              "      <th>Idle Max</th>\n",
              "      <th>Idle Min</th>\n",
              "      <th>Label</th>\n",
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              "  </thead>\n",
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              "      <td>18016315184</td>\n",
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              "      <td>6.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>20.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10420151</td>\n",
              "      <td>46440.0</td>\n",
              "      <td>123125115190</td>\n",
              "      <td>80.0</td>\n",
              "      <td>6.0</td>\n",
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              "      <td>0.0</td>\n",
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              "<p>5 rows × 83 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Source IP  Source Port  Destination IP  Destination Port  Protocol  \\\n",
              "0     1042042      47294.0       772344443              80.0       6.0   \n",
              "1  9813922543        443.0         1042042           55559.0       6.0   \n",
              "2     1042042      58579.0     18016315184              80.0       6.0   \n",
              "3    10420151      46440.0    123125115190              80.0       6.0   \n",
              "4  1191467434         80.0        10420211           51215.0       6.0   \n",
              "\n",
              "   Flow Duration  Total Fwd Packets  Total Backward Packets  \\\n",
              "0      1305334.0                8.0                     6.0   \n",
              "1         2910.0                1.0                     1.0   \n",
              "2     43792665.0                2.0                     0.0   \n",
              "3      2461988.0                2.0                     0.0   \n",
              "4           36.0                2.0                     0.0   \n",
              "\n",
              "   Total Length of Fwd Packets  Total Length of Bwd Packets  ...  \\\n",
              "0                        580.0                        254.0  ...   \n",
              "1                          0.0                          0.0  ...   \n",
              "2                          0.0                          0.0  ...   \n",
              "3                          0.0                          0.0  ...   \n",
              "4                          0.0                          0.0  ...   \n",
              "\n",
              "   min_seg_size_forward  Active Mean  Active Std  Active Max  Active Min  \\\n",
              "0                  32.0          0.0         0.0         0.0         0.0   \n",
              "1                  32.0          0.0         0.0         0.0         0.0   \n",
              "2                  20.0          0.0         0.0         0.0         0.0   \n",
              "3                  20.0          0.0         0.0         0.0         0.0   \n",
              "4                  20.0          0.0         0.0         0.0         0.0   \n",
              "\n",
              "   Idle Mean  Idle Std  Idle Max  Idle Min  Label  \n",
              "0        0.0       0.0       0.0       0.0      0  \n",
              "1        0.0       0.0       0.0       0.0      0  \n",
              "2        0.0       0.0       0.0       0.0      7  \n",
              "3        0.0       0.0       0.0       0.0      8  \n",
              "4        0.0       0.0       0.0       0.0      3  \n",
              "\n",
              "[5 rows x 83 columns]"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Load csv file into a dataframe\n",
        "import pandas as pd\n",
        "Data = pd.read_csv(\"train.csv\")\n",
        "Data.drop([\"Flow ID\",\"Timestamp\"],axis=1,inplace=True)\n",
        "Data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F866vonl4FBW",
        "outputId": "516a5893-9b66-4c93-b6bc-f19d8dd01f2e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0     44517\n",
              "1     39765\n",
              "2     33678\n",
              "3     33618\n",
              "4     22197\n",
              "5     15026\n",
              "6     12371\n",
              "7      7429\n",
              "8      6401\n",
              "9      6065\n",
              "10     5993\n",
              "11      419\n",
              "Name: Label, dtype: int64"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Data['Label'].map({1:0,2:1,3:2,4:3,5:4,6:5,7:6,8:7,9:8,10:9,11:10,12:11})\n",
        "Data['Label'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "FkAcLrCG4Ntk"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "D1,D2=train_test_split(Data,test_size=0.5,random_state = 100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KCl9SmOz4RaQ",
        "outputId": "3fca775c-4c09-4bfb-ff32-3b0e839fdf3e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(113739, 83)"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "D1.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "HwFyl_XQ4YRI"
      },
      "outputs": [],
      "source": [
        "D1_y = D1['Label']\n",
        "D1_x = D1.drop('Label', axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "v8tbHA3I4geu"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "D3,D4=train_test_split(D2,test_size=0.2,random_state = 100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "ezLFtvkn4hZM"
      },
      "outputs": [],
      "source": [
        "D4_y = D4['Label']\n",
        "D4_x = D4.drop('Label', axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "_kGPNI_a4lL_"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "D1_x_train, D1_x_test, D1_y_train, D1_y_test = train_test_split(D1_x,D1_y,test_size=0.2, random_state =100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "TqBH2LyA5Qcf"
      },
      "outputs": [],
      "source": [
        "D3_y = D3['Label']\n",
