1_CNN_from_scratch.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "1.CNN_from_scratch.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "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/lorebianchi98/BrainTumorClassification/blob/main/1_CNN_from_scratch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Brain Tumor Classification using CNN from scratch\n",
        "In this notebook we are going to perform the classification of the brain tumor using a CNN from scratch. We will try different structures, and our aim is to find the best model using a trial and error approach."
      ],
      "metadata": {
        "id": "r3M2Y_ftTQ7j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import and Costants"
      ],
      "metadata": {
        "id": "l1ATd77JsQyY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras import layers\n",
        "from keras import models\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tensorflow import keras\n",
        "import os\n",
        "import shutil\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "from google.colab.patches import cv2_imshow\n",
        "import imutils\n",
        "from tqdm import tqdm\n",
        "from google.colab import drive"
      ],
      "metadata": {
        "id": "PmOIGAcXsSUk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Connection to Google Drive"
      ],
      "metadata": {
        "id": "tvcSz_khiYIR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/gdrive', force_remount=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SWrkZz1siY_U",
        "outputId": "1507f3bb-e72f-4100-b5e4-ce64e53e3895"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "% run /content/gdrive/MyDrive/BrainTumorClassifier/utilities.ipynb"
      ],
      "metadata": {
        "id": "tTZdVYQ4sWjL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We also perform the download and the unzipping of the dataset from the drive folder."
      ],
      "metadata": {
        "id": "M4pwhya_W3Nj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip -q '/content/gdrive/MyDrive/BrainTumorClassifier/brain-tumor-mri-splits.zip' -d '/'"
      ],
      "metadata": {
        "id": "8MpTHXtjW_PA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Hardware Accelerator test"
      ],
      "metadata": {
        "id": "Q1peprlbhd3O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First we need to check if the hardware accelaration is enabled, since training a CNN on a CPU could be infeasible."
      ],
      "metadata": {
        "id": "Q6nkuByCSine"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#check hardware acceleration\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QAWrdrxOSn6Y",
        "outputId": "e66ec5de-7f0e-41a8-d3f7-d2c90adbae1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataset loading"
      ],
      "metadata": {
        "id": "gKUZHvmhczLT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we need to create the Dataset objects from the sets directory. We use the [image_dataset_from_directory](https://www.tensorflow.org/api_docs/python/tf/keras/utils/image_dataset_from_directory) function provided by Keras. "
      ],
      "metadata": {
        "id": "8MRY8zlUibs-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds, val_ds, test_ds = load_data_splits(IMAGE_SIZE, BATCH_SIZE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "01Hu-W8UjrwJ",
        "outputId": "30a7aaa1-64b6-4aa0-c2ac-0470d9903cd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 4213 files belonging to 4 classes.\n",
            "Found 1404 files belonging to 4 classes.\n",
            "Found 1406 files belonging to 4 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# One Dense layer with 256 neurons, One Dropout with 0.3"
      ],
      "metadata": {
        "id": "2ZeXjTpGk6As"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we define the model that we want to train."
      ],
      "metadata": {
        "id": "il6mVLSNtcPK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = keras.Input(shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3))\n",
        "\n",
        "x = data_augmentation_layers(inputs)\n",
        "x = layers.Rescaling(1./255)(x)\n",
        "x = layers.Conv2D(filters=32, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=128, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=256, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=5)(x)\n",
        "x = layers.Flatten()(x)\n",
        "x = layers.Dense(256, activation='relu', name='hidden_classifier') (x)\n",
        "x = layers.Dropout(0.3)(x)\n",
        "outputs = layers.Dense(4, activation=\"softmax\")(x)\n",
        "model = keras.Model(inputs=inputs, outputs=outputs)\n",
        "model = compile_model(model, loss=\"sparse_categorical_crossentropy\")\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "vGUGnwPmtzS6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0aa02546-5652-40f7-e94a-50146fc01baa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " sequential (Sequential)     (None, 224, 224, 3)       0         \n",
            "                                                                 \n",
            " rescaling (Rescaling)       (None, 224, 224, 3)       0         \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 222, 222, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 111, 111, 32)     0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 109, 109, 64)      18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 54, 54, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 52, 52, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 26, 26, 128)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 24, 24, 256)       295168    \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 4, 4, 256)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 4096)              0         \n",
            "                                                                 \n",
            " hidden_classifier (Dense)   (None, 256)               1048832   \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 256)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 4)                 1028      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,438,276\n",
            "Trainable params: 1,438,276\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model, history = run_model(model, \"Dense256Neurons_OneDropout0-3\", train_ds, val_ds, 50, patience=3, monitor='val_loss')\n",
        "plot_accuracy_and_loss_history(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "HgUYOdoINEIU",
        "outputId": "83eb2a95-1c87-4452-dbbe-3f3b6a4b6c12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 1.2015 - accuracy: 0.4125\n",
            "Epoch 1: val_loss improved from inf to 0.84518, saving model to model/Dense256Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 19s 111ms/step - loss: 1.2015 - accuracy: 0.4125 - val_loss: 0.8452 - val_accuracy: 0.6261\n",
            "Epoch 2/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.7588 - accuracy: 0.6876\n",
            "Epoch 2: val_loss improved from 0.84518 to 0.60243, saving model to model/Dense256Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 7s 101ms/step - loss: 0.7588 - accuracy: 0.6876 - val_loss: 0.6024 - val_accuracy: 0.7856\n",
            "Epoch 3/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.6531 - accuracy: 0.7422\n",
            "Epoch 3: val_loss improved from 0.60243 to 0.51072, saving model to model/Dense256Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 7s 101ms/step - loss: 0.6531 - accuracy: 0.7422 - val_loss: 0.5107 - val_accuracy: 0.7877\n",
            "Epoch 4/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.5049 - accuracy: 0.8139\n",
            "Epoch 4: val_loss improved from 0.51072 to 0.40031, saving model to model/Dense256Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 7s 102ms/step - loss: 0.5049 - accuracy: 0.8139 - val_loss: 0.4003 - val_accuracy: 0.8640\n",
            "Epoch 5/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.4313 - accuracy: 0.8507\n",
            "Epoch 5: val_loss improved from 0.40031 to 0.36184, saving model to model/Dense256Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 7s 102ms/step - loss: 0.4313 - accuracy: 0.8507 - val_loss: 0.3618 - val_accuracy: 0.8789\n",
            "Epoch 6/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.3700 - accuracy: 0.8690\n",
            "Epoch 6: val_loss improved from 0.36184 to 0.32033, saving model to model/Dense256Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 7s 103ms/step - loss: 0.3700 - accuracy: 0.8690 - val_loss: 0.3203 - val_accuracy: 0.8860\n",
            "Epoch 7/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.3399 - accuracy: 0.8794\n",
            "Epoch 7: val_loss improved from 0.32033 to 0.28945, saving model to model/Dense256Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 7s 102ms/step - loss: 0.3399 - accuracy: 0.8794 - val_loss: 0.2895 - val_accuracy: 0.8989\n",
            "Epoch 8/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2769 - accuracy: 0.9046\n",
            "Epoch 8: val_loss improved from 0.28945 to 0.27274, saving model to model/Dense256Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 7s 102ms/step - loss: 0.2769 - accuracy: 0.9046 - val_loss: 0.2727 - val_accuracy: 0.9003\n",
            "Epoch 9/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2392 - accuracy: 0.9167\n",
            "Epoch 9: val_loss improved from 0.27274 to 0.27175, saving model to model/Dense256Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 7s 102ms/step - loss: 0.2392 - accuracy: 0.9167 - val_loss: 0.2718 - val_accuracy: 0.9067\n",
            "Epoch 10/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2176 - accuracy: 0.9245\n",
            "Epoch 10: val_loss improved from 0.27175 to 0.21265, saving model to model/Dense256Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 7s 102ms/step - loss: 0.2176 - accuracy: 0.9245 - val_loss: 0.2126 - val_accuracy: 0.9202\n",
            "Epoch 11/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1851 - accuracy: 0.9343\n",
            "Epoch 11: val_loss did not improve from 0.21265\n",
            "66/66 [==============================] - 7s 100ms/step - loss: 0.1851 - accuracy: 0.9343 - val_loss: 0.2441 - val_accuracy: 0.9060\n",
            "Epoch 12/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1567 - accuracy: 0.9480\n",
            "Epoch 12: val_loss improved from 0.21265 to 0.19614, saving model to model/Dense256Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 7s 101ms/step - loss: 0.1567 - accuracy: 0.9480 - val_loss: 0.1961 - val_accuracy: 0.9181\n",
            "Epoch 13/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1451 - accuracy: 0.9483\n",
            "Epoch 13: val_loss did not improve from 0.19614\n",
            "66/66 [==============================] - 7s 100ms/step - loss: 0.1451 - accuracy: 0.9483 - val_loss: 0.1966 - val_accuracy: 0.9288\n",
            "Epoch 14/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1246 - accuracy: 0.9585\n",
            "Epoch 14: val_loss did not improve from 0.19614\n",
            "66/66 [==============================] - 7s 102ms/step - loss: 0.1246 - accuracy: 0.9585 - val_loss: 0.2170 - val_accuracy: 0.9288\n",
            "Epoch 15/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1202 - accuracy: 0.9615\n",
            "Epoch 15: val_loss did not improve from 0.19614\n",
            "66/66 [==============================] - 7s 101ms/step - loss: 0.1202 - accuracy: 0.9615 - val_loss: 0.1998 - val_accuracy: 0.9295\n",
            "Saving /content/gdrive/MyDrive/BrainTumorClassifier/Models/Dense256Neurons_OneDropout0-3/history.pkl\n",
            "INFO:tensorflow:Assets written to: ram://517ae1ad-5d17-4bc7-a8f7-6ea18ac8a1ab/assets\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(model, test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sC1dk8kFdJL5",
        "outputId": "5df66c1c-4864-4332-c84e-ff94ff72512b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22/22 [==============================] - 2s 66ms/step - loss: 0.2174 - accuracy: 0.9139\n",
            "Loss on test set: 0.21739162504673004\n",
            "Accuracy on test set: 0.9139402508735657\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      glioma     0.9055    0.9138    0.9096       325\n",
            "  meningioma     0.8136    0.8754    0.8433       329\n",
            "     notumor     0.9585    0.9825    0.9704       400\n",
            "   pituitary     0.9777    0.8722    0.9219       352\n",
            "\n",
            "    accuracy                         0.9139      1406\n",
            "   macro avg     0.9138    0.9110    0.9113      1406\n",
            "weighted avg     0.9171    0.9139    0.9145      1406\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusionmatrix(model, test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "id": "1g8oiHNKfqdp",
        "outputId": "5d798e09-4b08-461d-c850-7e2bd3128fec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22/22 [==============================] - 1s 61ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# One Dense layer with 128 neurons, One Dropout with 0.3"
      ],
      "metadata": {
        "id": "YRibcD7WlH6G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we define the model that we want to train."
