Tumor_Detector.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "gpuType": "A100",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RohanNankani/Computer-Fundamentals-CS50/blob/main/Tumor_Detector.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qe0GXCoCdzGI",
        "outputId": "90a5030c-067d-4436-d423-b3909c8bc30a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U tensorflow"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fW1W4PG9fRnO",
        "outputId": "bc989823-b841-4412-e7d2-b65cbd800b6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.17.0)\n",
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            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow) (3.0.0)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.config.optimizer.set_jit(False)\n"
      ],
      "metadata": {
        "id": "rMDQqvL_kxJC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "cb58e24a-7db6-443a-f5de-95e1e3e3b87a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'tf' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-a513266561be>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_jit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'tf' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "-PValvcCdKYe",
        "outputId": "bfcf74f0-39e4-48ff-9d0b-077e56d2dc7c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 446 files belonging to 4 classes.\n",
            "Found 394 files belonging to 4 classes.\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "\u001b[1m87910968/87910968\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 0us/step\n",
            "Epoch 1/10\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "UnimplementedError",
          "evalue": "Graph execution error:\n\nDetected at node gradient_tape/sequential_2_1/inception_v3_1/max_pooling2d_7_1/MaxPool2d/MaxPoolGrad defined at (most recent call last):\n<stack traces unavailable>\nGPU MaxPool gradient ops do not yet have a deterministic XLA implementation.\n\t [[{{node gradient_tape/sequential_2_1/inception_v3_1/max_pooling2d_7_1/MaxPool2d/MaxPoolGrad}}]]\n\ttf2xla conversion failed while converting __inference_one_step_on_data_52597[]. Run with TF_DUMP_GRAPH_PREFIX=/path/to/dump/dir and --vmodule=xla_compiler=2 to obtain a dump of the compiled functions.\n\t [[StatefulPartitionedCall]] [Op:__inference_one_step_on_iterator_54190]",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mUnimplementedError\u001b[0m                        Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-f083117745ca>\u001b[0m in \u001b[0;36m<cell line: 118>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    116\u001b[0m )\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m history = model.fit(\n\u001b[0m\u001b[1;32m    119\u001b[0m     \u001b[0mds_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0mvalidation_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mds_valid\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    120\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;31m# `keras.config.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     51\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m     54\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[1;32m     55\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mUnimplementedError\u001b[0m: Graph execution error:\n\nDetected at node gradient_tape/sequential_2_1/inception_v3_1/max_pooling2d_7_1/MaxPool2d/MaxPoolGrad defined at (most recent call last):\n<stack traces unavailable>\nGPU MaxPool gradient ops do not yet have a deterministic XLA implementation.\n\t [[{{node gradient_tape/sequential_2_1/inception_v3_1/max_pooling2d_7_1/MaxPool2d/MaxPoolGrad}}]]\n\ttf2xla conversion failed while converting __inference_one_step_on_data_52597[]. Run with TF_DUMP_GRAPH_PREFIX=/path/to/dump/dir and --vmodule=xla_compiler=2 to obtain a dump of the compiled functions.\n\t [[StatefulPartitionedCall]] [Op:__inference_one_step_on_iterator_54190]"
          ]
        }
      ],
      "source": [
        "# Imports\n",
        "import os, warnings\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import gridspec\n",
        "import tensorflow as tf\n",
        "\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "# import pandas as pd\n",
