2.Image recognition.ipynb

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  "metadata": {
    "colab": {
      "name": "Model_construction_for_image_pattern_recognition_17Nov2021+15Dec different split of data.ipynb",
      "provenance": [],
      "collapsed_sections": [
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        "MBk9xGPbC3Bn"
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      "toc_visible": true
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    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.11"
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gbouzioto/chemical_polution/blob/main/notebooks/model_exploration.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0pIA_GPjAZqN"
      },
      "source": [
        "## Import Statements"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_g8VpZLbPRAZ",
        "outputId": "da6d3f68-c5c5-42c1-f547-af46d9c3b28d"
      },
      "source": [
        "pip install tensorflow_addons"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tensorflow_addons\n",
            "  Downloading tensorflow_addons-0.15.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)\n",
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            "\u001b[?25hRequirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow_addons) (2.7.1)\n",
            "Installing collected packages: tensorflow-addons\n",
            "Successfully installed tensorflow-addons-0.15.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XbyZT9iGxPdK"
      },
      "source": [
        "from keras import backend as K\n",
        "def recall_m(y_true, y_pred):\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
        "    recall = true_positives / (possible_positives + K.epsilon())\n",
        "    return recall\n",
        "\n",
        "def precision_m(y_true, y_pred):\n",
        "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
        "    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
        "    precision = true_positives / (predicted_positives + K.epsilon())\n",
        "    return precision\n",
        "\n",
        "def f1_m(y_true, y_pred):\n",
        "    precision = precision_m(y_true, y_pred)\n",
        "    recall = recall_m(y_true, y_pred)\n",
        "    return 2*((precision*recall)/(precision+recall+K.epsilon()))"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z9qaEwiW5_KT"
      },
      "source": [
        "import os\n",
        "import numpy\n",
        "numpy.random.seed(1337)\n",
        "import urllib\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import zipfile\n",
        "import tensorflow as tf\n",
        "import tensorflow_addons as tfa\n",
        "import os\n",
        "import PIL\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.applications import InceptionV3, MobileNetV2, ResNet50\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout\n",
        "from tensorflow.keras.utils import get_file\n",
        "from tensorflow import expand_dims\n",
        "from tensorflow.nn import softmax\n",
        "from tensorflow.keras.models import Sequential, save_model, load_model\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.models import Sequential\n",
        "#Tutorial https://www.tensorflow.org/tutorials/images/classification"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sTQAgmxDAuhZ"
      },
      "source": [
        "## Load Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lOCgLHS3AuB7"
      },
      "source": [
        "project_dir = os.path.join('/', 'content')\n",
        "dataset_file = os.path.join(project_dir, 'BlackSea.zip')\n",
        "dataset_file_path = os.path.join(project_dir, 'BlackSea')\n",
        "\n",
        "if not os.path.isdir(dataset_file_path):\n",
        "    # download dataset if it does not exist\n",
        "    url = 'http://dsfp.chem.uoa.gr/BlackSea/Predicted%20spatial%20distribution%20dataset_different%20split%201.zip'\n",
        "    urllib.request.urlretrieve(url, dataset_file)\n",
        "    # extract images\n",
        "    with zipfile.ZipFile(dataset_file, 'r') as zip_ref:\n",
        "        zip_ref.extractall(dataset_file_path)\n",
        "\n",
        "dataset_train = os.path.join(dataset_file_path, 'train')\n",
        "dataset_evaluation = os.path.join(dataset_file_path, 'evaluation')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "k3fKEBZS0BaQ",
        "outputId": "31e275c8-3d49-43cb-e345-2f3558b78d55"
      },
      "source": [
        "dataset_train"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content/BlackSea/train'"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "c7bUOEw30BaR",
        "outputId": "e3822b5a-f584-494f-e4a9-3afe6a92fa50"
      },
      "source": [
        "dataset_evaluation"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content/BlackSea/evaluation'"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F1EmdPK7A5Tj"
      },
      "source": [
        "## Train, Validation and Test Generators"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GOzbLuAW2AG2"
      },
      "source": [
        "batch_size = 32\n",
        "img_height = 150\n",
        "img_width = 150"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lzdCiOx4B9vh"
      },
      "source": [
        "datagen = image.ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    validation_split=0.2)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yf1__QmcA4qa",
        "outputId": "4fdc6249-8090-4240-b444-172f96e07c24"
      },
      "source": [
        "train_generator = datagen.flow_from_directory(\n",
        "    dataset_train, \n",
        "    target_size=(img_height, img_width), \n",
        "    batch_size=batch_size, \n",
        "    class_mode='categorical', \n",
        "    subset='training')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1126 images belonging to 3 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "48IOOsS84H0R",
        "outputId": "8f9e916b-4b9a-4efe-fbbc-a94ede75ceb2"
      },
      "source": [
        "train_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "  'BlackSea/train',\n",
        "  validation_split=0.2,\n",
        "  subset=\"training\",\n",
        "  seed=123,\n",
        "  image_size=(img_height, img_width),\n",
        "  batch_size=batch_size)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1406 files belonging to 3 classes.\n",
            "Using 1125 files for training.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "cBmbVCBi7VxS",
        "outputId": "114cb5a0-094b-4846-a24e-7750102a1b75"
      },
      "source": [
        "class_names = train_ds.class_names\n",
        "print(class_names)\n",
        "\n",
        "plt.figure(figsize=(18, 18))\n",
        "for images, labels in train_ds.take(1):\n",
        "  for i in range(3):\n",
        "    ax = plt.subplot(3, 3, i + 1)\n",
        "    plt.imshow(images[i].numpy().astype(\"uint8\"))\n",
        "    plt.title(class_names[labels[i]])\n",
        "    plt.axis(\"off\")"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Danube', 'Dnieper', 'UnknownOrigin']\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x1296 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L-i9sojx0BaU",
        "outputId": "fec84818-4e19-404b-991a-b29a2daa26c1"
      },
      "source": [
        "validation_generator = datagen.flow_from_directory(\n",
        "    dataset_train, \n",
        "    target_size=(img_height, img_width), \n",