        "D3_x = D3.drop('Label', axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "425fsXBj5Wb3"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "D3_x_train, D3_x_test, D3_y_train, D3_y_test = train_test_split(D3_x,D3_y,test_size=0.2, random_state =100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "q5OeRflX5cvh"
      },
      "outputs": [],
      "source": [
        "# scale data using MinMaxScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "nor = MinMaxScaler()\n",
        "\n",
        "nor.fit(D1_x_train)\n",
        "D1_x_train_scaled = nor.transform(D1_x_train)\n",
        "nor.fit(D1_x_test)\n",
        "D1_x_test_scaled = nor.transform(D1_x_test)\n",
        "\n",
        "# scale data using MinMaxScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "nor = MinMaxScaler()\n",
        "\n",
        "nor.fit(D3_x_train)\n",
        "D3_x_train_scaled = nor.transform(D3_x_train)\n",
        "nor.fit(D3_x_test)\n",
        "D3_x_test_scaled = nor.transform(D3_x_test)\n",
        "\n",
        "# scale data using MinMaxScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "nor = MinMaxScaler()\n",
        "\n",
        "nor.fit(D4_x)\n",
        "D4_x_scaled = nor.transform(D4_x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F8ytZ9T_5oqA",
        "outputId": "f075186d-f312-4bdf-9c53-e9a1253452d2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"autoencoder\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " encoder_input (InputLayer)  [(None, 82)]              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 246)               20418     \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 246)              984       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 164)               40508     \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 164)              656       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 82)                13530     \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 82)               328       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 72)                5976      \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 72)               288       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 62)                4526      \n",
            "                                                                 \n",
            " batch_normalization_4 (Batc  (None, 62)               248       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 52)                3276      \n",
            "                                                                 \n",
            " batch_normalization_5 (Batc  (None, 52)               208       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 42)                2226      \n",
            "                                                                 \n",
            " batch_normalization_6 (Batc  (None, 42)               168       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 32)                1376      \n",
            "                                                                 \n",
            " batch_normalization_7 (Batc  (None, 32)               128       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 22)                726       \n",
            "                                                                 \n",
            " batch_normalization_8 (Batc  (None, 22)               88        \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 12)                276       \n",
            "                                                                 \n",
            " batch_normalization_9 (Batc  (None, 12)               48        \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 100)               1300      \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 100)               10100     \n",
            "                                                                 \n",
            " dense_12 (Dense)            (None, 12)                1212      \n",
            "                                                                 \n",
            " batch_normalization_10 (Bat  (None, 12)               48        \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_13 (Dense)            (None, 22)                286       \n",
            "                                                                 \n",
            " batch_normalization_11 (Bat  (None, 22)               88        \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_14 (Dense)            (None, 32)                736       \n",
            "                                                                 \n",
            " batch_normalization_12 (Bat  (None, 32)               128       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_15 (Dense)            (None, 42)                1386      \n",
            "                                                                 \n",
            " batch_normalization_13 (Bat  (None, 42)               168       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_16 (Dense)            (None, 52)                2236      \n",
            "                                                                 \n",
            " batch_normalization_14 (Bat  (None, 52)               208       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_17 (Dense)            (None, 62)                3286      \n",
            "                                                                 \n",
            " batch_normalization_15 (Bat  (None, 62)               248       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_18 (Dense)            (None, 72)                4536      \n",
            "                                                                 \n",
            " batch_normalization_16 (Bat  (None, 72)               288       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_19 (Dense)            (None, 82)                5986      \n",
            "                                                                 \n",
            " batch_normalization_17 (Bat  (None, 82)               328       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_20 (Dense)            (None, 164)               13612     \n",