        "from tensorflow.keras import layers\n",
        "# import tensorflow_hub as hub\n",
        "from tensorflow.keras.preprocessing import image_dataset_from_directory\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense\n",
        "from tensorflow.keras.applications import InceptionV3\n",
        "\n",
        "\n",
        "# Reproducability\n",
        "def set_seed(seed=31415):\n",
        "    np.random.seed(seed)\n",
        "    tf.random.set_seed(seed)\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    os.environ['TF_DETERMINISTIC_OPS'] = '1'\n",
        "set_seed()\n",
        "\n",
        "# Set Matplotlib defaults\n",
        "plt.rc('figure', autolayout=True)\n",
        "plt.rc('axes', labelweight='bold', labelsize='large',\n",
        "       titleweight='bold', titlesize=18, titlepad=10)\n",
        "plt.rc('image', cmap='magma')\n",
        "warnings.filterwarnings(\"ignore\") # to clean up output cells\n",
        "\n",
        "\n",
        "# Load training and validation sets\n",
        "ds_train = image_dataset_from_directory(\n",
        "    '/content/drive/MyDrive/Data-sets/Brain-Tumor-Classification-DataSet-master/Training',\n",
        "    labels='inferred',\n",
        "    image_size=[512, 512],\n",
        "    interpolation='nearest',\n",
        "    batch_size=25,\n",
        "    shuffle=True,\n",
        ")\n",
        "\n",
        "ds_valid = image_dataset_from_directory(\n",
        "    '/content/drive/MyDrive/Data-sets/Brain-Tumor-Classification-DataSet-master/Testing',\n",
        "    labels='inferred',\n",
        "    image_size=[512, 512],\n",
        "    interpolation='nearest',\n",
        "    batch_size=25,\n",
        "    shuffle=False,\n",
        ")\n",
        "\n",
        "# Data Pipeline\n",
        "def convert_to_float(image, label):\n",
        "    image = tf.image.convert_image_dtype(image, dtype=tf.float32)\n",
        "    return image, label\n",
        "\n",
        "AUTOTUNE = tf.data.experimental.AUTOTUNE\n",
        "\n",
        "data_augmentation = keras.Sequential([\n",
        "    layers.RandomFlip(\"horizontal_and_vertical\"),\n",
        "    layers.RandomRotation(0.2),\n",
        "    layers.RandomZoom(0.2),\n",
        "])\n",
        "\n",
        "def augment(image, label):\n",
        "    image = data_augmentation(image)\n",
        "    return image, label\n",
        "\n",
        "ds_train = (\n",
        "    ds_train\n",
        "    .map(convert_to_float)\n",
        "    .map(augment, num_parallel_calls=AUTOTUNE)\n",
        "    .cache()\n",
        "    .prefetch(buffer_size=AUTOTUNE)\n",
        ")\n",
        "\n",
        "ds_valid = (\n",
        "    ds_valid\n",
        "    .map(convert_to_float)\n",
        "    .cache()\n",
        "    .prefetch(buffer_size=AUTOTUNE)\n",
        ")\n",
        "\n",
        "pre_trained_base = InceptionV3(\n",
        "    input_shape=(512, 512, 3),\n",
        "    include_top=False,\n",
        "    weights='imagenet'\n",
        ")\n",
        "\n",
        "model = keras.Sequential([\n",
        "    pre_trained_base,\n",
        "    GlobalAveragePooling2D(),\n",
        "    layers.Dense(10, activation='relu'),\n",
        "    layers.Dropout(0.2),\n",
        "    layers.BatchNormalization(),\n",
        "    layers.Dense(10, activation='relu'),\n",
        "    layers.Dropout(0.2),\n",
        "    layers.BatchNormalization(),\n",
        "    layers.Dense(1, activation='sigmoid'),\n",
        "])\n",
        "\n",
        "model.compile(\n",
        "    optimizer = 'adam',\n",
        "    loss = 'binary_crossentropy',\n",
        "    metrics = ['binary_accuracy'],\n",
        ")\n",
        "\n",
        "early_stopping = keras.callbacks.EarlyStopping(\n",
        "    patience=10,\n",
        "    min_delta=0.001,\n",
        "    restore_best_weights=True,\n",
        ")\n",
        "\n",
        "history = model.fit(\n",
        "    ds_train,\n",
        "    validation_data = ds_valid,\n",
        "    epochs=10,\n",
        "    callbacks=[early_stopping],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "12bpS0k-fQoM"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}