        "    batch_size=batch_size, \n",
        "    class_mode='categorical', \n",
        "    subset='validation')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 280 images belonging to 3 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WWhKPEQH4MyV",
        "outputId": "b75c0552-feb0-40dd-db53-ae82dbc740ab"
      },
      "source": [
        "val_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "  'BlackSea/train',\n",
        "  validation_split=0.2,\n",
        "  subset=\"validation\",\n",
        "  seed=123,\n",
        "  image_size=(img_height, img_width),\n",
        "  batch_size=batch_size)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1406 files belonging to 3 classes.\n",
            "Using 281 files for validation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PGS9G1yY7lTF"
      },
      "source": [
        "#AUTOTUNE = tf.data.AUTOTUNE\n",
        "#\n",
        "#train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)\n",
        "#val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EFlYlQW-0BaU",
        "outputId": "1e728e67-bcbb-4d85-9657-811e2ae1a873"
      },
      "source": [
        "test_generator = datagen.flow_from_directory(\n",
        "    dataset_evaluation, \n",
        "    target_size=(img_height, img_height), \n",
        "    batch_size=batch_size, \n",
        "    shuffle = False,\n",
        "    class_mode='categorical')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 224 images belonging to 3 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U1R-mmwD4ahk",
        "outputId": "dd89b646-ced9-4e3b-ae4d-1e19decdba18"
      },
      "source": [
        "test_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "  'BlackSea/evaluation',\n",
        "  seed=123,\n",
        "  image_size=(img_height, img_width),\n",
        "  shuffle = False,\n",
        "  batch_size=batch_size)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 224 files belonging to 3 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TJSJFYZb0BaV"
      },
      "source": [
        "The number of images are correct. We have in total 1406 which were split in training set (1126 images) and validation set (280 images). Finally there are 224 images as the test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KCOzteRGBUAd",
        "outputId": "1e010ef3-7c47-4da8-cf37-d7ac57177a1f"
      },
      "source": [
        "test_steps_per_epoch = numpy.math.ceil(test_generator.samples / test_generator.batch_size)\n",
        "print(test_steps_per_epoch)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AN3P4Glv7-14"
      },
      "source": [
        "num_classes = 3"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gaNIZX4b0BaW"
      },
      "source": [
        "# 0.Baseline model implementation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EWB7tgFm0BaW"
      },
      "source": [
        "Set the architecture of the neural network model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4L-P1FVy0BaW"
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Flatten(),\n",
        "  tf.keras.layers.Dense(1500, activation='relu'),\n",
        "  tf.keras.layers.Dense(3, activation='softmax')\n",
        "])"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tvckU7V90BaW"
      },
      "source": [
        "Set the loss and the optimizer function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9tVN-cST0BaW"
      },
      "source": [
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=f1_m) #[tfa.metrics.F1Score(num_classes=3)])"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b0owkNts3OEr"
      },
      "source": [
        "callback = tf.keras.callbacks.EarlyStopping(monitor='f1_m', patience=10)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lx-k3KEE0BaX",
        "outputId": "ca668dce-a5a7-40e1-811a-fb2209c74174"
      },
      "source": [
        "epochs=100\n",
        "history = model.fit(\n",
        "    train_generator,\n",
        "    validation_data=validation_generator,\n",
        "    epochs=epochs,\n",
        "    callbacks=[callback]\n",
        ")"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "36/36 [==============================] - 30s 801ms/step - loss: 66.9340 - f1_m: 0.6431 - val_loss: 0.5450 - val_f1_m: 0.9757\n",
            "Epoch 2/100\n",
            "36/36 [==============================] - 28s 787ms/step - loss: 1.9015 - f1_m: 0.9306 - val_loss: 0.3946 - val_f1_m: 0.9815\n",
            "Epoch 3/100\n",
            "36/36 [==============================] - 29s 808ms/step - loss: 0.6871 - f1_m: 0.9601 - val_loss: 0.1002 - val_f1_m: 0.9850\n",
            "Epoch 4/100\n",
            "36/36 [==============================] - 28s 782ms/step - loss: 0.5686 - f1_m: 0.9653 - val_loss: 0.1697 - val_f1_m: 0.9757\n",
            "Epoch 5/100\n",
            "36/36 [==============================] - 28s 786ms/step - loss: 0.3764 - f1_m: 0.9774 - val_loss: 0.2715 - val_f1_m: 0.9641\n",
            "Epoch 6/100\n",
            "36/36 [==============================] - 28s 783ms/step - loss: 0.6181 - f1_m: 0.9601 - val_loss: 0.1437 - val_f1_m: 0.9861\n",
            "Epoch 7/100\n",
            "36/36 [==============================] - 28s 785ms/step - loss: 0.4781 - f1_m: 0.9731 - val_loss: 0.3469 - val_f1_m: 0.9711\n",
            "Epoch 8/100\n",
            "36/36 [==============================] - 29s 811ms/step - loss: 1.7073 - f1_m: 0.9120 - val_loss: 0.8985 - val_f1_m: 0.9537\n",
            "Epoch 9/100\n",
            "36/36 [==============================] - 28s 783ms/step - loss: 0.8969 - f1_m: 0.9540 - val_loss: 0.0846 - val_f1_m: 0.9826\n",
            "Epoch 10/100\n",
            "36/36 [==============================] - 28s 784ms/step - loss: 0.6719 - f1_m: 0.9601 - val_loss: 0.0750 - val_f1_m: 0.9965\n",
            "Epoch 11/100\n",
            "36/36 [==============================] - 28s 784ms/step - loss: 0.2715 - f1_m: 0.9766 - val_loss: 0.4517 - val_f1_m: 0.9722\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "26QTRdxm0BaX",
        "outputId": "97a21574-4f5a-4a2f-82ec-0384cea76263"
      },
      "source": [
        "score = model.evaluate(test_generator, verbose=0)\n",
        "score"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.0804450511932373, 0.9553571343421936]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BRSE0C9-0BaX",
        "outputId": "b15a71bf-e014-4d34-cfb8-bded81d22cfc"
      },
      "source": [
        "print(\"Test loss:\", score[0])\n",
        "print(\"Test accuracy:\", score[1])"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 1.0804450511932373\n",
            "Test accuracy: 0.9553571343421936\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dkGmNUnc6HAS",
        "outputId": "3a917f0a-90d4-4742-80c4-aefee9433587"
      },
      "source": [
        "# Save the model\n",
        "filepath = './saved_model_simple'\n",
        "save_model(model, filepath)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: ./saved_model_simple/assets\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KxYYYFpG6Jx3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92ddff28-1cea-4ac9-f0ff-03bedc473847"
      },
      "source": [
        "# Load the model\n",
        "model = load_model(filepath, compile = False)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:Unable to restore custom metric. Please ensure that the layer implements `get_config` and `from_config` when saving. In addition, please use the `custom_objects` arg when calling `load_model()`.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "jr0tTEr-6N2k",
