Colab_Classification_Pipeline.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JNPauli/Protect_app_master_thesis/blob/main/Colab_Classification_Pipeline.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f3485813",
      "metadata": {
        "id": "f3485813"
      },
      "source": [
        "0. This notebook is the continuation to the preprocessing.ipynb notebook."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b60fcc27",
      "metadata": {
        "id": "b60fcc27"
      },
      "source": [
        "1. Import all neccessary modules "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "73af1d9d",
      "metadata": {
        "id": "73af1d9d"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwWpOPN-SkYk",
        "outputId": "b82ccd4d-1da8-4664-cd61-d26e4da3f606"
      },
      "id": "xwWpOPN-SkYk",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0bab0d03",
      "metadata": {
        "id": "0bab0d03"
      },
      "source": [
        "2. Import X and y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0e61570a",
      "metadata": {
        "id": "0e61570a"
      },
      "outputs": [],
      "source": [
        "import torchvision\n",
        "from torchvision import transforms\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from PIL import Image"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.chdir('/content/drive/MyDrive/Thesis')"
      ],
      "metadata": {
        "id": "Q822x4HZTzzr"
      },
      "id": "Q822x4HZTzzr",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_all = pd.read_csv('All_ratings_train.csv')"
      ],
      "metadata": {
        "id": "OrPVmqIRJCK6"
      },
      "id": "OrPVmqIRJCK6",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "99428f03",
      "metadata": {
        "id": "99428f03"
      },
      "outputs": [],
      "source": [
        "y = pd.read_csv('Arousal_frames_emodb.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1bd70573",
      "metadata": {
        "id": "1bd70573"
      },
      "outputs": [],
      "source": [
        "y = y.iloc[:,[1]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "986e4ca0",
      "metadata": {
        "id": "986e4ca0"
      },
      "outputs": [],
      "source": [
        "y_8000_rows = y[0:10000]\n",
        "#y = y[0:14276]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e5f1e95f",
      "metadata": {
        "id": "e5f1e95f"
      },
      "outputs": [],
      "source": [
        "y_arr = y_8000_rows.to_numpy()\n",
        "#y_arr = y.to_numpy()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "daf7012a",
      "metadata": {
        "id": "daf7012a"
      },
      "source": [
        "NEW APPROACH:\n",
        "\n",
        "Fit CNN on X"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4fdc2364",
      "metadata": {
        "id": "4fdc2364"
      },
      "source": [
        "1. Read `n-rows`from X."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if torch.cuda.is_available():\n",
        "  device = torch.device(\"cuda\")\n",
        "else:\n",
        "  device = torch.device(\"cpu\")\n",
        "\n",
        "print(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r367TL-IeTgB",
        "outputId": "6b3d6a05-984b-4cb1-d5a4-c58921aca368"
      },
      "id": "r367TL-IeTgB",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "27e2352f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "27e2352f",
        "outputId": "6e112b33-9d2c-47cc-9726-392db9bb56d7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processed 1/120 chunks\n",
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          ]
        }
      ],
      "source": [
        "chunk = 100\n",
        "i=0\n",
        "X=[]\n",
        "import pandas as pd\n",
        "for df in pd.read_csv('X_matrix.csv',chunksize = chunk):\n",
        "    X.append(df)\n",
        "    i=i+1\n",
        "    print('Processed {i}/120 chunks'.format(i=i))\n",
        "    if i == 100:\n",
        "      break\n",
        "   \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "deea97ec",
      "metadata": {
        "id": "deea97ec"
      },
      "outputs": [],
      "source": [
        "X = pd.concat(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e6be9487",
      "metadata": {
        "id": "e6be9487"
      },
      "outputs": [],
      "source": [
        "del X [X.columns[0]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d148f00c",
      "metadata": {
        "id": "d148f00c"
      },
      "outputs": [],
      "source": [
        "X_arr = X.to_numpy()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_arr.shape"
      ],
      "metadata": {
        "id": "hiLqmaAuVgWK"
      },
      "id": "hiLqmaAuVgWK",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "06126843",
