CNN_GarbageClassification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "V28",
      "authorship_tag": "ABX9TyOuYApg1DR4uMfxMfJeFg2i"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**Garbage Classification**\n"
      ],
      "metadata": {
        "id": "tzGa25HarWL_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1) Business Problem\n",
        "\n",
        "Classification of solid waste\n",
        "(Automating solid waste classification and transferring human labor to machines)"
      ],
      "metadata": {
        "id": "wbnVNT-sqy4-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2) Data Understanding\n",
        "\n",
        "The dataset we will use within the scope of this project is the dataset named TrashNet. The data set consists of six different classes. There are a total of 2527 images in the dataset, including glass, paper, cardboard, plastic, metal and trash.\n",
        "\n",
        "Distribution of images:\n",
        "- 501 glass\n",
        "- 594 paper\n",
        "- 403 cardboard\n",
        "- 482 plastic\n",
        "- 410 metal\n",
        "- 137 trash"
      ],
      "metadata": {
        "id": "_AJp1dXNrzLA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "2.1) İmport libraries"
      ],
      "metadata": {
        "id": "AHF4rUzTuGKn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# for reading and processing data\n",
        "!pip install imutils\n",
        "\n",
        "import cv2\n",
        "import urllib\n",
        "import itertools\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import random, os, glob\n",
        "from imutils import paths\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.utils import shuffle\n",
        "from urllib.request import urlopen\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# for model evaluation\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "\n",
        "# for model\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "from tensorflow.keras.layers import Conv2D, Flatten, MaxPooling2D, Dense, Dropout, SpatialDropout2D\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img, array_to_img\n"
      ],
      "metadata": {
        "id": "UPU4IinXrjDt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b444133-4f89-48d9-a065-a7440fe0f06a"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting imutils\n",
            "  Downloading imutils-0.5.4.tar.gz (17 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: imutils\n",
            "  Building wheel for imutils (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for imutils: filename=imutils-0.5.4-py3-none-any.whl size=25837 sha256=87b6de6db8b0e6b161b5398608adc4f62f42dfd92961a7181e683c210c5c3222\n",
            "  Stored in directory: /root/.cache/pip/wheels/85/cf/3a/e265e975a1e7c7e54eb3692d6aa4e2e7d6a3945d29da46f2d7\n",
            "Successfully built imutils\n",
            "Installing collected packages: imutils\n",
            "Successfully installed imutils-0.5.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2.2) Read dataset"
      ],
      "metadata": {
        "id": "4LpLkjTQxG9f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NZOMA2PbwRug",
        "outputId": "7828993d-c182-424f-f609-5ab31e839154"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target_size = (224, 224)\n",
        "waste_labels = {'cardboard': 0, 'glass':1, 'metal': 2, 'paper': 3, 'plastic': 4, 'trash':5}\n"
      ],
      "metadata": {
        "id": "vdQGVhYK1AfL"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def load_datasets(path, waste_labels, target_size):\n",
        "    x = []\n",
        "    labels = []\n",
        "\n",
        "    image_paths = sorted(glob.glob(os.path.join(path, \"*/*.jpg\")))\n",
        "\n",
        "    for image_path in image_paths:\n",
        "        img = cv2.imread(image_path)\n",
        "        img = cv2.resize(img, target_size)\n",
        "        x.append(img)\n",
        "        label = os.path.basename(os.path.dirname(image_path))\n",
        "        labels.append(waste_labels[label])\n",
        "\n",
        "    x, labels = shuffle(x, labels, random_state=42)\n",
        "\n",
        "    print(f\"X size: {np.array(x).shape}\")\n",
        "    print(f\"Number of label classes: {len(np.unique(labels))}, Number of observations: {len(labels)}\")\n",
        "\n",
        "    return np.array(x), np.array(labels)\n",
        "\n",
        "# Define the directory path\n",
        "dir_path = '/content/drive/MyDrive/Garbage classification'\n",
        "\n",
        "# Call the function to load datasets\n",
        "x, labels = load_datasets(dir_path, waste_labels, target_size)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ExueC53HyR8u",
        "outputId": "5fad3a7c-ce42-4522-c203-e6b5dd9d4aec"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X size: (2527, 224, 224, 3)\n",
            "Number of label classes: 6, Number of observations: 2527\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_shape = (np.array(x[0]).shape[1], np.array(x[0]).shape[1],3)\n",
        "print(input_shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0vgvgj710oH",
        "outputId": "bb46e61f-4956-4fd1-bc8e-2995e3ad3abd"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(224, 224, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2.3) Veri Setinden Örnekler Gösterilmesi"
      ],
      "metadata": {
        "id": "j6zu_GFnUHS6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def visualize_img(image_batch, label_batch):\n",
        "  plt.figure(figsize=(10, 10))\n",
        "  for n in range(10):\n",
        "    ax = plt.subplot(5,5,n+1)\n",
        "    plt.imshow(image_batch[n])\n",
        "    plt.title(np.array(list(waste_labels.keys()))[to_categorical(labels, num_classes=6)[n]==1][0].title())\n",
        "    plt.axis('off')"
      ],
      "metadata": {
        "id": "XF61axN5TEkt"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "visualize_img(x, labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "3KgioVh7aqdO",
        "outputId": "8662669c-de29-4fdf-e75a-508ffcbe227f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3) Data Preparation"
      ],
      "metadata": {
        "id": "0aeoata_a_Fn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train = ImageDataGenerator(horizontal_flip=True,\n",
        "                           vertical_flip=True,\n",
        "                           validation_split=0.1,\n",
        "                           rescale=1./255,\n",
        "                           shear_range=0.1,\n",
        "                           zoom_range=0.1,\n",
        "                           width_shift_range=0.1,\n",
        "                           height_shift_range=0.1)\n",
        "\n",
        "test = ImageDataGenerator(rescale=1/255, validation_split=0.1)"
      ],
      "metadata": {
        "id": "vAZr0vDua2mu"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_generator = train.flow_from_directory(directory = dir_path,\n",
        "                                            target_size = (target_size),\n",
        "                                            class_mode = 'categorical',\n",
        "                                            subset = 'training')\n",
        "\n",
        "test_generator = train.flow_from_directory(directory = dir_path,\n",
        "                                           target_size = (target_size),\n",
        "                                           class_mode = 'categorical',\n",