      ],
      "metadata": {
        "id": "FjdDqxIElH6H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = keras.Input(shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3))\n",
        "\n",
        "x = data_augmentation_layers(inputs)\n",
        "x = layers.Rescaling(1./255)(x)\n",
        "x = layers.Conv2D(filters=32, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=128, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=256, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=5)(x)\n",
        "x = layers.Flatten()(x)\n",
        "x = layers.Dense(128, activation='relu', name='hidden_classifier') (x)\n",
        "x = layers.Dropout(0.3)(x)\n",
        "outputs = layers.Dense(4, activation=\"softmax\")(x)\n",
        "model = keras.Model(inputs=inputs, outputs=outputs)\n",
        "model = compile_model(model, loss=\"sparse_categorical_crossentropy\")\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3189c2da-b815-4e63-b58f-b4ceefa349a0",
        "id": "rOlh5TYdlH6I"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_3 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " sequential_6 (Sequential)   (None, 224, 224, 3)       0         \n",
            "                                                                 \n",
            " rescaling_2 (Rescaling)     (None, 224, 224, 3)       0         \n",
            "                                                                 \n",
            " conv2d_8 (Conv2D)           (None, 222, 222, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d_8 (MaxPooling  (None, 111, 111, 32)     0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_9 (Conv2D)           (None, 109, 109, 64)      18496     \n",
            "                                                                 \n",
            " max_pooling2d_9 (MaxPooling  (None, 54, 54, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_10 (Conv2D)          (None, 52, 52, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_10 (MaxPoolin  (None, 26, 26, 128)      0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_11 (Conv2D)          (None, 24, 24, 256)       295168    \n",
            "                                                                 \n",
            " max_pooling2d_11 (MaxPoolin  (None, 4, 4, 256)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_2 (Flatten)         (None, 4096)              0         \n",
            "                                                                 \n",
            " hidden_classifier (Dense)   (None, 128)               524416    \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 4)                 516       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 913,348\n",
            "Trainable params: 913,348\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model, history = run_model(model, \"Dense128Neurons_OneDropout0-3\", train_ds, val_ds, 50, patience=3, monitor='val_loss')\n",
        "plot_accuracy_and_loss_history(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "51a26123-5f1c-4836-a1d2-502ff0b42fd4",
        "id": "5OZKe5gdlH6J"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 1.1749 - accuracy: 0.4413\n",
            "Epoch 1: val_loss improved from inf to 0.94101, saving model to model/Dense128Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 11s 150ms/step - loss: 1.1749 - accuracy: 0.4413 - val_loss: 0.9410 - val_accuracy: 0.5484\n",
            "Epoch 2/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.7480 - accuracy: 0.6940\n",
            "Epoch 2: val_loss improved from 0.94101 to 0.60715, saving model to model/Dense128Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 148ms/step - loss: 0.7480 - accuracy: 0.6940 - val_loss: 0.6072 - val_accuracy: 0.7664\n",
            "Epoch 3/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.6010 - accuracy: 0.7743\n",
            "Epoch 3: val_loss improved from 0.60715 to 0.48818, saving model to model/Dense128Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 149ms/step - loss: 0.6010 - accuracy: 0.7743 - val_loss: 0.4882 - val_accuracy: 0.8013\n",
            "Epoch 4/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.4667 - accuracy: 0.8263\n",
            "Epoch 4: val_loss improved from 0.48818 to 0.39119, saving model to model/Dense128Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 149ms/step - loss: 0.4667 - accuracy: 0.8263 - val_loss: 0.3912 - val_accuracy: 0.8583\n",
            "Epoch 5/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.4225 - accuracy: 0.8441\n",
            "Epoch 5: val_loss improved from 0.39119 to 0.34447, saving model to model/Dense128Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 152ms/step - loss: 0.4225 - accuracy: 0.8441 - val_loss: 0.3445 - val_accuracy: 0.8789\n",
            "Epoch 6/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.3942 - accuracy: 0.8547\n",
            "Epoch 6: val_loss did not improve from 0.34447\n",
            "66/66 [==============================] - 10s 149ms/step - loss: 0.3942 - accuracy: 0.8547 - val_loss: 0.3523 - val_accuracy: 0.8640\n",
            "Epoch 7/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.3607 - accuracy: 0.8680\n",
            "Epoch 7: val_loss improved from 0.34447 to 0.34372, saving model to model/Dense128Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 151ms/step - loss: 0.3607 - accuracy: 0.8680 - val_loss: 0.3437 - val_accuracy: 0.8754\n",
            "Epoch 8/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.3219 - accuracy: 0.8858\n",
            "Epoch 8: val_loss improved from 0.34372 to 0.29743, saving model to model/Dense128Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 152ms/step - loss: 0.3219 - accuracy: 0.8858 - val_loss: 0.2974 - val_accuracy: 0.8903\n",
            "Epoch 9/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2698 - accuracy: 0.9036\n",
            "Epoch 9: val_loss improved from 0.29743 to 0.26471, saving model to model/Dense128Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 151ms/step - loss: 0.2698 - accuracy: 0.9036 - val_loss: 0.2647 - val_accuracy: 0.8981\n",
            "Epoch 10/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2471 - accuracy: 0.9117\n",
            "Epoch 10: val_loss improved from 0.26471 to 0.23463, saving model to model/Dense128Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 150ms/step - loss: 0.2471 - accuracy: 0.9117 - val_loss: 0.2346 - val_accuracy: 0.9160\n",
            "Epoch 11/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2460 - accuracy: 0.9148\n",
            "Epoch 11: val_loss improved from 0.23463 to 0.22951, saving model to model/Dense128Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 151ms/step - loss: 0.2460 - accuracy: 0.9148 - val_loss: 0.2295 - val_accuracy: 0.9117\n",
            "Epoch 12/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2063 - accuracy: 0.9243\n",
            "Epoch 12: val_loss did not improve from 0.22951\n",
            "66/66 [==============================] - 10s 150ms/step - loss: 0.2063 - accuracy: 0.9243 - val_loss: 0.2349 - val_accuracy: 0.9031\n",
            "Epoch 13/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1988 - accuracy: 0.9297\n",
            "Epoch 13: val_loss improved from 0.22951 to 0.22693, saving model to model/Dense128Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 151ms/step - loss: 0.1988 - accuracy: 0.9297 - val_loss: 0.2269 - val_accuracy: 0.9067\n",
            "Epoch 14/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1616 - accuracy: 0.9418\n",
            "Epoch 14: val_loss improved from 0.22693 to 0.22327, saving model to model/Dense128Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 153ms/step - loss: 0.1616 - accuracy: 0.9418 - val_loss: 0.2233 - val_accuracy: 0.9174\n",
            "Epoch 15/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1505 - accuracy: 0.9445\n",
            "Epoch 15: val_loss improved from 0.22327 to 0.18412, saving model to model/Dense128Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 152ms/step - loss: 0.1505 - accuracy: 0.9445 - val_loss: 0.1841 - val_accuracy: 0.9281\n",
            "Epoch 16/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1318 - accuracy: 0.9551\n",
            "Epoch 16: val_loss did not improve from 0.18412\n",
            "66/66 [==============================] - 10s 151ms/step - loss: 0.1318 - accuracy: 0.9551 - val_loss: 0.2104 - val_accuracy: 0.9274\n",
            "Epoch 17/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1290 - accuracy: 0.9561\n",
            "Epoch 17: val_loss improved from 0.18412 to 0.17064, saving model to model/Dense128Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 152ms/step - loss: 0.1290 - accuracy: 0.9561 - val_loss: 0.1706 - val_accuracy: 0.9345\n",
            "Epoch 18/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0980 - accuracy: 0.9670\n",
            "Epoch 18: val_loss did not improve from 0.17064\n",
            "66/66 [==============================] - 10s 151ms/step - loss: 0.0980 - accuracy: 0.9670 - val_loss: 0.1977 - val_accuracy: 0.9259\n",
            "Epoch 19/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1054 - accuracy: 0.9604\n",
            "Epoch 19: val_loss improved from 0.17064 to 0.16501, saving model to model/Dense128Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 151ms/step - loss: 0.1054 - accuracy: 0.9604 - val_loss: 0.1650 - val_accuracy: 0.9409\n",
            "Epoch 20/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0915 - accuracy: 0.9658\n",
            "Epoch 20: val_loss improved from 0.16501 to 0.16321, saving model to model/Dense128Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 152ms/step - loss: 0.0915 - accuracy: 0.9658 - val_loss: 0.1632 - val_accuracy: 0.9459\n",
            "Epoch 21/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1121 - accuracy: 0.9601\n",
            "Epoch 21: val_loss did not improve from 0.16321\n",
            "66/66 [==============================] - 10s 151ms/step - loss: 0.1121 - accuracy: 0.9601 - val_loss: 0.1796 - val_accuracy: 0.9373\n",
            "Epoch 22/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0851 - accuracy: 0.9715\n",
            "Epoch 22: val_loss did not improve from 0.16321\n",
            "66/66 [==============================] - 10s 151ms/step - loss: 0.0851 - accuracy: 0.9715 - val_loss: 0.1718 - val_accuracy: 0.9452\n",
            "Epoch 23/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0790 - accuracy: 0.9718\n",
            "Epoch 23: val_loss improved from 0.16321 to 0.14620, saving model to model/Dense128Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 151ms/step - loss: 0.0790 - accuracy: 0.9718 - val_loss: 0.1462 - val_accuracy: 0.9459\n",
            "Epoch 24/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0745 - accuracy: 0.9744\n",
            "Epoch 24: val_loss did not improve from 0.14620\n",
            "66/66 [==============================] - 10s 150ms/step - loss: 0.0745 - accuracy: 0.9744 - val_loss: 0.1631 - val_accuracy: 0.9509\n",
            "Epoch 25/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0675 - accuracy: 0.9772\n",
            "Epoch 25: val_loss did not improve from 0.14620\n",
            "66/66 [==============================] - 10s 152ms/step - loss: 0.0675 - accuracy: 0.9772 - val_loss: 0.1486 - val_accuracy: 0.9494\n",
            "Epoch 26/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0627 - accuracy: 0.9786\n",
            "Epoch 26: val_loss did not improve from 0.14620\n",
            "66/66 [==============================] - 10s 151ms/step - loss: 0.0627 - accuracy: 0.9786 - val_loss: 0.1534 - val_accuracy: 0.9544\n",
            "Saving /content/gdrive/MyDrive/BrainTumorClassifier/Models/Dense128Neurons_OneDropout0-3/history.pkl\n",