      "metadata": {
        "id": "06126843"
      },
      "outputs": [],
      "source": [
        "X_arr = np.reshape(X_arr,(10000,224,224))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3d763e78",
      "metadata": {
        "id": "3d763e78"
      },
      "outputs": [],
      "source": [
        "X_arr.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gc"
      ],
      "metadata": {
        "id": "OZ7bGS3HsJH9"
      },
      "id": "OZ7bGS3HsJH9",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "del X\n",
        "gc.collect"
      ],
      "metadata": {
        "id": "2Y9KVzHHVwZY"
      },
      "id": "2Y9KVzHHVwZY",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "0fd74af2",
      "metadata": {
        "id": "0fd74af2"
      },
      "source": [
        "Adding new dimension for CNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7f4c005c",
      "metadata": {
        "id": "7f4c005c"
      },
      "outputs": [],
      "source": [
        "X_ = X_arr[:,:,np.newaxis]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fc2f2ce4",
      "metadata": {
        "id": "fc2f2ce4"
      },
      "outputs": [],
      "source": [
        "X_ = np.swapaxes(X_,2,1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "92fc96c3",
      "metadata": {
        "id": "92fc96c3"
      },
      "outputs": [],
      "source": [
        "X_.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9c5e8485",
      "metadata": {
        "id": "9c5e8485"
      },
      "outputs": [],
      "source": [
        "X_train,X_test,y_train,y_test = train_test_split(X_,y_arr,test_size=0.2,random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#X_train, X_val, y_train, y_val = train_test_split(X_train,y_train, test_size = 0.2,random_state=42)"
      ],
      "metadata": {
        "id": "J0CTpvPgzB9e"
      },
      "id": "J0CTpvPgzB9e",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Clear variables that are not in use anymore"
      ],
      "metadata": {
        "id": "vn2qVMNLq5tx"
      },
      "id": "vn2qVMNLq5tx"
    },
    {
      "cell_type": "code",
      "source": [
        "import gc"
      ],
      "metadata": {
        "id": "EJSProaYrS1e"
      },
      "id": "EJSProaYrS1e",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "del X_\n",
        "gc.collect()\n",
        "del X_arr\n",
        "gc.collect()"
      ],
      "metadata": {
        "id": "2FJJxFyZqqD_"
      },
      "id": "2FJJxFyZqqD_",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "del y_arr\n",
        "gc.collect()"
      ],
      "metadata": {
        "id": "CdEhzIJzq9_r"
      },
      "id": "CdEhzIJzq9_r",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "43fc4d4d",
      "metadata": {
        "id": "43fc4d4d"
      },
      "source": [
        "Converting X to appropriate Datatype..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "95e21cb9",
      "metadata": {
        "id": "95e21cb9"
      },
      "outputs": [],
      "source": [
        "X_tensor_train = torch.from_numpy(X_train)\n",
        "X_tensor_test = torch.from_numpy(X_test)\n",
        "#X_tensor_val = torch.from_numpy(X_val)\n",
        "\n",
        "y_tensor_train = torch.from_numpy(y_train)\n",
        "y_tensor_test = torch.from_numpy(y_test)\n",
        "#y_tensor_val = torch.from_numpy(y_val)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "del X_train, X_test, y_train, y_test#, X_val, y_val\n",
        "gc.collect()"
      ],
      "metadata": {
        "id": "yCQ3jMJ1evT2"
      },
      "id": "yCQ3jMJ1evT2",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "83ce427f",
      "metadata": {
        "id": "83ce427f"
      },
      "outputs": [],
      "source": [
        "X_tensor_train = torch.tensor(X_tensor_train,dtype = torch.float32)\n",
        "X_tensor_test = torch.tensor(X_tensor_test,dtype = torch.float32)\n",
        "#X_tensor_val = torch.tensor(X_tensor_val,dtype=torch.float32)\n",
        "\n",
        "y_tensor_train = y_tensor_train.type(torch.LongTensor)\n",
        "y_tensor_test = y_tensor_test.type(torch.LongTensor)\n",
        "#y_tensor_val = y_tensor_val.type(torch.LongTensor)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "82e920b8",
      "metadata": {
        "id": "82e920b8"
      },
      "source": [
        "Pytorch needs the true labels to be in the range N-1. Thus, substract 1 one from each label."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fe7f344c",
      "metadata": {
        "id": "fe7f344c"
      },
      "outputs": [],
      "source": [
        "y_tensor_train = y_tensor_train-1\n",
        "y_tensor_test = y_tensor_test-1\n",
        "#y_tensor_val = y_tensor_val-1"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Loading remaining data..."