        "outputId": "326973c8-06a5-4733-9a5d-a663fb75765f"
      },
      "source": [
        "acc = history.history['val_f1_m']\n",
        "val_acc = history.history['val_f1_m']\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs_range = range(len(acc))\n",
        "epochs_range\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs_range, loss, label='Training Loss')\n",
        "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.show()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4af5sCR46Rgc",
        "outputId": "704254ec-01b9-4036-bff4-b08a3468ddf6"
      },
      "source": [
        "test_generator"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.preprocessing.image.DirectoryIterator at 0x7f9bd6de8050>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rb6reyWL0BaY",
        "outputId": "a3dc4673-7084-4d90-d6a1-7d136dac8a26"
      },
      "source": [
        "predictions = model.predict(test_generator)\n",
        "predictions[range(5)]"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.0000000e+00, 0.0000000e+00, 0.0000000e+00],\n",
              "       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00],\n",
              "       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00],\n",
              "       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00],\n",
              "       [1.0000000e+00, 0.0000000e+00, 1.1536658e-37]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zCoS1Wfh0BaY",
        "outputId": "fb588e88-ada1-4607-e03c-f471b7c50d72"
      },
      "source": [
        "predicted_classes= numpy.argmax(predictions, axis=1)\n",
        "predicted_classes"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "       1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 0, 1, 1, 1, 1, 2, 2, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
              "       2, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 2, 0, 2, 2, 0, 2, 2, 2, 2, 2, 0,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "085EHm0V0BaY",
        "outputId": "c5608cbf-77f8-49b9-c95a-69c8d64f007f"
      },
      "source": [
        "true_classes = test_generator.classes\n",
        "true_classes"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pBX2s8WS0BaY",
        "outputId": "ac8e3aa2-1892-4354-b60e-619b6b4dbb58"
      },
      "source": [
        "class_labels = list(test_generator.class_indices.keys())\n",
        "class_labels"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Danube', 'Dnieper', 'UnknownOrigin']"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dCeN1xmx0BaZ",
        "outputId": "aca88f13-dd6d-405d-aab7-e95e5bb20f88"
      },
      "source": [
        "report = classification_report(true_classes, predicted_classes, target_names=class_labels, zero_division=0)\n",
        "print(report)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               precision    recall  f1-score   support\n",
            "\n",
            "       Danube       0.94      1.00      0.97       109\n",
            "      Dnieper       1.00      0.90      0.95        78\n",
            "UnknownOrigin       0.89      0.92      0.91        37\n",
            "\n",
            "     accuracy                           0.95       224\n",
            "    macro avg       0.94      0.94      0.94       224\n",
            " weighted avg       0.95      0.95      0.95       224\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "-rvHbWGU0BaZ",
        "outputId": "6140fa35-df82-4ccd-a2b6-6e4aae8df8f4"
      },
      "source": [
        "cmrforest = confusion_matrix(true_classes, predicted_classes)\n",
        "plt.figure(figsize=(10,7))\n",
        "sns.heatmap(cmrforest ,annot=True , fmt = 'd')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')\n",
        "plt.title('Confusion Report')\n",
        "plt.show()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0egjiA5w0BaZ"
      },
      "source": [
        "# 1.Basic tensorflow CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WEyY0lrz0Bab"
      },
      "source": [
        "model_CNN1 = Sequential([\n",
        "  layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),\n",
        "  layers.Conv2D(16, 3, padding='same', activation='relu'),\n",
        "  layers.MaxPooling2D(),\n",
        "  layers.Conv2D(32, 3, padding='same', activation='relu'),\n",
        "  layers.MaxPooling2D(),\n",
        "  layers.Dropout(0.2),\n",
        "  layers.Flatten(),\n",
        "  layers.Dense(128, activation='relu'),\n",
        "  layers.Dense(num_classes)\n",
        "])"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-iRqtbGO0Bab"
      },
      "source": [
        "model_CNN1.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "               metrics=['acc', f1_m, precision_m, recall_m])"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "emVI8BQU0Bab",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bade9e6f-c7a9-4a00-80e7-b0ea92ccc3a4"
      },
      "source": [
        "model_CNN1.summary()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " rescaling (Rescaling)       (None, 150, 150, 3)       0         \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 150, 150, 16)      448       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 75, 75, 16)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 75, 75, 32)        4640      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 37, 37, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 37, 37, 32)        0         \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 43808)             0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               5607552   \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 3)                 387       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5,613,027\n",
            "Trainable params: 5,613,027\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Iyobyk_0Bac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f34e47bf-fa28-42e4-8d80-ac3f9c4ac981"
      },
      "source": [
        "history_CNN1 = model_CNN1.fit(train_ds,\n",
        "                    validation_data=val_ds,\n",
        "                    callbacks=[callback],\n",
        "                    epochs=epochs)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "36/36 [==============================] - 23s 596ms/step - loss: 1.2781 - acc: 0.8196 - f1_m: 0.7499 - precision_m: 0.5812 - recall_m: 1.1790 - val_loss: 0.0765 - val_acc: 0.9893 - val_f1_m: 0.6976 - val_precision_m: 0.5096 - val_recall_m: 1.1218\n",
            "Epoch 2/100\n",
            "36/36 [==============================] - 22s 586ms/step - loss: 0.0328 - acc: 0.9938 - f1_m: 0.7989 - precision_m: 0.5848 - recall_m: 1.2870 - val_loss: 0.0752 - val_acc: 0.9893 - val_f1_m: 0.7023 - val_precision_m: 0.5233 - val_recall_m: 1.0843\n",
            "Epoch 3/100\n",
            "36/36 [==============================] - 22s 587ms/step - loss: 0.0401 - acc: 0.9876 - f1_m: 0.7287 - precision_m: 0.5555 - recall_m: 1.0716 - val_loss: 0.0499 - val_acc: 0.9929 - val_f1_m: 0.6771 - val_precision_m: 0.5083 - val_recall_m: 1.0482\n",
            "Epoch 4/100\n",
            "36/36 [==============================] - 22s 587ms/step - loss: 0.0296 - acc: 0.9893 - f1_m: 0.7591 - precision_m: 0.5669 - recall_m: 1.1784 - val_loss: 0.0830 - val_acc: 0.9822 - val_f1_m: 0.6997 - val_precision_m: 0.5210 - val_recall_m: 1.1033\n",
