thesis_model_mobilenetv2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/fadillarizalul/thesis-alzheimer/blob/main/thesis_model_mobilenetv2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# set up dan init"
      ],
      "metadata": {
        "id": "SptVS2iG6sGF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import zipfile\n",
        "import cv2\n",
        "from skimage import io"
      ],
      "metadata": {
        "id": "bHtFRXg_-ld1"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.python.keras import Sequential\n",
        "from tensorflow.keras import layers, optimizers\n",
        "from tensorflow.keras.layers import *\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.initializers import glorot_uniform\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint, LearningRateScheduler\n",
        "import tensorflow.keras.backend as K"
      ],
      "metadata": {
        "id": "6H1B0JDL-lnG"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import glob\n",
        "from sklearn.preprocessing import StandardScaler, normalize\n",
        "from IPython.display import display"
      ],
      "metadata": {
        "id": "0Lm01W2e-lwF"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ACrdbwJ3YyR",
        "outputId": "d6d48c83-ae74-4317-d8fb-1051f6c33b13"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "\n",
        "#import data dari drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget --no-check-certificate \\\n",
        "https://github.com/fadillarizalul/thesis-alzheimer/blob/main/dataset/3-classes-alzheimer-20-patients.zip?raw=true \\\n",
        "  -O 3-classes-alzheimer.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VMINoiIO8q9r",
        "outputId": "02e85a8d-5a41-4b92-8b1a-cf5fb5a01a7c"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-09-17 15:49:11--  https://github.com/fadillarizalul/thesis-alzheimer/blob/main/dataset/3-classes-alzheimer-20-patients.zip?raw=true\n",
            "Resolving github.com (github.com)... 192.30.255.112\n",
            "Connecting to github.com (github.com)|192.30.255.112|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github.com/fadillarizalul/thesis-alzheimer/raw/main/dataset/3-classes-alzheimer-20-patients.zip [following]\n",
            "--2022-09-17 15:49:11--  https://github.com/fadillarizalul/thesis-alzheimer/raw/main/dataset/3-classes-alzheimer-20-patients.zip\n",
            "Reusing existing connection to github.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/fadillarizalul/thesis-alzheimer/main/dataset/3-classes-alzheimer-20-patients.zip [following]\n",
            "--2022-09-17 15:49:12--  https://raw.githubusercontent.com/fadillarizalul/thesis-alzheimer/main/dataset/3-classes-alzheimer-20-patients.zip\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 9859577 (9.4M) [application/zip]\n",
            "Saving to: ‘3-classes-alzheimer.zip’\n",
            "\n",
            "3-classes-alzheimer 100%[===================>]   9.40M  --.-KB/s    in 0.08s   \n",
            "\n",
            "2022-09-17 15:49:12 (123 MB/s) - ‘3-classes-alzheimer.zip’ saved [9859577/9859577]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/3-classes-alzheimer.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yl7-H0rf9NFS",
        "outputId": "590bf007-f1cf-4889-afd8-ff61e287ba96"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/3-classes-alzheimer.zip\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        " #lokasi direktori dasar\n",
        "dir_base ='/content/3-classes-alzheimer-20-patients'\n",
        "os.listdir(dir_base)\n",
        "print(os.listdir(dir_base))\n",
        "\n",
        "#hitung jumlah file per direktori\n",
        "data_cn = len(os.listdir('/content/3-classes-alzheimer-20-patients/CN'))\n",
        "data_mci = len(os.listdir('/content/3-classes-alzheimer-20-patients/MCI')) \n",