            "INFO:tensorflow:Assets written to: ram://1a047ec5-fc1a-4f53-9b99-fc8d3f8a2dff/assets\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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yjiMSL3RRVNLBHW68MQxSqfzPLCcn1L67dq34uO46WLt2330lc9h8TTZsCM0oL74YRkvu3g2HHw5Dh8L558Opp6qWK+mjNnSpc598EpL5e+/FXn/QQeHn/7JlYWKn9etr3mcq/6muWRMuaL74Yoh5797wR2TYsJDETzxx36HqIumgNnRJqurauBctggsvDD005s+veh/btsFf/hK6+P3rX+H9vHlhzo6qDBoEd94ZholHzy9dG+6hxn/ffWF4eV4eXH11mFr1hhtg5szwh+a++8IkU0rmkgniqqGb2WDgQSAH+G93v6vS+s7An4B2wEbgEncvqm6fqqFnpqp6idx9d0jgjz4aentcfz3813+FSZgS6SYYa//NmsGZZ4ammFmzQjI+8MAwx0dp/+o+fcrnFikpCX8kVq2CoqLwKH0dvaz0GPn5oRY+bBgce6zmDJH6bb/mciEk8aXAkUBTYA5wbKUyzwGXRl5/B3i6pv3qomhmqmqkpVkYcThunPvateXlkz1sfuNG95deCvN+9OpVvs/Wrd0HDHDv1CnEUTm+xo3DuoEDwzD5665zf/BB98WLU/RBiaQI+3NR1MxOBO5w9+9F3t8c+UPwf6PKzAcGu/tXZmbAFnevdsiCauiZqap+3xBGKnbrtu/yVM7dvXZt+Wx+X34ZBuZ06BAeeXnlz4ceqmYTyQ77ewu6POCrqPdFwAmVyswBhhGaZYYCLc2sjbtvqBTIGGAMQKdk3S9L6kRJSZgX++CDYePGfdd37hw7mUNI3qm6+cLhh8PIkeEh0tAlq85yPXCamX0GnAasAvZWLuTuk9y9wN0L2lV39UvSbvfuMD/1PffAkCHhYuW3vhU7mefmhlq3iKRXPAl9FdAx6n2HyLIy7r7a3Ye5e19gfGTZ5qRFKSk1eXJoCjELIxyPPTY8n3hi6PFRWAj/8R/w+OPhBgVPP127kZYiklrxNLnMALqbWVdCIh8BXBxdwMzaAhvdvQS4mdDjRTLAr38NEyaEftcAW7aELoTf/S78+Mehy95hh1Xc5qijwvB2Ealfaqyhu3sxMA54A/gCmOru883sTjMbEik2CCg0s0XAYYB+gNdj7vDGG2EOkvHjy5N5qdL28mHD9k3mIlJ/aaRoA7JrVxjM89vfwuefhx4hq1fHLptJNzQWaUg0UrSB27QJ7rorzJVy2WUhWT/5ZOjm17lz7G3UCUkk8yih17E1a+CKK+Dii0MvklT68kv42c+gY0e4+eYwavOvf4U5c+BHPwp3r5k4MfRSiaZeKyKZSQm9jpSUwB/+AMccA888A6+9FnqRnHwyTJu2bzt2bRUXhxkCL7ooXLx85BG44IKQxEvbzaOHtu/P/OAiUr8oodeBuXNDb5GxY8M9IefODZNAPfhgmFtk6FA4+uiQfKPnMIlXSUm4BdrVV0ObNqGHytSp4d6Tv/0tPPEEHHdc1duPGhXmVSkpCc9K5iKZSQk9hf797zCFbL9+YVj800/DW29Bjx4h2V5zTejXPXVqGIF51VWh7XrCBFi3rvp9u4cZAX/+87DNqafCf/93OGaprVvhppuqv+OPiGQPJfQUmT49jKz8zW9g9GhYuDD03a48k1/jxmG62Y8/DndiP/lk+NWvQtPHmDFhu2iffw633grdu8Pxx8P/+3/hD8bkyaGLYeWmmx07QtdEEcl+6raYZKtXhzuqP/dcaC//4x/DNK+JKCyE++8PPVF27gw3HO7fP9y3cv78MMnUGWfAiBGhuebgg8N2id4wWUQyj+5YVAf27g0XPW+5JfT3vu02+MUvQk+S2lq/Hh5+GH7/+3C39lNOCUn8/PNjD/jp0iWxucdFJPOoH3qKzZ0LAwfCuHHhTj2ffx6aOfYnmUOYEOuoo8q7Fa5cCa1aVT16U10QRRq2eOZykWqsWRPavQ84ILRjjxyZvDveVL57z4oV4T3E7olSuixVc4+LSP2mJpf9dNllIfHOnx8uVCaTmlBEpDI1uaTIJ5+EPt7XXZf8ZA6hlp3IchFp2JTQa6mkJPQjP/zw0I0wXpMnh5p3o0bhubo+4lXNp6J5VkQkFiX0Wpo8OfQdv+suaNky/m3GjAnNKO7lbeJVJXVd5BSRRKgNvRa2bYOePcOkVx99FP/Nh2vTJp7KGyyLSObZ35tESyW//nXo3fLSS4ndSb42beKpvMGyiGQXNbkkaOlSuO++MP3sCScktq3axEUklZTQE/Tzn4cBQ3fdlfi2ahMXkVRSQk/Am2/Cyy+HNu327cOyRHqtaO5xEUklXRSN05490KdPmKdl/nxo1mzfkZwQatxK0iKSKhpYlASPPAILFoT282bNwrLx4/e9IYWmqxWRdFFCj8P69XD77eH2bT/4QflyjeQUkfpECT0OEyaEvucPPFBx4i31WhGR+kQJvQZz5oQ28XHj4NhjK65TrxURqU+U0KvhHuZrOeSQ0ORSmXqtiEh9opGi1Xj++XCfzz/8ofw2b5VpJKeI1BeqoVdhxw64/vrQVfGKK9IdjYhIzVRDr8I994TeKk8/DTk56Y5GRKRmcdXQzWywmRWa2RIzuynG+k5m9q6ZfWZmc83s7OSHWndWroS774bhw+HUU9MdjYhIfGpM6GaWAzwEfB84FhhpZpX6e3ArMNXd+wIjgIeTHWhduuGG8HzPPemNQ0QkEfHU0AcAS9x9mbvvBqYA51Uq48BBkdetgNXJC7FuPfwwPPss3Hij+pOLSGaJJ6HnAV9FvS+KLIt2B3CJmRUB04GfxtqRmY0xs5lmNnP9+vW1CDe1nnoKrr46jAa95ZZ0RyMikphk9XIZCTzh7h2As4GnzWyffbv7JHcvcPeCdu3aJenQyfHCC3DZZXDGGTB1KjRpku6IREQSE09CXwV0jHrfIbIs2n8CUwHc/SOgOdA2GQHWhddfh5Ej4dvfhmnToHnzdEckIpK4eBL6DKC7mXU1s6aEi56vVCqzEjgDwMyOIST0+temEsP778PQofCtb8Grr0KLFumOSESkdmpM6O5eDIwD3gC+IPRmmW9md5rZkEixnwM/NrM5wF+A0Z6uidYTMGMGnHsudO0Kb7wBrVunOyIRkdqLa2CRu08nXOyMXjYh6vUC4KTkhpZa8+bB974HbduGOxHVsyZ9EZGENcih/4sWhbnNDzgA3n4b8qL67CRySzkRkfqkwQ39X7ECzjwT9u6Fd98NzS2lKt9SbsWK8B40AZeI1H8Nqoa+dm1I5lu3wl//CsccU3G9biknIpmswdTQN2wIzSxr1oQ287599y2jW8qJSCZrEDX0rVth8GBYvBhefhlOPDF2Od1STkQyWdYn9J07Q9fE2bPhuefCSNCq6JZyIpLJsj6hv/46fPBBuDXcD35QfVndUk5EMlnWt6F/8UV4vuCC+MrrlnIikqmyvoZeWAjt20PLlumOREQktRpEQu/ZM91RiIikXlYndHcldBFpOLI6oX/9NWzapIQuIg1DVif0wsLwrIQuIg1BVif0RYvCsxK6iDQEWZ3QCwuhadMwa6KISLbL+oR+1FGQk5PuSEREUi/rE7qaW0SkocjahF5cDEuXQo8e6Y5ERKRuZG1C//JL2LNHNXQRaTiyNqGry6KINDRK6CIiWSKrE3qbNuEhItIQZG1CX7RItXMRaViyNqGry6KINDRZmdC3boW1a5XQRaRhycqEXnpBVH3QRaQhyeqErhq6iDQkWZvQGzWCbt3SHYmISN2JK6Gb2WAzKzSzJWZ2U4z195vZ7MhjkZltTn6o8SsshK5doVmzdEYhIlK3GtdUwMxygIeAs4AiYIaZveLuC0rLuPt1UeV/CvRNQaxxUw8XEWmI4qmhDwCWuPsyd98NTAHOq6b8SOAvyQiuNkpKYPHikNAnTw5zoTdqFJ4nT05XVCIiqRdPQs8Dvop6XxRZtg8z6wx0Bd6pYv0YM5tpZjPXr1+faKxxKSqCb76BzZthzBhYsSLcLHrFivBeSV1EslWyL4qOAJ53972xVrr7JHcvcPeCdu3aJfnQQWkPl+nTYceOiut27IDx41NyWBGRtIsnoa8COka97xBZFssI0tjcAuUJfd262OtXrqy7WERE6lI8CX0G0N3MuppZU0LSfqVyITM7GjgY+Ci5ISamsBBatIBOnWKvr2q5iEimqzGhu3sxMA54A/gCmOru883sTjMbElV0BDDF3T01ocantIfLr38NubkV1+XmwsSJ6YlLRCTVauy2CODu04HplZZNqPT+juSFVXuFhXDyyTBqVHg/fnxoZunUKSTz0uUiItkmroSeKXbsCMm7tA/6qFFK4CLScGTV0P/Fi8OzBhWJSEOUVQl90aLwrIQuIg1RViX00i6L3bunNw4RkXTIuoTesSMceGC6IxERqXtZl9B1UwsRaaiyJqG7a5ZFEWnYsiahr1sX7iWqhC4iDVXWJHTddk5EGjoldBGRLJE1CX3RImjeXJNviUjDlTUJvbAw9D9vlDVnJCKSmKxJf+rhIiINXVYk9N27Ydky9UEXkYYtKxL6smWwd69q6CLSsGVFQlcPFxERJXQRkayRNQn90EOhdet0RyIikj5ZkdAXLVLtXEQkKxK6uiyKiGRBQt+0CdavV0IXEcn4hK4LoiIiQdYkdA0qEpGGLisSeuPGcOSR6Y5ERCS9siKhH3kkNGmS7khERNIrKxK62s9FRDI8oe/dC0uWKKGLiECcCd3MBptZoZktMbObqigz3MwWmNl8M/tzcsOMbeVK2LVLCV1EBKBxTQXMLAd4CDgLKAJmmNkr7r4gqkx34GbgJHffZGaHpirgaOqyKCJSLp4a+gBgibsvc/fdwBTgvEplfgw85O6bANz9X8kNMzYldBGRcvEk9Dzgq6j3RZFl0XoAPczsH2b2TzMbnKwAq1NYCK1aQbt2dXE0EZH6rcYmlwT20x0YBHQA3jez3u6+ObqQmY0BxgB0SsLdnEt7uJjt965ERDJePDX0VUDHqPcdIsuiFQGvuPsed/8SWERI8BW4+yR3L3D3gnZJqFary6KISLl4EvoMoLuZdTWzpsAI4JVKZaYRaueYWVtCE8yyJMa5j+3bYdUqJXQRkVI1JnR3LwbGAW8AXwBT3X2+md1pZkMixd4ANpjZAuBd4BfuviFVQUOYAx2U0EVESsXVhu7u04HplZZNiHrtwH9FHnVCCV1EpKKMHSlaWBguhh51VLojERGpHzI6oXfuDAcckO5IRETqh4xO6GpuEREpl5EJ3T20oeumFiIi5TIyoa9eHbotqoYuIlIuIxO65nAREdmXErqISJbI2ISemwt5lacIExFpwDIyoZdeEG2UkdGLiKRGRqZEdVkUEdlXxiX0Xbtg+XIldBGRyjIuoS9ZAiUl6oMuIlJZxiV09XAREYktYxO6augiIhUl6xZ0deayy2DAADjooHRHIiJSv2RcQj/88PAQEZGKMq7JRUREYlNCFxHJEkroIiJZQgldRCRLKKGLiGQJJXQRkSyhhC4ikiWU0EVEsoQSuohIlsi4kaIi2WjPnj0UFRWxc+fOdIci9UTz5s3p0KEDTZo0iXsbJXSReqCoqIiWLVvSpUsXzCzd4UiauTsbNmygqKiIrl27xr2dmlxE6oGdO3fSpk0bJXMBwMxo06ZNwr/Y4kroZjbYzArNbImZ3RRj/WgzW29msyOPKxKKQkSUzKWC2vx7qLHJxcxygIeAs4AiYIaZveLuCyoVfdbdxyUcgYiIJEU8NfQBwBJ3X+buu4EpwHmpDUtEqjN5MnTpAo0ahefJk/dvfxs2bKBPnz706dOHww8/nLy8vLL3u3fvrnbbmTNncs0119R4jIEDB+5fkFKjeC6K5gFfRb0vAk6IUe58MzsVWARc5+5fVS5gZmOAMQCdOnVKPFoRYfJkGDMGduwI71esCO8BRo2q3T7btGnD7NmzAbjjjjto0aIF119/fdn64uJiGjeOnS4KCgooKCio8Rgffvhh7YJLo71795KTk5PuMOKWrIui/wN0cffjgDeBJ2MVcvdJ7l7g7gXt2rVL0qFFGpbx48uTeakdO8LyZBo9ejQ/+clPOOGEE7jhhhv45JNPOPHEE+nbty8DBw6kMHI/yPfee49zzz0XCH8MLr/8cgYNGsSRRx7J7373u7L9tWjRoqz8oEGDuOCCC0g6f3EAAAz9SURBVDj66KMZNWoU7g7A9OnTOfroo+nfvz/XXHNN2X6jLV++nFNOOYV+/frRr1+/Cn8o7r77bnr37k1+fj433RQu9y1ZsoQzzzyT/Px8+vXrx9KlSyvEDDBu3DieeOIJALp06cKNN95Iv379eO6553j00Uc5/vjjyc/P5/zzz2dH5MNft24dQ4cOJT8/n/z8fD788EMmTJjAAw88ULbf8ePH8+CDD+73dxGveGroq4COUe87RJaVcfcNUW//G/jN/ocmIrGsXJnY8v1RVFTEhx9+SE5ODlu3buWDDz6gcePGvPXWW9xyyy288MIL+2yzcOFC3n33XbZt20bPnj0ZO3bsPn2pP/vsM+bPn88RRxzBSSedxD/+8Q8KCgq48soref/99+natSsjR46MGdOhhx7Km2++SfPmzVm8eDEjR45k5syZvPbaa7z88st8/PHH5ObmsnHjRgBGjRrFTTfdxNChQ9m5cyclJSV89dU+DQgVtGnThlmzZgGhOerHP/4xALfeeiuPPfYYP/3pT7nmmms47bTTeOmll9i7dy/bt2/niCOOYNiwYVx77bWUlJQwZcoUPvnkk4Q/99qKJ6HPALqbWVdCIh8BXBxdwMzau/uayNshwBdJjVJEynTqFJpZYi1PtgsvvLCsyWHLli1ceumlLF68GDNjz549Mbc555xzaNasGc2aNePQQw9l3bp1dOjQoUKZAQMGlC3r06cPy5cvp0WLFhx55JFl/a5HjhzJpEmT9tn/nj17GDduHLNnzyYnJ4dFixYB8NZbb3HZZZeRm5sLwCGHHMK2bdtYtWoVQ4cOBcJgnXhcdNFFZa8///xzbr31VjZv3sz27dv53ve+B8A777zDU089BUBOTg6tWrWiVatWtGnThs8++4x169bRt29f2rRpE9cxk6HGhO7uxWY2DngDyAH+5O7zzexOYKa7vwJcY2ZDgGJgIzA6hTGLNGgTJ1ZsQwfIzQ3Lk+3AAw8se33bbbdx+umn89JLL7F8+XIGDRoUc5tmzZqVvc7JyaG4uLhWZapy//33c9hhhzFnzhxKSkriTtLRGjduTElJSdn7yv29o8979OjRTJs2jfz8fJ544gnee++9avd9xRVX8MQTT7B27Vouv/zyhGPbH3G1obv7dHfv4e7d3H1iZNmESDLH3W92917unu/up7v7wlQGLdKQjRoFkyZB585gFp4nTar9BdF4bdmyhby8PICy9uZk6tmzJ8uWLWP58uUAPPvss1XG0b59exo1asTTTz/N3r17ATjrrLN4/PHHy9q4N27cSMuWLenQoQPTpk0DYNeuXezYsYPOnTuzYMECdu3axebNm3n77berjGvbtm20b9+ePXv2MDmqO9EZZ5zBI488AoSLp1u2bAFg6NChvP7668yYMaOsNl9XNFJUJAONGgXLl0NJSXhOdTIHuOGGG7j55pvp27dvQjXqeB1wwAE8/PDDDB48mP79+9OyZUtatWq1T7mrrrqKJ598kvz8fBYuXFhWmx48eDBDhgyhoKCAPn36cO+99wLw9NNP87vf/Y7jjjuOgQMHsnbtWjp27Mjw4cP51re+xfDhw+nbt2+Vcf3yl7/khBNO4KSTTuLoo48uW/7ggw/y7rvv0rt3b/r378+CBWFoTtOmTTn99NMZPnx4nfeQsdKry3WtoKDAZ86cmZZji9Q3X3zxBcccc0y6w0i77du306JFC9ydq6++mu7du3PdddelO6yElJSUlPWQ6d69+37tK9a/CzP71N1j9hNVDV1E6o1HH32UPn360KtXL7Zs2cKVV16Z7pASsmDBAo466ijOOOOM/U7mtaHZFkWk3rjuuusyrkYe7dhjj2XZsmVpO75q6CIiWUIJXUQkSyihi4hkCSV0EZEsoYQuIpx++um88cYbFZY98MADjB07tsptBg0aRGnX47PPPpvNmzfvU+aOO+4o6w9elWnTppX14QaYMGECb731ViLhS4QSuogwcuRIpkyZUmHZlClTqpwgq7Lp06fTunXrWh27ckK/8847OfPMM2u1r3QpHa2abkroIvXMtdfCoEHJfVx7bfXHvOCCC3j11VfLbmaxfPlyVq9ezSmnnMLYsWMpKCigV69e3H777TG379KlC19//TUAEydOpEePHpx88sllU+wCMaeh/fDDD3nllVf4xS9+QZ8+fVi6dCmjR4/m+eefB+Dtt9+mb9++9O7dm8svv5xdu3aVHe/222+nX79+9O7dm4UL951tpCFOs6uELiIccsghDBgwgNdeew0ItfPhw4djZkycOJGZM2cyd+5c/va3vzF37twq9/Ppp58yZcoUZs+ezfTp05kxY0bZumHDhjFjxgzmzJnDMcccw2OPPcbAgQMZMmQI99xzD7Nnz6Zbt25l5Xfu3Mno0aN59tlnmTdvHsXFxWVzpwC0bduWWbNmMXbs2JjNOqXT7M6aNYtnn3227K5K0dPszpkzhxtuuAEI0+xeffXVzJkzhw8//JD27dvX+LmVTrM7YsSImOcHlE2zO2fOHGbNmkWvXr24/PLLy2ZqLJ1m95JLLqnxeDXRwCKReiaq4lanSptdzjvvPKZMmVKWkKZOncqkSZMoLi5mzZo1LFiwgOOOOy7mPj744AOGDh1aNoXtkCFDytZVNQ1tVQoLC+natSs9evQA4NJLL+Whhx7i2sjPjWHDhgHQv39/XnzxxX22b4jT7GZUDT3Z91EUkXLnnXceb7/9NrNmzWLHjh3079+fL7/8knvvvZe3336buXPncs455+wz1Wy8Ro8eze9//3vmzZvH7bffXuv9lCqdgreq6Xejp9mdOXNmjfdGjSXRaXYTOb/SaXYff/zxpE2zmzEJvfQ+iitWgHv5fRSV1EWSo0WLFpx++ulcfvnlZRdDt27dyoEHHkirVq1Yt25dWZNMVU499VSmTZvGN998w7Zt2/if//mfsnVVTUPbsmVLtm3bts++evbsyfLly1myZAkQZk087bTT4j6fhjjNbsYk9Lq6j6JIQzZy5EjmzJlTltDz8/Pp27cvRx99NBdffDEnnXRStdv369ePiy66iPz8fL7//e9z/PHHl62rahraESNGcM8999C3b1+WLl1atrx58+Y8/vjjXHjhhfTu3ZtGjRrxk5/8JO5zaYjT7GbM9LmNGoWaeWVmYU5okUym6XMbnnim2c3a6XOrul9iKu6jKCKSSqmaZjdjernU5X0URURSKVXT7GZMDT1d91EUqSvpav6U+qk2/x4ypoYOIXkrgUs2at68ORs2bKBNmzaYWbrDkTRzdzZs2BB3f/hSGZXQRbJVhw4dKCoqYv369ekOReqJ5s2b06FDh4S2UUIXqQeaNGlC165d0x2GZLiMaUMXEZHqKaGLiGQJJXQRkSyRtpGiZrYeWFHLzdsCXycxnEygc24YdM4Nw/6cc2d3bxdrRdoS+v4ws5lVDX3NVjrnhkHn3DCk6pzV5CIikiWU0EVEskSmJvRJ6Q4gDXTODYPOuWFIyTlnZBu6iIjsK1Nr6CIiUokSuohIlsi4hG5mg82s0MyWmNlN6Y6nLpjZcjObZ2azzSz+2zxlEDP7k5n9y8w+j1p2iJm9aWaLI88HpzPGZKvinO8ws1WR73q2mZ2dzhiTycw6mtm7ZrbAzOab2c8iy7P2e67mnFPyPWdUG7qZ5QCLgLOAImAGMNLdF6Q1sBQzs+VAgbtn7eALMzsV2A485e7fiiz7DbDR3e+K/PE+2N1vTGecyVTFOd8BbHf3e9MZWyqYWXugvbvPMrOWwKfAfwCjydLvuZpzHk4KvudMq6EPAJa4+zJ33w1MAc5Lc0ySBO7+PrCx0uLzgCcjr58k/EfIGlWcc9Zy9zXuPivyehvwBZBHFn/P1ZxzSmRaQs8Dvop6X0QKP5x6xIG/mtmnZjYm3cHUocPcfU3k9VrgsHQGU4fGmdncSJNM1jQ/RDOzLkBf4GMayPdc6ZwhBd9zpiX0hupkd+8HfB+4OvJTvUHx0DaYOe2DtfcI0A3oA6wBfpvecJLPzFoALwDXuvvW6HXZ+j3HOOeUfM+ZltBXAR2j3neILMtq7r4q8vwv4CVC01NDsC7SBlnaFvmvNMeTcu6+zt33unsJ8ChZ9l2bWRNCYpvs7i9GFmf19xzrnFP1PWdaQp8BdDezrmbWFBgBvJLmmFLKzA6MXEzBzA4Evgt8Xv1WWeMV4NLI60uBl9MYS50oTWwRQ8mi79rCzVIfA75w9/uiVmXt91zVOafqe86oXi4Ake49DwA5wJ/cfWKaQ0opMzuSUCuHcMvAP2fjOZvZX4BBhGlF1wG3A9OAqUAnwlTLw909ay4iVnHOgwg/wx1YDlwZ1b6c0czsZOADYB5QEll8C6FNOSu/52rOeSQp+J4zLqGLiEhsmdbkIiIiVVBCFxHJEkroIiJZQgldRCRLKKGLiGQJJXQRkSyhhC4ikiX+PwyiGpblt5zoAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(model, test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e00786f-727c-4a09-93f1-93e0a4807030",
        "id": "i2jy3C9PlH6K"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22/22 [==============================] - 12s 85ms/step - loss: 0.1659 - accuracy: 0.9467\n",
            "Loss on test set: 0.16589902341365814\n",
            "Accuracy on test set: 0.9466571807861328\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      glioma     0.9132    0.9385    0.9256       325\n",
            "  meningioma     0.8985    0.9149    0.9066       329\n",
            "     notumor     0.9825    0.9850    0.9838       400\n",
            "   pituitary     0.9851    0.9403    0.9622       352\n",
            "\n",
            "    accuracy                         0.9467      1406\n",
            "   macro avg     0.9448    0.9447    0.9446      1406\n",
            "weighted avg     0.9475    0.9467    0.9469      1406\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusionmatrix(model, test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "outputId": "439863e7-0b14-4f97-aae4-5528924a779a",
        "id": "kLtSga94lH6L"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22/22 [==============================] - 2s 75ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# One Dense layer with 512 neurons, One Dropout with 0.3"
      ],
      "metadata": {
        "id": "j0VEMPeBnp4_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we define the model that we want to train."