            "                                                                 \n",
            " batch_normalization_18 (Bat  (None, 164)              656       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_21 (Dense)            (None, 246)               40590     \n",
            "                                                                 \n",
            " batch_normalization_19 (Bat  (None, 246)              984       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_22 (Dense)            (None, 82)                20254     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 204,646\n",
            "Trainable params: 201,502\n",
            "Non-trainable params: 3,144\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#APPLY AUTOENCODERS \n",
        "#from tensorflow.keras.optimizers import SGD\n",
        "from tensorflow.keras.layers import Input, Dense, Activation, BatchNormalization\n",
        "from tensorflow.keras.models import Model, load_model\n",
        "#from tensorflow.keras.initializers import glorot_uniform\n",
        "#from keras.optimizers import SGD\n",
        "\n",
        "encoding_dim = 100\n",
        "\n",
        "n_inputs= D3_x_train_scaled.shape[1]\n",
        "\n",
        "#Now, we want to define our encoder.\n",
        "encoder_input = Input(shape=(n_inputs, ),name='encoder_input')\n",
        "\n",
        "x1 = Dense(n_inputs*3, activation=\"relu\")(encoder_input)\n",
        "x1 = BatchNormalization()(x1)\n",
        "\n",
        "x2 = Dense(n_inputs*2, activation=\"relu\")(x1)\n",
        "x2 = BatchNormalization()(x2)\n",
        "\n",
        "x3 = Dense(n_inputs, activation=\"relu\")(x2)\n",
        "x3 = BatchNormalization()(x3)\n",
        "\n",
        "x4 = Dense(n_inputs-10, activation=\"relu\")(x3)\n",
        "x4 = BatchNormalization()(x4)\n",
        "\n",
        "x5 = Dense(n_inputs-20, activation=\"relu\")(x4)\n",
        "x5 = BatchNormalization()(x5)\n",
        "\n",
        "x6 = Dense(n_inputs-30, activation=\"relu\")(x5)\n",
        "x6 = BatchNormalization()(x6)\n",
        "\n",
        "x7 = Dense(n_inputs-40, activation=\"relu\")(x6)\n",
        "x7 = BatchNormalization()(x7)\n",
        "\n",
        "x8 = Dense (n_inputs-50, activation= \"relu\")(x7)\n",
        "x8 = BatchNormalization()(x8)\n",
        "\n",
        "x9 = Dense (n_inputs-60, activation= \"relu\")(x8)\n",
        "x9 = BatchNormalization()(x9)\n",
        "\n",
        "x10 = Dense (n_inputs-70, activation= \"relu\")(x9)\n",
        "x10 = BatchNormalization()(x10)\n",
        "\n",
        "encoder_output = Dense (encoding_dim, activation= \"relu\")(x10)\n",
        "\n",
        "#Encoder Model\n",
        "encoder = Model(encoder_input, encoder_output, name='encoder')\n",
        "\n",
        "#Now, we want to define our decoder.\n",
        "decoder_input = Dense(encoding_dim, activation=\"relu\")(encoder_output)\n",
        "\n",
        "y1 = Dense(n_inputs-70, activation=\"relu\")(decoder_input)\n",
        "y1 = BatchNormalization()(y1)            \n",
        "\n",
        "y2 = Dense (n_inputs-60, activation= \"relu\")(y1)\n",
        "y2 = BatchNormalization()(y2) \n",
        "\n",
        "y3 = Dense (n_inputs-50, activation= \"relu\")(y2)\n",
        "y3 = BatchNormalization()(y3) \n",
        "\n",
        "y4 = Dense (n_inputs-40, activation= \"relu\")(y3)\n",
        "y4 = BatchNormalization()(y4) \n",
        "\n",
        "y5 = Dense (n_inputs-30, activation= \"relu\")(y4)\n",
        "y5 = BatchNormalization()(y5) \n",
        "                 \n",
        "y6 = Dense (n_inputs-20, activation= \"relu\")(y5)\n",
        "y6 = BatchNormalization()(y6) \n",
        "\n",
        "y7 = Dense (n_inputs-10, activation= \"relu\")(y6)\n",
        "y7 = BatchNormalization()(y7) \n",
        "\n",
        "y8 = Dense (n_inputs, activation= \"relu\")(y7)\n",
        "y8 = BatchNormalization()(y8) \n",
        "\n",
        "y9 = Dense (n_inputs*2, activation= \"relu\")(y8)\n",
        "y9 = BatchNormalization()(y9) \n",
        "                                                        \n",
        "y10 = Dense (n_inputs*3, activation= \"relu\")(y9)\n",
        "y10 = BatchNormalization()(y10) \n",
        "\n",
        "decoder_output = Dense (n_inputs, activation= \"linear\")(y10)\n",
        "\n",
        "#Decoder Model\n",
        "decoder = Model(encoder_output, decoder_output, name= 'decoder')\n",
        "\n",
        "#We'll also combine this encoder and decoder into a singular \"autoencoder\" model:\n",
        "autoencoder = Model(encoder_input, decoder_output, name='autoencoder')\n",
        "\n",
        "#Now that the model architecture is done, we'll set an optimizer:\n",
        "import tensorflow as tf  \n",
        "opt = tf.keras.optimizers.Adam(learning_rate=0.0001, decay=1e-6)\n",
        "\n",
        "autoencoder.summary()\n",
        "\n",
        "#We'll now compile our model with the optimizer and a loss metric. We'll use mean squared error for loss (mse).\n",
        "autoencoder.compile(optimizer= opt, loss='mean_squared_error')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MunsNWiv5-DR",
        "outputId": "f3f54f4b-d107-4e16-fd44-9c70658387e1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "4550/4550 - 40s - loss: 0.1387 - val_loss: 0.2977 - 40s/epoch - 9ms/step\n",
            "Epoch 2/100\n",
            "4550/4550 - 31s - loss: 0.0237 - val_loss: 0.0887 - 31s/epoch - 7ms/step\n",
            "Epoch 3/100\n",
            "4550/4550 - 30s - loss: 0.0135 - val_loss: 0.0662 - 30s/epoch - 7ms/step\n",
            "Epoch 4/100\n",
            "4550/4550 - 30s - loss: 0.0099 - val_loss: 0.0604 - 30s/epoch - 7ms/step\n",
            "Epoch 5/100\n",
            "4550/4550 - 44s - loss: 0.0084 - val_loss: 0.0559 - 44s/epoch - 10ms/step\n",
            "Epoch 6/100\n",
            "4550/4550 - 45s - loss: 0.0075 - val_loss: 0.0539 - 45s/epoch - 10ms/step\n",
            "Epoch 7/100\n",