        "                                           subset = 'validation')"
      ],
      "metadata": {
        "id": "KOTYzS9McFeE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6e4a5a5-c747-4baa-a63a-dfaa2d53f546"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2276 images belonging to 6 classes.\n",
            "Found 251 images belonging to 6 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4) CNN Model\n",
        "\n",
        "- Sequential\n",
        "- Convolution Layer, Conv2D\n",
        "- Pooling Layer\n",
        "- Activation Layer\n",
        "- Flattening Layer\n",
        "- Dense Layer\n",
        "- Dropout Layer\n",
        "\n"
      ],
      "metadata": {
        "id": "Za8Ah008VW2e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(filters=32, kernel_size=(3,3), padding='same', input_shape=(input_shape), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=2, strides=(2,2)))\n",
        "\n",
        "model.add(Conv2D(filters=64, kernel_size=(3,3), padding='same', input_shape=(input_shape), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=2, strides=(2,2)))\n",
        "\n",
        "model.add(Conv2D(filters=32, kernel_size=(3,3), padding='same', input_shape=(input_shape), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=2, strides=(2,2)))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(units=64, activation='relu'))\n",
        "model.add(Dropout(rate=0.2))\n",
        "\n",
        "model.add(Dense(units=6, activation='softmax'))\n"
      ],
      "metadata": {
        "id": "bbH-uNQNU9b4"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "4.2) Model Summary"
      ],
      "metadata": {
        "id": "3yGp8t7azhug"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h7peH7-szY8f",
        "outputId": "085546fd-ea24-4746-a28f-aea0ff3e358d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 224, 224, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2  (None, 112, 112, 32)      0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 112, 112, 64)      18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPoolin  (None, 56, 56, 64)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 56, 56, 32)        18464     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPoolin  (None, 28, 28, 32)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 25088)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                1605696   \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 6)                 390       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1643942 (6.27 MB)\n",
            "Trainable params: 1643942 (6.27 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4.3) Optimizasyon ve Değerlendirme Metriklerinin Ayarlanması\n"
      ],
      "metadata": {
        "id": "XWbRBFzeKMak"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss='categorical_crossentropy',\n",
        "              metrics=(tf.keras.metrics.Precision(), tf.keras.metrics.Recall(),'acc'))"
      ],
      "metadata": {
        "id": "eRYW9Kkrzpd1"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "callbacks = (EarlyStopping(monitor='val_loss', patience=50, verbose=1, mode=\"min\"),\n",
        "             ModelCheckpoint(filepath='mymodel.h5', monitor='val_loss', mode=\"min\", save_best_only=True, save_weights_only=False, verbose=1))\n"
      ],
      "metadata": {
        "id": "c3lpa5xUKxda"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "4.4) Training the Model"
      ],
      "metadata": {
        "id": "IqdmKJrVLxLh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit_generator(generator=train_generator,\n",
        "                              epochs=100,\n",
        "                              validation_data=test_generator,\n",
        "                              callbacks=callbacks,\n",
        "                              workers=4,\n",
        "                              steps_per_epoch=2276//32,\n",
        "                              validation_steps=251//32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LcQZLs_mLpuF",
        "outputId": "3d404fc3-022c-40d0-f448-36524cc9f218"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 1.6035 - precision: 0.7363 - recall: 0.0597 - acc: 0.3191\n",
            "Epoch 1: val_loss improved from inf to 1.54724, saving model to mymodel.h5\n",
            "71/71 [==============================] - 30s 397ms/step - loss: 1.6035 - precision: 0.7363 - recall: 0.0597 - acc: 0.3191 - val_loss: 1.5472 - val_precision: 0.8000 - val_recall: 0.0536 - val_acc: 0.3973\n",
            "Epoch 2/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 1.4570 - precision: 0.7146 - recall: 0.1373 - acc: 0.3993\n",
            "Epoch 2: val_loss improved from 1.54724 to 1.41959, saving model to mymodel.h5\n",
            "71/71 [==============================] - 28s 387ms/step - loss: 1.4570 - precision: 0.7146 - recall: 0.1373 - acc: 0.3993 - val_loss: 1.4196 - val_precision: 0.6667 - val_recall: 0.1339 - val_acc: 0.4375\n",
            "Epoch 3/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 1.3821 - precision: 0.7135 - recall: 0.1720 - acc: 0.4256\n",
            "Epoch 3: val_loss improved from 1.41959 to 1.41536, saving model to mymodel.h5\n",
            "71/71 [==============================] - 28s 389ms/step - loss: 1.3821 - precision: 0.7135 - recall: 0.1720 - acc: 0.4256 - val_loss: 1.4154 - val_precision: 0.7750 - val_recall: 0.1384 - val_acc: 0.4152\n",
            "Epoch 4/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 1.3755 - precision: 0.7221 - recall: 0.2050 - acc: 0.4394\n",
            "Epoch 4: val_loss improved from 1.41536 to 1.38471, saving model to mymodel.h5\n",
            "71/71 [==============================] - 28s 385ms/step - loss: 1.3755 - precision: 0.7221 - recall: 0.2050 - acc: 0.4394 - val_loss: 1.3847 - val_precision: 0.7949 - val_recall: 0.1384 - val_acc: 0.4464\n",
            "Epoch 5/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 1.3201 - precision: 0.7208 - recall: 0.2197 - acc: 0.4724\n",
            "Epoch 5: val_loss improved from 1.38471 to 1.28961, saving model to mymodel.h5\n",
            "71/71 [==============================] - 32s 458ms/step - loss: 1.3201 - precision: 0.7208 - recall: 0.2197 - acc: 0.4724 - val_loss: 1.2896 - val_precision: 0.7727 - val_recall: 0.2277 - val_acc: 0.4688\n",
            "Epoch 6/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 1.2591 - precision: 0.7434 - recall: 0.2750 - acc: 0.5147\n",
            "Epoch 6: val_loss improved from 1.28961 to 1.26077, saving model to mymodel.h5\n",
            "71/71 [==============================] - 28s 384ms/step - loss: 1.2591 - precision: 0.7434 - recall: 0.2750 - acc: 0.5147 - val_loss: 1.2608 - val_precision: 0.7973 - val_recall: 0.2634 - val_acc: 0.4955\n",
            "Epoch 7/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 1.2330 - precision: 0.7132 - recall: 0.3026 - acc: 0.5160\n",
            "Epoch 7: val_loss did not improve from 1.26077\n",
            "71/71 [==============================] - 32s 448ms/step - loss: 1.2330 - precision: 0.7132 - recall: 0.3026 - acc: 0.5160 - val_loss: 1.3938 - val_precision: 0.6349 - val_recall: 0.1786 - val_acc: 0.4688\n",
            "Epoch 8/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 1.2165 - precision: 0.7313 - recall: 0.3142 - acc: 0.5245\n",
            "Epoch 8: val_loss did not improve from 1.26077\n",