      ],
      "metadata": {
        "id": "r7aY3Efznp4_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = keras.Input(shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3))\n",
        "\n",
        "x = data_augmentation_layers(inputs)\n",
        "x = layers.Rescaling(1./255)(x)\n",
        "x = layers.Conv2D(filters=32, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=128, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=256, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=5)(x)\n",
        "x = layers.Flatten()(x)\n",
        "x = layers.Dense(512, activation='relu', name='hidden_classifier') (x)\n",
        "x = layers.Dropout(0.3)(x)\n",
        "outputs = layers.Dense(4, activation=\"softmax\")(x)\n",
        "model = keras.Model(inputs=inputs, outputs=outputs)\n",
        "model = compile_model(model, loss=\"sparse_categorical_crossentropy\")\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d89e66c8-be18-4d18-ae38-ada673e21a3f",
        "id": "-MOXrwbRnp5A"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_4 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " sequential_6 (Sequential)   (None, 224, 224, 3)       0         \n",
            "                                                                 \n",
            " rescaling_3 (Rescaling)     (None, 224, 224, 3)       0         \n",
            "                                                                 \n",
            " conv2d_12 (Conv2D)          (None, 222, 222, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d_12 (MaxPoolin  (None, 111, 111, 32)     0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_13 (Conv2D)          (None, 109, 109, 64)      18496     \n",
            "                                                                 \n",
            " max_pooling2d_13 (MaxPoolin  (None, 54, 54, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_14 (Conv2D)          (None, 52, 52, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_14 (MaxPoolin  (None, 26, 26, 128)      0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_15 (Conv2D)          (None, 24, 24, 256)       295168    \n",
            "                                                                 \n",
            " max_pooling2d_15 (MaxPoolin  (None, 4, 4, 256)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_3 (Flatten)         (None, 4096)              0         \n",
            "                                                                 \n",
            " hidden_classifier (Dense)   (None, 512)               2097664   \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 4)                 2052      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,488,132\n",
            "Trainable params: 2,488,132\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model, history = run_model(model, \"Dense512Neurons_OneDropout0-3\", train_ds, val_ds, 50, patience=3, monitor='val_loss')\n",
        "plot_accuracy_and_loss_history(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "afe9c516-0f27-4d1d-8399-3fe0b5202236",
        "id": "hXEiQHVGnp5B"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 1.1048 - accuracy: 0.4790\n",
            "Epoch 1: val_loss improved from inf to 0.77765, saving model to model/Dense512Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 11s 153ms/step - loss: 1.1048 - accuracy: 0.4790 - val_loss: 0.7777 - val_accuracy: 0.7158\n",
            "Epoch 2/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.7255 - accuracy: 0.7164\n",
            "Epoch 2: val_loss improved from 0.77765 to 0.59774, saving model to model/Dense512Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 152ms/step - loss: 0.7255 - accuracy: 0.7164 - val_loss: 0.5977 - val_accuracy: 0.7792\n",
            "Epoch 3/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.5379 - accuracy: 0.7982\n",
            "Epoch 3: val_loss did not improve from 0.59774\n",
            "66/66 [==============================] - 10s 153ms/step - loss: 0.5379 - accuracy: 0.7982 - val_loss: 0.6524 - val_accuracy: 0.7564\n",
            "Epoch 4/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.4941 - accuracy: 0.8194\n",
            "Epoch 4: val_loss improved from 0.59774 to 0.46785, saving model to model/Dense512Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 11s 157ms/step - loss: 0.4941 - accuracy: 0.8194 - val_loss: 0.4678 - val_accuracy: 0.8291\n",
            "Epoch 5/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.3985 - accuracy: 0.8592\n",
            "Epoch 5: val_loss did not improve from 0.46785\n",
            "66/66 [==============================] - 10s 152ms/step - loss: 0.3985 - accuracy: 0.8592 - val_loss: 0.4920 - val_accuracy: 0.7984\n",
            "Epoch 6/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.3172 - accuracy: 0.8887\n",
            "Epoch 6: val_loss improved from 0.46785 to 0.33301, saving model to model/Dense512Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 151ms/step - loss: 0.3172 - accuracy: 0.8887 - val_loss: 0.3330 - val_accuracy: 0.8718\n",
            "Epoch 7/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2718 - accuracy: 0.9003\n",
            "Epoch 7: val_loss improved from 0.33301 to 0.25021, saving model to model/Dense512Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 152ms/step - loss: 0.2718 - accuracy: 0.9003 - val_loss: 0.2502 - val_accuracy: 0.9010\n",
            "Epoch 8/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2172 - accuracy: 0.9238\n",
            "Epoch 8: val_loss improved from 0.25021 to 0.22235, saving model to model/Dense512Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 151ms/step - loss: 0.2172 - accuracy: 0.9238 - val_loss: 0.2224 - val_accuracy: 0.9124\n",
            "Epoch 9/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2036 - accuracy: 0.9255\n",
            "Epoch 9: val_loss did not improve from 0.22235\n",
            "66/66 [==============================] - 10s 150ms/step - loss: 0.2036 - accuracy: 0.9255 - val_loss: 0.2306 - val_accuracy: 0.9160\n",
            "Epoch 10/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1576 - accuracy: 0.9492\n",
            "Epoch 10: val_loss improved from 0.22235 to 0.20446, saving model to model/Dense512Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 153ms/step - loss: 0.1576 - accuracy: 0.9492 - val_loss: 0.2045 - val_accuracy: 0.9274\n",
            "Epoch 11/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1320 - accuracy: 0.9561\n",
            "Epoch 11: val_loss improved from 0.20446 to 0.20103, saving model to model/Dense512Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 154ms/step - loss: 0.1320 - accuracy: 0.9561 - val_loss: 0.2010 - val_accuracy: 0.9245\n",
            "Epoch 12/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1150 - accuracy: 0.9611\n",
            "Epoch 12: val_loss did not improve from 0.20103\n",
            "66/66 [==============================] - 10s 151ms/step - loss: 0.1150 - accuracy: 0.9611 - val_loss: 0.2649 - val_accuracy: 0.9053\n",
            "Epoch 13/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1013 - accuracy: 0.9639\n",
            "Epoch 13: val_loss improved from 0.20103 to 0.16351, saving model to model/Dense512Neurons_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 153ms/step - loss: 0.1013 - accuracy: 0.9639 - val_loss: 0.1635 - val_accuracy: 0.9402\n",
            "Epoch 14/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0806 - accuracy: 0.9715\n",
            "Epoch 14: val_loss did not improve from 0.16351\n",
            "66/66 [==============================] - 10s 150ms/step - loss: 0.0806 - accuracy: 0.9715 - val_loss: 0.2227 - val_accuracy: 0.9281\n",
            "Epoch 15/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0762 - accuracy: 0.9756\n",
            "Epoch 15: val_loss did not improve from 0.16351\n",
            "66/66 [==============================] - 10s 150ms/step - loss: 0.0762 - accuracy: 0.9756 - val_loss: 0.1752 - val_accuracy: 0.9452\n",
            "Epoch 16/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0688 - accuracy: 0.9756\n",
            "Epoch 16: val_loss did not improve from 0.16351\n",
            "66/66 [==============================] - 10s 151ms/step - loss: 0.0688 - accuracy: 0.9756 - val_loss: 0.1788 - val_accuracy: 0.9466\n",
            "Saving /content/gdrive/MyDrive/BrainTumorClassifier/Models/Dense512Neurons_OneDropout0-3/history.pkl\n",
            "INFO:tensorflow:Assets written to: ram://dd01d4da-bad2-4e54-abad-e51f9c70defa/assets\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(model, test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6d4c5b3-782c-413d-874e-f888aa8f0271",
        "id": "vJqqt4Qknp5B"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22/22 [==============================] - 11s 74ms/step - loss: 0.1731 - accuracy: 0.9481\n",
            "Loss on test set: 0.17310363054275513\n",
            "Accuracy on test set: 0.9480796456336975\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      glioma     0.9589    0.9323    0.9454       325\n",
            "  meningioma     0.9297    0.8845    0.9065       329\n",
            "     notumor     0.9523    0.9975    0.9744       400\n",
            "   pituitary     0.9497    0.9659    0.9577       352\n",
            "\n",
            "    accuracy                         0.9481      1406\n",
            "   macro avg     0.9476    0.9451    0.9460      1406\n",
            "weighted avg     0.9479    0.9481    0.9476      1406\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusionmatrix(model, test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "outputId": "1a28caf4-a7f3-4973-900f-ad3a29809239",
        "id": "f71oXu3Anp5B"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22/22 [==============================] - 2s 73ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# One Dense layer with 512 neurons, One Dropout with 0.5"
      ],
      "metadata": {
        "id": "rifxJlKypc36"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we define the model that we want to train."