            "4550/4550 - 45s - loss: 0.0069 - val_loss: 0.0533 - 45s/epoch - 10ms/step\n",
            "Epoch 8/100\n",
            "4550/4550 - 46s - loss: 0.0066 - val_loss: 0.0501 - 46s/epoch - 10ms/step\n",
            "Epoch 9/100\n",
            "4550/4550 - 46s - loss: 0.0062 - val_loss: 0.0517 - 46s/epoch - 10ms/step\n",
            "Epoch 10/100\n",
            "4550/4550 - 46s - loss: 0.0060 - val_loss: 0.0522 - 46s/epoch - 10ms/step\n",
            "Epoch 11/100\n",
            "4550/4550 - 45s - loss: 0.0058 - val_loss: 0.0504 - 45s/epoch - 10ms/step\n",
            "Epoch 12/100\n",
            "4550/4550 - 46s - loss: 0.0056 - val_loss: 0.0515 - 46s/epoch - 10ms/step\n",
            "Epoch 13/100\n",
            "4550/4550 - 46s - loss: 0.0054 - val_loss: 0.0516 - 46s/epoch - 10ms/step\n",
            "Epoch 14/100\n",
            "4550/4550 - 46s - loss: 0.0053 - val_loss: 0.0518 - 46s/epoch - 10ms/step\n",
            "Epoch 15/100\n",
            "4550/4550 - 46s - loss: 0.0051 - val_loss: 0.0521 - 46s/epoch - 10ms/step\n",
            "Epoch 16/100\n",
            "4550/4550 - 46s - loss: 0.0051 - val_loss: 0.0523 - 46s/epoch - 10ms/step\n",
            "Epoch 17/100\n",
            "4550/4550 - 45s - loss: 0.0049 - val_loss: 0.0510 - 45s/epoch - 10ms/step\n",
            "Epoch 18/100\n",
            "4550/4550 - 47s - loss: 0.0049 - val_loss: 0.0511 - 47s/epoch - 10ms/step\n",
            "Epoch 19/100\n",
            "4550/4550 - 46s - loss: 0.0048 - val_loss: 0.0527 - 46s/epoch - 10ms/step\n",
            "Epoch 20/100\n",
            "4550/4550 - 47s - loss: 0.0047 - val_loss: 0.0518 - 47s/epoch - 10ms/step\n",
            "Epoch 21/100\n",
            "4550/4550 - 46s - loss: 0.0046 - val_loss: 0.0512 - 46s/epoch - 10ms/step\n",
            "Epoch 22/100\n",
            "4550/4550 - 46s - loss: 0.0046 - val_loss: 0.0524 - 46s/epoch - 10ms/step\n",
            "Epoch 23/100\n",
            "4550/4550 - 44s - loss: 0.0045 - val_loss: 0.0530 - 44s/epoch - 10ms/step\n",
            "Epoch 24/100\n",
            "4550/4550 - 50s - loss: 0.0044 - val_loss: 0.0512 - 50s/epoch - 11ms/step\n",
            "Epoch 25/100\n",
            "4550/4550 - 47s - loss: 0.0044 - val_loss: 0.0517 - 47s/epoch - 10ms/step\n",
            "Epoch 26/100\n",
            "4550/4550 - 52s - loss: 0.0043 - val_loss: 0.0527 - 52s/epoch - 11ms/step\n",
            "Epoch 27/100\n",
            "4550/4550 - 52s - loss: 0.0043 - val_loss: 0.0522 - 52s/epoch - 11ms/step\n",
            "Epoch 28/100\n",
            "4550/4550 - 47s - loss: 0.0043 - val_loss: 0.0524 - 47s/epoch - 10ms/step\n",
            "Epoch 29/100\n",
            "4550/4550 - 55s - loss: 0.0043 - val_loss: 0.0548 - 55s/epoch - 12ms/step\n",
            "Epoch 30/100\n",
            "4550/4550 - 54s - loss: 0.0042 - val_loss: 0.0535 - 54s/epoch - 12ms/step\n",
            "Epoch 31/100\n",
            "4550/4550 - 54s - loss: 0.0041 - val_loss: 0.0554 - 54s/epoch - 12ms/step\n",
            "Epoch 32/100\n",
            "4550/4550 - 55s - loss: 0.0041 - val_loss: 0.0529 - 55s/epoch - 12ms/step\n",
            "Epoch 33/100\n",
            "4550/4550 - 54s - loss: 0.0040 - val_loss: 0.0569 - 54s/epoch - 12ms/step\n",
            "Epoch 34/100\n",
            "4550/4550 - 53s - loss: 0.0040 - val_loss: 0.0559 - 53s/epoch - 12ms/step\n",
            "Epoch 35/100\n",
            "4550/4550 - 52s - loss: 0.0040 - val_loss: 0.0567 - 52s/epoch - 11ms/step\n",
            "Epoch 36/100\n",
            "4550/4550 - 46s - loss: 0.0040 - val_loss: 0.0577 - 46s/epoch - 10ms/step\n",
            "Epoch 37/100\n",
            "4550/4550 - 36s - loss: 0.0039 - val_loss: 0.0537 - 36s/epoch - 8ms/step\n",
            "Epoch 38/100\n",
            "4550/4550 - 36s - loss: 0.0040 - val_loss: 0.0545 - 36s/epoch - 8ms/step\n",
            "Epoch 39/100\n",
            "4550/4550 - 36s - loss: 0.0039 - val_loss: 0.0543 - 36s/epoch - 8ms/step\n",
            "Epoch 40/100\n",
            "4550/4550 - 36s - loss: 0.0038 - val_loss: 0.0579 - 36s/epoch - 8ms/step\n",
            "Epoch 41/100\n",
            "4550/4550 - 37s - loss: 0.0038 - val_loss: 0.0553 - 37s/epoch - 8ms/step\n",
            "Epoch 42/100\n",
            "4550/4550 - 39s - loss: 0.0038 - val_loss: 0.0555 - 39s/epoch - 9ms/step\n",
            "Epoch 43/100\n",
            "4550/4550 - 37s - loss: 0.0038 - val_loss: 0.0557 - 37s/epoch - 8ms/step\n",
            "Epoch 44/100\n",
            "4550/4550 - 37s - loss: 0.0037 - val_loss: 0.0533 - 37s/epoch - 8ms/step\n",
            "Epoch 45/100\n",
            "4550/4550 - 39s - loss: 0.0036 - val_loss: 0.0520 - 39s/epoch - 9ms/step\n",
            "Epoch 46/100\n",
            "4550/4550 - 46s - loss: 0.0036 - val_loss: 0.0527 - 46s/epoch - 10ms/step\n",
            "Epoch 47/100\n",
            "4550/4550 - 39s - loss: 0.0036 - val_loss: 0.0530 - 39s/epoch - 9ms/step\n",
            "Epoch 48/100\n",
            "4550/4550 - 38s - loss: 0.0036 - val_loss: 0.0530 - 38s/epoch - 8ms/step\n",
            "Epoch 49/100\n",
            "4550/4550 - 40s - loss: 0.0035 - val_loss: 0.0520 - 40s/epoch - 9ms/step\n",
            "Epoch 50/100\n",
            "4550/4550 - 39s - loss: 0.0035 - val_loss: 0.0534 - 39s/epoch - 9ms/step\n",
            "Epoch 51/100\n",
            "4550/4550 - 40s - loss: 0.0035 - val_loss: 0.0547 - 40s/epoch - 9ms/step\n",
            "Epoch 52/100\n",
            "4550/4550 - 39s - loss: 0.0035 - val_loss: 0.0532 - 39s/epoch - 8ms/step\n",
            "Epoch 53/100\n",
            "4550/4550 - 38s - loss: 0.0035 - val_loss: 0.0545 - 38s/epoch - 8ms/step\n",
            "Epoch 54/100\n",
            "4550/4550 - 39s - loss: 0.0034 - val_loss: 0.0529 - 39s/epoch - 8ms/step\n",
            "Epoch 55/100\n",
            "4550/4550 - 40s - loss: 0.0034 - val_loss: 0.0541 - 40s/epoch - 9ms/step\n",
            "Epoch 56/100\n",
            "4550/4550 - 39s - loss: 0.0034 - val_loss: 0.0540 - 39s/epoch - 9ms/step\n",
            "Epoch 57/100\n",
            "4550/4550 - 39s - loss: 0.0033 - val_loss: 0.0534 - 39s/epoch - 9ms/step\n",
