Parkinson_Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Parkinson-Prediction.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMKOoCHqoW2TSPe2NQlwL5s",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ahmadmardeni1/Parkinson-Prediction/blob/main/Parkinson_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZIG479my3pme",
        "outputId": "8942acce-55e5-49ab-f4f7-3b8cb24fd059"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AVvOEvl-3tyb"
      },
      "source": [
        "import os\n",
        "os.environ['KAGGLE_CONFIG_DIR'] = \"/content/gdrive/My Drive/Kaggle\""
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eZDlwFJR3urQ",
        "outputId": "7769c01d-5cb1-40b3-bba5-994abf3d2644"
      },
      "source": [
        "!kaggle datasets download -d kmader/parkinsons-drawings"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading parkinsons-drawings.zip to /content\n",
            " 80% 33.0M/41.4M [00:00<00:00, 34.2MB/s]\n",
            "100% 41.4M/41.4M [00:00<00:00, 65.3MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-BRl-Bhe3xxQ",
        "outputId": "f96a4508-ea9e-4d81-a90a-c16a44859f5f"
      },
      "source": [
        "!unzip \\*.zip  && rm *.zip"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  parkinsons-drawings.zip\n",
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            "  inflating: wave/training/parkinson/V15PO03.png  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vscYUlwi4Ntg"
      },
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ibb9igNP4R2W"
      },
      "source": [
        "model = keras.Sequential([\n",
        "        layers.Conv2D(32,(3,3),input_shape=(128, 128, 3),activation='relu'),\n",
        "        layers.MaxPooling2D(pool_size=(2,2)),\n",
        "        layers.Conv2D(32,(3,3),activation='relu'),\n",
        "        layers.MaxPooling2D(pool_size=(2,2)),\n",
        "        layers.Flatten(),\n",
        "        layers.Dense(activation='relu',units=128),\n",
        "        layers.Dense(activation='sigmoid',units=1),\n",
        "])"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FC_Bcn5Y4U5o"
      },
      "source": [
        "from keras.optimizers import Adam\n",
        "from keras.callbacks import EarlyStopping\n",
        "from keras.preprocessing.image import ImageDataGenerator"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5tzR-RCNzSIy",
        "outputId": "74c20ddc-93dd-4ffc-b09e-13d59e1e17ff"
      },
      "source": [
        "train_datagen = ImageDataGenerator(rescale = 1./255, \n",
        "                                  shear_range = 0.2, \n",
        "                                  zoom_range = 0.2, \n",
        "                                  horizontal_flip = True)\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale = 1./255)\n",
        "\n",
        "spiral_train_generator = train_datagen.flow_from_directory('spiral/training',\n",
        "                                                   target_size = (128,128),\n",
        "                                                   batch_size = 32,\n",
        "                                                   class_mode = 'binary')\n",
        "\n",
        "spiral_test_generator = test_datagen.flow_from_directory('spiral/testing',\n",
        "                                                   target_size = (128,128),\n",
        "                                                   batch_size = 32,\n",
        "                                                   class_mode = 'binary')\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 72 images belonging to 2 classes.\n",
            "Found 30 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c8BxrTMYzu9v",
        "outputId": "7faf2c81-ef3f-41f7-8e35-2af8ca2dec0c"
      },
      "source": [
        "wave_train_generator = train_datagen.flow_from_directory('wave/training',\n",
        "                                                   target_size = (128,128),\n",
        "                                                   batch_size = 32,\n",
        "                                                   class_mode = 'binary')\n",
        "\n",
        "wave_test_generator = test_datagen.flow_from_directory('wave/testing',\n",
        "                                                   target_size = (128,128),\n",
        "                                                   batch_size = 32,\n",
        "                                                   class_mode = 'binary')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 72 images belonging to 2 classes.\n",
            "Found 30 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XHBf_2md4XHg"
      },
      "source": [
        "early_stopping = keras.callbacks.EarlyStopping(\n",
        "    patience=10,\n",
        "    min_delta=0.001,\n",
        "    restore_best_weights=True,\n",
        ")\n",
        "\n",
        "model.compile(\n",
        "    optimizer=Adam(lr=0.01),\n",
        "    loss='binary_crossentropy',\n",
        "    metrics=['accuracy'],\n",
        ")\n"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hOUYzpuB0mq4",
        "outputId": "34a4fd5d-0ea1-4258-b569-77c79f67fc2d"
      },
      "source": [
        "history = model.fit_generator(\n",
        "        spiral_train_generator,\n",
        "        steps_per_epoch=spiral_train_generator.n//spiral_train_generator.batch_size,\n",
        "        epochs=48,\n",
        "        validation_data=spiral_test_generator,\n",
        "        validation_steps=spiral_test_generator.n//spiral_test_generator.batch_size,\n",
        "        callbacks=early_stopping)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/48\n",
            "2/2 [==============================] - 2s 707ms/step - loss: 0.7188 - accuracy: 0.4896\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 2/48\n",
            "2/2 [==============================] - 1s 324ms/step - loss: 1.3471 - accuracy: 0.5000\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 3/48\n",
            "2/2 [==============================] - 1s 320ms/step - loss: 1.5051 - accuracy: 0.5396\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 4/48\n",
            "2/2 [==============================] - 1s 578ms/step - loss: 1.5028 - accuracy: 0.5417\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 5/48\n",
            "2/2 [==============================] - 1s 574ms/step - loss: 0.6968 - accuracy: 0.4583\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 6/48\n",
            "2/2 [==============================] - 1s 578ms/step - loss: 0.6931 - accuracy: 0.5104\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 7/48\n",
            "2/2 [==============================] - 1s 588ms/step - loss: 0.6931 - accuracy: 0.5000\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 8/48\n",
            "2/2 [==============================] - 1s 301ms/step - loss: 0.6935 - accuracy: 0.4833\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 9/48\n",