            "Epoch 5/100\n",
            "36/36 [==============================] - 22s 590ms/step - loss: 0.0416 - acc: 0.9884 - f1_m: 0.7554 - precision_m: 0.5727 - recall_m: 1.1371 - val_loss: 0.0460 - val_acc: 0.9929 - val_f1_m: 0.6756 - val_precision_m: 0.5049 - val_recall_m: 1.0310\n",
            "Epoch 6/100\n",
            "36/36 [==============================] - 22s 587ms/step - loss: 0.0408 - acc: 0.9858 - f1_m: 0.7436 - precision_m: 0.5624 - recall_m: 1.1050 - val_loss: 0.0815 - val_acc: 0.9822 - val_f1_m: 0.7148 - val_precision_m: 0.5315 - val_recall_m: 1.1135\n",
            "Epoch 7/100\n",
            "36/36 [==============================] - 22s 586ms/step - loss: 0.0324 - acc: 0.9902 - f1_m: 0.7424 - precision_m: 0.5606 - recall_m: 1.1147 - val_loss: 0.0473 - val_acc: 0.9929 - val_f1_m: 0.6885 - val_precision_m: 0.5125 - val_recall_m: 1.0582\n",
            "Epoch 8/100\n",
            "36/36 [==============================] - 22s 588ms/step - loss: 0.0375 - acc: 0.9858 - f1_m: 0.7373 - precision_m: 0.5603 - recall_m: 1.0923 - val_loss: 0.0660 - val_acc: 0.9893 - val_f1_m: 0.6730 - val_precision_m: 0.5083 - val_recall_m: 1.0382\n",
            "Epoch 9/100\n",
            "36/36 [==============================] - 22s 585ms/step - loss: 0.0234 - acc: 0.9902 - f1_m: 0.7404 - precision_m: 0.5625 - recall_m: 1.1055 - val_loss: 0.0489 - val_acc: 0.9929 - val_f1_m: 0.6700 - val_precision_m: 0.5032 - val_recall_m: 1.0237\n",
            "Epoch 10/100\n",
            "36/36 [==============================] - 22s 588ms/step - loss: 0.0427 - acc: 0.9902 - f1_m: 0.7195 - precision_m: 0.5470 - recall_m: 1.0778 - val_loss: 0.0406 - val_acc: 0.9929 - val_f1_m: 0.6719 - val_precision_m: 0.5042 - val_recall_m: 1.0283\n",
            "Epoch 11/100\n",
            "36/36 [==============================] - 22s 587ms/step - loss: 0.0310 - acc: 0.9876 - f1_m: 0.7254 - precision_m: 0.5547 - recall_m: 1.0721 - val_loss: 0.0363 - val_acc: 0.9929 - val_f1_m: 0.6662 - val_precision_m: 0.4999 - val_recall_m: 1.0209\n",
            "Epoch 12/100\n",
            "36/36 [==============================] - 22s 590ms/step - loss: 0.0356 - acc: 0.9876 - f1_m: 0.7439 - precision_m: 0.5687 - recall_m: 1.0961 - val_loss: 0.0455 - val_acc: 0.9929 - val_f1_m: 0.6605 - val_precision_m: 0.4974 - val_recall_m: 1.0182\n",
            "Epoch 13/100\n",
            "36/36 [==============================] - 22s 593ms/step - loss: 0.0452 - acc: 0.9831 - f1_m: 0.7333 - precision_m: 0.5613 - recall_m: 1.0662 - val_loss: 0.0432 - val_acc: 0.9929 - val_f1_m: 0.6730 - val_precision_m: 0.5087 - val_recall_m: 1.0294\n",
            "Epoch 14/100\n",
            "36/36 [==============================] - 22s 596ms/step - loss: 0.0291 - acc: 0.9902 - f1_m: 0.7512 - precision_m: 0.5721 - recall_m: 1.1227 - val_loss: 0.0526 - val_acc: 0.9929 - val_f1_m: 0.6825 - val_precision_m: 0.5103 - val_recall_m: 1.0384\n",
            "Epoch 15/100\n",
            "36/36 [==============================] - 22s 597ms/step - loss: 0.0219 - acc: 0.9911 - f1_m: 0.7265 - precision_m: 0.5540 - recall_m: 1.0783 - val_loss: 0.0569 - val_acc: 0.9929 - val_f1_m: 0.6772 - val_precision_m: 0.5114 - val_recall_m: 1.0423\n",
            "Epoch 16/100\n",
            "36/36 [==============================] - 22s 596ms/step - loss: 0.0263 - acc: 0.9911 - f1_m: 0.7300 - precision_m: 0.5589 - recall_m: 1.0707 - val_loss: 0.0494 - val_acc: 0.9929 - val_f1_m: 0.6761 - val_precision_m: 0.5077 - val_recall_m: 1.0362\n",
            "Epoch 17/100\n",
            "36/36 [==============================] - 22s 596ms/step - loss: 0.0252 - acc: 0.9902 - f1_m: 0.7328 - precision_m: 0.5580 - recall_m: 1.0801 - val_loss: 0.0427 - val_acc: 0.9929 - val_f1_m: 0.6794 - val_precision_m: 0.5075 - val_recall_m: 1.0391\n",
            "Epoch 18/100\n",
            "36/36 [==============================] - 22s 596ms/step - loss: 0.0261 - acc: 0.9902 - f1_m: 0.7182 - precision_m: 0.5504 - recall_m: 1.0544 - val_loss: 0.0482 - val_acc: 0.9929 - val_f1_m: 0.6718 - val_precision_m: 0.5076 - val_recall_m: 1.0267\n",
            "Epoch 19/100\n",
            "36/36 [==============================] - 22s 598ms/step - loss: 0.0218 - acc: 0.9911 - f1_m: 0.7297 - precision_m: 0.5596 - recall_m: 1.0678 - val_loss: 0.0454 - val_acc: 0.9893 - val_f1_m: 0.6753 - val_precision_m: 0.5062 - val_recall_m: 1.0340\n",
            "Epoch 20/100\n",
            "36/36 [==============================] - 22s 598ms/step - loss: 0.0232 - acc: 0.9920 - f1_m: 0.7194 - precision_m: 0.5499 - recall_m: 1.0528 - val_loss: 0.0519 - val_acc: 0.9893 - val_f1_m: 0.6746 - val_precision_m: 0.5072 - val_recall_m: 1.0321\n",
            "Epoch 21/100\n",
            "36/36 [==============================] - 22s 599ms/step - loss: 0.0257 - acc: 0.9902 - f1_m: 0.7204 - precision_m: 0.5507 - recall_m: 1.0608 - val_loss: 0.0512 - val_acc: 0.9929 - val_f1_m: 0.6878 - val_precision_m: 0.5131 - val_recall_m: 1.0516\n",
            "Epoch 22/100\n",
            "36/36 [==============================] - 22s 599ms/step - loss: 0.0237 - acc: 0.9902 - f1_m: 0.7218 - precision_m: 0.5519 - recall_m: 1.0591 - val_loss: 0.0379 - val_acc: 0.9929 - val_f1_m: 0.6594 - val_precision_m: 0.4961 - val_recall_m: 1.0111\n",
            "Epoch 23/100\n",
            "36/36 [==============================] - 22s 596ms/step - loss: 0.0268 - acc: 0.9902 - f1_m: 0.7184 - precision_m: 0.5491 - recall_m: 1.0570 - val_loss: 0.0444 - val_acc: 0.9893 - val_f1_m: 0.6734 - val_precision_m: 0.5043 - val_recall_m: 1.0236\n",
            "Epoch 24/100\n",
            "36/36 [==============================] - 22s 598ms/step - loss: 0.0237 - acc: 0.9911 - f1_m: 0.7196 - precision_m: 0.5518 - recall_m: 1.0493 - val_loss: 0.0476 - val_acc: 0.9929 - val_f1_m: 0.6790 - val_precision_m: 0.5087 - val_recall_m: 1.0405\n",
            "Epoch 25/100\n",
            "36/36 [==============================] - 22s 597ms/step - loss: 0.0214 - acc: 0.9911 - f1_m: 0.7265 - precision_m: 0.5571 - recall_m: 1.0640 - val_loss: 0.0475 - val_acc: 0.9893 - val_f1_m: 0.6785 - val_precision_m: 0.5084 - val_recall_m: 1.0287\n",
            "Epoch 26/100\n",
            "36/36 [==============================] - 22s 596ms/step - loss: 0.0477 - acc: 0.9867 - f1_m: 0.7305 - precision_m: 0.5598 - recall_m: 1.0655 - val_loss: 0.0487 - val_acc: 0.9929 - val_f1_m: 0.6689 - val_precision_m: 0.5017 - val_recall_m: 1.0167\n",
            "Epoch 27/100\n",
            "36/36 [==============================] - 22s 596ms/step - loss: 0.0241 - acc: 0.9902 - f1_m: 0.7115 - precision_m: 0.5480 - recall_m: 1.0331 - val_loss: 0.0633 - val_acc: 0.9929 - val_f1_m: 0.6857 - val_precision_m: 0.5161 - val_recall_m: 1.0444\n",
            "Epoch 28/100\n",
            "36/36 [==============================] - 22s 598ms/step - loss: 0.0268 - acc: 0.9902 - f1_m: 0.7233 - precision_m: 0.5581 - recall_m: 1.0506 - val_loss: 0.0486 - val_acc: 0.9929 - val_f1_m: 0.6779 - val_precision_m: 0.5096 - val_recall_m: 1.0228\n",
            "Epoch 29/100\n",
            "36/36 [==============================] - 22s 597ms/step - loss: 0.0193 - acc: 0.9920 - f1_m: 0.7267 - precision_m: 0.5570 - recall_m: 1.0602 - val_loss: 0.0494 - val_acc: 0.9929 - val_f1_m: 0.6756 - val_precision_m: 0.5113 - val_recall_m: 1.0355\n",
            "Epoch 30/100\n",