      ],
      "metadata": {
        "id": "VTboVRypHS5X"
      },
      "id": "VTboVRypHS5X"
    },
    {
      "cell_type": "code",
      "source": [
        "y_frames = y[14276:].to_numpy()"
      ],
      "metadata": {
        "id": "KliKIV86uGlk"
      },
      "id": "KliKIV86uGlk",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_frames.shape"
      ],
      "metadata": {
        "id": "_TokEr1ruucd"
      },
      "id": "_TokEr1ruucd",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "frames = []\n",
        "i = 0\n",
        "for x in pd.read_csv('X_frames.csv', chunksize = 100):\n",
        "  frames.append(x)\n",
        "  i=i+1\n",
        "  print('processing')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NlREINiXvk2M",
        "outputId": "777b9d77-2a0c-4cc7-d859-0bdf809d5c2f"
      },
      "id": "NlREINiXvk2M",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "processing\n",
            "processing\n",
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            "processing\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "frames = pd.concat(frames)"
      ],
      "metadata": {
        "id": "zxiuzSsyuGqe"
      },
      "id": "zxiuzSsyuGqe",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "del frames [frames.columns[0]]"
      ],
      "metadata": {
        "id": "qo_geADpyYta"
      },
      "id": "qo_geADpyYta",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "frames = frames.to_numpy()"
      ],
      "metadata": {
        "id": "22ryBOTNuGse"
      },
      "id": "22ryBOTNuGse",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "frames.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bltsX832yAfB",
        "outputId": "5a5734fe-ade5-4fde-89db-625c69ca616b"
      },
      "id": "bltsX832yAfB",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7348, 50176)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "frames = np.reshape(frames,(7348,224,224))"
      ],
      "metadata": {
        "id": "7A4SYSzxuGur"
      },
      "id": "7A4SYSzxuGur",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "frames = frames[:,:,np.newaxis]"
      ],
      "metadata": {
        "id": "0r3LLMN6uGw0"
      },
      "id": "0r3LLMN6uGw0",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "frames = np.swapaxes(frames,2,1)"
      ],
      "metadata": {
        "id": "INzhKWZtuGzF"
      },
      "id": "INzhKWZtuGzF",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(frames, y_frames, test_size = 0.2, random_state=42)"
      ],
      "metadata": {
        "id": "U2YtuSrEuXic"
      },
      "id": "U2YtuSrEuXic",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "del frames, y_frames\n",
        "gc.collect()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KXepwVWVuXrY",
        "outputId": "b62193c7-7784-4c4c-95c4-35a249ac5574"
      },
      "id": "KXepwVWVuXrY",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_tensor_train_fr = torch.from_numpy(X_train)\n",
        "X_tensor_test_fr = torch.from_numpy(X_test)\n",
        "\n",
        "y_tensor_train_fr = torch.from_numpy(y_train)\n",
        "y_tensor_test_fr = torch.from_numpy(y_test)\n"
      ],
      "metadata": {
        "id": "vDHrKuusuXwv"
      },
      "id": "vDHrKuusuXwv",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "del X_train, X_test, y_train, y_test\n",
        "gc.collect()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t0lNaFdhuga_",
        "outputId": "194dbccc-0875-4abb-9894-2e7ae2fbcee6"
      },
      "id": "t0lNaFdhuga_",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_tensor_train_fr = torch.tensor(X_tensor_train_fr,dtype = torch.float32)\n",
        "X_tensor_test_fr = torch.tensor(X_tensor_test_fr,dtype = torch.float32)\n",
        "\n",
        "y_tensor_train_fr = y_tensor_train_fr.type(torch.LongTensor)\n",
        "y_tensor_test_fr = y_tensor_test_fr.type(torch.LongTensor)"
      ],
      "metadata": {
        "id": "MqyfDFp_ugfz"
      },
      "id": "MqyfDFp_ugfz",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_tensor_train_fr = y_tensor_train_fr -1\n",
        "y_tensor_test_fr = y_tensor_test_fr -1"
      ],