            "2/2 [==============================] - 1s 820ms/step - loss: 0.6917 - accuracy: 0.6000\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 10/48\n",
            "2/2 [==============================] - 1s 777ms/step - loss: 0.6928 - accuracy: 0.5250\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 11/48\n",
            "2/2 [==============================] - 1s 593ms/step - loss: 0.6940 - accuracy: 0.5208\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 12/48\n",
            "2/2 [==============================] - 1s 574ms/step - loss: 0.6953 - accuracy: 0.5000\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 13/48\n",
            "2/2 [==============================] - 1s 292ms/step - loss: 0.6963 - accuracy: 0.4292\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 14/48\n",
            "2/2 [==============================] - 1s 289ms/step - loss: 0.6959 - accuracy: 0.4937\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 15/48\n",
            "2/2 [==============================] - 1s 587ms/step - loss: 0.6978 - accuracy: 0.5000\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 16/48\n",
            "2/2 [==============================] - 1s 305ms/step - loss: 0.7042 - accuracy: 0.5104\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 17/48\n",
            "2/2 [==============================] - 1s 772ms/step - loss: 0.7256 - accuracy: 0.5917\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 18/48\n",
            "2/2 [==============================] - 1s 799ms/step - loss: 0.6200 - accuracy: 0.6833\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 19/48\n",
            "2/2 [==============================] - 1s 563ms/step - loss: 0.7351 - accuracy: 0.4688\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 20/48\n",
            "2/2 [==============================] - 1s 586ms/step - loss: 0.6941 - accuracy: 0.4479\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 21/48\n",
            "2/2 [==============================] - 1s 568ms/step - loss: 0.6939 - accuracy: 0.4583\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 22/48\n",
            "2/2 [==============================] - 1s 287ms/step - loss: 0.6929 - accuracy: 0.5333\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 23/48\n",
            "2/2 [==============================] - 1s 818ms/step - loss: 0.6915 - accuracy: 0.6750\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 24/48\n",
            "2/2 [==============================] - 1s 796ms/step - loss: 0.6978 - accuracy: 0.3417\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 25/48\n",
            "2/2 [==============================] - 1s 767ms/step - loss: 0.6888 - accuracy: 0.6500\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 26/48\n",
            "2/2 [==============================] - 1s 816ms/step - loss: 0.6911 - accuracy: 0.5583\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 27/48\n",
            "2/2 [==============================] - 1s 284ms/step - loss: 0.6925 - accuracy: 0.5167\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 28/48\n",
            "2/2 [==============================] - 1s 319ms/step - loss: 0.6950 - accuracy: 0.4833\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 29/48\n",
            "2/2 [==============================] - 1s 815ms/step - loss: 0.6867 - accuracy: 0.5917\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 30/48\n",
            "2/2 [==============================] - 1s 313ms/step - loss: 0.6941 - accuracy: 0.5000\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 31/48\n",
            "2/2 [==============================] - 1s 286ms/step - loss: 0.6943 - accuracy: 0.5000\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 32/48\n",
            "2/2 [==============================] - 1s 786ms/step - loss: 0.6939 - accuracy: 0.5083\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 33/48\n",
            "2/2 [==============================] - 1s 299ms/step - loss: 0.6952 - accuracy: 0.4937\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 34/48\n",
            "2/2 [==============================] - 1s 778ms/step - loss: 0.7100 - accuracy: 0.3333\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 35/48\n",
            "2/2 [==============================] - 1s 284ms/step - loss: 0.6909 - accuracy: 0.5375\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 36/48\n",
            "2/2 [==============================] - 1s 314ms/step - loss: 0.6888 - accuracy: 0.5917\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 37/48\n",
            "2/2 [==============================] - 1s 298ms/step - loss: 0.6904 - accuracy: 0.5979\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 38/48\n",
            "2/2 [==============================] - 1s 332ms/step - loss: 0.6924 - accuracy: 0.5375\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 39/48\n",
            "2/2 [==============================] - 1s 580ms/step - loss: 0.6926 - accuracy: 0.5417\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 40/48\n",
            "2/2 [==============================] - 1s 581ms/step - loss: 0.6931 - accuracy: 0.5000\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 41/48\n",
            "2/2 [==============================] - 1s 826ms/step - loss: 0.6932 - accuracy: 0.4250\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 42/48\n",
            "2/2 [==============================] - 1s 312ms/step - loss: 0.6934 - accuracy: 0.4729\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 43/48\n",
            "2/2 [==============================] - 1s 801ms/step - loss: 0.6917 - accuracy: 0.6167\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 44/48\n",
            "2/2 [==============================] - 1s 583ms/step - loss: 0.6922 - accuracy: 0.5417\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 45/48\n",
            "2/2 [==============================] - 1s 307ms/step - loss: 0.6938 - accuracy: 0.4792\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 46/48\n",
            "2/2 [==============================] - 1s 581ms/step - loss: 0.6966 - accuracy: 0.4167\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 47/48\n",
            "2/2 [==============================] - 1s 337ms/step - loss: 0.6930 - accuracy: 0.5104\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "Epoch 48/48\n",
            "2/2 [==============================] - 1s 583ms/step - loss: 0.6950 - accuracy: 0.4688\n",
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "ANEbfA_52YUh",
        "outputId": "8a9a9c3e-6577-4a84-d6d7-193a90be5030"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.ylabel('Accuracy')\n",
        "plt.plot(history.history['accuracy'], color = 'blue', label='Training Accuracy')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f8c3fd3b290>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQ7lBcPP21b6"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}