            "Epoch 58/100\n",
            "4550/4550 - 39s - loss: 0.0034 - val_loss: 0.0543 - 39s/epoch - 9ms/step\n",
            "Epoch 59/100\n",
            "4550/4550 - 40s - loss: 0.0033 - val_loss: 0.0535 - 40s/epoch - 9ms/step\n",
            "Epoch 60/100\n",
            "4550/4550 - 41s - loss: 0.0033 - val_loss: 0.0539 - 41s/epoch - 9ms/step\n",
            "Epoch 61/100\n",
            "4550/4550 - 41s - loss: 0.0033 - val_loss: 0.0548 - 41s/epoch - 9ms/step\n",
            "Epoch 62/100\n",
            "4550/4550 - 41s - loss: 0.0033 - val_loss: 0.0538 - 41s/epoch - 9ms/step\n",
            "Epoch 63/100\n",
            "4550/4550 - 42s - loss: 0.0033 - val_loss: 0.0550 - 42s/epoch - 9ms/step\n",
            "Epoch 64/100\n",
            "4550/4550 - 40s - loss: 0.0033 - val_loss: 0.0546 - 40s/epoch - 9ms/step\n",
            "Epoch 65/100\n",
            "4550/4550 - 40s - loss: 0.0033 - val_loss: 0.0550 - 40s/epoch - 9ms/step\n",
            "Epoch 66/100\n",
            "4550/4550 - 39s - loss: 0.0032 - val_loss: 0.0553 - 39s/epoch - 9ms/step\n",
            "Epoch 67/100\n",
            "4550/4550 - 38s - loss: 0.0032 - val_loss: 0.0545 - 38s/epoch - 8ms/step\n",
            "Epoch 68/100\n",
            "4550/4550 - 39s - loss: 0.0032 - val_loss: 0.0543 - 39s/epoch - 9ms/step\n",
            "Epoch 69/100\n",
            "4550/4550 - 39s - loss: 0.0032 - val_loss: 0.0538 - 39s/epoch - 9ms/step\n",
            "Epoch 70/100\n",
            "4550/4550 - 40s - loss: 0.0032 - val_loss: 0.0549 - 40s/epoch - 9ms/step\n",
            "Epoch 71/100\n",
            "4550/4550 - 39s - loss: 0.0032 - val_loss: 0.0551 - 39s/epoch - 9ms/step\n",
            "Epoch 72/100\n",
            "4550/4550 - 42s - loss: 0.0032 - val_loss: 0.0547 - 42s/epoch - 9ms/step\n",
            "Epoch 73/100\n",
            "4550/4550 - 42s - loss: 0.0031 - val_loss: 0.0549 - 42s/epoch - 9ms/step\n",
            "Epoch 74/100\n",
            "4550/4550 - 38s - loss: 0.0031 - val_loss: 0.0551 - 38s/epoch - 8ms/step\n",
            "Epoch 75/100\n",
            "4550/4550 - 38s - loss: 0.0031 - val_loss: 0.0557 - 38s/epoch - 8ms/step\n",
            "Epoch 76/100\n",
            "4550/4550 - 40s - loss: 0.0032 - val_loss: 0.0535 - 40s/epoch - 9ms/step\n",
            "Epoch 77/100\n",
            "4550/4550 - 41s - loss: 0.0031 - val_loss: 0.0558 - 41s/epoch - 9ms/step\n",
            "Epoch 78/100\n",
            "4550/4550 - 41s - loss: 0.0031 - val_loss: 0.0550 - 41s/epoch - 9ms/step\n",
            "Epoch 79/100\n",
            "4550/4550 - 42s - loss: 0.0031 - val_loss: 0.0546 - 42s/epoch - 9ms/step\n",
            "Epoch 80/100\n",
            "4550/4550 - 41s - loss: 0.0031 - val_loss: 0.0562 - 41s/epoch - 9ms/step\n",
            "Epoch 81/100\n",
            "4550/4550 - 40s - loss: 0.0031 - val_loss: 0.0549 - 40s/epoch - 9ms/step\n",
            "Epoch 82/100\n",
            "4550/4550 - 41s - loss: 0.0030 - val_loss: 0.0546 - 41s/epoch - 9ms/step\n",
            "Epoch 83/100\n",
            "4550/4550 - 42s - loss: 0.0030 - val_loss: 0.0545 - 42s/epoch - 9ms/step\n",
            "Epoch 84/100\n",
            "4550/4550 - 41s - loss: 0.0030 - val_loss: 0.0552 - 41s/epoch - 9ms/step\n",
            "Epoch 85/100\n",
            "4550/4550 - 42s - loss: 0.0030 - val_loss: 0.0558 - 42s/epoch - 9ms/step\n",
            "Epoch 86/100\n",
            "4550/4550 - 41s - loss: 0.0030 - val_loss: 0.0565 - 41s/epoch - 9ms/step\n",
            "Epoch 87/100\n",
            "4550/4550 - 45s - loss: 0.0030 - val_loss: 0.0560 - 45s/epoch - 10ms/step\n",
            "Epoch 88/100\n",
            "4550/4550 - 42s - loss: 0.0030 - val_loss: 0.0558 - 42s/epoch - 9ms/step\n",
            "Epoch 89/100\n",
            "4550/4550 - 42s - loss: 0.0029 - val_loss: 0.0544 - 42s/epoch - 9ms/step\n",
            "Epoch 90/100\n",
            "4550/4550 - 42s - loss: 0.0030 - val_loss: 0.0552 - 42s/epoch - 9ms/step\n",
            "Epoch 91/100\n",
            "4550/4550 - 42s - loss: 0.0030 - val_loss: 0.0567 - 42s/epoch - 9ms/step\n",
            "Epoch 92/100\n",
            "4550/4550 - 44s - loss: 0.0030 - val_loss: 0.0565 - 44s/epoch - 10ms/step\n",
            "Epoch 93/100\n",
            "4550/4550 - 42s - loss: 0.0029 - val_loss: 0.0558 - 42s/epoch - 9ms/step\n",
            "Epoch 94/100\n",
            "4550/4550 - 44s - loss: 0.0029 - val_loss: 0.0571 - 44s/epoch - 10ms/step\n",
            "Epoch 95/100\n",
            "4550/4550 - 44s - loss: 0.0029 - val_loss: 0.0552 - 44s/epoch - 10ms/step\n",
            "Epoch 96/100\n",
            "4550/4550 - 43s - loss: 0.0029 - val_loss: 0.0579 - 43s/epoch - 9ms/step\n",
            "Epoch 97/100\n",
            "4550/4550 - 43s - loss: 0.0029 - val_loss: 0.0577 - 43s/epoch - 9ms/step\n",
            "Epoch 98/100\n",
            "4550/4550 - 41s - loss: 0.0030 - val_loss: 0.0579 - 41s/epoch - 9ms/step\n",
            "Epoch 99/100\n",
            "4550/4550 - 40s - loss: 0.0029 - val_loss: 0.0554 - 40s/epoch - 9ms/step\n",
            "Epoch 100/100\n",
            "4550/4550 - 42s - loss: 0.0029 - val_loss: 0.0569 - 42s/epoch - 9ms/step\n"
          ]
        }
      ],
      "source": [
        "#fit the autoencoder model \n",
        "ae = autoencoder.fit(D3_x_train_scaled, D3_x_train_scaled, epochs= 100, batch_size=16, verbose=2, validation_data=(D3_x_test_scaled,D3_x_test_scaled))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "LRELHdDa-lqk",
        "outputId": "118fb1af-d03f-4449-9f8e-fa5e78bf0b55"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# plot for loss (epoch 100 )\n",
        "from matplotlib import pyplot\n",
        "pyplot.plot(ae.history['loss'], label='train')\n",
        "pyplot.plot(ae.history['val_loss'], label='test')\n",
        "pyplot.legend()\n",
        "pyplot.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: ram://6e3611ed-a45a-4940-aa4a-14926d2644b2/assets\n"
          ]
        }
      ],
      "source": [
        "import pickle\n",
        "filename = 'finalized_model.sav'\n",