      ],
      "metadata": {
        "id": "sESygF7xpc4J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = keras.Input(shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3))\n",
        "\n",
        "x = data_augmentation_layers(inputs)\n",
        "x = layers.Rescaling(1./255)(x)\n",
        "x = layers.Conv2D(filters=32, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=128, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=256, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=5)(x)\n",
        "x = layers.Flatten()(x)\n",
        "x = layers.Dense(512, activation='relu', name='hidden_classifier') (x)\n",
        "x = layers.Dropout(0.5)(x)\n",
        "outputs = layers.Dense(4, activation=\"softmax\")(x)\n",
        "model = keras.Model(inputs=inputs, outputs=outputs)\n",
        "model = compile_model(model, loss=\"sparse_categorical_crossentropy\")\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9e74c6e-469d-45f6-d0eb-69ab1e3d16d0",
        "id": "ETOw_vvYpc4J"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_5\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_6 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " sequential_6 (Sequential)   (None, 224, 224, 3)       0         \n",
            "                                                                 \n",
            " rescaling_5 (Rescaling)     (None, 224, 224, 3)       0         \n",
            "                                                                 \n",
            " conv2d_20 (Conv2D)          (None, 222, 222, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d_20 (MaxPoolin  (None, 111, 111, 32)     0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_21 (Conv2D)          (None, 109, 109, 64)      18496     \n",
            "                                                                 \n",
            " max_pooling2d_21 (MaxPoolin  (None, 54, 54, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_22 (Conv2D)          (None, 52, 52, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_22 (MaxPoolin  (None, 26, 26, 128)      0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_23 (Conv2D)          (None, 24, 24, 256)       295168    \n",
            "                                                                 \n",
            " max_pooling2d_23 (MaxPoolin  (None, 4, 4, 256)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_5 (Flatten)         (None, 4096)              0         \n",
            "                                                                 \n",
            " hidden_classifier (Dense)   (None, 512)               2097664   \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 4)                 2052      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,488,132\n",
            "Trainable params: 2,488,132\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model, history = run_model(model, \"Dense512Neurons_OneDropout0-5\", train_ds, val_ds, 50, patience=3, monitor='val_loss')\n",
        "plot_accuracy_and_loss_history(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1eb27447-3db5-4273-a086-cc454ce0a7ad",
        "id": "8c5AQpyJpc4L"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 1.1455 - accuracy: 0.4529\n",
            "Epoch 1: val_loss improved from inf to 0.83994, saving model to model/Dense512Neurons_OneDropout0-5.h5\n",
            "66/66 [==============================] - 11s 153ms/step - loss: 1.1455 - accuracy: 0.4529 - val_loss: 0.8399 - val_accuracy: 0.6631\n",
            "Epoch 2/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.7563 - accuracy: 0.7021\n",
            "Epoch 2: val_loss improved from 0.83994 to 0.62915, saving model to model/Dense512Neurons_OneDropout0-5.h5\n",
            "66/66 [==============================] - 10s 153ms/step - loss: 0.7563 - accuracy: 0.7021 - val_loss: 0.6291 - val_accuracy: 0.7557\n",
            "Epoch 3/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.5730 - accuracy: 0.7833\n",
            "Epoch 3: val_loss improved from 0.62915 to 0.43186, saving model to model/Dense512Neurons_OneDropout0-5.h5\n",
            "66/66 [==============================] - 10s 153ms/step - loss: 0.5730 - accuracy: 0.7833 - val_loss: 0.4319 - val_accuracy: 0.8412\n",
            "Epoch 4/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.5197 - accuracy: 0.8020\n",
            "Epoch 4: val_loss did not improve from 0.43186\n",
            "66/66 [==============================] - 10s 151ms/step - loss: 0.5197 - accuracy: 0.8020 - val_loss: 0.4658 - val_accuracy: 0.8490\n",
            "Epoch 5/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.4412 - accuracy: 0.8424\n",
            "Epoch 5: val_loss improved from 0.43186 to 0.34381, saving model to model/Dense512Neurons_OneDropout0-5.h5\n",
            "66/66 [==============================] - 10s 152ms/step - loss: 0.4412 - accuracy: 0.8424 - val_loss: 0.3438 - val_accuracy: 0.8775\n",
            "Epoch 6/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.3447 - accuracy: 0.8725\n",
            "Epoch 6: val_loss improved from 0.34381 to 0.30318, saving model to model/Dense512Neurons_OneDropout0-5.h5\n",
            "66/66 [==============================] - 10s 153ms/step - loss: 0.3447 - accuracy: 0.8725 - val_loss: 0.3032 - val_accuracy: 0.8839\n",
            "Epoch 7/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.3207 - accuracy: 0.8868\n",
            "Epoch 7: val_loss improved from 0.30318 to 0.28340, saving model to model/Dense512Neurons_OneDropout0-5.h5\n",
            "66/66 [==============================] - 10s 153ms/step - loss: 0.3207 - accuracy: 0.8868 - val_loss: 0.2834 - val_accuracy: 0.8967\n",
            "Epoch 8/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2753 - accuracy: 0.9065\n",
            "Epoch 8: val_loss improved from 0.28340 to 0.26809, saving model to model/Dense512Neurons_OneDropout0-5.h5\n",
            "66/66 [==============================] - 10s 153ms/step - loss: 0.2753 - accuracy: 0.9065 - val_loss: 0.2681 - val_accuracy: 0.9060\n",
            "Epoch 9/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2382 - accuracy: 0.9179\n",
            "Epoch 9: val_loss improved from 0.26809 to 0.23076, saving model to model/Dense512Neurons_OneDropout0-5.h5\n",
            "66/66 [==============================] - 10s 152ms/step - loss: 0.2382 - accuracy: 0.9179 - val_loss: 0.2308 - val_accuracy: 0.9188\n",
            "Epoch 10/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2069 - accuracy: 0.9340\n",
            "Epoch 10: val_loss did not improve from 0.23076\n",
            "66/66 [==============================] - 10s 152ms/step - loss: 0.2069 - accuracy: 0.9340 - val_loss: 0.2331 - val_accuracy: 0.9138\n",
            "Epoch 11/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2057 - accuracy: 0.9248\n",
            "Epoch 11: val_loss improved from 0.23076 to 0.21903, saving model to model/Dense512Neurons_OneDropout0-5.h5\n",
            "66/66 [==============================] - 11s 154ms/step - loss: 0.2057 - accuracy: 0.9248 - val_loss: 0.2190 - val_accuracy: 0.9224\n",
            "Epoch 12/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1717 - accuracy: 0.9376\n",
            "Epoch 12: val_loss improved from 0.21903 to 0.20716, saving model to model/Dense512Neurons_OneDropout0-5.h5\n",
            "66/66 [==============================] - 10s 152ms/step - loss: 0.1717 - accuracy: 0.9376 - val_loss: 0.2072 - val_accuracy: 0.9217\n",
            "Epoch 13/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1409 - accuracy: 0.9547\n",
            "Epoch 13: val_loss improved from 0.20716 to 0.17382, saving model to model/Dense512Neurons_OneDropout0-5.h5\n",
            "66/66 [==============================] - 10s 152ms/step - loss: 0.1409 - accuracy: 0.9547 - val_loss: 0.1738 - val_accuracy: 0.9380\n",
            "Epoch 14/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1128 - accuracy: 0.9634\n",
            "Epoch 14: val_loss did not improve from 0.17382\n",
            "66/66 [==============================] - 10s 150ms/step - loss: 0.1128 - accuracy: 0.9634 - val_loss: 0.1772 - val_accuracy: 0.9338\n",
            "Epoch 15/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1138 - accuracy: 0.9587\n",
            "Epoch 15: val_loss did not improve from 0.17382\n",
            "66/66 [==============================] - 10s 151ms/step - loss: 0.1138 - accuracy: 0.9587 - val_loss: 0.1767 - val_accuracy: 0.9380\n",
            "Epoch 16/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1139 - accuracy: 0.9587\n",
            "Epoch 16: val_loss did not improve from 0.17382\n",
            "66/66 [==============================] - 10s 150ms/step - loss: 0.1139 - accuracy: 0.9587 - val_loss: 0.1931 - val_accuracy: 0.9302\n",
            "Saving /content/gdrive/MyDrive/BrainTumorClassifier/Models/Dense512Neurons_OneDropout0-5/history.pkl\n",
            "INFO:tensorflow:Assets written to: ram://0b44fbc4-5512-4e90-9dd9-c8396f889772/assets\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(model, test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1724c946-1a08-46d9-c0e7-b7fb80b44b1a",
        "id": "BBjT2HL1pc4L"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22/22 [==============================] - 2s 63ms/step - loss: 0.1903 - accuracy: 0.9339\n",
            "Loss on test set: 0.19032692909240723\n",
            "Accuracy on test set: 0.933854877948761\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      glioma     0.9631    0.8831    0.9213       325\n",
            "  meningioma     0.8612    0.9240    0.8915       329\n",
            "     notumor     0.9496    0.9900    0.9694       400\n",
            "   pituitary     0.9645    0.9261    0.9449       352\n",
            "\n",
            "    accuracy                         0.9339      1406\n",
            "   macro avg     0.9346    0.9308    0.9318      1406\n",
            "weighted avg     0.9358    0.9339    0.9339      1406\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusionmatrix(model, test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "outputId": "1303fcec-6f43-4b56-c54e-b41de4122f24",
        "id": "ClReJVvFpc4M"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22/22 [==============================] - 2s 71ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# One Conv2D layer with 256 filters, One Dropout with 0.3"
      ],
      "metadata": {
        "id": "xXLQljleq_cX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we define the model that we want to train."
      ],
      "metadata": {
        "id": "YiwL3Yq-q_cl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = keras.Input(shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3))\n",
        "\n",
        "x = data_augmentation_layers(inputs)\n",
        "x = layers.Rescaling(1./255)(x)\n",
        "x = layers.Conv2D(filters=32, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=128, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=256, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=3)(x)\n",
        "x = layers.Conv2D(filters=256, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.Flatten()(x)\n",
        "x = layers.Dropout(0.3)(x)\n",
        "outputs = layers.Dense(4, activation=\"softmax\")(x)\n",
        "model = keras.Model(inputs=inputs, outputs=outputs)\n",
        "model = compile_model(model, loss=\"sparse_categorical_crossentropy\")\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c9567643-8685-4c98-8d26-6d51d1c6924c",
        "id": "_6uXZx7Jq_cl"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_7 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " sequential_6 (Sequential)   (None, 224, 224, 3)       0         \n",
            "                                                                 \n",
            " rescaling_6 (Rescaling)     (None, 224, 224, 3)       0         \n",
            "                                                                 \n",
            " conv2d_24 (Conv2D)          (None, 222, 222, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d_24 (MaxPoolin  (None, 111, 111, 32)     0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_25 (Conv2D)          (None, 109, 109, 64)      18496     \n",
            "                                                                 \n",
            " max_pooling2d_25 (MaxPoolin  (None, 54, 54, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_26 (Conv2D)          (None, 52, 52, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_26 (MaxPoolin  (None, 26, 26, 128)      0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_27 (Conv2D)          (None, 24, 24, 256)       295168    \n",
            "                                                                 \n",
            " max_pooling2d_27 (MaxPoolin  (None, 8, 8, 256)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_28 (Conv2D)          (None, 6, 6, 256)         590080    \n",
            "                                                                 \n",
            " flatten_6 (Flatten)         (None, 9216)              0         \n",
            "                                                                 \n",
            " dropout_6 (Dropout)         (None, 9216)              0         \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 4)                 36868     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,015,364\n",
            "Trainable params: 1,015,364\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model, history = run_model(model, \"Conv2D256Filters_OneDropout0-3\", train_ds, val_ds, 50, patience=3, monitor='val_loss')\n",
        "plot_accuracy_and_loss_history(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1b5ba12b-e102-4e2e-e067-da1893ccc7ce",
        "id": "UlwnHN8mq_cn"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 1.1586 - accuracy: 0.4553\n",
            "Epoch 1: val_loss improved from inf to 1.01144, saving model to model/Conv2D256Filters_OneDropout0-3.h5\n",
            "66/66 [==============================] - 11s 154ms/step - loss: 1.1586 - accuracy: 0.4553 - val_loss: 1.0114 - val_accuracy: 0.6161\n",
            "Epoch 2/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.7762 - accuracy: 0.6950\n",
            "Epoch 2: val_loss improved from 1.01144 to 0.60666, saving model to model/Conv2D256Filters_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 150ms/step - loss: 0.7762 - accuracy: 0.6950 - val_loss: 0.6067 - val_accuracy: 0.7657\n",
            "Epoch 3/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.5829 - accuracy: 0.7726\n",
            "Epoch 3: val_loss did not improve from 0.60666\n",
            "66/66 [==============================] - 10s 150ms/step - loss: 0.5829 - accuracy: 0.7726 - val_loss: 0.6186 - val_accuracy: 0.7457\n",
            "Epoch 4/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.4960 - accuracy: 0.8118\n",
            "Epoch 4: val_loss improved from 0.60666 to 0.39929, saving model to model/Conv2D256Filters_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 151ms/step - loss: 0.4960 - accuracy: 0.8118 - val_loss: 0.3993 - val_accuracy: 0.8490\n",
            "Epoch 5/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.4163 - accuracy: 0.8483\n",
            "Epoch 5: val_loss improved from 0.39929 to 0.36485, saving model to model/Conv2D256Filters_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 152ms/step - loss: 0.4163 - accuracy: 0.8483 - val_loss: 0.3649 - val_accuracy: 0.8632\n",
            "Epoch 6/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.3684 - accuracy: 0.8652\n",
            "Epoch 6: val_loss did not improve from 0.36485\n",
            "66/66 [==============================] - 10s 151ms/step - loss: 0.3684 - accuracy: 0.8652 - val_loss: 0.3945 - val_accuracy: 0.8369\n",
            "Epoch 7/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.3212 - accuracy: 0.8820\n",
            "Epoch 7: val_loss improved from 0.36485 to 0.29362, saving model to model/Conv2D256Filters_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 152ms/step - loss: 0.3212 - accuracy: 0.8820 - val_loss: 0.2936 - val_accuracy: 0.8882\n",
            "Epoch 8/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2412 - accuracy: 0.9186\n",
            "Epoch 8: val_loss did not improve from 0.29362\n",
            "66/66 [==============================] - 10s 151ms/step - loss: 0.2412 - accuracy: 0.9186 - val_loss: 0.2966 - val_accuracy: 0.8882\n",
            "Epoch 9/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2239 - accuracy: 0.9176\n",
            "Epoch 9: val_loss improved from 0.29362 to 0.23545, saving model to model/Conv2D256Filters_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 152ms/step - loss: 0.2239 - accuracy: 0.9176 - val_loss: 0.2355 - val_accuracy: 0.9138\n",
            "Epoch 10/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1909 - accuracy: 0.9340\n",
            "Epoch 10: val_loss did not improve from 0.23545\n",
            "66/66 [==============================] - 10s 151ms/step - loss: 0.1909 - accuracy: 0.9340 - val_loss: 0.2358 - val_accuracy: 0.9067\n",
            "Epoch 11/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1679 - accuracy: 0.9404\n",
            "Epoch 11: val_loss did not improve from 0.23545\n",
            "66/66 [==============================] - 10s 152ms/step - loss: 0.1679 - accuracy: 0.9404 - val_loss: 0.2404 - val_accuracy: 0.9074\n",
            "Epoch 12/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1420 - accuracy: 0.9504\n",
            "Epoch 12: val_loss improved from 0.23545 to 0.18556, saving model to model/Conv2D256Filters_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 152ms/step - loss: 0.1420 - accuracy: 0.9504 - val_loss: 0.1856 - val_accuracy: 0.9345\n",
            "Epoch 13/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1134 - accuracy: 0.9613\n",
            "Epoch 13: val_loss did not improve from 0.18556\n",
            "66/66 [==============================] - 10s 154ms/step - loss: 0.1134 - accuracy: 0.9613 - val_loss: 0.2094 - val_accuracy: 0.9252\n",
            "Epoch 14/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1230 - accuracy: 0.9556\n",
            "Epoch 14: val_loss improved from 0.18556 to 0.18369, saving model to model/Conv2D256Filters_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 153ms/step - loss: 0.1230 - accuracy: 0.9556 - val_loss: 0.1837 - val_accuracy: 0.9437\n",
            "Epoch 15/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0886 - accuracy: 0.9696\n",
            "Epoch 15: val_loss did not improve from 0.18369\n",
            "66/66 [==============================] - 10s 152ms/step - loss: 0.0886 - accuracy: 0.9696 - val_loss: 0.1925 - val_accuracy: 0.9338\n",
            "Epoch 16/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0766 - accuracy: 0.9779\n",
            "Epoch 16: val_loss did not improve from 0.18369\n",
            "66/66 [==============================] - 10s 153ms/step - loss: 0.0766 - accuracy: 0.9779 - val_loss: 0.2407 - val_accuracy: 0.9302\n",
            "Epoch 17/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0619 - accuracy: 0.9791\n",
            "Epoch 17: val_loss improved from 0.18369 to 0.17161, saving model to model/Conv2D256Filters_OneDropout0-3.h5\n",
            "66/66 [==============================] - 10s 154ms/step - loss: 0.0619 - accuracy: 0.9791 - val_loss: 0.1716 - val_accuracy: 0.9494\n",
            "Epoch 18/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0733 - accuracy: 0.9746\n",
            "Epoch 18: val_loss did not improve from 0.17161\n",
            "66/66 [==============================] - 10s 153ms/step - loss: 0.0733 - accuracy: 0.9746 - val_loss: 0.2118 - val_accuracy: 0.9437\n",
            "Epoch 19/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0476 - accuracy: 0.9836\n",
            "Epoch 19: val_loss did not improve from 0.17161\n",
            "66/66 [==============================] - 10s 153ms/step - loss: 0.0476 - accuracy: 0.9836 - val_loss: 0.2055 - val_accuracy: 0.9430\n",
            "Epoch 20/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0457 - accuracy: 0.9839\n",
            "Epoch 20: val_loss did not improve from 0.17161\n",
            "66/66 [==============================] - 10s 154ms/step - loss: 0.0457 - accuracy: 0.9839 - val_loss: 0.2213 - val_accuracy: 0.9416\n",
            "Saving /content/gdrive/MyDrive/BrainTumorClassifier/Models/Conv2D256Filters_OneDropout0-3/history.pkl\n",
            "INFO:tensorflow:Assets written to: ram://9624de57-c6bc-4bb0-8cb8-460148da061a/assets\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
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          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(model, test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3aa766b7-19db-41ba-fb16-3a792a63216e",
        "id": "lFYZz6Gtq_cn"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22/22 [==============================] - 2s 63ms/step - loss: 0.1875 - accuracy: 0.9395\n",
            "Loss on test set: 0.18747489154338837\n",
            "Accuracy on test set: 0.9395447969436646\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      glioma     0.9294    0.9323    0.9309       325\n",
            "  meningioma     0.9042    0.8602    0.8816       329\n",
            "     notumor     0.9662    1.0000    0.9828       400\n",
            "   pituitary     0.9490    0.9517    0.9504       352\n",
            "\n",
            "    accuracy                         0.9395      1406\n",
            "   macro avg     0.9372    0.9360    0.9364      1406\n",
            "weighted avg     0.9389    0.9395    0.9390      1406\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusionmatrix(model, test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 630
        },
        "outputId": "574b31ff-07a1-49cd-a757-fc1c087dce36",
        "id": "QjpYKx7Fq_co"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22/22 [==============================] - 2s 69ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# One Conv2D layer with 256 filters, Two Dropouts with 0.3"
      ],
      "metadata": {
        "id": "ZB8gCxchtc8k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we define the model that we want to train."