        "data_ad = len(os.listdir('/content/3-classes-alzheimer-20-patients/AD')) \n",
        "\n",
        "print(\"Amount of CN class images:\", data_cn)\n",
        "print(\"Amount of MCI class images:\", data_mci)\n",
        "print(\"Amount of AD class images:\", data_ad)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lynqr5NO9P2V",
        "outputId": "a68dde05-8015-4b24-adf1-0a9ab227ffde"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['MCI', 'AD', 'CN']\n",
            "Amount of CN class images: 101\n",
            "Amount of MCI class images: 100\n",
            "Amount of AD class images: 108\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 32\n",
        "IMG_SHAPE = 256"
      ],
      "metadata": {
        "id": "2ud0YsRg9mqM"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Split into train, test, and validation sets"
      ],
      "metadata": {
        "id": "luG777wx9rQp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# from sklearn.model_selection import train_test_split\n",
        "# from keras_preprocessing.image import ImageDataGenerator\n",
        "\n",
        "# # First split into train and test sets at 80:20 ratio\n",
        "# # Second split into train validation at 75:25 ratio\n",
        "# # - results in 60:20:20 ratio (train, val, test)\n",
        "# train, test = train_test_split(dir_base, test_size=0.2, random_state=1)\n",
        "# train, val = train_test_split(train, test_size=0.25, random_state=1)"
      ],
      "metadata": {
        "id": "3bFnjlrS9TgA"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Generation and Augmentation"
      ],
      "metadata": {
        "id": "Kvo8SqDf-N1b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
      ],
      "metadata": {
        "id": "T6LPDcEMKJ-X"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
        "                                  # rotation_range=30,\n",
        "                                  # zoom_range=0.2,\n",
        "                                  # horizontal_flip=True,\n",
        "                                  # vertical_flip=True,\n",
        "                                   validation_split = 0.2\n",
        "                                   )\n",
        "\n",
        "valid_datagen = ImageDataGenerator(rescale = 1./255\n",
        "#                                    validation_split = 0.2\n",
        "                                  )\n",
        "\n",
        "# test_datagen  = ImageDataGenerator(rescale = 1./255)"
      ],
      "metadata": {
        "id": "2UezJlZOAr1O"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_generator = train_datagen.flow_from_directory(\n",
        "    directory = dir_base,\n",
        "    target_size = (IMG_SHAPE,IMG_SHAPE),\n",
        "    batch_size = BATCH_SIZE,\n",
        "    class_mode = 'categorical',\n",
        "    subset='training'\n",
        ")\n",
        " \n",
        "val_generator = train_datagen.flow_from_directory(\n",
        "    directory = dir_base,\n",
        "    target_size = (IMG_SHAPE,IMG_SHAPE),\n",
        "    batch_size = BATCH_SIZE,\n",
        "    class_mode = 'categorical',\n",
        "    subset='validation'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SBYdf_aiAwID",
        "outputId": "8e59625b-5e9d-4640-f2f7-822c8393fb8a"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 248 images belonging to 3 classes.\n",
            "Found 61 images belonging to 3 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # TRAIN\n",
        "# image_gen_train = ImageDataGenerator(rescale=1./255., \n",
        "#                                      horizontal_flip=True, \n",
        "#                                      rotation_range=20, \n",
        "#                                      zoom_range=0.2)\n",
        "\n",
        "# train_generator = image_gen_train.flow_from_directory(\n",