        "pickle.dump(ae, open(filename, 'wb'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "7JRVEwlMCCrR"
      },
      "outputs": [],
      "source": [
        "encoder.Trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wZwCL9zLCP75",
        "outputId": "d5d3b19e-361c-4f3d-c02c-4cd1198273e0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Model was constructed with shape (None, 82) for input KerasTensor(type_spec=TensorSpec(shape=(None, 82), dtype=tf.float32, name='encoder_input'), name='encoder_input', description=\"created by layer 'encoder_input'\"), but it was called on an input with incompatible shape (None, 470).\n"
          ]
        },
        {
          "ename": "ValueError",
          "evalue": "Exception encountered when calling layer \"encoder\" (type Functional).\n\nInput 0 of layer \"dense\" is incompatible with the layer: expected axis -1 of input shape to have value 82, but received input with shape (None, 470)\n\nCall arguments received by layer \"encoder\" (type Functional):\n  • inputs=tf.Tensor(shape=(None, 470), dtype=float32)\n  • training=False\n  • mask=None",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[1;32mc:\\Users\\ATHARVA\\Documents\\Atharva\\Malware Detection IP\\rl_v2\\autoencoder.ipynb Cell 17\u001b[0m in \u001b[0;36m<cell line: 50>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/ATHARVA/Documents/Atharva/Malware%20Detection%20IP/rl_v2/autoencoder.ipynb#X21sZmlsZQ%3D%3D?line=45'>46</a>\u001b[0m   model \u001b[39m=\u001b[39m Model(\u001b[39minput\u001b[39m, output)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/ATHARVA/Documents/Atharva/Malware%20Detection%20IP/rl_v2/autoencoder.ipynb#X21sZmlsZQ%3D%3D?line=47'>48</a>\u001b[0m   \u001b[39mreturn\u001b[39;00m model\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/ATHARVA/Documents/Atharva/Malware%20Detection%20IP/rl_v2/autoencoder.ipynb#X21sZmlsZQ%3D%3D?line=49'>50</a>\u001b[0m final_model \u001b[39m=\u001b[39m fcModel()\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/ATHARVA/Documents/Atharva/Malware%20Detection%20IP/rl_v2/autoencoder.ipynb#X21sZmlsZQ%3D%3D?line=50'>51</a>\u001b[0m final_model\u001b[39m.\u001b[39msummary()\n",
            "\u001b[1;32mc:\\Users\\ATHARVA\\Documents\\Atharva\\Malware Detection IP\\rl_v2\\autoencoder.ipynb Cell 17\u001b[0m in \u001b[0;36mfcModel\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/ATHARVA/Documents/Atharva/Malware%20Detection%20IP/rl_v2/autoencoder.ipynb#X21sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfcModel\u001b[39m():\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/ATHARVA/Documents/Atharva/Malware%20Detection%20IP/rl_v2/autoencoder.ipynb#X21sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m   \u001b[39minput\u001b[39m \u001b[39m=\u001b[39m Input(shape\u001b[39m=\u001b[39m(\u001b[39m470\u001b[39m,))\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/ATHARVA/Documents/Atharva/Malware%20Detection%20IP/rl_v2/autoencoder.ipynb#X21sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m   enco \u001b[39m=\u001b[39m encoder(\u001b[39minput\u001b[39;49m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/ATHARVA/Documents/Atharva/Malware%20Detection%20IP/rl_v2/autoencoder.ipynb#X21sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m   z1 \u001b[39m=\u001b[39m Dense(n_inputs\u001b[39m*\u001b[39m\u001b[39m3\u001b[39m, activation\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mrelu\u001b[39m\u001b[39m\"\u001b[39m)(enco)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/ATHARVA/Documents/Atharva/Malware%20Detection%20IP/rl_v2/autoencoder.ipynb#X21sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m   z1 \u001b[39m=\u001b[39m BatchNormalization()(z1)\n",
            "File \u001b[1;32mc:\\Users\\ATHARVA\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\utils\\traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n\u001b[0;32m     68\u001b[0m     \u001b[39m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m     69\u001b[0m     \u001b[39m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m---> 70\u001b[0m     \u001b[39mraise\u001b[39;00m e\u001b[39m.\u001b[39mwith_traceback(filtered_tb) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39m\n\u001b[0;32m     71\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m     72\u001b[0m     \u001b[39mdel\u001b[39;00m filtered_tb\n",
            "File \u001b[1;32mc:\\Users\\ATHARVA\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\engine\\input_spec.py:277\u001b[0m, in \u001b[0;36massert_input_compatibility\u001b[1;34m(input_spec, inputs, layer_name)\u001b[0m\n\u001b[0;32m    272\u001b[0m             value \u001b[39m=\u001b[39m value\u001b[39m.\u001b[39mvalue\n\u001b[0;32m    273\u001b[0m         \u001b[39mif\u001b[39;00m value \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m shape_as_list[\u001b[39mint\u001b[39m(axis)] \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m {\n\u001b[0;32m    274\u001b[0m             value,\n\u001b[0;32m    275\u001b[0m             \u001b[39mNone\u001b[39;00m,\n\u001b[0;32m    276\u001b[0m         }:\n\u001b[1;32m--> 277\u001b[0m             \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m    278\u001b[0m                 \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mInput \u001b[39m\u001b[39m{\u001b[39;00minput_index\u001b[39m}\u001b[39;00m\u001b[39m of layer \u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mlayer_name\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m is \u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m    279\u001b[0m                 \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mincompatible with the layer: expected axis \u001b[39m\u001b[39m{\u001b[39;00maxis\u001b[39m}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    280\u001b[0m                 \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mof input shape to have value \u001b[39m\u001b[39m{\u001b[39;00mvalue\u001b[39m}\u001b[39;00m\u001b[39m, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    281\u001b[0m                 \u001b[39m\"\u001b[39m\u001b[39mbut received input with \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    282\u001b[0m                 \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mshape \u001b[39m\u001b[39m{\u001b[39;00mdisplay_shape(x\u001b[39m.\u001b[39mshape)\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[0;32m    283\u001b[0m             )\n\u001b[0;32m    284\u001b[0m \u001b[39m# Check shape.\u001b[39;00m\n\u001b[0;32m    285\u001b[0m \u001b[39mif\u001b[39;00m spec\u001b[39m.\u001b[39mshape \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m shape\u001b[39m.\u001b[39mrank \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