      ],
      "metadata": {
        "id": "nTCDYEWGtc8l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = keras.Input(shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3))\n",
        "\n",
        "x = data_augmentation_layers(inputs)\n",
        "x = layers.Rescaling(1./255)(x)\n",
        "x = layers.Conv2D(filters=32, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=128, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=256, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=3)(x)\n",
        "x = layers.Dropout(0.3)(x)\n",
        "x = layers.Conv2D(filters=256, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.Dropout(0.3)(x)\n",
        "x = layers.Flatten()(x)\n",
        "outputs = layers.Dense(4, activation=\"softmax\")(x)\n",
        "model = keras.Model(inputs=inputs, outputs=outputs)\n",
        "model = compile_model(model, loss=\"sparse_categorical_crossentropy\")\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b73a91c-5d9f-489a-aec5-5c6bfc39f5cd",
        "id": "U8Hudxwwtc8l"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_7\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_8 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " sequential_6 (Sequential)   (None, 224, 224, 3)       0         \n",
            "                                                                 \n",
            " rescaling_7 (Rescaling)     (None, 224, 224, 3)       0         \n",
            "                                                                 \n",
            " conv2d_29 (Conv2D)          (None, 222, 222, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d_28 (MaxPoolin  (None, 111, 111, 32)     0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_30 (Conv2D)          (None, 109, 109, 64)      18496     \n",
            "                                                                 \n",
            " max_pooling2d_29 (MaxPoolin  (None, 54, 54, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_31 (Conv2D)          (None, 52, 52, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_30 (MaxPoolin  (None, 26, 26, 128)      0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_32 (Conv2D)          (None, 24, 24, 256)       295168    \n",
            "                                                                 \n",
            " max_pooling2d_31 (MaxPoolin  (None, 8, 8, 256)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_7 (Dropout)         (None, 8, 8, 256)         0         \n",
            "                                                                 \n",
            " conv2d_33 (Conv2D)          (None, 6, 6, 256)         590080    \n",
            "                                                                 \n",
            " dropout_8 (Dropout)         (None, 6, 6, 256)         0         \n",
            "                                                                 \n",
            " flatten_7 (Flatten)         (None, 9216)              0         \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 4)                 36868     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,015,364\n",
            "Trainable params: 1,015,364\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model, history = run_model(model, \"Conv2D256Filters_TwoDropouts0-3\", train_ds, val_ds, 50, patience=3, monitor='val_loss')\n",
        "plot_accuracy_and_loss_history(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "791872a3-f4a8-4e91-c6fa-296c049bc0e2",
        "id": "6rFMmPMztc8m"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 1.1458 - accuracy: 0.4579\n",
            "Epoch 1: val_loss improved from inf to 0.83912, saving model to model/Conv2D256Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 11s 155ms/step - loss: 1.1458 - accuracy: 0.4579 - val_loss: 0.8391 - val_accuracy: 0.6595\n",
            "Epoch 2/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.7520 - accuracy: 0.7005\n",
            "Epoch 2: val_loss improved from 0.83912 to 0.59759, saving model to model/Conv2D256Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 11s 156ms/step - loss: 0.7520 - accuracy: 0.7005 - val_loss: 0.5976 - val_accuracy: 0.7735\n",
            "Epoch 3/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.5862 - accuracy: 0.7788\n",
            "Epoch 3: val_loss improved from 0.59759 to 0.49192, saving model to model/Conv2D256Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 11s 156ms/step - loss: 0.5862 - accuracy: 0.7788 - val_loss: 0.4919 - val_accuracy: 0.8226\n",
            "Epoch 4/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.4787 - accuracy: 0.8251\n",
            "Epoch 4: val_loss improved from 0.49192 to 0.48258, saving model to model/Conv2D256Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 10s 154ms/step - loss: 0.4787 - accuracy: 0.8251 - val_loss: 0.4826 - val_accuracy: 0.8312\n",
            "Epoch 5/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.4200 - accuracy: 0.8545\n",
            "Epoch 5: val_loss improved from 0.48258 to 0.39658, saving model to model/Conv2D256Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 10s 154ms/step - loss: 0.4200 - accuracy: 0.8545 - val_loss: 0.3966 - val_accuracy: 0.8526\n",
            "Epoch 6/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.3480 - accuracy: 0.8711\n",
            "Epoch 6: val_loss improved from 0.39658 to 0.33495, saving model to model/Conv2D256Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 10s 154ms/step - loss: 0.3480 - accuracy: 0.8711 - val_loss: 0.3350 - val_accuracy: 0.8625\n",
            "Epoch 7/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2947 - accuracy: 0.8911\n",
            "Epoch 7: val_loss improved from 0.33495 to 0.27516, saving model to model/Conv2D256Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 10s 153ms/step - loss: 0.2947 - accuracy: 0.8911 - val_loss: 0.2752 - val_accuracy: 0.8974\n",
            "Epoch 8/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2580 - accuracy: 0.9027\n",
            "Epoch 8: val_loss did not improve from 0.27516\n",
            "66/66 [==============================] - 10s 154ms/step - loss: 0.2580 - accuracy: 0.9027 - val_loss: 0.3099 - val_accuracy: 0.8896\n",
            "Epoch 9/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2251 - accuracy: 0.9179\n",
            "Epoch 9: val_loss improved from 0.27516 to 0.26391, saving model to model/Conv2D256Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 10s 154ms/step - loss: 0.2251 - accuracy: 0.9179 - val_loss: 0.2639 - val_accuracy: 0.9017\n",
            "Epoch 10/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1894 - accuracy: 0.9347\n",
            "Epoch 10: val_loss improved from 0.26391 to 0.21196, saving model to model/Conv2D256Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 11s 154ms/step - loss: 0.1894 - accuracy: 0.9347 - val_loss: 0.2120 - val_accuracy: 0.9224\n",
            "Epoch 11/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1545 - accuracy: 0.9445\n",
            "Epoch 11: val_loss did not improve from 0.21196\n",
            "66/66 [==============================] - 11s 155ms/step - loss: 0.1545 - accuracy: 0.9445 - val_loss: 0.2121 - val_accuracy: 0.9252\n",
            "Epoch 12/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1290 - accuracy: 0.9587\n",
            "Epoch 12: val_loss did not improve from 0.21196\n",
            "66/66 [==============================] - 10s 153ms/step - loss: 0.1290 - accuracy: 0.9587 - val_loss: 0.2121 - val_accuracy: 0.9323\n",
            "Epoch 13/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1280 - accuracy: 0.9570\n",
            "Epoch 13: val_loss improved from 0.21196 to 0.19336, saving model to model/Conv2D256Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 11s 154ms/step - loss: 0.1280 - accuracy: 0.9570 - val_loss: 0.1934 - val_accuracy: 0.9380\n",
            "Epoch 14/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1271 - accuracy: 0.9559\n",
            "Epoch 14: val_loss improved from 0.19336 to 0.19239, saving model to model/Conv2D256Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 11s 155ms/step - loss: 0.1271 - accuracy: 0.9559 - val_loss: 0.1924 - val_accuracy: 0.9373\n",
            "Epoch 15/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0956 - accuracy: 0.9675\n",
            "Epoch 15: val_loss improved from 0.19239 to 0.16914, saving model to model/Conv2D256Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 11s 155ms/step - loss: 0.0956 - accuracy: 0.9675 - val_loss: 0.1691 - val_accuracy: 0.9473\n",
            "Epoch 16/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0900 - accuracy: 0.9668\n",
            "Epoch 16: val_loss did not improve from 0.16914\n",
            "66/66 [==============================] - 11s 156ms/step - loss: 0.0900 - accuracy: 0.9668 - val_loss: 0.2229 - val_accuracy: 0.9238\n",
            "Epoch 17/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0853 - accuracy: 0.9713\n",
            "Epoch 17: val_loss improved from 0.16914 to 0.16183, saving model to model/Conv2D256Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 10s 154ms/step - loss: 0.0853 - accuracy: 0.9713 - val_loss: 0.1618 - val_accuracy: 0.9487\n",
            "Epoch 18/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0744 - accuracy: 0.9722\n",
            "Epoch 18: val_loss did not improve from 0.16183\n",
            "66/66 [==============================] - 10s 152ms/step - loss: 0.0744 - accuracy: 0.9722 - val_loss: 0.1736 - val_accuracy: 0.9509\n",
            "Epoch 19/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0658 - accuracy: 0.9763\n",
            "Epoch 19: val_loss did not improve from 0.16183\n",
            "66/66 [==============================] - 10s 153ms/step - loss: 0.0658 - accuracy: 0.9763 - val_loss: 0.1624 - val_accuracy: 0.9480\n",
            "Epoch 20/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0653 - accuracy: 0.9772\n",
            "Epoch 20: val_loss did not improve from 0.16183\n",
            "66/66 [==============================] - 10s 153ms/step - loss: 0.0653 - accuracy: 0.9772 - val_loss: 0.2100 - val_accuracy: 0.9466\n",
            "Saving /content/gdrive/MyDrive/BrainTumorClassifier/Models/Conv2D256Filters_TwoDropouts0-3/history.pkl\n",
            "INFO:tensorflow:Assets written to: ram://7c26625f-ff35-4ef5-b723-2b676544e1b1/assets\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = models.load_model(MODELS_PATH + \"/Conv2D256Filters_TwoDropouts0-3/Conv2D256Filters_TwoDropouts0-3.h5\")"
      ],
      "metadata": {
        "id": "xEzmYR5wlMH8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(model, test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5264d58-ad76-4b5d-e446-1fd05a156f94",
        "id": "Vx7Z-ccatc8m"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22/22 [==============================] - 2s 64ms/step - loss: 0.1611 - accuracy: 0.9481\n",
            "Loss on test set: 0.161067932844162\n",
            "Accuracy on test set: 0.9480796456336975\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      glioma     0.9078    0.9692    0.9375       325\n",
            "  meningioma     0.9295    0.8815    0.9048       329\n",
            "     notumor     0.9777    0.9850    0.9813       400\n",
            "   pituitary     0.9709    0.9489    0.9598       352\n",
            "\n",
            "    accuracy                         0.9481      1406\n",
            "   macro avg     0.9465    0.9461    0.9459      1406\n",
            "weighted avg     0.9486    0.9481    0.9479      1406\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusionmatrix(model, test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "outputId": "bf34b9af-2f2f-44aa-9f21-d5002c238b80",
        "id": "7xs21jhntc8n"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22/22 [==============================] - 2s 70ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# One Conv2D layer with 512 filters, Two Dropouts with 0.3, Additional layer"
      ],
      "metadata": {
        "id": "7KDoqR-ExpOj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we define the model that we want to train."