        "#     train,\n",
        "#     directory = dir_base,\n",
        "#     target_size = (IMG_SHAPE,IMG_SHAPE),\n",
        "#     batch_size = BATCH_SIZE,\n",
        "#     class_mode = 'categorical'\n",
        "# )"
      ],
      "metadata": {
        "id": "c_i30C1W9ui_"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # VALID\n",
        "# image_gen_val = ImageDataGenerator(rescale=1./255.)\n",
        "\n",
        "# val_generator = image_gen_val.flow_from_directory(\n",
        "#     val,\n",
        "#     directory = dir_base,\n",
        "#     target_size = (IMG_SHAPE,IMG_SHAPE),\n",
        "#     batch_size = BATCH_SIZE,\n",
        "#     class_mode = 'categorical'\n",
        "# )"
      ],
      "metadata": {
        "id": "7Pm2Z80i-slL"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # TEST\n",
        "# image_gen_test = ImageDataGenerator(rescale=1./255.)\n",
        "\n",
        "# test_generator = image_gen_val.flow_from_directory(\n",
        "#     test,\n",
        "#     directory = dir_base,\n",
        "#     target_size = (IMG_SHAPE,IMG_SHAPE),\n",
        "#     batch_size = BATCH_SIZE,\n",
        "#     class_mode = 'categorical'\n",
        "# )"
      ],
      "metadata": {
        "id": "4gmdIJKg_54q"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create CNN model"
      ],
      "metadata": {
        "id": "qEIQMrzcAWkn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.applications import MobileNetV2\n",
        "# get base models\n",
        "base_model = MobileNetV2(\n",
        "    input_shape=(IMG_SHAPE,IMG_SHAPE,3),\n",
        "    include_top=False,\n",
        "    weights='imagenet',\n",
        "    classes=3,\n",
        ")\n",
        "\n",
        "from tensorflow.keras import layers,Sequential\n",
        "from tensorflow.keras.models import Model\n",
        "\n",
        "num_class = 3\n",
        "\n",
        "#Adding custom layers\n",
        "x = base_model.output\n",
        "x = layers.GlobalAveragePooling2D()(x)\n",
        "x = layers.Dropout(0.2)(x)\n",
        "x = layers.Dense(1024, activation=\"relu\")(x)\n",
        "predictions = layers.Dense(num_class, activation=\"softmax\")(x)\n",
        "model = Model(inputs=base_model.input, outputs=predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aHjMKzMP_n53",
        "outputId": "37de087c-c9ec-4db5-ed15-b7c59d6dbadd"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n",
            "9412608/9406464 [==============================] - 0s 0us/step\n",
            "9420800/9406464 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Input\n",
        "from tensorflow.keras.applications import ResNet50\n",
        "from tensorflow.keras.applications import ResNet152V2\n",
        "\n",
        "classes = 3\n",
        "\n",
        "model = tf.keras.models.Sequential([\n",
        "    MobileNetV2(include_top=False, weights='imagenet', classes=classes, input_tensor=Input(shape=(150, 150, 3))),\n",
        "    tf.keras.layers.GlobalAveragePooling2D(),\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    tf.keras.layers.Dense(1024, activation='relu'),\n",
        "    tf.keras.layers.Dense(512, activation='relu'),\n",
        "    tf.keras.layers.Dense(3, activation='softmax')\n",
        "])\n",
        "model.layers[0].trainable = False\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kl8mnzmeAbSI",
        "outputId": "7585bb99-7f99-494b-f325-7c5cd32bf8f5"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " mobilenetv2_1.00_224 (Funct  (None, 5, 5, 1280)       2257984   \n",
            " ional)                                                          \n",
            "                                                                 \n",
            " global_average_pooling2d_3   (None, 1280)             0         \n",