            "\u001b[1;31mValueError\u001b[0m: Exception encountered when calling layer \"encoder\" (type Functional).\n\nInput 0 of layer \"dense\" is incompatible with the layer: expected axis -1 of input shape to have value 82, but received input with shape (None, 470)\n\nCall arguments received by layer \"encoder\" (type Functional):\n  • inputs=tf.Tensor(shape=(None, 470), dtype=float32)\n  • training=False\n  • mask=None"
          ]
        }
      ],
      "source": [
        "def fcModel():\n",
        "  input = Input(shape=(D1_x_train.shape[1],))\n",
        "  enco = encoder(input)\n",
        "\n",
        "  z1 = Dense(n_inputs*3, activation=\"relu\")(enco)\n",
        "  z1 = BatchNormalization()(z1)\n",
        "\n",
        "  z2 = Dense(n_inputs*2, activation=\"relu\")(z1)\n",
        "  z2 = BatchNormalization()(z2)\n",
        "\n",
        "  z3 = Dense(n_inputs, activation=\"relu\")(z2)\n",
        "  z3 = BatchNormalization()(z3)\n",
        "\n",
        "  z4 = Dense(n_inputs-7, activation=\"relu\")(z3)\n",
        "  z4 = BatchNormalization()(z4)\n",
        "\n",
        "  z5 = Dense(n_inputs-14, activation=\"relu\")(z4)\n",
        "  z5 = BatchNormalization()(z5)\n",
        "\n",
        "  z6 = Dense(n_inputs-30, activation=\"relu\")(z5)\n",
        "  z6 = BatchNormalization()(z6)\n",
        "\n",
        "  z7 = Dense(n_inputs-40, activation=\"relu\")(z6)\n",
        "  z7 = BatchNormalization()(z7)\n",
        "\n",
        "  z8 = Dense (n_inputs-50, activation= \"relu\")(z7)\n",
        "  z8 = BatchNormalization()(z8)\n",
        "\n",
        "  z9 = Dense (n_inputs-60, activation= \"relu\")(z8)\n",
        "  z9 = BatchNormalization()(z9)\n",
        "\n",
        "  z10 = Dense (n_inputs-70, activation= \"relu\")(z9)\n",
        "  z10 = BatchNormalization()(z10)\n",
        "\n",
        "  z11 = Dense (n_inputs-80, activation= \"relu\")(z10)\n",
        "  z11 = BatchNormalization()(z11)\n",
        "\n",
        "  z12 = Dense (n_inputs-450, activation= \"relu\")(z11)\n",
        "  z12 = BatchNormalization()(z12)\n",
        "\n",
        "  z13 = Dense (n_inputs-460, activation= \"relu\")(z12)\n",
        "  z13 = BatchNormalization()(z13)\n",
        "  \n",
        "  output = Dense (5, activation= \"softmax\")(z13)\n",
        "  \n",
        "  model = Model(input, output)\n",
        "\n",
        "  return model\n",
        "\n",
        "final_model = fcModel()\n",
        "final_model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lwI3Fix9C6SC",
        "outputId": "9c2e3908-6189-4c74-c913-6439b7d3e131"
      },
      "outputs": [
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mRunning cells with 'Python 3.9.12 ('atharva': venv)' requires ipykernel package.\n",
            "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
            "\u001b[1;31mCommand: '\"c:/Users/ATHARVA/Documents/Atharva/Malware Detection IP/atharva/Scripts/python.exe\" -m pip install ipykernel -U --force-reinstall'"
          ]
        }
      ],
      "source": [
        "import tensorflow\n",
        "tensorflow.keras.utils.plot_model(final_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OYTie5KTDDmo",
        "outputId": "624e03e3-49ad-46b6-a184-ae952d0ea27d"
      },
      "outputs": [
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mRunning cells with 'Python 3.9.12 ('atharva': venv)' requires ipykernel package.\n",
            "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
            "\u001b[1;31mCommand: '\"c:/Users/ATHARVA/Documents/Atharva/Malware Detection IP/atharva/Scripts/python.exe\" -m pip install ipykernel -U --force-reinstall'"
          ]
        }
      ],
      "source": [
        "#Now that the model architecture is done, we'll set an optimizer:\n",
        "import tensorflow as tf  \n",
        "optimizer = tf.keras.optimizers.Adam(lr=0.0001, decay=1e-6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eRi4EeGUDWSA"
      },
      "outputs": [
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mRunning cells with 'Python 3.9.12 ('atharva': venv)' requires ipykernel package.\n",
            "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
            "\u001b[1;31mCommand: '\"c:/Users/ATHARVA/Documents/Atharva/Malware Detection IP/atharva/Scripts/python.exe\" -m pip install ipykernel -U --force-reinstall'"
          ]
        }
      ],
      "source": [
        "#We'll now compile our model with the optimizer and a loss metric. We'll use mean squared error for loss (mse).\n",
        "import keras\n",
        "final_model.compile(optimizer= optimizer, loss= keras.losses.SparseCategoricalCrossentropy(),metrics=[keras.metrics.SparseCategoricalAccuracy()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "-MqK-csSDcfk",
        "outputId": "2e1b0365-19d7-472e-b6ca-8e8520cda598"
      },
      "outputs": [
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mRunning cells with 'Python 3.9.12 ('atharva': venv)' requires ipykernel package.\n",
            "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
            "\u001b[1;31mCommand: '\"c:/Users/ATHARVA/Documents/Atharva/Malware Detection IP/atharva/Scripts/python.exe\" -m pip install ipykernel -U --force-reinstall'"
          ]
        }
      ],
      "source": [
        "vc = D1_y_train.value_counts().to_frame().reset_index()\n",