            "71/71 [==============================] - 27s 381ms/step - loss: 1.2165 - precision: 0.7313 - recall: 0.3142 - acc: 0.5245 - val_loss: 1.2820 - val_precision: 0.7534 - val_recall: 0.2455 - val_acc: 0.4643\n",
            "Epoch 9/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 1.1782 - precision: 0.7519 - recall: 0.3538 - acc: 0.5490\n",
            "Epoch 9: val_loss improved from 1.26077 to 1.23343, saving model to mymodel.h5\n",
            "71/71 [==============================] - 28s 384ms/step - loss: 1.1782 - precision: 0.7519 - recall: 0.3538 - acc: 0.5490 - val_loss: 1.2334 - val_precision: 0.7624 - val_recall: 0.3438 - val_acc: 0.5402\n",
            "Epoch 10/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 1.1686 - precision: 0.7412 - recall: 0.3561 - acc: 0.5535\n",
            "Epoch 10: val_loss did not improve from 1.23343\n",
            "71/71 [==============================] - 27s 380ms/step - loss: 1.1686 - precision: 0.7412 - recall: 0.3561 - acc: 0.5535 - val_loss: 1.2904 - val_precision: 0.7879 - val_recall: 0.2321 - val_acc: 0.5134\n",
            "Epoch 11/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 1.1429 - precision: 0.7674 - recall: 0.3645 - acc: 0.5606\n",
            "Epoch 11: val_loss improved from 1.23343 to 1.19141, saving model to mymodel.h5\n",
            "71/71 [==============================] - 27s 379ms/step - loss: 1.1429 - precision: 0.7674 - recall: 0.3645 - acc: 0.5606 - val_loss: 1.1914 - val_precision: 0.7619 - val_recall: 0.3571 - val_acc: 0.5848\n",
            "Epoch 12/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 1.1671 - precision: 0.7268 - recall: 0.3414 - acc: 0.5419\n",
            "Epoch 12: val_loss improved from 1.19141 to 1.13529, saving model to mymodel.h5\n",
            "71/71 [==============================] - 28s 383ms/step - loss: 1.1671 - precision: 0.7268 - recall: 0.3414 - acc: 0.5419 - val_loss: 1.1353 - val_precision: 0.7732 - val_recall: 0.3348 - val_acc: 0.5625\n",
            "Epoch 13/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 1.1176 - precision: 0.7786 - recall: 0.3792 - acc: 0.5731\n",
            "Epoch 13: val_loss improved from 1.13529 to 1.12463, saving model to mymodel.h5\n",
            "71/71 [==============================] - 28s 385ms/step - loss: 1.1176 - precision: 0.7786 - recall: 0.3792 - acc: 0.5731 - val_loss: 1.1246 - val_precision: 0.7300 - val_recall: 0.3259 - val_acc: 0.5402\n",
            "Epoch 14/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 1.0887 - precision: 0.7670 - recall: 0.4020 - acc: 0.5704\n",
            "Epoch 14: val_loss improved from 1.12463 to 1.10924, saving model to mymodel.h5\n",
            "71/71 [==============================] - 28s 383ms/step - loss: 1.0887 - precision: 0.7670 - recall: 0.4020 - acc: 0.5704 - val_loss: 1.1092 - val_precision: 0.8370 - val_recall: 0.3438 - val_acc: 0.5446\n",
            "Epoch 15/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 1.0595 - precision: 0.7960 - recall: 0.4225 - acc: 0.6020\n",
            "Epoch 15: val_loss improved from 1.10924 to 1.08673, saving model to mymodel.h5\n",
            "71/71 [==============================] - 27s 381ms/step - loss: 1.0595 - precision: 0.7960 - recall: 0.4225 - acc: 0.6020 - val_loss: 1.0867 - val_precision: 0.7797 - val_recall: 0.4107 - val_acc: 0.5446\n",
            "Epoch 16/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 1.0296 - precision: 0.7836 - recall: 0.4340 - acc: 0.6181\n",
            "Epoch 16: val_loss did not improve from 1.08673\n",
            "71/71 [==============================] - 28s 383ms/step - loss: 1.0296 - precision: 0.7836 - recall: 0.4340 - acc: 0.6181 - val_loss: 1.1041 - val_precision: 0.7227 - val_recall: 0.3839 - val_acc: 0.5580\n",
            "Epoch 17/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 1.0359 - precision: 0.7689 - recall: 0.4345 - acc: 0.6078\n",
            "Epoch 17: val_loss did not improve from 1.08673\n",
            "71/71 [==============================] - 28s 385ms/step - loss: 1.0359 - precision: 0.7689 - recall: 0.4345 - acc: 0.6078 - val_loss: 1.0982 - val_precision: 0.7946 - val_recall: 0.3973 - val_acc: 0.5804\n",
            "Epoch 18/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 1.0289 - precision: 0.7755 - recall: 0.4403 - acc: 0.6150\n",
            "Epoch 18: val_loss improved from 1.08673 to 1.05979, saving model to mymodel.h5\n",
            "71/71 [==============================] - 28s 387ms/step - loss: 1.0289 - precision: 0.7755 - recall: 0.4403 - acc: 0.6150 - val_loss: 1.0598 - val_precision: 0.7377 - val_recall: 0.4018 - val_acc: 0.5625\n",
            "Epoch 19/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 1.0079 - precision: 0.7815 - recall: 0.4510 - acc: 0.6163\n",
            "Epoch 19: val_loss improved from 1.05979 to 1.03167, saving model to mymodel.h5\n",
            "71/71 [==============================] - 28s 381ms/step - loss: 1.0079 - precision: 0.7815 - recall: 0.4510 - acc: 0.6163 - val_loss: 1.0317 - val_precision: 0.8286 - val_recall: 0.3884 - val_acc: 0.6205\n",
            "Epoch 20/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.9664 - precision: 0.7945 - recall: 0.4857 - acc: 0.6444\n",
            "Epoch 20: val_loss did not improve from 1.03167\n",
            "71/71 [==============================] - 28s 384ms/step - loss: 0.9664 - precision: 0.7945 - recall: 0.4857 - acc: 0.6444 - val_loss: 1.0915 - val_precision: 0.7279 - val_recall: 0.4420 - val_acc: 0.5848\n",
            "Epoch 21/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.9537 - precision: 0.7906 - recall: 0.4947 - acc: 0.6480\n",
            "Epoch 21: val_loss improved from 1.03167 to 0.99686, saving model to mymodel.h5\n",
            "71/71 [==============================] - 28s 382ms/step - loss: 0.9537 - precision: 0.7906 - recall: 0.4947 - acc: 0.6480 - val_loss: 0.9969 - val_precision: 0.8182 - val_recall: 0.4821 - val_acc: 0.6429\n",
            "Epoch 22/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.9516 - precision: 0.8134 - recall: 0.4973 - acc: 0.6462\n",
            "Epoch 22: val_loss did not improve from 0.99686\n",
            "71/71 [==============================] - 28s 386ms/step - loss: 0.9516 - precision: 0.8134 - recall: 0.4973 - acc: 0.6462 - val_loss: 1.0631 - val_precision: 0.7869 - val_recall: 0.4286 - val_acc: 0.5848\n",
            "Epoch 23/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.9420 - precision: 0.7986 - recall: 0.5018 - acc: 0.6457\n",
            "Epoch 23: val_loss improved from 0.99686 to 0.94357, saving model to mymodel.h5\n",
            "71/71 [==============================] - 28s 382ms/step - loss: 0.9420 - precision: 0.7986 - recall: 0.5018 - acc: 0.6457 - val_loss: 0.9436 - val_precision: 0.8504 - val_recall: 0.4821 - val_acc: 0.6250\n",
            "Epoch 24/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.9231 - precision: 0.7953 - recall: 0.4987 - acc: 0.6546\n",
            "Epoch 24: val_loss did not improve from 0.94357\n",
            "71/71 [==============================] - 28s 384ms/step - loss: 0.9231 - precision: 0.7953 - recall: 0.4987 - acc: 0.6546 - val_loss: 1.0176 - val_precision: 0.7574 - val_recall: 0.4598 - val_acc: 0.6339\n",
            "Epoch 25/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.9230 - precision: 0.8026 - recall: 0.5036 - acc: 0.6529\n",
            "Epoch 25: val_loss improved from 0.94357 to 0.93945, saving model to mymodel.h5\n",