            " (GlobalAveragePooling2D)                                        \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 1280)              0         \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 1024)              1311744   \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 512)               524800    \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 3)                 1539      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,096,067\n",
            "Trainable params: 1,838,083\n",
            "Non-trainable params: 2,257,984\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Compile the Model"
      ],
      "metadata": {
        "id": "DH80h3KVBDVz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.optimizers import RMSprop\n",
        "# compile model dengan 'adam' optimizer loss function 'categorical_crossentropy' \n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer=tf.optimizers.Adam(),\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "-_P2GzYpA-Iv"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "early stopping"
      ],
      "metadata": {
        "id": "kO04UOZrBJER"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#callback\n",
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('accuracy')>0.95 and logs.get('val_accuracy')>0.95):\n",
        "      print(\"\\n akurasi telah mencapai >95%!\")\n",
        "      self.model.stop_training = True\n",
        "callbacks = myCallback()"
      ],
      "metadata": {
        "id": "UyyTFDsZBFtH"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# latih model dengan model.fit \n",
        "history = model.fit(\n",
        "      train_generator,\n",
        "      #steps_per_epoch=12,  # berapa batch yang akan dieksekusi pada setiap epoch\n",
        "      epochs= 30, # tambahkan eposchs jika akurasi model belum optimal\n",
        "      validation_data=val_generator, # menampilkan akurasi pengujian data validasi\n",
        "      #validation_steps=5,  # berapa batch yang akan dieksekusi pada setiap epoch\n",
        "      verbose=2,\n",
        "      callbacks=[callbacks]\n",
        "      )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eu48IHgeBKqa",
        "outputId": "04f5be15-c36d-4d1b-d9f9-9bfe275956f3"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "8/8 - 11s - loss: 1.0841 - accuracy: 0.5323 - val_loss: 1.2286 - val_accuracy: 0.3607 - 11s/epoch - 1s/step\n",
            "Epoch 2/30\n",
            "8/8 - 11s - loss: 1.0137 - accuracy: 0.4516 - val_loss: 1.1752 - val_accuracy: 0.3279 - 11s/epoch - 1s/step\n",
            "Epoch 3/30\n",
            "8/8 - 11s - loss: 0.8919 - accuracy: 0.5323 - val_loss: 1.5200 - val_accuracy: 0.3279 - 11s/epoch - 1s/step\n",
            "Epoch 4/30\n",
            "8/8 - 11s - loss: 0.8927 - accuracy: 0.5363 - val_loss: 1.3002 - val_accuracy: 0.2951 - 11s/epoch - 1s/step\n",
            "Epoch 5/30\n",
            "8/8 - 11s - loss: 0.8013 - accuracy: 0.6613 - val_loss: 1.3320 - val_accuracy: 0.3115 - 11s/epoch - 1s/step\n",
            "Epoch 6/30\n",
            "8/8 - 11s - loss: 0.7093 - accuracy: 0.6815 - val_loss: 1.4910 - val_accuracy: 0.2623 - 11s/epoch - 1s/step\n",
            "Epoch 7/30\n",
            "8/8 - 10s - loss: 0.6104 - accuracy: 0.7419 - val_loss: 1.7244 - val_accuracy: 0.2623 - 10s/epoch - 1s/step\n",
            "Epoch 8/30\n",
            "8/8 - 11s - loss: 0.6827 - accuracy: 0.6935 - val_loss: 2.0867 - val_accuracy: 0.3115 - 11s/epoch - 1s/step\n",
            "Epoch 9/30\n",
            "8/8 - 11s - loss: 0.6269 - accuracy: 0.7218 - val_loss: 2.1195 - val_accuracy: 0.3115 - 11s/epoch - 1s/step\n",
            "Epoch 10/30\n",
            "8/8 - 11s - loss: 0.5214 - accuracy: 0.7621 - val_loss: 1.7468 - val_accuracy: 0.2459 - 11s/epoch - 1s/step\n",
            "Epoch 11/30\n",
            "8/8 - 11s - loss: 0.5338 - accuracy: 0.7661 - val_loss: 1.8739 - val_accuracy: 0.2623 - 11s/epoch - 1s/step\n",