            "36/36 [==============================] - 22s 597ms/step - loss: 0.0225 - acc: 0.9911 - f1_m: 0.7249 - precision_m: 0.5603 - recall_m: 1.0551 - val_loss: 0.0544 - val_acc: 0.9893 - val_f1_m: 0.6737 - val_precision_m: 0.5068 - val_recall_m: 1.0294\n",
            "Epoch 31/100\n",
            "36/36 [==============================] - 22s 598ms/step - loss: 0.0217 - acc: 0.9947 - f1_m: 0.7281 - precision_m: 0.5599 - recall_m: 1.0553 - val_loss: 0.0599 - val_acc: 0.9929 - val_f1_m: 0.6846 - val_precision_m: 0.5120 - val_recall_m: 1.0373\n",
            "Epoch 32/100\n",
            "36/36 [==============================] - 22s 596ms/step - loss: 0.0206 - acc: 0.9911 - f1_m: 0.7279 - precision_m: 0.5586 - recall_m: 1.0677 - val_loss: 0.0470 - val_acc: 0.9929 - val_f1_m: 0.6746 - val_precision_m: 0.5075 - val_recall_m: 1.0245\n",
            "Epoch 33/100\n",
            "36/36 [==============================] - 22s 593ms/step - loss: 0.0220 - acc: 0.9902 - f1_m: 0.7152 - precision_m: 0.5500 - recall_m: 1.0495 - val_loss: 0.0464 - val_acc: 0.9929 - val_f1_m: 0.6676 - val_precision_m: 0.5027 - val_recall_m: 1.0202\n",
            "Epoch 34/100\n",
            "36/36 [==============================] - 22s 596ms/step - loss: 0.0218 - acc: 0.9929 - f1_m: 0.7306 - precision_m: 0.5624 - recall_m: 1.0608 - val_loss: 0.0545 - val_acc: 0.9929 - val_f1_m: 0.6742 - val_precision_m: 0.5088 - val_recall_m: 1.0341\n",
            "Epoch 35/100\n",
            "36/36 [==============================] - 22s 597ms/step - loss: 0.0265 - acc: 0.9911 - f1_m: 0.7113 - precision_m: 0.5470 - recall_m: 1.0415 - val_loss: 0.0456 - val_acc: 0.9929 - val_f1_m: 0.6706 - val_precision_m: 0.5038 - val_recall_m: 1.0133\n",
            "Epoch 36/100\n",
            "36/36 [==============================] - 22s 595ms/step - loss: 0.0214 - acc: 0.9929 - f1_m: 0.7145 - precision_m: 0.5468 - recall_m: 1.0520 - val_loss: 0.0537 - val_acc: 0.9929 - val_f1_m: 0.6857 - val_precision_m: 0.5135 - val_recall_m: 1.0451\n",
            "Epoch 37/100\n",
            "36/36 [==============================] - 22s 598ms/step - loss: 0.0204 - acc: 0.9911 - f1_m: 0.7192 - precision_m: 0.5529 - recall_m: 1.0450 - val_loss: 0.0534 - val_acc: 0.9929 - val_f1_m: 0.6684 - val_precision_m: 0.5016 - val_recall_m: 1.0131\n",
            "Epoch 38/100\n",
            "36/36 [==============================] - 22s 595ms/step - loss: 0.0197 - acc: 0.9920 - f1_m: 0.7201 - precision_m: 0.5530 - recall_m: 1.0547 - val_loss: 0.0497 - val_acc: 0.9929 - val_f1_m: 0.6744 - val_precision_m: 0.5049 - val_recall_m: 1.0193\n",
            "Epoch 39/100\n",
            "36/36 [==============================] - 22s 596ms/step - loss: 0.0205 - acc: 0.9893 - f1_m: 0.7272 - precision_m: 0.5609 - recall_m: 1.0556 - val_loss: 0.0497 - val_acc: 0.9893 - val_f1_m: 0.6724 - val_precision_m: 0.5051 - val_recall_m: 1.0204\n",
            "Epoch 40/100\n",
            "36/36 [==============================] - 22s 595ms/step - loss: 0.0195 - acc: 0.9920 - f1_m: 0.7249 - precision_m: 0.5583 - recall_m: 1.0476 - val_loss: 0.0564 - val_acc: 0.9893 - val_f1_m: 0.6801 - val_precision_m: 0.5101 - val_recall_m: 1.0333\n",
            "Epoch 41/100\n",
            "36/36 [==============================] - 22s 597ms/step - loss: 0.0288 - acc: 0.9911 - f1_m: 0.7332 - precision_m: 0.5635 - recall_m: 1.0757 - val_loss: 0.0570 - val_acc: 0.9929 - val_f1_m: 0.6808 - val_precision_m: 0.5182 - val_recall_m: 1.0413\n",
            "Epoch 42/100\n",
            "36/36 [==============================] - 22s 601ms/step - loss: 0.0224 - acc: 0.9911 - f1_m: 0.7426 - precision_m: 0.5702 - recall_m: 1.0909 - val_loss: 0.0565 - val_acc: 0.9929 - val_f1_m: 0.6859 - val_precision_m: 0.5142 - val_recall_m: 1.0454\n",
            "Epoch 43/100\n",
            "36/36 [==============================] - 22s 598ms/step - loss: 0.0366 - acc: 0.9884 - f1_m: 0.7139 - precision_m: 0.5522 - recall_m: 1.0315 - val_loss: 0.0596 - val_acc: 0.9893 - val_f1_m: 0.6838 - val_precision_m: 0.5123 - val_recall_m: 1.0362\n",
            "Epoch 44/100\n",
            "36/36 [==============================] - 22s 597ms/step - loss: 0.0265 - acc: 0.9911 - f1_m: 0.7319 - precision_m: 0.5612 - recall_m: 1.0688 - val_loss: 0.0547 - val_acc: 0.9929 - val_f1_m: 0.6731 - val_precision_m: 0.5089 - val_recall_m: 1.0271\n",
            "Epoch 45/100\n",
            "36/36 [==============================] - 22s 598ms/step - loss: 0.0316 - acc: 0.9902 - f1_m: 0.7229 - precision_m: 0.5577 - recall_m: 1.0473 - val_loss: 0.0466 - val_acc: 0.9929 - val_f1_m: 0.6640 - val_precision_m: 0.4993 - val_recall_m: 1.0145\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vr39Ffrf0goB",
        "outputId": "e944b5b4-9fe0-4d15-b34c-ada3a2adb90d"
      },
      "source": [
        "score = model_CNN1.evaluate(test_ds, verbose=0)\n",
        "print(\"Test loss:\", score[0])\n",
        "print(\"Test accuracy:\", score[1])\n",
        "\n",
        "\n",
        "# Save the model\n",
        "filepath = './model_CNN1'\n",
        "save_model(model, filepath)\n",
        "\n",
        "# Load the model\n",
        "model = load_model(filepath, compile = False)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 0.06954865157604218\n",
            "Test accuracy: 0.9821428656578064\n",
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
            "INFO:tensorflow:Assets written to: ./model_CNN1/assets\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sOt2_T1F-vAJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "18a17045-e3aa-462a-97f1-14f9dc8f40fd"
      },
      "source": [
        "acc = history_CNN1.history['f1_m']\n",
        "val_acc = history_CNN1.history['val_f1_m']\n",
        "\n",
        "loss = history_CNN1.history['loss']\n",
        "val_loss = history_CNN1.history['val_loss']\n",
        "\n",
        "epochs_range = range(len(acc))\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs_range, loss, label='Training Loss')\n",
        "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.show()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9QnrNrEr0Bac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3964ed58-cfc9-43ef-adc4-8759f1aceefa"
      },
      "source": [
        "predictions = model_CNN1.predict(test_ds, steps=test_steps_per_epoch)\n",
        "# Get most likely class\n",
        "predicted_classes = numpy.argmax(predictions, axis=1)\n",
        "predicted_classes"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzK37VbIE2bW",
        "outputId": "dd47859b-419c-4af4-8ed1-7e1c1e809194"
      },
      "source": [
        "true_classes = test_generator.classes #true_classes = test_ds.class_names\n",
        "true_classes"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vM5U0Mc90Bac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4c5bada-861f-4cfc-fe77-139541d517ae"
      },
      "source": [
        "class_labels = list(test_generator.class_indices.keys())\n",
        "class_labels"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Danube', 'Dnieper', 'UnknownOrigin']"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ItJdCNnF0Bac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        },
        "outputId": "ea4c2264-5ade-430c-badf-3ee354753c7e"
      },
      "source": [
        "report = classification_report(true_classes, predicted_classes, target_names=class_labels, zero_division=0)\n",
        "print(report)\n",
        "cmrforest = confusion_matrix(true_classes, predicted_classes)\n",
