costum_cnn_for_feature_training.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "costum_cnn_for_feature_training.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/iamrajee/Machine-Learning/blob/master/costum_cnn_for_feature_training.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "yTM_rAE8jKJU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "06bae0fe-6f87-4dc5-8af1-6129cc41c572"
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "datalab  drive\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "pEkuXOk6ktYp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# !apt-get install -y -qq software-properties-common python-software-properties module-init-tools\n",
        "# !add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null\n",
        "# !apt-get update -qq 2>&1 > /dev/null\n",
        "# !apt-get -y install -qq google-drive-ocamlfuse fuse\n",
        "# from google.colab import auth\n",
        "# auth.authenticate_user()\n",
        "# from oauth2client.client import GoogleCredentials\n",
        "# creds = GoogleCredentials.get_application_default()\n",
        "# import getpass\n",
        "# !google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret} < /dev/null 2>&1 | grep URL\n",
        "# vcode = getpass.getpass()\n",
        "# !echo {vcode} | google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret}\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# !mkdir -p drive\n",
        "# !google-drive-ocamlfuse drive\n",
        "\n",
        "\n",
        "# !ls"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FoALOF1Wk0Km",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WEkpZYs1k3tr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"drive/custom/masterfile5.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FJ0pGBZimGHR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "outputId": "a36b62c6-aca8-4b61-9963-285178df3a9f"
      },
      "cell_type": "code",
      "source": [
        "print(df.columns)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['Name', 'Year', 'f1', 'f2', 'f3', 'f4', 'f5', 'f6', 'f7', 'f8',\n",
            "       ...\n",
            "       'f4087', 'f4088', 'f4089', 'f4090', 'f4091', 'f4092', 'f4093', 'f4094',\n",
            "       'f4095', 'f4096'],\n",
            "      dtype='object', length=4098)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "iO98D4OpmL21",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "975078cb-a3d2-46b6-adb2-c023f9499c9c"
      },
      "cell_type": "code",
      "source": [
        "namelist = df['Name']\n",
        "yearlist = df['Year']\n",
        "\n",
        "print(len(namelist),len(yearlist))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10278 10278\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "yU-kfXKY0_Ye",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "outputId": "bca57d0c-ae3b-423c-e4a8-41d1f554903b"
      },
      "cell_type": "code",
      "source": [
        "del df['Name']\n",
        "del df['Year']\n",
        "\n",
        "print(df.columns)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['f1', 'f2', 'f3', 'f4', 'f5', 'f6', 'f7', 'f8', 'f9', 'f10',\n",
            "       ...\n",
            "       'f4087', 'f4088', 'f4089', 'f4090', 'f4091', 'f4092', 'f4093', 'f4094',\n",
            "       'f4095', 'f4096'],\n",
            "      dtype='object', length=4096)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "74C92vzimN7A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "95e3e401-c05e-41c4-ed29-b49b4c7cf728"
      },
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10278, 4096)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "0kF2HGU-n_yO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "fdd33bfa-53f3-47da-f5c9-fa8620944508"
      },
      "cell_type": "code",
      "source": [
        "rows = np.shape(df)[0]\n",
        "columns = np.shape(df)[1]\n",
        "print(rows,columns)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10278 4096\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "EgOFF2fjp_b_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "e2d6187b-f50e-43d4-881e-ad4b120824d3"
      },
      "cell_type": "code",
      "source": [
        "print(df.iloc[10277][4095])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "KLK77Wm0owMr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1740
        },
        "outputId": "dc622c97-9a19-4920-c39e-7baf6867e98c"
      },
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "flist = []\n",
        "for i in range(0,rows):\n",
        "  tempele = df.iloc[i]\n",
        "#   print(type(tempele), np.shape(tempele), np.shape(np.array(tempele)))\n",
        "  flist.append(np.array(tempele))\n",
        "  if i%100 == 0:\n",
        "    print(i)\n",
        "  \n",
        "print(np.shape(flist))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "100\n",
            "200\n",
            "300\n",
            "400\n",
            "500\n",
            "600\n",
            "700\n",
            "800\n",
            "900\n",
            "1000\n",
            "1100\n",
            "1200\n",
            "1300\n",
            "1400\n",
            "1500\n",
            "1600\n",