            "71/71 [==============================] - 28s 385ms/step - loss: 0.9230 - precision: 0.8026 - recall: 0.5036 - acc: 0.6529 - val_loss: 0.9395 - val_precision: 0.8268 - val_recall: 0.4688 - val_acc: 0.6696\n",
            "Epoch 26/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.8816 - precision: 0.8123 - recall: 0.5401 - acc: 0.6751\n",
            "Epoch 26: val_loss did not improve from 0.93945\n",
            "71/71 [==============================] - 32s 445ms/step - loss: 0.8816 - precision: 0.8123 - recall: 0.5401 - acc: 0.6751 - val_loss: 0.9977 - val_precision: 0.8450 - val_recall: 0.4866 - val_acc: 0.6562\n",
            "Epoch 27/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.9024 - precision: 0.8007 - recall: 0.5192 - acc: 0.6693\n",
            "Epoch 27: val_loss did not improve from 0.93945\n",
            "71/71 [==============================] - 28s 385ms/step - loss: 0.9024 - precision: 0.8007 - recall: 0.5192 - acc: 0.6693 - val_loss: 0.9999 - val_precision: 0.7851 - val_recall: 0.4241 - val_acc: 0.6205\n",
            "Epoch 28/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.8581 - precision: 0.8080 - recall: 0.5250 - acc: 0.6742\n",
            "Epoch 28: val_loss improved from 0.93945 to 0.92771, saving model to mymodel.h5\n",
            "71/71 [==============================] - 28s 385ms/step - loss: 0.8581 - precision: 0.8080 - recall: 0.5250 - acc: 0.6742 - val_loss: 0.9277 - val_precision: 0.8397 - val_recall: 0.4911 - val_acc: 0.6607\n",
            "Epoch 29/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.8413 - precision: 0.8192 - recall: 0.5673 - acc: 0.6965\n",
            "Epoch 29: val_loss did not improve from 0.92771\n",
            "71/71 [==============================] - 28s 387ms/step - loss: 0.8413 - precision: 0.8192 - recall: 0.5673 - acc: 0.6965 - val_loss: 0.9784 - val_precision: 0.8042 - val_recall: 0.5134 - val_acc: 0.6339\n",
            "Epoch 30/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.8806 - precision: 0.8048 - recall: 0.5183 - acc: 0.6751\n",
            "Epoch 30: val_loss did not improve from 0.92771\n",
            "71/71 [==============================] - 28s 385ms/step - loss: 0.8806 - precision: 0.8048 - recall: 0.5183 - acc: 0.6751 - val_loss: 1.0137 - val_precision: 0.8197 - val_recall: 0.4464 - val_acc: 0.6295\n",
            "Epoch 31/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.8297 - precision: 0.8087 - recall: 0.5539 - acc: 0.6867\n",
            "Epoch 31: val_loss did not improve from 0.92771\n",
            "71/71 [==============================] - 32s 450ms/step - loss: 0.8297 - precision: 0.8087 - recall: 0.5539 - acc: 0.6867 - val_loss: 0.9703 - val_precision: 0.7673 - val_recall: 0.5446 - val_acc: 0.6607\n",
            "Epoch 32/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.8341 - precision: 0.8258 - recall: 0.5744 - acc: 0.6930\n",
            "Epoch 32: val_loss did not improve from 0.92771\n",
            "71/71 [==============================] - 28s 384ms/step - loss: 0.8341 - precision: 0.8258 - recall: 0.5744 - acc: 0.6930 - val_loss: 0.9698 - val_precision: 0.7826 - val_recall: 0.4821 - val_acc: 0.6429\n",
            "Epoch 33/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.8497 - precision: 0.8024 - recall: 0.5700 - acc: 0.6919\n",
            "Epoch 33: val_loss did not improve from 0.92771\n",
            "71/71 [==============================] - 28s 387ms/step - loss: 0.8497 - precision: 0.8024 - recall: 0.5700 - acc: 0.6919 - val_loss: 1.0814 - val_precision: 0.7445 - val_recall: 0.4554 - val_acc: 0.5938\n",
            "Epoch 34/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.7952 - precision: 0.8166 - recall: 0.5775 - acc: 0.7045\n",
            "Epoch 34: val_loss improved from 0.92771 to 0.89887, saving model to mymodel.h5\n",
            "71/71 [==============================] - 28s 386ms/step - loss: 0.7952 - precision: 0.8166 - recall: 0.5775 - acc: 0.7045 - val_loss: 0.8989 - val_precision: 0.8108 - val_recall: 0.5357 - val_acc: 0.6518\n",
            "Epoch 35/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.8415 - precision: 0.8091 - recall: 0.5762 - acc: 0.6881\n",
            "Epoch 35: val_loss did not improve from 0.89887\n",
            "71/71 [==============================] - 28s 382ms/step - loss: 0.8415 - precision: 0.8091 - recall: 0.5762 - acc: 0.6881 - val_loss: 0.9220 - val_precision: 0.8000 - val_recall: 0.5536 - val_acc: 0.6607\n",
            "Epoch 36/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.8021 - precision: 0.8188 - recall: 0.5860 - acc: 0.7045\n",
            "Epoch 36: val_loss did not improve from 0.89887\n",
            "71/71 [==============================] - 27s 379ms/step - loss: 0.8021 - precision: 0.8188 - recall: 0.5860 - acc: 0.7045 - val_loss: 1.0426 - val_precision: 0.7848 - val_recall: 0.5536 - val_acc: 0.6116\n",
            "Epoch 37/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.7991 - precision: 0.8117 - recall: 0.5860 - acc: 0.7068\n",
            "Epoch 37: val_loss did not improve from 0.89887\n",
            "71/71 [==============================] - 28s 381ms/step - loss: 0.7991 - precision: 0.8117 - recall: 0.5860 - acc: 0.7068 - val_loss: 1.2136 - val_precision: 0.6957 - val_recall: 0.5000 - val_acc: 0.5938\n",
            "Epoch 38/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.7964 - precision: 0.8117 - recall: 0.5954 - acc: 0.7099\n",
            "Epoch 38: val_loss did not improve from 0.89887\n",
            "71/71 [==============================] - 28s 382ms/step - loss: 0.7964 - precision: 0.8117 - recall: 0.5954 - acc: 0.7099 - val_loss: 1.0481 - val_precision: 0.7566 - val_recall: 0.5134 - val_acc: 0.6250\n",
            "Epoch 39/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.7591 - precision: 0.8288 - recall: 0.6105 - acc: 0.7188\n",
            "Epoch 39: val_loss did not improve from 0.89887\n",
            "71/71 [==============================] - 28s 381ms/step - loss: 0.7591 - precision: 0.8288 - recall: 0.6105 - acc: 0.7188 - val_loss: 0.9823 - val_precision: 0.7051 - val_recall: 0.4911 - val_acc: 0.6429\n",
            "Epoch 40/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.7235 - precision: 0.8202 - recall: 0.6381 - acc: 0.7447\n",
            "Epoch 40: val_loss did not improve from 0.89887\n",
            "71/71 [==============================] - 28s 383ms/step - loss: 0.7235 - precision: 0.8202 - recall: 0.6381 - acc: 0.7447 - val_loss: 1.2390 - val_precision: 0.7041 - val_recall: 0.5312 - val_acc: 0.6250\n",
            "Epoch 41/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.8090 - precision: 0.7931 - recall: 0.5963 - acc: 0.7001\n",
            "Epoch 41: val_loss did not improve from 0.89887\n",
            "71/71 [==============================] - 28s 383ms/step - loss: 0.8090 - precision: 0.7931 - recall: 0.5963 - acc: 0.7001 - val_loss: 0.9985 - val_precision: 0.7667 - val_recall: 0.5134 - val_acc: 0.6250\n",
            "Epoch 42/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.7122 - precision: 0.8351 - recall: 0.6364 - acc: 0.7478\n",
            "Epoch 42: val_loss improved from 0.89887 to 0.81440, saving model to mymodel.h5\n",
            "71/71 [==============================] - 28s 383ms/step - loss: 0.7122 - precision: 0.8351 - recall: 0.6364 - acc: 0.7478 - val_loss: 0.8144 - val_precision: 0.7895 - val_recall: 0.6027 - val_acc: 0.6920\n",
            "Epoch 43/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.7259 - precision: 0.8201 - recall: 0.6279 - acc: 0.7246\n",