      ],
      "metadata": {
        "id": "4ZRSvtLJxpOj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = keras.Input(shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3))\n",
        "\n",
        "x = data_augmentation_layers(inputs)\n",
        "x = layers.Rescaling(1./255)(x)\n",
        "x = layers.Conv2D(filters=32, padding='same', kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=64, padding='same', kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=128, padding='same', kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=256, padding='same', kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=3)(x)\n",
        "x = layers.Conv2D(filters=512, padding='same', kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=3)(x)\n",
        "x = layers.Dropout(0.3)(x)\n",
        "x = layers.Conv2D(filters=512, padding='same', kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.Dropout(0.3)(x)\n",
        "x = layers.Flatten()(x)\n",
        "outputs = layers.Dense(4, activation=\"softmax\")(x)\n",
        "model = keras.Model(inputs=inputs, outputs=outputs)\n",
        "model = compile_model(model, loss=\"sparse_categorical_crossentropy\")\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "97b98107-11f4-4438-8b70-59fbc2285b90",
        "id": "NytGlbJOxpOk"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_9\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_11 (InputLayer)       [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " sequential_6 (Sequential)   (None, 224, 224, 3)       0         \n",
            "                                                                 \n",
            " rescaling_10 (Rescaling)    (None, 224, 224, 3)       0         \n",
            "                                                                 \n",
            " conv2d_46 (Conv2D)          (None, 224, 224, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d_42 (MaxPoolin  (None, 112, 112, 32)     0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_47 (Conv2D)          (None, 112, 112, 64)      18496     \n",
            "                                                                 \n",
            " max_pooling2d_43 (MaxPoolin  (None, 56, 56, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_48 (Conv2D)          (None, 56, 56, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_44 (MaxPoolin  (None, 28, 28, 128)      0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_49 (Conv2D)          (None, 28, 28, 256)       295168    \n",
            "                                                                 \n",
            " max_pooling2d_45 (MaxPoolin  (None, 9, 9, 256)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_50 (Conv2D)          (None, 9, 9, 512)         1180160   \n",
            "                                                                 \n",
            " max_pooling2d_46 (MaxPoolin  (None, 3, 3, 512)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_12 (Dropout)        (None, 3, 3, 512)         0         \n",
            "                                                                 \n",
            " conv2d_51 (Conv2D)          (None, 3, 3, 512)         2359808   \n",
            "                                                                 \n",
            " dropout_13 (Dropout)        (None, 3, 3, 512)         0         \n",
            "                                                                 \n",
            " flatten_9 (Flatten)         (None, 4608)              0         \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 4)                 18436     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,946,820\n",
            "Trainable params: 3,946,820\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model, history = run_model(model, \"Conv2D512Filters_TwoDropouts0-3\", train_ds, val_ds, 50, patience=3, monitor='val_loss')\n",
        "plot_accuracy_and_loss_history(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0a293cd7-5a84-4167-f1fb-94ad4e30ebcf",
        "id": "MlggPZtVxpOk"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 1.1663 - accuracy: 0.4515\n",
            "Epoch 1: val_loss improved from inf to 0.88793, saving model to model/Conv2D512Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 16s 198ms/step - loss: 1.1663 - accuracy: 0.4515 - val_loss: 0.8879 - val_accuracy: 0.6546\n",
            "Epoch 2/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.8022 - accuracy: 0.6596\n",
            "Epoch 2: val_loss improved from 0.88793 to 0.60291, saving model to model/Conv2D512Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 11s 165ms/step - loss: 0.8022 - accuracy: 0.6596 - val_loss: 0.6029 - val_accuracy: 0.7785\n",
            "Epoch 3/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.5820 - accuracy: 0.7700\n",
            "Epoch 3: val_loss did not improve from 0.60291\n",
            "66/66 [==============================] - 11s 165ms/step - loss: 0.5820 - accuracy: 0.7700 - val_loss: 0.7255 - val_accuracy: 0.7101\n",
            "Epoch 4/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.4963 - accuracy: 0.8134\n",
            "Epoch 4: val_loss improved from 0.60291 to 0.40857, saving model to model/Conv2D512Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 11s 164ms/step - loss: 0.4963 - accuracy: 0.8134 - val_loss: 0.4086 - val_accuracy: 0.8454\n",
            "Epoch 5/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.3792 - accuracy: 0.8614\n",
            "Epoch 5: val_loss improved from 0.40857 to 0.35972, saving model to model/Conv2D512Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 11s 166ms/step - loss: 0.3792 - accuracy: 0.8614 - val_loss: 0.3597 - val_accuracy: 0.8668\n",
            "Epoch 6/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.3224 - accuracy: 0.8770\n",
            "Epoch 6: val_loss improved from 0.35972 to 0.30987, saving model to model/Conv2D512Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 11s 166ms/step - loss: 0.3224 - accuracy: 0.8770 - val_loss: 0.3099 - val_accuracy: 0.8796\n",
            "Epoch 7/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2442 - accuracy: 0.9098\n",
            "Epoch 7: val_loss improved from 0.30987 to 0.21218, saving model to model/Conv2D512Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 11s 166ms/step - loss: 0.2442 - accuracy: 0.9098 - val_loss: 0.2122 - val_accuracy: 0.9160\n",
            "Epoch 8/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1876 - accuracy: 0.9302\n",
            "Epoch 8: val_loss did not improve from 0.21218\n",
            "66/66 [==============================] - 11s 166ms/step - loss: 0.1876 - accuracy: 0.9302 - val_loss: 0.2279 - val_accuracy: 0.9274\n",
            "Epoch 9/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1383 - accuracy: 0.9540\n",
            "Epoch 9: val_loss improved from 0.21218 to 0.19239, saving model to model/Conv2D512Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 11s 167ms/step - loss: 0.1383 - accuracy: 0.9540 - val_loss: 0.1924 - val_accuracy: 0.9338\n",
            "Epoch 10/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1167 - accuracy: 0.9608\n",
            "Epoch 10: val_loss improved from 0.19239 to 0.17381, saving model to model/Conv2D512Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 12s 171ms/step - loss: 0.1167 - accuracy: 0.9608 - val_loss: 0.1738 - val_accuracy: 0.9309\n",
            "Epoch 11/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1071 - accuracy: 0.9649\n",
            "Epoch 11: val_loss did not improve from 0.17381\n",
            "66/66 [==============================] - 11s 165ms/step - loss: 0.1071 - accuracy: 0.9649 - val_loss: 0.2102 - val_accuracy: 0.9323\n",
            "Epoch 12/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1015 - accuracy: 0.9680\n",
            "Epoch 12: val_loss improved from 0.17381 to 0.15541, saving model to model/Conv2D512Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 11s 168ms/step - loss: 0.1015 - accuracy: 0.9680 - val_loss: 0.1554 - val_accuracy: 0.9523\n",
            "Epoch 13/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0598 - accuracy: 0.9791\n",
            "Epoch 13: val_loss improved from 0.15541 to 0.15220, saving model to model/Conv2D512Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 11s 169ms/step - loss: 0.0598 - accuracy: 0.9791 - val_loss: 0.1522 - val_accuracy: 0.9537\n",
            "Epoch 14/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0555 - accuracy: 0.9805\n",
            "Epoch 14: val_loss did not improve from 0.15220\n",
            "66/66 [==============================] - 11s 169ms/step - loss: 0.0555 - accuracy: 0.9805 - val_loss: 0.2142 - val_accuracy: 0.9330\n",
            "Epoch 15/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0569 - accuracy: 0.9803\n",
            "Epoch 15: val_loss improved from 0.15220 to 0.13690, saving model to model/Conv2D512Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 11s 169ms/step - loss: 0.0569 - accuracy: 0.9803 - val_loss: 0.1369 - val_accuracy: 0.9651\n",
            "Epoch 16/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0482 - accuracy: 0.9877\n",
            "Epoch 16: val_loss improved from 0.13690 to 0.13457, saving model to model/Conv2D512Filters_TwoDropouts0-3.h5\n",
            "66/66 [==============================] - 11s 168ms/step - loss: 0.0482 - accuracy: 0.9877 - val_loss: 0.1346 - val_accuracy: 0.9623\n",
            "Epoch 17/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0396 - accuracy: 0.9865\n",
            "Epoch 17: val_loss did not improve from 0.13457\n",
            "66/66 [==============================] - 12s 167ms/step - loss: 0.0396 - accuracy: 0.9865 - val_loss: 0.2101 - val_accuracy: 0.9309\n",
            "Epoch 18/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0397 - accuracy: 0.9879\n",
            "Epoch 18: val_loss did not improve from 0.13457\n",
            "66/66 [==============================] - 11s 169ms/step - loss: 0.0397 - accuracy: 0.9879 - val_loss: 0.1705 - val_accuracy: 0.9566\n",
            "Epoch 19/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0414 - accuracy: 0.9853\n",
            "Epoch 19: val_loss did not improve from 0.13457\n",
            "66/66 [==============================] - 12s 170ms/step - loss: 0.0414 - accuracy: 0.9853 - val_loss: 0.1960 - val_accuracy: 0.9452\n",
            "Saving /content/gdrive/MyDrive/BrainTumorClassifier/Models/Conv2D512Filters_TwoDropouts0-3/history.pkl\n",
            "INFO:tensorflow:Assets written to: ram://ca4926da-eeba-47e3-8d1a-9b9b65c211ac/assets\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(model, test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d79ca7c5-8ffe-49eb-cabe-143bcd59bb5c",
        "id": "hx4xm0U4xpOl"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22/22 [==============================] - 2s 72ms/step - loss: 0.1541 - accuracy: 0.9538\n",
            "Loss on test set: 0.15409797430038452\n",
            "Accuracy on test set: 0.9537695646286011\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      glioma     0.9538    0.9538    0.9538       325\n",
            "  meningioma     0.9452    0.8906    0.9171       329\n",
            "     notumor     0.9523    0.9975    0.9744       400\n",
            "   pituitary     0.9631    0.9631    0.9631       352\n",
            "\n",
            "    accuracy                         0.9538      1406\n",
            "   macro avg     0.9536    0.9512    0.9521      1406\n",
            "weighted avg     0.9537    0.9538    0.9534      1406\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusionmatrix(model, test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "outputId": "76e08bed-345c-4c1e-aec8-85b7a16e1676",
        "id": "tt7aNIhlxpOl"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22/22 [==============================] - 2s 78ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# One Dense layer with 512 neurons, Two Dropouts with 0.3, Additional layer"
      ],
      "metadata": {
        "id": "IXTpYGBC0b0O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we define the model that we want to train."
      ],
      "metadata": {
        "id": "1PtENVsO0b0P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = keras.Input(shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3))\n",
        "\n",
        "x = data_augmentation_layers(inputs)\n",
        "x = layers.Rescaling(1./255)(x)\n",
        "x = layers.Conv2D(filters=32, padding='same', kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=64, padding='same', kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=128, padding='same', kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=256, padding='same', kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=3)(x)\n",
        "x = layers.Conv2D(filters=512, padding='same', kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=3)(x)\n",
        "x = layers.Flatten()(x)\n",
        "x = layers.Dropout(0.3)(x)\n",
        "x = layers.Dense(512, activation='relu', name='hidden_classifier')(x)\n",
        "x = layers.Dropout(0.3)(x)\n",
        "outputs = layers.Dense(4, activation=\"softmax\")(x)\n",
        "model = keras.Model(inputs=inputs, outputs=outputs)\n",
        "model = compile_model(model, loss=\"sparse_categorical_crossentropy\")\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d16b5f36-9e3b-4478-e741-6d8cf0dd81c1",
        "id": "fTjQEZbj0b0Q"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_10\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_13 (InputLayer)       [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " sequential_6 (Sequential)   (None, 224, 224, 3)       0         \n",
            "                                                                 \n",
            " rescaling_12 (Rescaling)    (None, 224, 224, 3)       0         \n",
            "                                                                 \n",
            " conv2d_57 (Conv2D)          (None, 224, 224, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d_52 (MaxPoolin  (None, 112, 112, 32)     0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_58 (Conv2D)          (None, 112, 112, 64)      18496     \n",
            "                                                                 \n",
            " max_pooling2d_53 (MaxPoolin  (None, 56, 56, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_59 (Conv2D)          (None, 56, 56, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_54 (MaxPoolin  (None, 28, 28, 128)      0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_60 (Conv2D)          (None, 28, 28, 256)       295168    \n",
            "                                                                 \n",
            " max_pooling2d_55 (MaxPoolin  (None, 9, 9, 256)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_61 (Conv2D)          (None, 9, 9, 512)         1180160   \n",
            "                                                                 \n",
            " max_pooling2d_56 (MaxPoolin  (None, 3, 3, 512)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_11 (Flatten)        (None, 4608)              0         \n",
            "                                                                 \n",
            " dropout_16 (Dropout)        (None, 4608)              0         \n",
            "                                                                 \n",
            " hidden_classifier (Dense)   (None, 512)               2359808   \n",
            "                                                                 \n",
            " dropout_17 (Dropout)        (None, 512)               0         \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 4)                 2052      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,930,436\n",
            "Trainable params: 3,930,436\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model, history = run_model(model, \"Dense512Neurons_TwoDropouts0-3_AdditionalLayer\", train_ds, val_ds, 50, patience=3, monitor='val_loss')\n",
        "plot_accuracy_and_loss_history(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cc7df82a-0f21-464d-aeed-87e43e0b3434",
        "id": "gCD00Wr60b0R"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 1.1012 - accuracy: 0.4776\n",
            "Epoch 1: val_loss improved from inf to 0.87358, saving model to model/Dense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 12s 161ms/step - loss: 1.1012 - accuracy: 0.4776 - val_loss: 0.8736 - val_accuracy: 0.6211\n",
            "Epoch 2/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.7430 - accuracy: 0.6969\n",
            "Epoch 2: val_loss improved from 0.87358 to 0.64165, saving model to model/Dense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 160ms/step - loss: 0.7430 - accuracy: 0.6969 - val_loss: 0.6417 - val_accuracy: 0.7464\n",
            "Epoch 3/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.5534 - accuracy: 0.7850\n",
            "Epoch 3: val_loss improved from 0.64165 to 0.44315, saving model to model/Dense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 161ms/step - loss: 0.5534 - accuracy: 0.7850 - val_loss: 0.4432 - val_accuracy: 0.8390\n",
            "Epoch 4/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.4413 - accuracy: 0.8386\n",
            "Epoch 4: val_loss improved from 0.44315 to 0.38653, saving model to model/Dense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 161ms/step - loss: 0.4413 - accuracy: 0.8386 - val_loss: 0.3865 - val_accuracy: 0.8668\n",
            "Epoch 5/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.3628 - accuracy: 0.8685\n",
            "Epoch 5: val_loss improved from 0.38653 to 0.32948, saving model to model/Dense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 161ms/step - loss: 0.3628 - accuracy: 0.8685 - val_loss: 0.3295 - val_accuracy: 0.8782\n",
            "Epoch 6/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2946 - accuracy: 0.8939\n",
            "Epoch 6: val_loss improved from 0.32948 to 0.29248, saving model to model/Dense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 162ms/step - loss: 0.2946 - accuracy: 0.8939 - val_loss: 0.2925 - val_accuracy: 0.8989\n",
            "Epoch 7/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2309 - accuracy: 0.9138\n",
            "Epoch 7: val_loss improved from 0.29248 to 0.19789, saving model to model/Dense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 164ms/step - loss: 0.2309 - accuracy: 0.9138 - val_loss: 0.1979 - val_accuracy: 0.9274\n",
            "Epoch 8/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1822 - accuracy: 0.9366\n",
            "Epoch 8: val_loss improved from 0.19789 to 0.19421, saving model to model/Dense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 163ms/step - loss: 0.1822 - accuracy: 0.9366 - val_loss: 0.1942 - val_accuracy: 0.9288\n",
            "Epoch 9/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1869 - accuracy: 0.9371\n",
            "Epoch 9: val_loss did not improve from 0.19421\n",
            "66/66 [==============================] - 11s 159ms/step - loss: 0.1869 - accuracy: 0.9371 - val_loss: 0.3134 - val_accuracy: 0.8868\n",
            "Epoch 10/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1296 - accuracy: 0.9580\n",
            "Epoch 10: val_loss improved from 0.19421 to 0.13815, saving model to model/Dense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 161ms/step - loss: 0.1296 - accuracy: 0.9580 - val_loss: 0.1381 - val_accuracy: 0.9509\n",
            "Epoch 11/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1173 - accuracy: 0.9589\n",
            "Epoch 11: val_loss did not improve from 0.13815\n",
            "66/66 [==============================] - 11s 160ms/step - loss: 0.1173 - accuracy: 0.9589 - val_loss: 0.1741 - val_accuracy: 0.9459\n",
            "Epoch 12/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0995 - accuracy: 0.9663\n",
            "Epoch 12: val_loss did not improve from 0.13815\n",
            "66/66 [==============================] - 11s 161ms/step - loss: 0.0995 - accuracy: 0.9663 - val_loss: 0.1582 - val_accuracy: 0.9387\n",
            "Epoch 13/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0604 - accuracy: 0.9801\n",
            "Epoch 13: val_loss improved from 0.13815 to 0.12972, saving model to model/Dense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 163ms/step - loss: 0.0604 - accuracy: 0.9801 - val_loss: 0.1297 - val_accuracy: 0.9544\n",
            "Epoch 14/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0770 - accuracy: 0.9737\n",
            "Epoch 14: val_loss did not improve from 0.12972\n",
            "66/66 [==============================] - 11s 167ms/step - loss: 0.0770 - accuracy: 0.9737 - val_loss: 0.2487 - val_accuracy: 0.9217\n",
            "Epoch 15/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0803 - accuracy: 0.9701\n",
            "Epoch 15: val_loss improved from 0.12972 to 0.11360, saving model to model/Dense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 167ms/step - loss: 0.0803 - accuracy: 0.9701 - val_loss: 0.1136 - val_accuracy: 0.9630\n",
            "Epoch 16/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0421 - accuracy: 0.9869\n",
            "Epoch 16: val_loss improved from 0.11360 to 0.11177, saving model to model/Dense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 163ms/step - loss: 0.0421 - accuracy: 0.9869 - val_loss: 0.1118 - val_accuracy: 0.9608\n",
            "Epoch 17/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0481 - accuracy: 0.9834\n",
            "Epoch 17: val_loss did not improve from 0.11177\n",
            "66/66 [==============================] - 11s 162ms/step - loss: 0.0481 - accuracy: 0.9834 - val_loss: 0.1185 - val_accuracy: 0.9658\n",
            "Epoch 18/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0357 - accuracy: 0.9860\n",
            "Epoch 18: val_loss did not improve from 0.11177\n",
            "66/66 [==============================] - 11s 161ms/step - loss: 0.0357 - accuracy: 0.9860 - val_loss: 0.1157 - val_accuracy: 0.9694\n",
            "Epoch 19/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0378 - accuracy: 0.9872\n",
            "Epoch 19: val_loss improved from 0.11177 to 0.11142, saving model to model/Dense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 163ms/step - loss: 0.0378 - accuracy: 0.9872 - val_loss: 0.1114 - val_accuracy: 0.9665\n",
            "Epoch 20/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0245 - accuracy: 0.9915\n",
            "Epoch 20: val_loss improved from 0.11142 to 0.10035, saving model to model/Dense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 163ms/step - loss: 0.0245 - accuracy: 0.9915 - val_loss: 0.1004 - val_accuracy: 0.9672\n",
            "Epoch 21/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0254 - accuracy: 0.9912\n",
            "Epoch 21: val_loss did not improve from 0.10035\n",
            "66/66 [==============================] - 11s 161ms/step - loss: 0.0254 - accuracy: 0.9912 - val_loss: 0.1043 - val_accuracy: 0.9708\n",
            "Epoch 22/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0246 - accuracy: 0.9910\n",
            "Epoch 22: val_loss did not improve from 0.10035\n",
            "66/66 [==============================] - 11s 160ms/step - loss: 0.0246 - accuracy: 0.9910 - val_loss: 0.1297 - val_accuracy: 0.9658\n",
            "Epoch 23/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0215 - accuracy: 0.9931\n",
            "Epoch 23: val_loss did not improve from 0.10035\n",
            "66/66 [==============================] - 11s 160ms/step - loss: 0.0215 - accuracy: 0.9931 - val_loss: 0.1631 - val_accuracy: 0.9587\n",
            "Saving /content/gdrive/MyDrive/BrainTumorClassifier/Models/Dense512Neurons_TwoDropouts0-3_AdditionalLayer/history.pkl\n",
            "INFO:tensorflow:Assets written to: ram://36261166-1101-43e0-ad66-81a4ecc832c5/assets\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(model, test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63c9f9eb-c0f8-4aab-91ae-d5d40189f95b",
        "id": "Cyd7QQXZ0b0S"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22/22 [==============================] - 2s 66ms/step - loss: 0.1380 - accuracy: 0.9623\n",
            "Loss on test set: 0.13795408606529236\n",
            "Accuracy on test set: 0.962304413318634\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      glioma     0.9601    0.9631    0.9616       325\n",
            "  meningioma     0.9281    0.9422    0.9351       329\n",
            "     notumor     0.9826    0.9875    0.9850       400\n",
            "   pituitary     0.9738    0.9517    0.9626       352\n",
            "\n",
            "    accuracy                         0.9623      1406\n",
            "   macro avg     0.9612    0.9611    0.9611      1406\n",
            "weighted avg     0.9625    0.9623    0.9623      1406\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusionmatrix(model, test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "outputId": "a5837724-d29f-4f1e-c306-db9d6cf17ddb",
        "id": "RQv7ATPq0b0T"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22/22 [==============================] - 2s 77ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Two Dense layer with 512 neurons, Two Dropouts with 0.3, Additional layer"
      ],
      "metadata": {
        "id": "FsHeMox53NFH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we define the model that we want to train."