      "metadata": {
        "id": "XrPItkOFulia"
      },
      "id": "XrPItkOFulia",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_tensor_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h_e7ANOg43l-",
        "outputId": "01645664-f493-41e5-c79a-9e0651614d16"
      },
      "id": "h_e7ANOg43l-",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([8000, 1, 224, 224])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xyz = torch.cat((X_tensor_train,X_tensor_train_fr),0)"
      ],
      "metadata": {
        "id": "4g_RFcKk47am"
      },
      "id": "4g_RFcKk47am",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "del X_tensor_train, X_tensor_train_fr\n",
        "gc.collect()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xjk4ZqoD5gAP",
        "outputId": "1fef1b94-582a-4431-d843-0e13a3afce67"
      },
      "id": "xjk4ZqoD5gAP",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "76"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#X_tensor_train = torch.cat((X_tensor_train,X_tensor_train_fr),0)\n",
        "X_tensor_test_cat = torch.cat((X_tensor_test,X_tensor_test_fr),0)\n",
        "del X_tensor_test, X_tensor_test_fr\n",
        "gc.collect()\n",
        "\n",
        "y_tensor_train_cat = torch.cat((y_tensor_train,y_tensor_train_fr),0)\n",
        "del y_tensor_train,y_tensor_train_fr\n",
        "gc.collect()\n",
        "y_tensor_test_cat = torch.cat((y_tensor_test,y_tensor_test_fr),0)\n",
        "del y_tensor_test, y_tensor_test_fr\n",
        "gc.collect()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W3geHvraulk7",
        "outputId": "da4caa1a-6156-46c3-86ce-5f217b729f68"
      },
      "id": "W3geHvraulk7",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('training shape X is {}, test shape X is{}'.format(xyz.shape,X_tensor_test_cat.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w3CAcuLCulnT",
        "outputId": "f9a7b705-9cdb-40cf-9023-8e0836ae2018"
      },
      "id": "w3CAcuLCulnT",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "training shape X is torch.Size([13878, 1, 224, 224]), test shape X istorch.Size([3470, 1, 224, 224])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5379ae48",
      "metadata": {
        "id": "5379ae48"
      },
      "source": [
        "Setup custom dataset class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "76e13d73",
      "metadata": {
        "id": "76e13d73"
      },
      "outputs": [],
      "source": [
        "class SimpleDataset(Dataset):\n",
        "    def __init__(self, X, y):\n",
        "        self.X = X\n",
        "        self.y = y\n",
        "        \n",
        "    def __len__(self):\n",
        "        return len(self.X)\n",
        "    \n",
        "    def __getitem__(self, idx):\n",
        "        X = self.X[idx]\n",
        "        y = self.y[idx]\n",
        "        return X, y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "af6a5a92",
      "metadata": {
        "id": "af6a5a92"
      },
      "outputs": [],
      "source": [
        "dataset_train = SimpleDataset(xyz,y_tensor_train_cat)\n",
        "dataloader_train = DataLoader(dataset_train, batch_size=32, shuffle=True)\n",
        "\n",
        "#dataset_val = SimpleDataset(X_tensor_val,y_tensor_val)\n",
        "#dataloader_val = DataLoader(dataset_val,batch_size=32)\n",
        "\n",
        "dataset_test = SimpleDataset(X_tensor_test_cat,y_tensor_test_cat)\n",
        "dataloader_test = DataLoader(dataset_test, batch_size = 32)#,shuffle=True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9c4df5ea",
      "metadata": {
        "id": "9c4df5ea"
      },
      "source": [
        "Setting up the CNN."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c5b23ab6",
      "metadata": {
        "id": "c5b23ab6"
      },
      "outputs": [],
      "source": [
        "import torch.nn.functional as F\n",
        "\n",
        "class CNN(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(CNN, self).__init__()\n",
        "        self.conv1 = nn.Conv2d(1, 32, 3)\n",
        "        self.conv2 = nn.Conv2d(32, 64, 3)\n",
        "        self.dropout1 = nn.Dropout(0.5)\n",