            "1700\n",
            "1800\n",
            "1900\n",
            "2000\n",
            "2100\n",
            "2200\n",
            "2300\n",
            "2400\n",
            "2500\n",
            "2600\n",
            "2700\n",
            "2800\n",
            "2900\n",
            "3000\n",
            "3100\n",
            "3200\n",
            "3300\n",
            "3400\n",
            "3500\n",
            "3600\n",
            "3700\n",
            "3800\n",
            "3900\n",
            "4000\n",
            "4100\n",
            "4200\n",
            "4300\n",
            "4400\n",
            "4500\n",
            "4600\n",
            "4700\n",
            "4800\n",
            "4900\n",
            "5000\n",
            "5100\n",
            "5200\n",
            "5300\n",
            "5400\n",
            "5500\n",
            "5600\n",
            "5700\n",
            "5800\n",
            "5900\n",
            "6000\n",
            "6100\n",
            "6200\n",
            "6300\n",
            "6400\n",
            "6500\n",
            "6600\n",
            "6700\n",
            "6800\n",
            "6900\n",
            "7000\n",
            "7100\n",
            "7200\n",
            "7300\n",
            "7400\n",
            "7500\n",
            "7600\n",
            "7700\n",
            "7800\n",
            "7900\n",
            "8000\n",
            "8100\n",
            "8200\n",
            "8300\n",
            "8400\n",
            "8500\n",
            "8600\n",
            "8700\n",
            "8800\n",
            "8900\n",
            "9000\n",
            "9100\n",
            "9200\n",
            "9300\n",
            "9400\n",
            "9500\n",
            "9600\n",
            "9700\n",
            "9800\n",
            "9900\n",
            "10000\n",
            "10100\n",
            "10200\n",
            "(10278, 4096)\n",
            "CPU times: user 3.12 s, sys: 253 ms, total: 3.37 s\n",
            "Wall time: 3.32 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "cgtXyWNyqi6c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "3e150266-2c5c-4950-bdd5-d925245dc3bf"
      },
      "cell_type": "code",
      "source": [
        "print(type(flist),type(flist[0]))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'list'> <class 'numpy.ndarray'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "58jB5VWu3E2z",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "flist = np.array(flist)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pqCCVEXJ3Jpa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "7c1df1c5-3563-4c06-9a0b-83d988e01e5c"
      },
      "cell_type": "code",
      "source": [
        "print(type(flist),type(flist[0]))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'numpy.ndarray'> <class 'numpy.ndarray'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "iUazzPSI3MNt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "yearlist = np.array(yearlist)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "92p7xoZQ3hBY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "b52a6620-bfc8-453e-b2e5-b2917a4fc036"
      },
      "cell_type": "code",
      "source": [
        "print(type(yearlist))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'numpy.ndarray'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "89UK49i83k08",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "01ae04c7-c1da-4b45-8e6f-bde9becfdf37"
      },
      "cell_type": "code",
      "source": [
        "import keras\n",
        "\n",
        "#\n",
        "import os\n",
        "import time\n",
        "from keras.preprocessing import image\n",
        "from keras.applications.imagenet_utils import preprocess_input\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.cross_validation import train_test_split\n",
        "from keras import optimizers\n",
        "#\n",
        "\n",
        "\n",
        "from keras import applications\n",
        "from keras import optimizers\n",
        "from keras import backend as k\n",
        "\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential, Model\n",
        "from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D\n",
        "from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard,EarlyStopping"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
            "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "PZDDMZva4v9f",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelBinarizer\n",
        "encoder = LabelBinarizer()\n",
        "\n",
        "y_train3 = encoder.fit_transform(y_train2)\n",
        "y_test3 = encoder.fit_transform(y_test2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Z0-mF2Ea3689",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "n_class = 15\n",
        "x1 = model.output\n",
        "x1 = Flatten()(x1)\n",
        "x1 = Dense(1024, activation=\"relu\")(x1)\n",
        "x1 = Dropout(0.5)(x1)\n",
        "x1 = Dense(1024,activation=\"relu\")(x1)\n",
        "predictions = Dense(n_class,activation=\"softmax\", name='output_layer')(x1)\n",
        "\n",
        "model_final = Model(input=model.input, output = predictions)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}