      ],
      "metadata": {
        "id": "KD6ZhnEV3NFH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = keras.Input(shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3))\n",
        "\n",
        "x = data_augmentation_layers(inputs)\n",
        "x = layers.Rescaling(1./255)(x)\n",
        "x = layers.Conv2D(filters=32, padding='same', kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=64, padding='same', kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=128, padding='same', kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=256, padding='same', kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=3)(x)\n",
        "x = layers.Conv2D(filters=512, padding='same', kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=3)(x)\n",
        "x = layers.Flatten()(x)\n",
        "x = layers.Dense(512, activation='relu', name='hidden_classifier0')(x)\n",
        "x = layers.Dropout(0.3)(x)\n",
        "x = layers.Dense(512, activation='relu', name='hidden_classifier1')(x)\n",
        "x = layers.Dropout(0.3)(x)\n",
        "outputs = layers.Dense(4, activation=\"softmax\")(x)\n",
        "model = keras.Model(inputs=inputs, outputs=outputs)\n",
        "model = compile_model(model, loss=\"sparse_categorical_crossentropy\")\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "86874b49-456c-4c36-ea3c-20152b25341e",
        "id": "kqkhcIyx3NFI"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_11\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_14 (InputLayer)       [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " sequential_6 (Sequential)   (None, 224, 224, 3)       0         \n",
            "                                                                 \n",
            " rescaling_13 (Rescaling)    (None, 224, 224, 3)       0         \n",
            "                                                                 \n",
            " conv2d_62 (Conv2D)          (None, 224, 224, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d_57 (MaxPoolin  (None, 112, 112, 32)     0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_63 (Conv2D)          (None, 112, 112, 64)      18496     \n",
            "                                                                 \n",
            " max_pooling2d_58 (MaxPoolin  (None, 56, 56, 64)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_64 (Conv2D)          (None, 56, 56, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_59 (MaxPoolin  (None, 28, 28, 128)      0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_65 (Conv2D)          (None, 28, 28, 256)       295168    \n",
            "                                                                 \n",
            " max_pooling2d_60 (MaxPoolin  (None, 9, 9, 256)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_66 (Conv2D)          (None, 9, 9, 512)         1180160   \n",
            "                                                                 \n",
            " max_pooling2d_61 (MaxPoolin  (None, 3, 3, 512)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_12 (Flatten)        (None, 4608)              0         \n",
            "                                                                 \n",
            " hidden_classifier0 (Dense)  (None, 512)               2359808   \n",
            "                                                                 \n",
            " dropout_18 (Dropout)        (None, 512)               0         \n",
            "                                                                 \n",
            " hidden_classifier1 (Dense)  (None, 512)               262656    \n",
            "                                                                 \n",
            " dropout_19 (Dropout)        (None, 512)               0         \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 4)                 2052      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,193,092\n",
            "Trainable params: 4,193,092\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model, history = run_model(model, \"TwoDense512Neurons_TwoDropouts0-3_AdditionalLayer\", train_ds, val_ds, 50, patience=3, monitor='val_loss')\n",
        "plot_accuracy_and_loss_history(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "55ffa888-417b-4870-aa19-366b0955da05",
        "id": "ebWDN4Ki3NFI"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 1.1668 - accuracy: 0.4313\n",
            "Epoch 1: val_loss improved from inf to 0.87245, saving model to model/TwoDense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 12s 161ms/step - loss: 1.1668 - accuracy: 0.4313 - val_loss: 0.8724 - val_accuracy: 0.6353\n",
            "Epoch 2/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.8799 - accuracy: 0.6167\n",
            "Epoch 2: val_loss improved from 0.87245 to 0.69889, saving model to model/TwoDense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 159ms/step - loss: 0.8799 - accuracy: 0.6167 - val_loss: 0.6989 - val_accuracy: 0.7158\n",
            "Epoch 3/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.6683 - accuracy: 0.7270\n",
            "Epoch 3: val_loss improved from 0.69889 to 0.54309, saving model to model/TwoDense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 161ms/step - loss: 0.6683 - accuracy: 0.7270 - val_loss: 0.5431 - val_accuracy: 0.7849\n",
            "Epoch 4/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.5488 - accuracy: 0.7842\n",
            "Epoch 4: val_loss improved from 0.54309 to 0.51605, saving model to model/TwoDense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 160ms/step - loss: 0.5488 - accuracy: 0.7842 - val_loss: 0.5161 - val_accuracy: 0.8034\n",
            "Epoch 5/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.4655 - accuracy: 0.8291\n",
            "Epoch 5: val_loss improved from 0.51605 to 0.34934, saving model to model/TwoDense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 160ms/step - loss: 0.4655 - accuracy: 0.8291 - val_loss: 0.3493 - val_accuracy: 0.8761\n",
            "Epoch 6/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.3397 - accuracy: 0.8775\n",
            "Epoch 6: val_loss improved from 0.34934 to 0.26794, saving model to model/TwoDense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 161ms/step - loss: 0.3397 - accuracy: 0.8775 - val_loss: 0.2679 - val_accuracy: 0.8989\n",
            "Epoch 7/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2666 - accuracy: 0.9046\n",
            "Epoch 7: val_loss did not improve from 0.26794\n",
            "66/66 [==============================] - 11s 159ms/step - loss: 0.2666 - accuracy: 0.9046 - val_loss: 0.4074 - val_accuracy: 0.8476\n",
            "Epoch 8/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.2236 - accuracy: 0.9214\n",
            "Epoch 8: val_loss improved from 0.26794 to 0.20610, saving model to model/TwoDense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 164ms/step - loss: 0.2236 - accuracy: 0.9214 - val_loss: 0.2061 - val_accuracy: 0.9281\n",
            "Epoch 9/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1564 - accuracy: 0.9435\n",
            "Epoch 9: val_loss did not improve from 0.20610\n",
            "66/66 [==============================] - 11s 160ms/step - loss: 0.1564 - accuracy: 0.9435 - val_loss: 0.2271 - val_accuracy: 0.9160\n",
            "Epoch 10/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1275 - accuracy: 0.9544\n",
            "Epoch 10: val_loss improved from 0.20610 to 0.17026, saving model to model/TwoDense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 162ms/step - loss: 0.1275 - accuracy: 0.9544 - val_loss: 0.1703 - val_accuracy: 0.9437\n",
            "Epoch 11/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0837 - accuracy: 0.9706\n",
            "Epoch 11: val_loss did not improve from 0.17026\n",
            "66/66 [==============================] - 11s 160ms/step - loss: 0.0837 - accuracy: 0.9706 - val_loss: 0.1841 - val_accuracy: 0.9459\n",
            "Epoch 12/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.1159 - accuracy: 0.9594\n",
            "Epoch 12: val_loss improved from 0.17026 to 0.16664, saving model to model/TwoDense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 162ms/step - loss: 0.1159 - accuracy: 0.9594 - val_loss: 0.1666 - val_accuracy: 0.9466\n",
            "Epoch 13/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0536 - accuracy: 0.9817\n",
            "Epoch 13: val_loss improved from 0.16664 to 0.12910, saving model to model/TwoDense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 161ms/step - loss: 0.0536 - accuracy: 0.9817 - val_loss: 0.1291 - val_accuracy: 0.9558\n",
            "Epoch 14/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0448 - accuracy: 0.9853\n",
            "Epoch 14: val_loss did not improve from 0.12910\n",
            "66/66 [==============================] - 11s 158ms/step - loss: 0.0448 - accuracy: 0.9853 - val_loss: 0.1672 - val_accuracy: 0.9509\n",
            "Epoch 15/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0505 - accuracy: 0.9831\n",
            "Epoch 15: val_loss improved from 0.12910 to 0.12045, saving model to model/TwoDense512Neurons_TwoDropouts0-3_AdditionalLayer.h5\n",
            "66/66 [==============================] - 11s 161ms/step - loss: 0.0505 - accuracy: 0.9831 - val_loss: 0.1204 - val_accuracy: 0.9658\n",
            "Epoch 16/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0294 - accuracy: 0.9891\n",
            "Epoch 16: val_loss did not improve from 0.12045\n",
            "66/66 [==============================] - 11s 160ms/step - loss: 0.0294 - accuracy: 0.9891 - val_loss: 0.1642 - val_accuracy: 0.9530\n",
            "Epoch 17/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0390 - accuracy: 0.9867\n",
            "Epoch 17: val_loss did not improve from 0.12045\n",
            "66/66 [==============================] - 11s 161ms/step - loss: 0.0390 - accuracy: 0.9867 - val_loss: 0.1364 - val_accuracy: 0.9637\n",
            "Epoch 18/50\n",
            "66/66 [==============================] - ETA: 0s - loss: 0.0234 - accuracy: 0.9934\n",
            "Epoch 18: val_loss did not improve from 0.12045\n",
            "66/66 [==============================] - 11s 162ms/step - loss: 0.0234 - accuracy: 0.9934 - val_loss: 0.1607 - val_accuracy: 0.9644\n",
            "Saving /content/gdrive/MyDrive/BrainTumorClassifier/Models/TwoDense512Neurons_TwoDropouts0-3_AdditionalLayer/history.pkl\n",
            "INFO:tensorflow:Assets written to: ram://1d7807bf-33e3-44b2-9075-3e6bdb7cee84/assets\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = models.load_model(MODELS_PATH + \"/Dense512Neurons_OneDropout0-5/Dense512Neurons_OneDropout0-5.h5\")"
      ],
      "metadata": {
        "id": "GeQwV7w8lS-l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(model, test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b4247dc-c01f-4082-b873-978f6f679041",
        "id": "TFM2pSwp3NFJ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22/22 [==============================] - 2s 71ms/step - loss: 0.1433 - accuracy: 0.9573\n",
            "Loss on test set: 0.1432587206363678\n",
            "Accuracy on test set: 0.9573257565498352\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "   pituitary       0.97      0.94      0.96       325\n",
            "  meningioma       0.93      0.92      0.93       329\n",
            "     notumor       0.97      0.99      0.98       400\n",
            "      glioma       0.96      0.96      0.96       352\n",
            "\n",
            "    accuracy                           0.96      1406\n",
            "   macro avg       0.96      0.96      0.96      1406\n",
            "weighted avg       0.96      0.96      0.96      1406\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusionmatrix(model, test_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "outputId": "e03df842-ba5b-4b55-e983-eeadf285bea3",
        "id": "Okz2hxne3NFJ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22/22 [==============================] - 2s 75ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}