            "Epoch 43: val_loss improved from 0.81440 to 0.79135, saving model to mymodel.h5\n",
            "71/71 [==============================] - 27s 380ms/step - loss: 0.7259 - precision: 0.8201 - recall: 0.6279 - acc: 0.7246 - val_loss: 0.7913 - val_precision: 0.8081 - val_recall: 0.6205 - val_acc: 0.6964\n",
            "Epoch 44/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.7783 - precision: 0.8257 - recall: 0.6078 - acc: 0.7179\n",
            "Epoch 44: val_loss did not improve from 0.79135\n",
            "71/71 [==============================] - 27s 380ms/step - loss: 0.7783 - precision: 0.8257 - recall: 0.6078 - acc: 0.7179 - val_loss: 0.8276 - val_precision: 0.8323 - val_recall: 0.6205 - val_acc: 0.7098\n",
            "Epoch 45/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.7172 - precision: 0.8248 - recall: 0.6502 - acc: 0.7429\n",
            "Epoch 45: val_loss did not improve from 0.79135\n",
            "71/71 [==============================] - 27s 379ms/step - loss: 0.7172 - precision: 0.8248 - recall: 0.6502 - acc: 0.7429 - val_loss: 0.8200 - val_precision: 0.7733 - val_recall: 0.5938 - val_acc: 0.6741\n",
            "Epoch 46/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.7125 - precision: 0.8224 - recall: 0.6355 - acc: 0.7228\n",
            "Epoch 46: val_loss did not improve from 0.79135\n",
            "71/71 [==============================] - 28s 384ms/step - loss: 0.7125 - precision: 0.8224 - recall: 0.6355 - acc: 0.7228 - val_loss: 0.9410 - val_precision: 0.7679 - val_recall: 0.5759 - val_acc: 0.6875\n",
            "Epoch 47/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.7426 - precision: 0.8255 - recall: 0.6283 - acc: 0.7344\n",
            "Epoch 47: val_loss improved from 0.79135 to 0.78668, saving model to mymodel.h5\n",
            "71/71 [==============================] - 28s 383ms/step - loss: 0.7426 - precision: 0.8255 - recall: 0.6283 - acc: 0.7344 - val_loss: 0.7867 - val_precision: 0.8059 - val_recall: 0.6116 - val_acc: 0.7188\n",
            "Epoch 48/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.6959 - precision: 0.8301 - recall: 0.6533 - acc: 0.7469\n",
            "Epoch 48: val_loss did not improve from 0.78668\n",
            "71/71 [==============================] - 27s 379ms/step - loss: 0.6959 - precision: 0.8301 - recall: 0.6533 - acc: 0.7469 - val_loss: 0.8506 - val_precision: 0.7725 - val_recall: 0.5759 - val_acc: 0.6964\n",
            "Epoch 49/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.7001 - precision: 0.8185 - recall: 0.6413 - acc: 0.7322\n",
            "Epoch 49: val_loss did not improve from 0.78668\n",
            "71/71 [==============================] - 28s 385ms/step - loss: 0.7001 - precision: 0.8185 - recall: 0.6413 - acc: 0.7322 - val_loss: 0.8979 - val_precision: 0.7824 - val_recall: 0.5938 - val_acc: 0.6696\n",
            "Epoch 50/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.6521 - precision: 0.8474 - recall: 0.6854 - acc: 0.7692\n",
            "Epoch 50: val_loss did not improve from 0.78668\n",
            "71/71 [==============================] - 27s 380ms/step - loss: 0.6521 - precision: 0.8474 - recall: 0.6854 - acc: 0.7692 - val_loss: 0.8086 - val_precision: 0.8125 - val_recall: 0.6384 - val_acc: 0.7411\n",
            "Epoch 51/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.6973 - precision: 0.8298 - recall: 0.6604 - acc: 0.7536\n",
            "Epoch 51: val_loss did not improve from 0.78668\n",
            "71/71 [==============================] - 28s 383ms/step - loss: 0.6973 - precision: 0.8298 - recall: 0.6604 - acc: 0.7536 - val_loss: 0.8891 - val_precision: 0.7917 - val_recall: 0.5938 - val_acc: 0.6830\n",
            "Epoch 52/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.7058 - precision: 0.8337 - recall: 0.6569 - acc: 0.7451\n",
            "Epoch 52: val_loss did not improve from 0.78668\n",
            "71/71 [==============================] - 27s 380ms/step - loss: 0.7058 - precision: 0.8337 - recall: 0.6569 - acc: 0.7451 - val_loss: 0.8670 - val_precision: 0.8050 - val_recall: 0.5714 - val_acc: 0.6518\n",
            "Epoch 53/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.6894 - precision: 0.8375 - recall: 0.6635 - acc: 0.7500\n",
            "Epoch 53: val_loss did not improve from 0.78668\n",
            "71/71 [==============================] - 28s 385ms/step - loss: 0.6894 - precision: 0.8375 - recall: 0.6635 - acc: 0.7500 - val_loss: 0.8083 - val_precision: 0.8314 - val_recall: 0.6384 - val_acc: 0.7188\n",
            "Epoch 54/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.6586 - precision: 0.8482 - recall: 0.6872 - acc: 0.7647\n",
            "Epoch 54: val_loss did not improve from 0.78668\n",
            "71/71 [==============================] - 28s 384ms/step - loss: 0.6586 - precision: 0.8482 - recall: 0.6872 - acc: 0.7647 - val_loss: 0.9359 - val_precision: 0.7758 - val_recall: 0.5714 - val_acc: 0.6607\n",
            "Epoch 55/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.6959 - precision: 0.8328 - recall: 0.6613 - acc: 0.7491\n",
            "Epoch 55: val_loss did not improve from 0.78668\n",
            "71/71 [==============================] - 32s 450ms/step - loss: 0.6959 - precision: 0.8328 - recall: 0.6613 - acc: 0.7491 - val_loss: 0.9504 - val_precision: 0.7484 - val_recall: 0.5179 - val_acc: 0.6295\n",
            "Epoch 56/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.6762 - precision: 0.8387 - recall: 0.6533 - acc: 0.7518\n",
            "Epoch 56: val_loss did not improve from 0.78668\n",
            "71/71 [==============================] - 28s 389ms/step - loss: 0.6762 - precision: 0.8387 - recall: 0.6533 - acc: 0.7518 - val_loss: 0.8388 - val_precision: 0.8056 - val_recall: 0.6473 - val_acc: 0.7054\n",
            "Epoch 57/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.6084 - precision: 0.8461 - recall: 0.7130 - acc: 0.7816\n",
            "Epoch 57: val_loss did not improve from 0.78668\n",
            "71/71 [==============================] - 28s 392ms/step - loss: 0.6084 - precision: 0.8461 - recall: 0.7130 - acc: 0.7816 - val_loss: 0.7983 - val_precision: 0.7772 - val_recall: 0.6384 - val_acc: 0.6830\n",
            "Epoch 58/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.6844 - precision: 0.8373 - recall: 0.6698 - acc: 0.7545\n",
            "Epoch 58: val_loss did not improve from 0.78668\n",
            "71/71 [==============================] - 28s 388ms/step - loss: 0.6844 - precision: 0.8373 - recall: 0.6698 - acc: 0.7545 - val_loss: 0.9049 - val_precision: 0.7919 - val_recall: 0.6116 - val_acc: 0.7009\n",
            "Epoch 59/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.6272 - precision: 0.8553 - recall: 0.7005 - acc: 0.7892\n",
            "Epoch 59: val_loss did not improve from 0.78668\n",
            "71/71 [==============================] - 28s 385ms/step - loss: 0.6272 - precision: 0.8553 - recall: 0.7005 - acc: 0.7892 - val_loss: 0.8985 - val_precision: 0.7923 - val_recall: 0.6473 - val_acc: 0.7143\n",
            "Epoch 60/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.6469 - precision: 0.8452 - recall: 0.6836 - acc: 0.7602\n",
            "Epoch 60: val_loss did not improve from 0.78668\n",
            "71/71 [==============================] - 28s 384ms/step - loss: 0.6469 - precision: 0.8452 - recall: 0.6836 - acc: 0.7602 - val_loss: 1.0354 - val_precision: 0.7558 - val_recall: 0.5804 - val_acc: 0.6786\n",