        "plt.figure(figsize=(10,7))\n",
        "sns.heatmap(cmrforest ,annot=True , fmt = 'd')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')\n",
        "plt.title('Confusion Report')\n",
        "plt.show()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               precision    recall  f1-score   support\n",
            "\n",
            "       Danube       0.99      0.97      0.98       109\n",
            "      Dnieper       0.97      1.00      0.99        78\n",
            "UnknownOrigin       0.97      0.97      0.97        37\n",
            "\n",
            "     accuracy                           0.98       224\n",
            "    macro avg       0.98      0.98      0.98       224\n",
            " weighted avg       0.98      0.98      0.98       224\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sv8vSqWOFlba"
      },
      "source": [
        "# 2.VGG Net"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c3FmYSoeFbYS",
        "outputId": "52be7af1-bb66-4cc1-d9c4-234bdc8f8c66"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Conv2D, MaxPooling2D\n",
        "from keras.layers import Flatten, Activation\n",
        "\n",
        "custom_vgg = Sequential([\n",
        "layers.Conv2D(32, (3, 3), strides = 1, padding = \"same\", activation = \"relu\", input_shape = (img_height, img_width, 3)),\n",
        "layers.Dropout(0.4),\n",
        "layers.Conv2D(32, (3, 3), strides = 1, padding = \"same\", activation = \"relu\"),\n",
        "layers.Dropout(0.4),\n",
        "layers.MaxPooling2D((2, 2)),\n",
        "\n",
        "layers.Conv2D(64, (3, 3), strides = 1, padding = \"same\", activation = \"relu\"),\n",
        "layers.Dropout(0.4),\n",
        "layers.Conv2D(64, (3, 3), strides = 1, padding = \"same\", activation = \"relu\"),\n",
        "layers.Dropout(0.4),\n",
        "layers.MaxPooling2D((2, 2)),\n",
        "\n",
        "layers.Conv2D(128, (3, 3), strides = 1, padding = \"same\", activation = \"relu\"),\n",
        "layers.Dropout(0.4),\n",
        "layers.Conv2D(128, (3, 3), strides = 1, padding = \"same\", activation = \"relu\"),\n",
        "layers.Dropout(0.4),\n",
        "layers.MaxPooling2D((2, 2)),\n",
        "\n",
        "layers.Flatten(),\n",
        "layers.Dense(num_classes, activation = \"relu\")\n",
        "])\n",
        "\n",
        "custom_vgg.compile(optimizer = \"adam\",\n",
        "                   loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "                   metrics=['acc', f1_m, precision_m, recall_m])\n",
        "\n",
        "custom_vgg.summary()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_2 (Conv2D)           (None, 150, 150, 32)      896       \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 150, 150, 32)      0         \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 150, 150, 32)      9248      \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 150, 150, 32)      0         \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 75, 75, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 75, 75, 64)        18496     \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 75, 75, 64)        0         \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 75, 75, 64)        36928     \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 75, 75, 64)        0         \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 37, 37, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_6 (Conv2D)           (None, 37, 37, 128)       73856     \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 37, 37, 128)       0         \n",
            "                                                                 \n",
            " conv2d_7 (Conv2D)           (None, 37, 37, 128)       147584    \n",
            "                                                                 \n",
            " dropout_6 (Dropout)         (None, 37, 37, 128)       0         \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPooling  (None, 18, 18, 128)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten_2 (Flatten)         (None, 41472)             0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 3)                 124419    \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 411,427\n",
            "Trainable params: 411,427\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L54jI3bxGaFy",
        "outputId": "5dc18249-6b7d-4106-80dc-1fe618d7cd38"
      },
      "source": [
        "history_vgg = custom_vgg.fit(train_ds,\n",
        "                    validation_data=val_ds,\n",
        "                    callbacks=[callback],\n",
        "                    epochs=epochs)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "36/36 [==============================] - 185s 5s/step - loss: 64.8417 - acc: 0.4453 - f1_m: 0.1005 - precision_m: 0.0774 - recall_m: 0.1812 - val_loss: 1.0986 - val_acc: 0.4982 - val_f1_m: 0.0000e+00 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
            "Epoch 2/100\n",
            "36/36 [==============================] - 183s 5s/step - loss: 1.0986 - acc: 0.4551 - f1_m: 0.0000e+00 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 1.0986 - val_acc: 0.4982 - val_f1_m: 0.0000e+00 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
            "Epoch 3/100\n",
            "36/36 [==============================] - 182s 5s/step - loss: 1.0986 - acc: 0.4551 - f1_m: 0.0000e+00 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 1.0986 - val_acc: 0.4982 - val_f1_m: 0.0000e+00 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
            "Epoch 4/100\n",
            "36/36 [==============================] - 182s 5s/step - loss: 1.0986 - acc: 0.4551 - f1_m: 0.0000e+00 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 1.0986 - val_acc: 0.4982 - val_f1_m: 0.0000e+00 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
            "Epoch 5/100\n",
            "36/36 [==============================] - 182s 5s/step - loss: 1.0986 - acc: 0.4551 - f1_m: 0.0000e+00 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 1.0986 - val_acc: 0.4982 - val_f1_m: 0.0000e+00 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
            "Epoch 6/100\n",
            "36/36 [==============================] - 182s 5s/step - loss: 1.0986 - acc: 0.4551 - f1_m: 0.0000e+00 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 1.0986 - val_acc: 0.4982 - val_f1_m: 0.0000e+00 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
            "Epoch 7/100\n",
            "36/36 [==============================] - 193s 5s/step - loss: 1.0986 - acc: 0.4551 - f1_m: 0.0000e+00 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 1.0986 - val_acc: 0.4982 - val_f1_m: 0.0000e+00 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
            "Epoch 8/100\n",
            "36/36 [==============================] - 182s 5s/step - loss: 1.0986 - acc: 0.4551 - f1_m: 0.0000e+00 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 1.0986 - val_acc: 0.4982 - val_f1_m: 0.0000e+00 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
            "Epoch 9/100\n",
            "36/36 [==============================] - 182s 5s/step - loss: 1.0986 - acc: 0.4551 - f1_m: 0.0000e+00 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 1.0986 - val_acc: 0.4982 - val_f1_m: 0.0000e+00 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
            "Epoch 10/100\n",