            "Epoch 12/30\n",
            "8/8 - 11s - loss: 0.4892 - accuracy: 0.7863 - val_loss: 1.9042 - val_accuracy: 0.2459 - 11s/epoch - 1s/step\n",
            "Epoch 13/30\n",
            "8/8 - 11s - loss: 0.4209 - accuracy: 0.7984 - val_loss: 2.2265 - val_accuracy: 0.3607 - 11s/epoch - 1s/step\n",
            "Epoch 14/30\n",
            "8/8 - 11s - loss: 0.4421 - accuracy: 0.8226 - val_loss: 2.2908 - val_accuracy: 0.3115 - 11s/epoch - 1s/step\n",
            "Epoch 15/30\n",
            "8/8 - 11s - loss: 0.3821 - accuracy: 0.8669 - val_loss: 2.4519 - val_accuracy: 0.2787 - 11s/epoch - 1s/step\n",
            "Epoch 16/30\n",
            "8/8 - 10s - loss: 0.4162 - accuracy: 0.8226 - val_loss: 2.3655 - val_accuracy: 0.3115 - 10s/epoch - 1s/step\n",
            "Epoch 17/30\n",
            "8/8 - 11s - loss: 0.4337 - accuracy: 0.8145 - val_loss: 2.3150 - val_accuracy: 0.2951 - 11s/epoch - 1s/step\n",
            "Epoch 18/30\n",
            "8/8 - 11s - loss: 0.3726 - accuracy: 0.8306 - val_loss: 2.6468 - val_accuracy: 0.3443 - 11s/epoch - 1s/step\n",
            "Epoch 19/30\n",
            "8/8 - 11s - loss: 0.3592 - accuracy: 0.8589 - val_loss: 2.2039 - val_accuracy: 0.3770 - 11s/epoch - 1s/step\n",
            "Epoch 20/30\n",
            "8/8 - 11s - loss: 0.3083 - accuracy: 0.8669 - val_loss: 2.1541 - val_accuracy: 0.3115 - 11s/epoch - 1s/step\n",
            "Epoch 21/30\n",
            "8/8 - 11s - loss: 0.3450 - accuracy: 0.8750 - val_loss: 2.2903 - val_accuracy: 0.3443 - 11s/epoch - 1s/step\n",
            "Epoch 22/30\n",
            "8/8 - 11s - loss: 0.3654 - accuracy: 0.8589 - val_loss: 2.4008 - val_accuracy: 0.2951 - 11s/epoch - 1s/step\n",
            "Epoch 23/30\n",
            "8/8 - 10s - loss: 0.2638 - accuracy: 0.9032 - val_loss: 2.5542 - val_accuracy: 0.2787 - 10s/epoch - 1s/step\n",
            "Epoch 24/30\n",
            "8/8 - 11s - loss: 0.2946 - accuracy: 0.8952 - val_loss: 2.5639 - val_accuracy: 0.2787 - 11s/epoch - 1s/step\n",
            "Epoch 25/30\n",
            "8/8 - 11s - loss: 0.3060 - accuracy: 0.8710 - val_loss: 2.6739 - val_accuracy: 0.3279 - 11s/epoch - 1s/step\n",
            "Epoch 26/30\n",
            "8/8 - 11s - loss: 0.3384 - accuracy: 0.8669 - val_loss: 2.7236 - val_accuracy: 0.3770 - 11s/epoch - 1s/step\n",
            "Epoch 27/30\n",
            "8/8 - 11s - loss: 0.4528 - accuracy: 0.8185 - val_loss: 2.5641 - val_accuracy: 0.2623 - 11s/epoch - 1s/step\n",
            "Epoch 28/30\n",
            "8/8 - 11s - loss: 0.3511 - accuracy: 0.8427 - val_loss: 2.4103 - val_accuracy: 0.3279 - 11s/epoch - 1s/step\n",
            "Epoch 29/30\n",
            "8/8 - 11s - loss: 0.3546 - accuracy: 0.8468 - val_loss: 2.2421 - val_accuracy: 0.3443 - 11s/epoch - 1s/step\n",
            "Epoch 30/30\n",
            "8/8 - 11s - loss: 0.3799 - accuracy: 0.8427 - val_loss: 2.2331 - val_accuracy: 0.2623 - 11s/epoch - 1s/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Summarize history for loss\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left', bbox_to_anchor=(1,1))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "4AdeNuZKFaUp",
        "outputId": "b92eb9a0-ae49-456e-a193-57e2b0725587"
      },
      "execution_count": 26,
      "outputs": [
        {
          "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": [
        "# Summarize history for loss\n",
        "\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left', bbox_to_anchor=(1,1))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "ygQgsK_yBdRW",
        "outputId": "efbd3397-323b-48e6-8991-58431db6dbeb"
      },
      "execution_count": 27,
      "outputs": [
        {
          "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": [],
      "metadata": {
        "id": "g-vCopHEFij_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}