            "Epoch 61/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.6319 - precision: 0.8548 - recall: 0.7005 - acc: 0.7723\n",
            "Epoch 61: val_loss did not improve from 0.78668\n",
            "71/71 [==============================] - 28s 384ms/step - loss: 0.6319 - precision: 0.8548 - recall: 0.7005 - acc: 0.7723 - val_loss: 0.8184 - val_precision: 0.7708 - val_recall: 0.6607 - val_acc: 0.7009\n",
            "Epoch 62/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.6193 - precision: 0.8402 - recall: 0.6934 - acc: 0.7678\n",
            "Epoch 62: val_loss did not improve from 0.78668\n",
            "71/71 [==============================] - 28s 388ms/step - loss: 0.6193 - precision: 0.8402 - recall: 0.6934 - acc: 0.7678 - val_loss: 0.8291 - val_precision: 0.7814 - val_recall: 0.6384 - val_acc: 0.7054\n",
            "Epoch 63/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.6180 - precision: 0.8531 - recall: 0.7014 - acc: 0.7750\n",
            "Epoch 63: val_loss did not improve from 0.78668\n",
            "71/71 [==============================] - 28s 384ms/step - loss: 0.6180 - precision: 0.8531 - recall: 0.7014 - acc: 0.7750 - val_loss: 0.8677 - val_precision: 0.7869 - val_recall: 0.6429 - val_acc: 0.7098\n",
            "Epoch 64/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.6203 - precision: 0.8437 - recall: 0.6974 - acc: 0.7665\n",
            "Epoch 64: val_loss improved from 0.78668 to 0.77077, saving model to mymodel.h5\n",
            "71/71 [==============================] - 28s 386ms/step - loss: 0.6203 - precision: 0.8437 - recall: 0.6974 - acc: 0.7665 - val_loss: 0.7708 - val_precision: 0.7766 - val_recall: 0.6518 - val_acc: 0.7143\n",
            "Epoch 65/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5679 - precision: 0.8485 - recall: 0.7161 - acc: 0.7865\n",
            "Epoch 65: val_loss did not improve from 0.77077\n",
            "71/71 [==============================] - 28s 381ms/step - loss: 0.5679 - precision: 0.8485 - recall: 0.7161 - acc: 0.7865 - val_loss: 0.8284 - val_precision: 0.7968 - val_recall: 0.6652 - val_acc: 0.7545\n",
            "Epoch 66/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5870 - precision: 0.8445 - recall: 0.7161 - acc: 0.7785\n",
            "Epoch 66: val_loss did not improve from 0.77077\n",
            "71/71 [==============================] - 28s 383ms/step - loss: 0.5870 - precision: 0.8445 - recall: 0.7161 - acc: 0.7785 - val_loss: 1.0317 - val_precision: 0.7021 - val_recall: 0.5893 - val_acc: 0.6607\n",
            "Epoch 67/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.6367 - precision: 0.8434 - recall: 0.6912 - acc: 0.7634\n",
            "Epoch 67: val_loss did not improve from 0.77077\n",
            "71/71 [==============================] - 28s 382ms/step - loss: 0.6367 - precision: 0.8434 - recall: 0.6912 - acc: 0.7634 - val_loss: 0.8309 - val_precision: 0.8077 - val_recall: 0.6562 - val_acc: 0.7277\n",
            "Epoch 68/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5967 - precision: 0.8527 - recall: 0.7094 - acc: 0.7803\n",
            "Epoch 68: val_loss did not improve from 0.77077\n",
            "71/71 [==============================] - 27s 381ms/step - loss: 0.5967 - precision: 0.8527 - recall: 0.7094 - acc: 0.7803 - val_loss: 0.9448 - val_precision: 0.7892 - val_recall: 0.6518 - val_acc: 0.7009\n",
            "Epoch 69/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.6064 - precision: 0.8597 - recall: 0.7019 - acc: 0.7807\n",
            "Epoch 69: val_loss did not improve from 0.77077\n",
            "71/71 [==============================] - 27s 380ms/step - loss: 0.6064 - precision: 0.8597 - recall: 0.7019 - acc: 0.7807 - val_loss: 0.8841 - val_precision: 0.7563 - val_recall: 0.6652 - val_acc: 0.6964\n",
            "Epoch 70/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5469 - precision: 0.8637 - recall: 0.7371 - acc: 0.8012\n",
            "Epoch 70: val_loss did not improve from 0.77077\n",
            "71/71 [==============================] - 27s 378ms/step - loss: 0.5469 - precision: 0.8637 - recall: 0.7371 - acc: 0.8012 - val_loss: 0.8179 - val_precision: 0.8212 - val_recall: 0.6562 - val_acc: 0.7411\n",
            "Epoch 71/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.6414 - precision: 0.8371 - recall: 0.6849 - acc: 0.7634\n",
            "Epoch 71: val_loss did not improve from 0.77077\n",
            "71/71 [==============================] - 28s 384ms/step - loss: 0.6414 - precision: 0.8371 - recall: 0.6849 - acc: 0.7634 - val_loss: 0.8841 - val_precision: 0.7884 - val_recall: 0.6652 - val_acc: 0.7321\n",
            "Epoch 72/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5934 - precision: 0.8597 - recall: 0.7184 - acc: 0.7843\n",
            "Epoch 72: val_loss did not improve from 0.77077\n",
            "71/71 [==============================] - 27s 375ms/step - loss: 0.5934 - precision: 0.8597 - recall: 0.7184 - acc: 0.7843 - val_loss: 0.7909 - val_precision: 0.8103 - val_recall: 0.7054 - val_acc: 0.7411\n",
            "Epoch 73/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5640 - precision: 0.8601 - recall: 0.7233 - acc: 0.7848\n",
            "Epoch 73: val_loss did not improve from 0.77077\n",
            "71/71 [==============================] - 28s 383ms/step - loss: 0.5640 - precision: 0.8601 - recall: 0.7233 - acc: 0.7848 - val_loss: 0.8167 - val_precision: 0.7923 - val_recall: 0.6473 - val_acc: 0.7098\n",
            "Epoch 74/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5265 - precision: 0.8674 - recall: 0.7491 - acc: 0.8061\n",
            "Epoch 74: val_loss did not improve from 0.77077\n",
            "71/71 [==============================] - 28s 386ms/step - loss: 0.5265 - precision: 0.8674 - recall: 0.7491 - acc: 0.8061 - val_loss: 0.8553 - val_precision: 0.7884 - val_recall: 0.6652 - val_acc: 0.6964\n",
            "Epoch 75/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5499 - precision: 0.8564 - recall: 0.7389 - acc: 0.7995\n",
            "Epoch 75: val_loss did not improve from 0.77077\n",
            "71/71 [==============================] - 27s 381ms/step - loss: 0.5499 - precision: 0.8564 - recall: 0.7389 - acc: 0.7995 - val_loss: 0.9250 - val_precision: 0.7312 - val_recall: 0.6071 - val_acc: 0.6696\n",
            "Epoch 76/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5627 - precision: 0.8560 - recall: 0.7286 - acc: 0.7977\n",
            "Epoch 76: val_loss did not improve from 0.77077\n",
            "71/71 [==============================] - 32s 448ms/step - loss: 0.5627 - precision: 0.8560 - recall: 0.7286 - acc: 0.7977 - val_loss: 0.9926 - val_precision: 0.7801 - val_recall: 0.6652 - val_acc: 0.7143\n",
            "Epoch 77/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5525 - precision: 0.8661 - recall: 0.7380 - acc: 0.7990\n",
            "Epoch 77: val_loss did not improve from 0.77077\n",
            "71/71 [==============================] - 27s 380ms/step - loss: 0.5525 - precision: 0.8661 - recall: 0.7380 - acc: 0.7990 - val_loss: 0.9090 - val_precision: 0.7551 - val_recall: 0.6607 - val_acc: 0.7009\n",
            "Epoch 78/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5867 - precision: 0.8511 - recall: 0.7206 - acc: 0.7830\n",
            "Epoch 78: val_loss did not improve from 0.77077\n",
            "71/71 [==============================] - 28s 384ms/step - loss: 0.5867 - precision: 0.8511 - recall: 0.7206 - acc: 0.7830 - val_loss: 0.8702 - val_precision: 0.7725 - val_recall: 0.6518 - val_acc: 0.7054\n",