        "vc['percent'] = vc[\"Class\"].apply(lambda x : round(100*float(x) / len(D1_y_train), 2))\n",
        "vc = vc.rename(columns = {\"index\" : \"Target\", \"Class\" : \"Count\"})\n",
        "vc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "FI2J2XFEDklS",
        "outputId": "b5fbc9d8-eef0-46d7-be7b-0b9c917928ca"
      },
      "outputs": [
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mRunning cells with 'Python 3.9.12 ('atharva': venv)' requires ipykernel package.\n",
            "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
            "\u001b[1;31mCommand: '\"c:/Users/ATHARVA/Documents/Atharva/Malware Detection IP/atharva/Scripts/python.exe\" -m pip install ipykernel -U --force-reinstall'"
          ]
        }
      ],
      "source": [
        "vc = D1_y_test.value_counts().to_frame().reset_index()\n",
        "vc['percent'] = vc[\"Class\"].apply(lambda x : round(100*float(x) / len(D1_y_test), 2))\n",
        "vc = vc.rename(columns = {\"index\" : \"Target\", \"Class\" : \"Count\"})\n",
        "vc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "seIIGEphDoly",
        "outputId": "ea1ece79-5989-496f-fbe2-80c8e0c68a9c"
      },
      "outputs": [
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mRunning cells with 'Python 3.9.12 ('atharva': venv)' requires ipykernel package.\n",
            "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
            "\u001b[1;31mCommand: '\"c:/Users/ATHARVA/Documents/Atharva/Malware Detection IP/atharva/Scripts/python.exe\" -m pip install ipykernel -U --force-reinstall'"
          ]
        }
      ],
      "source": [
        "#D1_y_train, D1_y_test(ForTraining the final model)\n",
        "\n",
        "D1ytrain = keras.utils.to_categorical(D1_y_train, dtype =\"int64\")\n",
        "print(D1ytrain.shape)\n",
        "D1ytest = keras.utils.to_categorical(D1_y_test, dtype =\"int64\")\n",
        "print(D1ytest.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6jlEvgeAF0eb",
        "outputId": "80e91921-6712-472a-83ef-860ec5244373"
      },
      "outputs": [
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mRunning cells with 'Python 3.9.12 ('atharva': venv)' requires ipykernel package.\n",
            "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
            "\u001b[1;31mCommand: '\"c:/Users/ATHARVA/Documents/Atharva/Malware Detection IP/atharva/Scripts/python.exe\" -m pip install ipykernel -U --force-reinstall'"
          ]
        }
      ],
      "source": [
        "final_model.fit(D1_x_train_scaled, D1_y_train,epochs=100,batch_size=16, verbose=2, validation_data=(D1_x_test_scaled,D1_y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zNWt2RcsSUy6",
        "outputId": "6798dae4-4866-4512-bc79-bd9de97cadb1"
      },
      "outputs": [
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mRunning cells with 'Python 3.9.12 ('atharva': venv)' requires ipykernel package.\n",
            "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
            "\u001b[1;31mCommand: '\"c:/Users/ATHARVA/Documents/Atharva/Malware Detection IP/atharva/Scripts/python.exe\" -m pip install ipykernel -U --force-reinstall'"
          ]
        }
      ],
      "source": [
        "#evaluate the network\n",
        "print(\"[INFO] evaluating network...\")\n",
        "score, acc = final_model.evaluate(D4_x_scaled, D4_y)\n",
        "print('test accuracy:', acc)\n",
        "print('score',score)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gyNr_MBmSfdv",
        "outputId": "74e6b791-845e-424f-fe33-5ef55bc8bf7b"
      },
      "outputs": [
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mRunning cells with 'Python 3.9.12 ('atharva': venv)' requires ipykernel package.\n",
            "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
            "\u001b[1;31mCommand: '\"c:/Users/ATHARVA/Documents/Atharva/Malware Detection IP/atharva/Scripts/python.exe\" -m pip install ipykernel -U --force-reinstall'"
          ]
        }
      ],
      "source": [
        "y_pred = final_model.predict(D4_x_scaled, batch_size=16)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MXBMsN98SmkJ",
        "outputId": "8eb28100-5aa9-4755-ee82-39a6492ffe7c"
      },
      "outputs": [
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mRunning cells with 'Python 3.9.12 ('atharva': venv)' requires ipykernel package.\n",
            "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
            "\u001b[1;31mCommand: '\"c:/Users/ATHARVA/Documents/Atharva/Malware Detection IP/atharva/Scripts/python.exe\" -m pip install ipykernel -U --force-reinstall'"
          ]
        }
      ],
      "source": [
        "y_predargmax = tf.argmax(y_pred, 1)\n",
        "print(y_predargmax)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EwC8xUWpSr3e",
        "outputId": "03aa2c64-0d40-472b-e960-a389f16fb70f"
      },
      "outputs": [
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mRunning cells with 'Python 3.9.12 ('atharva': venv)' requires ipykernel package.\n",
            "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
            "\u001b[1;31mCommand: '\"c:/Users/ATHARVA/Documents/Atharva/Malware Detection IP/atharva/Scripts/python.exe\" -m pip install ipykernel -U --force-reinstall'"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "print(accuracy_score(D4_y, y_predargmax))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mRunning cells with 'Python 3.9.12 ('atharva': venv)' requires ipykernel package.\n",
            "\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n",
            "\u001b[1;31mCommand: '\"c:/Users/ATHARVA/Documents/Atharva/Malware Detection IP/atharva/Scripts/python.exe\" -m pip install ipykernel -U --force-reinstall'"
          ]
        }
      ],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.10.1 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.1"
    },
    "vscode": {
      "interpreter": {
        "hash": "a0af84517f063bd45ee1b19a234400ee758964e44992839b9bc74f9265a5dcad"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}