        "        self.dropout2 = nn.Dropout(0.25)\n",
        "        self.fc1 = nn.Linear(774400, 128)\n",
        "        self.fc2 = nn.Linear(128, 10)\n",
        "        self.pool = nn.MaxPool2d(2)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        x = self.conv1(x)\n",
        "        x = F.relu(x)\n",
        "        x = self.conv2(x)\n",
        "        x = F.relu(x)\n",
        "        x = self.pool(x)\n",
        "        x = self.dropout1(x)\n",
        "        x = torch.flatten(x, 1)\n",
        "        x = self.fc1(x)\n",
        "        x = F.relu(x)\n",
        "        x = self.dropout2(x)\n",
        "        x = self.fc2(x)\n",
        "        return x\n",
        "#removed second layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ad94c3ad",
      "metadata": {
        "id": "ad94c3ad",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f537d6c0-b9fd-4d47-c6d2-2a4224b249c1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CNN(\n",
              "  (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n",
              "  (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n",
              "  (dropout1): Dropout(p=0.5, inplace=False)\n",
              "  (dropout2): Dropout(p=0.25, inplace=False)\n",
              "  (fc1): Linear(in_features=774400, out_features=128, bias=True)\n",
              "  (fc2): Linear(in_features=128, out_features=10, bias=True)\n",
              "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "SalienceNet = CNN()\n",
        "SalienceNet.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "criterion = nn.CrossEntropyLoss()"
      ],
      "metadata": {
        "id": "eMAlEzRt0VTw"
      },
      "id": "eMAlEzRt0VTw",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9997283d",
      "metadata": {
        "id": "9997283d"
      },
      "outputs": [],
      "source": [
        "def analysis(net, loss_fn, dataloader_train,dataloader_test,\n",
        "          epoch=100, learning_rate=0.001):\n",
        "    optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate,weight_decay=1e-4) # weight decay as a regularization technique to defy overfitting\n",
        "    train_loss = []\n",
        "   \n",
        "    train_total_loss = []\n",
        "    train_accuracy = []\n",
        "    for i in range(epoch):\n",
        "        total,correct = 0,0\n",
        "        \n",
        "        net.to(device)   \n",
        "        net.train()\n",
        "        for data, label in dataloader_train:\n",
        "            data, label = data.to(device), label.to(device)\n",
        "        #make prediction\n",
        "            output = net(data.float())\n",
        "\n",
        "            _, y_pred_train = torch.max(output,1) # get predicted train label\n",
        "\n",
        "            # Zero out the gradients\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            # Calculate loss.\n",
        "            loss = criterion(output,torch.max(label.long(), 1)[0])\n",
        "\n",
        "            # Backpropagation and gradient update.\n",
        "            loss.backward()# Calculate gradients. #retrain_graph=True\n",
        "\n",
        "            optimizer.step() # Apply gradient udpate.\n",
        "\n",
        "            total += label.size(0)\n",
        "            correct += (y_pred_train == torch.max(label,1)[0]).sum().item()\n",
        "            \n",
        "            train_loss.append(loss.item())\n",
        "            print('currently processing epoch {}'.format(i))\n",
        "            #print('batch accuracy of {}'.format(100*(correct/total)))\n",
        "            \n",
        "            #if (i + 1) % (epoch // 5) == 0:\n",
        "             #print(f'iteration {i + 1}/{epoch} | loss: {loss.item():.3f}')\n",
        "    \n",
        "\n",
        "    train_accuracy.append(correct/total)\n",
        "\n",
        "    net.eval()\n",
        "    test_loss = []\n",
        "    test_total,test_correct = 0,0\n",
        "    test_acc = []\n",
        "    for data, labels in dataloader_test:\n",
        "        print('testing...')\n",