            "Epoch 79/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5369 - precision: 0.8558 - recall: 0.7406 - acc: 0.7941\n",
            "Epoch 79: val_loss did not improve from 0.77077\n",
            "71/71 [==============================] - 27s 381ms/step - loss: 0.5369 - precision: 0.8558 - recall: 0.7406 - acc: 0.7941 - val_loss: 0.8473 - val_precision: 0.7487 - val_recall: 0.6384 - val_acc: 0.6786\n",
            "Epoch 80/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5433 - precision: 0.8573 - recall: 0.7282 - acc: 0.7950\n",
            "Epoch 80: val_loss did not improve from 0.77077\n",
            "71/71 [==============================] - 28s 384ms/step - loss: 0.5433 - precision: 0.8573 - recall: 0.7282 - acc: 0.7950 - val_loss: 0.8411 - val_precision: 0.8238 - val_recall: 0.7098 - val_acc: 0.7589\n",
            "Epoch 81/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5385 - precision: 0.8537 - recall: 0.7308 - acc: 0.7897\n",
            "Epoch 81: val_loss did not improve from 0.77077\n",
            "71/71 [==============================] - 28s 385ms/step - loss: 0.5385 - precision: 0.8537 - recall: 0.7308 - acc: 0.7897 - val_loss: 0.8483 - val_precision: 0.7766 - val_recall: 0.6518 - val_acc: 0.7098\n",
            "Epoch 82/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5618 - precision: 0.8589 - recall: 0.7322 - acc: 0.7874\n",
            "Epoch 82: val_loss did not improve from 0.77077\n",
            "71/71 [==============================] - 28s 384ms/step - loss: 0.5618 - precision: 0.8589 - recall: 0.7322 - acc: 0.7874 - val_loss: 0.9006 - val_precision: 0.7876 - val_recall: 0.6786 - val_acc: 0.7188\n",
            "Epoch 83/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5669 - precision: 0.8564 - recall: 0.7228 - acc: 0.7874\n",
            "Epoch 83: val_loss did not improve from 0.77077\n",
            "71/71 [==============================] - 28s 386ms/step - loss: 0.5669 - precision: 0.8564 - recall: 0.7228 - acc: 0.7874 - val_loss: 0.9598 - val_precision: 0.7713 - val_recall: 0.6473 - val_acc: 0.6920\n",
            "Epoch 84/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5373 - precision: 0.8635 - recall: 0.7442 - acc: 0.8021\n",
            "Epoch 84: val_loss did not improve from 0.77077\n",
            "71/71 [==============================] - 28s 391ms/step - loss: 0.5373 - precision: 0.8635 - recall: 0.7442 - acc: 0.8021 - val_loss: 0.8055 - val_precision: 0.7676 - val_recall: 0.6339 - val_acc: 0.6964\n",
            "Epoch 85/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5752 - precision: 0.8550 - recall: 0.7282 - acc: 0.7861\n",
            "Epoch 85: val_loss did not improve from 0.77077\n",
            "71/71 [==============================] - 28s 390ms/step - loss: 0.5752 - precision: 0.8550 - recall: 0.7282 - acc: 0.7861 - val_loss: 0.8716 - val_precision: 0.8010 - val_recall: 0.6830 - val_acc: 0.7321\n",
            "Epoch 86/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5554 - precision: 0.8553 - recall: 0.7375 - acc: 0.7959\n",
            "Epoch 86: val_loss improved from 0.77077 to 0.73347, saving model to mymodel.h5\n",
            "71/71 [==============================] - 28s 388ms/step - loss: 0.5554 - precision: 0.8553 - recall: 0.7375 - acc: 0.7959 - val_loss: 0.7335 - val_precision: 0.7817 - val_recall: 0.6875 - val_acc: 0.7321\n",
            "Epoch 87/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.6085 - precision: 0.8439 - recall: 0.7108 - acc: 0.7825\n",
            "Epoch 87: val_loss did not improve from 0.73347\n",
            "71/71 [==============================] - 28s 387ms/step - loss: 0.6085 - precision: 0.8439 - recall: 0.7108 - acc: 0.7825 - val_loss: 0.8479 - val_precision: 0.7772 - val_recall: 0.6384 - val_acc: 0.7054\n",
            "Epoch 88/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5380 - precision: 0.8562 - recall: 0.7322 - acc: 0.7986\n",
            "Epoch 88: val_loss did not improve from 0.73347\n",
            "71/71 [==============================] - 28s 387ms/step - loss: 0.5380 - precision: 0.8562 - recall: 0.7322 - acc: 0.7986 - val_loss: 0.7889 - val_precision: 0.7906 - val_recall: 0.6741 - val_acc: 0.7277\n",
            "Epoch 89/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.4976 - precision: 0.8819 - recall: 0.7652 - acc: 0.8275\n",
            "Epoch 89: val_loss did not improve from 0.73347\n",
            "71/71 [==============================] - 28s 383ms/step - loss: 0.4976 - precision: 0.8819 - recall: 0.7652 - acc: 0.8275 - val_loss: 0.8521 - val_precision: 0.8095 - val_recall: 0.6830 - val_acc: 0.7545\n",
            "Epoch 90/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5263 - precision: 0.8673 - recall: 0.7455 - acc: 0.8021\n",
            "Epoch 90: val_loss improved from 0.73347 to 0.71238, saving model to mymodel.h5\n",
            "71/71 [==============================] - 28s 387ms/step - loss: 0.5263 - precision: 0.8673 - recall: 0.7455 - acc: 0.8021 - val_loss: 0.7124 - val_precision: 0.8247 - val_recall: 0.7143 - val_acc: 0.7634\n",
            "Epoch 91/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.4677 - precision: 0.8678 - recall: 0.7754 - acc: 0.8142\n",
            "Epoch 91: val_loss did not improve from 0.71238\n",
            "71/71 [==============================] - 28s 386ms/step - loss: 0.4677 - precision: 0.8678 - recall: 0.7754 - acc: 0.8142 - val_loss: 0.7261 - val_precision: 0.8299 - val_recall: 0.7188 - val_acc: 0.7634\n",
            "Epoch 92/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5074 - precision: 0.8647 - recall: 0.7602 - acc: 0.8066\n",
            "Epoch 92: val_loss did not improve from 0.71238\n",
            "71/71 [==============================] - 28s 381ms/step - loss: 0.5074 - precision: 0.8647 - recall: 0.7602 - acc: 0.8066 - val_loss: 0.7129 - val_precision: 0.7846 - val_recall: 0.6830 - val_acc: 0.7500\n",
            "Epoch 93/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.4858 - precision: 0.8720 - recall: 0.7741 - acc: 0.8258\n",
            "Epoch 93: val_loss did not improve from 0.71238\n",
            "71/71 [==============================] - 28s 381ms/step - loss: 0.4858 - precision: 0.8720 - recall: 0.7741 - acc: 0.8258 - val_loss: 0.9635 - val_precision: 0.7594 - val_recall: 0.6339 - val_acc: 0.6875\n",
            "Epoch 94/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5000 - precision: 0.8662 - recall: 0.7643 - acc: 0.8222\n",
            "Epoch 94: val_loss did not improve from 0.71238\n",
            "71/71 [==============================] - 28s 386ms/step - loss: 0.5000 - precision: 0.8662 - recall: 0.7643 - acc: 0.8222 - val_loss: 0.8199 - val_precision: 0.8051 - val_recall: 0.7009 - val_acc: 0.7500\n",
            "Epoch 95/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.5038 - precision: 0.8707 - recall: 0.7625 - acc: 0.8106\n",
            "Epoch 95: val_loss did not improve from 0.71238\n",
            "71/71 [==============================] - 28s 390ms/step - loss: 0.5038 - precision: 0.8707 - recall: 0.7625 - acc: 0.8106 - val_loss: 0.8483 - val_precision: 0.7723 - val_recall: 0.6964 - val_acc: 0.7321\n",
            "Epoch 96/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.4870 - precision: 0.8678 - recall: 0.7754 - acc: 0.8191\n",
            "Epoch 96: val_loss did not improve from 0.71238\n",