            "36/36 [==============================] - 183s 5s/step - loss: 1.0986 - acc: 0.4551 - f1_m: 0.0000e+00 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 1.0986 - val_acc: 0.4982 - val_f1_m: 0.0000e+00 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
            "Epoch 11/100\n",
            "36/36 [==============================] - 183s 5s/step - loss: 1.0986 - acc: 0.4551 - f1_m: 0.0000e+00 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 1.0986 - val_acc: 0.4982 - val_f1_m: 0.0000e+00 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
            "Epoch 12/100\n",
            "36/36 [==============================] - 186s 5s/step - loss: 1.0986 - acc: 0.4551 - f1_m: 0.0000e+00 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 1.0986 - val_acc: 0.4982 - val_f1_m: 0.0000e+00 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Od15Nopy46pK",
        "outputId": "75ab7df1-03f6-41b9-f136-90430635a26b"
      },
      "source": [
        "score = custom_vgg.evaluate(test_ds, verbose=0)\n",
        "print(\"Test loss:\", score[0])\n",
        "print(\"Test accuracy:\", score[1])\n",
        "\n",
        "\n",
        "# Save the model\n",
        "filepath = './model_vgg'\n",
        "save_model(model, filepath)\n",
        "\n",
        "# Load the model\n",
        "model = load_model(filepath, compile = False)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 1.0986123085021973\n",
            "Test accuracy: 0.4866071343421936\n",
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
            "INFO:tensorflow:Assets written to: ./model_vgg/assets\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YEWLTcwWMLuj"
      },
      "source": [
        "acc = history_vgg.history['f1_m']\n",
        "val_acc = history_vgg.history['val_f1_m']\n",
        "\n",
        "loss = history_vgg.history['loss']\n",
        "val_loss = history_vgg.history['val_loss']\n",
        "\n",
        "epochs_range = range(len(acc))"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "VqB0QBPWML6l",
        "outputId": "a7f144e4-ca26-4249-948c-99c1342c70fc"
      },
      "source": [
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs_range, loss, label='Training Loss')\n",
        "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.show()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8BanTOWLML-T",
        "outputId": "4844d764-3672-494d-a8e9-531a9ff45d17"
      },
      "source": [
        "predictions = custom_vgg.predict(test_ds, steps=test_steps_per_epoch)\n",
        "# Get most likely class\n",
        "predicted_classes = numpy.argmax(predictions, axis=1)\n",
        "predicted_classes"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vrh0A_29MMBR",
        "outputId": "b8447f86-5335-41f1-e187-08733b4af82f"
      },
      "source": [
        "true_classes = test_generator.classes #true_classes = test_ds.class_names\n",
        "true_classes"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sZGWJorbMMEM",
        "outputId": "7b87f8c7-5467-4e95-ac90-afbd28ebd791"
      },
      "source": [
        "class_labels = list(test_generator.class_indices.keys())\n",
        "class_labels"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Danube', 'Dnieper', 'UnknownOrigin']"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        },
        "id": "hrbbFfhlMa8O",
        "outputId": "6b4a3650-533c-41ae-926b-dd7c0ec7a006"
      },
      "source": [
        "report = classification_report(true_classes, predicted_classes, target_names=class_labels, zero_division=0)\n",
        "print(report)\n",
        "cmrforest = confusion_matrix(true_classes, predicted_classes)\n",
        "plt.figure(figsize=(10,7))\n",
        "sns.heatmap(cmrforest ,annot=True , fmt = 'd')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')\n",
        "plt.title('Confusion Report')\n",
        "plt.show()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               precision    recall  f1-score   support\n",
            "\n",
            "       Danube       0.49      1.00      0.65       109\n",
            "      Dnieper       0.00      0.00      0.00        78\n",
            "UnknownOrigin       0.00      0.00      0.00        37\n",
            "\n",
            "     accuracy                           0.49       224\n",
            "    macro avg       0.16      0.33      0.22       224\n",
            " weighted avg       0.24      0.49      0.32       224\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VLUElIA4NMj7"
      },
      "source": [
        "# 3.ResNet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bTgp61f3NRff"
      },
      "source": [
        "stride = 1\n",
        "CHANNEL_AXIS = 3\n",
        "\n",
        "def res_layer(x, filters, pooling = False, dropout = 0.0):\n",
        "    temp = x\n",
        "    temp = tf.keras.layers.Conv2D(filters,(3,3),strides = stride,padding = \"same\")(temp)\n",
        "    temp = tf.keras.layers.BatchNormalization(axis = CHANNEL_AXIS)(temp)\n",
        "    temp = tf.keras.layers.Activation(\"relu\")(temp)\n",
        "    temp = tf.keras.layers.Conv2D(filters,(3,3),strides = stride,padding = \"same\")(temp)\n",
        "\n",
        "    x = tf.keras.layers.add([temp,tf.keras.layers.Conv2D(filters,(3,3),strides = stride,padding = \"same\")(x)])\n",
        "    if pooling:\n",
        "        x = tf.keras.layers.MaxPooling2D((2,2))(x)\n",
        "    if dropout != 0.0:\n",
        "        x = Dropout(dropout)(x)\n",
        "    x = tf.keras.layers.BatchNormalization(axis = CHANNEL_AXIS)(x)\n",
        "    x = tf.keras.layers.Activation(\"relu\")(x)\n",
        "    return x"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I_PLanL9NRoj",
        "outputId": "72961469-2135-44a8-9d9e-a17a4d59b8a1"
      },
      "source": [
        "inp = tf.keras.Input(shape = (img_height, img_width,3))\n",
        "x = inp\n",
        "x = tf.keras.layers.Conv2D(16,(3,3),strides = stride,padding = \"same\")(x)\n",
        "x = tf.keras.layers.BatchNormalization(axis = CHANNEL_AXIS)(x)\n",
        "x = tf.keras.layers.Activation(\"relu\")(x)\n",
        "\n",
        "x = res_layer(x,8,dropout = 0.2)\n",
        "x = res_layer(x,8,dropout = 0.3)\n",
        "x = res_layer(x,8,dropout = 0.4,pooling = True)\n",
        "x = res_layer(x,16,dropout = 0.2)\n",
        "x = res_layer(x,16,dropout = 0.2,pooling = True)\n",
        "x = res_layer(x,64,dropout = 0.4)\n",
        "x = tf.keras.layers.Flatten()(x)\n",
        "x = tf.keras.layers.Dropout(0.4)(x)\n",
        "x = tf.keras.layers.Dense(256,activation = \"relu\")(x)\n",
        "x = tf.keras.layers.Dropout(0.23)(x)\n",
        "x = tf.keras.layers.Dense(num_classes)(x)\n",
        "\n",
        "resnet_model = Model(inp,x,name = \"Resnet\")\n",
        "resnet_model.summary()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"Resnet\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 150, 150, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " conv2d_8 (Conv2D)              (None, 150, 150, 16  448         ['input_1[0][0]']                \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization (BatchNorm  (None, 150, 150, 16  64         ['conv2d_8[0][0]']               \n",
            " alization)                     )                                                                 \n",
            "                                                                                                  \n",
