arch_attv1_tests_4s.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "arch_attv1_tests_4s.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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": [
        "<a href=\"https://colab.research.google.com/github/iamsoroush/DeepEEG/blob/master/arch_attv1_tests_4s.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_JCUvRGHlz5M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#@title # Clone the repository and upgrade Keras {display-mode: \"form\"}\n",
        "\n",
        "!git clone https://github.com/iamsoroush/DeepEEG.git\n",
        "!pip install --upgrade keras"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7_sHB4_nsc8J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!rm -r DeepEEG"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-dMfhjVypjFa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#@title # Imports {display-mode: \"form\"}\n",
        "\n",
        "import os\n",
        "import pickle\n",
        "import sys\n",
        "sys.path.append('DeepEEG')\n",
        "\n",
        "PACKAGE_PARENT = '..'\n",
        "SCRIPT_DIR = os.path.dirname(os.path.realpath(os.getcwd()))\n",
        "sys.path.append(os.path.normpath(os.path.join(SCRIPT_DIR, PACKAGE_PARENT)))\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "from deepeeg.helpers import CrossValidator\n",
        "from deepeeg.models import DilatedDeepEEG\n",
        "from deepeeg.dataset import DataLoader, Splitter, FixedLenGenerator\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gwzjFnB5ptIL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#@title # Set data path {display-mode: \"form\"}\n",
        "\n",
        "#@markdown ---\n",
        "#@markdown Type in the folder in your google drive that contains numpy _data_ folder:\n",
        "\n",
        "parent_dir = 'soroush_deep_eeg'#@param {type:\"string\"}\n",
        "gdrive_path =  os.path.abspath(os.path.join('gdrive/My Drive', parent_dir))\n",
        "data_dir = os.path.join(gdrive_path, 'data')\n",
        "cv_results_dir = os.path.join(gdrive_path, 'cross_validation')\n",
        "if not os.path.exists(cv_results_dir):\n",
        "    os.mkdir(cv_results_dir)\n",
        "\n",
        "print('Data directory: ', data_dir)\n",
        "print('Cross validation results dir: ', cv_results_dir)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_NdDuoHWpwe4",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title # Set Parameters\n",
        "\n",
        "batch_size = 80\n",
        "epochs = 50\n",
        "k = 10\n",
        "t = 10\n",
        "instance_duration = 4 \n",
        "instance_overlap = 1 \n",
        "sampling_rate = 256 \n",
        "n_channels = 19 \n",
        "# task = 'rnr'\n",
        "data_mode = 'cross_subject'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U0p8XCLNfGTH",
        "colab_type": "text"
      },
      "source": [
        "# RnR"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wzWsxNZqfJQW",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title ## DilatedDeepEEG-AttV1\n",
        "\n",
        "task = 'rnr'\n",
        "model_name = 'DilatedDeepEEG-AttV1'\n",
        "\n",
        "train_generator = FixedLenGenerator(batch_size=batch_size,\n",
        "                                    duration=instance_duration,\n",
        "                                    overlap=instance_overlap,\n",
        "                                    sampling_rate=sampling_rate,\n",
        "                                    is_train=True)\n",
        "\n",
        "test_generator = FixedLenGenerator(batch_size=8,\n",
        "                                   duration=instance_duration,\n",
        "                                   overlap=instance_overlap,\n",
        "                                   sampling_rate=sampling_rate,\n",
        "                                   is_train=False)\n",
        "\n",
        "params = {'task': task,\n",
        "          'data_mode': data_mode,\n",
        "          'main_res_dir': cv_results_dir,\n",
        "          'model_name': model_name,\n",
        "          'epochs': epochs,\n",
        "          'train_generator': train_generator,\n",
        "          'test_generator': test_generator,\n",
        "          't': t,\n",
        "          'k': k,\n",
        "          'channel_drop': True}\n",
        "\n",
        "validator = CrossValidator(**params)\n",
        "\n",
        "dataloader = DataLoader(data_dir,\n",
        "                        task,\n",
        "                        data_mode,\n",
        "                        sampling_rate,\n",
        "                        instance_duration,\n",
        "                        instance_overlap)\n",
        "data, labels = dataloader.load_data()\n",
        "\n",
        "input_shape = (sampling_rate * instance_duration,\n",
        "               n_channels)\n",
        "\n",
        "model_obj = DilatedDeepEEG(input_shape,\n",
        "                           model_name=model_name,\n",
        "                           attention='v1')\n",
        "model = model_obj.create_model()\n",
        "model.summary()\n",
        "\n",
        "scores = validator.do_cv(model_obj,\n",
        "                         data,\n",
        "                         labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5i7ihqAufXj_",
        "colab_type": "text"
      },
      "source": [
        "# H-MDD"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aMPhCmkbfZnG",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title ## DilatedDeepEEG-AttV1\n",
        "\n",
        "task = 'hmdd'\n",
        "model_name = 'DilatedDeepEEG-AttV1'\n",
        "\n",
        "train_generator = FixedLenGenerator(batch_size=batch_size,\n",
        "                                    duration=instance_duration,\n",
        "                                    overlap=instance_overlap,\n",
        "                                    sampling_rate=sampling_rate,\n",
        "                                    is_train=True)\n",
        "\n",
        "test_generator = FixedLenGenerator(batch_size=8,\n",
        "                                   duration=instance_duration,\n",
        "                                   overlap=instance_overlap,\n",
        "                                   sampling_rate=sampling_rate,\n",
        "                                   is_train=False)\n",
        "\n",
        "params = {'task': task,\n",
        "          'data_mode': data_mode,\n",
        "          'main_res_dir': cv_results_dir,\n",
        "          'model_name': model_name,\n",
        "          'epochs': epochs,\n",
        "          'train_generator': train_generator,\n",
        "          'test_generator': test_generator,\n",
        "          't': t,\n",
        "          'k': k,\n",
        "          'channel_drop': True}\n",
        "\n",
        "validator = CrossValidator(**params)\n",
        "\n",
        "dataloader = DataLoader(data_dir,\n",
        "                        task,\n",
        "                        data_mode,\n",
        "                        sampling_rate,\n",
        "                        instance_duration,\n",
        "                        instance_overlap)\n",
        "data, labels = dataloader.load_data()\n",
        "\n",
        "input_shape = (sampling_rate * instance_duration,\n",
        "               n_channels)\n",
        "\n",
        "model_obj = DilatedDeepEEG(input_shape,\n",
        "                           model_name=model_name,\n",
        "                           attention='v1')\n",
        "model = model_obj.create_model()\n",
        "model.summary()\n",
        "\n",
        "scores = validator.do_cv(model_obj,\n",
        "                         data,\n",
        "                         labels)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}