            "71/71 [==============================] - 28s 385ms/step - loss: 0.4870 - precision: 0.8678 - recall: 0.7754 - acc: 0.8191 - val_loss: 0.8200 - val_precision: 0.7538 - val_recall: 0.6696 - val_acc: 0.7232\n",
            "Epoch 97/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.4846 - precision: 0.8598 - recall: 0.7705 - acc: 0.8106\n",
            "Epoch 97: val_loss did not improve from 0.71238\n",
            "71/71 [==============================] - 28s 387ms/step - loss: 0.4846 - precision: 0.8598 - recall: 0.7705 - acc: 0.8106 - val_loss: 1.0543 - val_precision: 0.7292 - val_recall: 0.6250 - val_acc: 0.6830\n",
            "Epoch 98/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.4980 - precision: 0.8800 - recall: 0.7616 - acc: 0.8213\n",
            "Epoch 98: val_loss did not improve from 0.71238\n",
            "71/71 [==============================] - 28s 389ms/step - loss: 0.4980 - precision: 0.8800 - recall: 0.7616 - acc: 0.8213 - val_loss: 0.9159 - val_precision: 0.7812 - val_recall: 0.6696 - val_acc: 0.7054\n",
            "Epoch 99/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.4551 - precision: 0.8762 - recall: 0.7825 - acc: 0.8298\n",
            "Epoch 99: val_loss did not improve from 0.71238\n",
            "71/71 [==============================] - 28s 390ms/step - loss: 0.4551 - precision: 0.8762 - recall: 0.7825 - acc: 0.8298 - val_loss: 0.7800 - val_precision: 0.7979 - val_recall: 0.6875 - val_acc: 0.7455\n",
            "Epoch 100/100\n",
            "71/71 [==============================] - ETA: 0s - loss: 0.4616 - precision: 0.8786 - recall: 0.7834 - acc: 0.8271\n",
            "Epoch 100: val_loss did not improve from 0.71238\n",
            "71/71 [==============================] - 28s 390ms/step - loss: 0.4616 - precision: 0.8786 - recall: 0.7834 - acc: 0.8271 - val_loss: 0.8016 - val_precision: 0.7463 - val_recall: 0.6696 - val_acc: 0.7054\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4.5) Accuracy and Loss Charts"
      ],
      "metadata": {
        "id": "0Fk_p_up9RJu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#################\n",
        "# Accuracy Chart\n",
        "#################\n",
        "\n",
        "plt.figure(figsize=(20, 5))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(history.history['acc'], label='Training Accuracy')\n",
        "plt.plot(history.history[\"val_acc\"], label=(\"Validation Accuracy\"))\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.xlabel(\"Epoch\", fontsize=16)\n",
        "plt.ylabel(\"Accuracy\", fontsize=16)\n",
        "plt.ylim([min(plt.ylim()), 1])\n",
        "plt.title(\"Training and Validation Accuracy\", fontsize=16)\n",
        "\n",
        "#################\n",
        "# Loss Chart\n",
        "#################\n",
        "\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history[\"val_loss\"], label=(\"Validation Loss\"))\n",
        "plt.legend(loc=\"upper right\")\n",
        "plt.xlabel(\"Epoch\", fontsize=16)\n",
        "plt.ylabel(\"Loss\", fontsize=16)\n",
        "plt.ylim([0,max(plt.ylim()),])\n",
        "plt.title(\"Training and Validation Loss\", fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "NYZZscbbMwtB",
        "outputId": "13730334-4c89-47c1-8370-3f3c0f55fafc"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5) Evaluation"
      ],
      "metadata": {
        "id": "-b-0J_K5BXLk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss, precision, recall, acc = model.evaluate(test_generator, batch_size=32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k0YEqDSMAR6_",
        "outputId": "94032b1f-f497-42e1-cdb5-59fcbaf12682"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 3s 405ms/step - loss: 0.7910 - precision: 0.7900 - recall: 0.6892 - acc: 0.7251\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nTest accuracy: %.1f%%\" % (100.0 * acc))\n",
        "print(\"\\nTest loss: %.1f%%\" % (100.0 * loss))\n",
        "print(\"\\nTest precision: %.1f%%\" % (100.0 * precision))\n",
        "print(\"\\nTest recall: %.1f%%\" % (100.0 * recall))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uUUgsUAvBvTv",
        "outputId": "ae23777d-a2c9-4f08-b6a9-942ccb003bce"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test accuracy: 72.5%\n",
            "\n",
            "Test loss: 79.1%\n",
            "\n",
            "Test precision: 79.0%\n",
            "\n",
            "Test recall: 68.9%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classification Report\n",
        "\n",
        "x_test, y_test = test_generator.next()\n",
        "\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "y_pred = np.argmax(y_pred, axis=1)\n",
        "\n",
        "y_test = np.argmax(y_test, axis=1)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VNkACClaCoaC",
        "outputId": "f46fc42c-d0db-47da-cc63-6f2981cb0790"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 178ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target_names = list(waste_labels.keys())"
      ],
      "metadata": {
        "id": "TI5-9Sc9DQmc"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred, target_names= target_names))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jFtZsc8UDvrH",
        "outputId": "8372eee4-d68b-47fa-da5e-dd9ad7b4ee04"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "   cardboard       1.00      0.71      0.83         7\n",
            "       glass       0.80      1.00      0.89         8\n",
            "       metal       0.60      1.00      0.75         3\n",
            "       paper       0.71      1.00      0.83         5\n",
            "     plastic       0.75      0.33      0.46         9\n",
            "       trash       0.00      0.00      0.00         0\n",
            "\n",
            "    accuracy                           0.75        32\n",
            "   macro avg       0.64      0.67      0.63        32\n",
            "weighted avg       0.80      0.75      0.73        32\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion Matrix\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title=\"Confusion matrix\",\n",
        "                          cmap=plt.cm.Blues):\n",
        "\n",
        "  if normalize:\n",
        "    cm = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "  plt.figure(figsize=(8, 6))\n",
        "  plt.imshow(cm, interpolation=\"nearest\", cmap=cmap)\n",
        "  plt.title(title)\n",
        "  plt.colorbar()\n",
        "  tick_marks = np.arange(len(classes))\n",
        "  plt.xticks(tick_marks, classes, rotation=45)\n",
        "  plt.yticks(tick_marks, classes)\n",
        "  fmt = \".2f\" if normalize else \"d\"\n",
        "  thresh = cm.max() / 2.\n",
        "  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "    plt.text(j, i, format(cm[i, j], fmt),\n",
        "             horizontalalignment=\"center\",\n",
        "             color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "  plt.tight_layout()\n",
        "  plt.ylabel(\"True label\", fontweight= \"bold\")\n",
        "  plt.xlabel(\"Predicted label\", fontweight=\"bold\")"
      ],
      "metadata": {
        "id": "ewCZ1aqsD7Nt"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(cm, waste_labels.keys(),\n",
        "                      title=\"Confusion matrix\",\n",
        "                      cmap=plt.cm.OrRd)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "id": "zF6Tf6ZbHpee",
        "outputId": "c99734f9-c6f9-42d4-f39a-ec027b44a21f"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}