        "\n",
        "        data, labels = data.to(device), labels.to(device)\n",
        "        outputs = net(data.float())\n",
        "\n",
        "        _, y_pred_test = torch.max(outputs,1) # get predicted label for test\n",
        "\n",
        "        test_l = criterion(outputs,torch.max(labels.long(), 1)[0])\n",
        "        test_loss.append(test_l.item())\n",
        "\n",
        "        test_total += labels.size(0)\n",
        "        test_correct += (y_pred_test==torch.max(labels,1)[0]).sum().item()\n",
        "        \n",
        "    test_acc.append(test_correct/test_total)\n",
        "    #y_pred_test = torch.softmax(outputs,dim=1).argmax(dim=1)\n",
        "\n",
        "    print('test acc is {}'.format(test_acc))\n",
        "\n",
        "    return train_loss,test_loss,train_accuracy,test_acc \n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.cuda.get_device_name(0)"
      ],
      "metadata": {
        "id": "yDqIlocyWRYY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0f456b56-0f4a-4032-b96a-3ad7f8276e53"
      },
      "id": "yDqIlocyWRYY",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Tesla T4'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7e318c98",
      "metadata": {
        "id": "7e318c98"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import Javascript\n",
        "\n",
        "display(Javascript('''google.colab.output.setIframeHeight(0, true, {maxHeight: 300})''')) "
      ],
      "metadata": {
        "id": "kGxhNDnUbZMD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "a81b662e-6882-4878-b0f5-9a498eacb8ef"
      },
      "id": "kGxhNDnUbZMD",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "google.colab.output.setIframeHeight(0, true, {maxHeight: 300})"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c5ddd06d",
      "metadata": {
        "id": "c5ddd06d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7660f959-7d61-4cfd-cc3d-09b4d104f1a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mDie letzten 5000 Zeilen der Streamingausgabe wurden abgeschnitten.\u001b[0m\n",
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            "testing...\n",
            "test acc is [0.19279538904899135]\n"
          ]
        }
      ],
      "source": [
        "np.random.seed(1)\n",
        "torch.manual_seed(1)\n",
        "\n",
        "train_loss,test_loss,train_acc,test_acc=analysis(net=SalienceNet,loss_fn=criterion,dataloader_train=dataloader_train,\n",
        "                  dataloader_test=dataloader_test,epoch=30)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(train_loss)\n",
        "plt.xlabel('Iterations of gradient descent')\n",
        "plt.ylabel('Cross entropy')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "fk-6haR7G5-q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "1d37dcd6-554c-4580-9afa-16fa8eb2f778"
      },
      "id": "fk-6haR7G5-q",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(test_loss)\n",
        "plt.xlabel('Iterations of gradient descent')\n",
        "plt.ylabel('Cross entropy')\n",
        "plt.title('Loss for test data')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "dtd4ymkVafCh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "782d6607-5b9d-4805-9911-7838755e3176"
      },
      "id": "dtd4ymkVafCh",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('training accuracy is {}'.format(train_acc))\n",
        "print('test accuracy is {}'.format(test_acc))"
      ],
      "metadata": {
        "id": "9TM45DtCbi99",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ab29fd1-4570-4a8d-e1b5-1164126a3d97"
      },
      "id": "9TM45DtCbi99",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "training accuracy is [0.682230869001297]\n",
            "test accuracy is [0.19279538904899135]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "bVtcGONpWq0Z"
      },
      "id": "bVtcGONpWq0Z"
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "YOLO",
      "language": "python",
      "name": "yolo"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.9"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}