            " activation (Activation)        (None, 150, 150, 16  0           ['batch_normalization[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_9 (Conv2D)              (None, 150, 150, 8)  1160        ['activation[0][0]']             \n",
            "                                                                                                  \n",
            " batch_normalization_1 (BatchNo  (None, 150, 150, 8)  32         ['conv2d_9[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_1 (Activation)      (None, 150, 150, 8)  0           ['batch_normalization_1[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_10 (Conv2D)             (None, 150, 150, 8)  584         ['activation_1[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_11 (Conv2D)             (None, 150, 150, 8)  1160        ['activation[0][0]']             \n",
            "                                                                                                  \n",
            " add (Add)                      (None, 150, 150, 8)  0           ['conv2d_10[0][0]',              \n",
            "                                                                  'conv2d_11[0][0]']              \n",
            "                                                                                                  \n",
            " dropout_7 (Dropout)            (None, 150, 150, 8)  0           ['add[0][0]']                    \n",
            "                                                                                                  \n",
            " batch_normalization_2 (BatchNo  (None, 150, 150, 8)  32         ['dropout_7[0][0]']              \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_2 (Activation)      (None, 150, 150, 8)  0           ['batch_normalization_2[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_12 (Conv2D)             (None, 150, 150, 8)  584         ['activation_2[0][0]']           \n",
            "                                                                                                  \n",
            " batch_normalization_3 (BatchNo  (None, 150, 150, 8)  32         ['conv2d_12[0][0]']              \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_3 (Activation)      (None, 150, 150, 8)  0           ['batch_normalization_3[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_13 (Conv2D)             (None, 150, 150, 8)  584         ['activation_3[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_14 (Conv2D)             (None, 150, 150, 8)  584         ['activation_2[0][0]']           \n",
            "                                                                                                  \n",
            " add_1 (Add)                    (None, 150, 150, 8)  0           ['conv2d_13[0][0]',              \n",
            "                                                                  'conv2d_14[0][0]']              \n",
            "                                                                                                  \n",
            " dropout_8 (Dropout)            (None, 150, 150, 8)  0           ['add_1[0][0]']                  \n",
            "                                                                                                  \n",
            " batch_normalization_4 (BatchNo  (None, 150, 150, 8)  32         ['dropout_8[0][0]']              \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_4 (Activation)      (None, 150, 150, 8)  0           ['batch_normalization_4[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_15 (Conv2D)             (None, 150, 150, 8)  584         ['activation_4[0][0]']           \n",
            "                                                                                                  \n",
            " batch_normalization_5 (BatchNo  (None, 150, 150, 8)  32         ['conv2d_15[0][0]']              \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_5 (Activation)      (None, 150, 150, 8)  0           ['batch_normalization_5[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_16 (Conv2D)             (None, 150, 150, 8)  584         ['activation_5[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_17 (Conv2D)             (None, 150, 150, 8)  584         ['activation_4[0][0]']           \n",
            "                                                                                                  \n",
            " add_2 (Add)                    (None, 150, 150, 8)  0           ['conv2d_16[0][0]',              \n",
            "                                                                  'conv2d_17[0][0]']              \n",
            "                                                                                                  \n",
            " max_pooling2d_5 (MaxPooling2D)  (None, 75, 75, 8)   0           ['add_2[0][0]']                  \n",
            "                                                                                                  \n",
            " dropout_9 (Dropout)            (None, 75, 75, 8)    0           ['max_pooling2d_5[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_6 (BatchNo  (None, 75, 75, 8)   32          ['dropout_9[0][0]']              \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_6 (Activation)      (None, 75, 75, 8)    0           ['batch_normalization_6[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_18 (Conv2D)             (None, 75, 75, 16)   1168        ['activation_6[0][0]']           \n",
            "                                                                                                  \n",
            " batch_normalization_7 (BatchNo  (None, 75, 75, 16)  64          ['conv2d_18[0][0]']              \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_7 (Activation)      (None, 75, 75, 16)   0           ['batch_normalization_7[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_19 (Conv2D)             (None, 75, 75, 16)   2320        ['activation_7[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_20 (Conv2D)             (None, 75, 75, 16)   1168        ['activation_6[0][0]']           \n",
            "                                                                                                  \n",
            " add_3 (Add)                    (None, 75, 75, 16)   0           ['conv2d_19[0][0]',              \n",
            "                                                                  'conv2d_20[0][0]']              \n",
            "                                                                                                  \n",
            " dropout_10 (Dropout)           (None, 75, 75, 16)   0           ['add_3[0][0]']                  \n",
            "                                                                                                  \n",
            " batch_normalization_8 (BatchNo  (None, 75, 75, 16)  64          ['dropout_10[0][0]']             \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_8 (Activation)      (None, 75, 75, 16)   0           ['batch_normalization_8[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_21 (Conv2D)             (None, 75, 75, 16)   2320        ['activation_8[0][0]']           \n",
            "                                                                                                  \n",
            " batch_normalization_9 (BatchNo  (None, 75, 75, 16)  64          ['conv2d_21[0][0]']              \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_9 (Activation)      (None, 75, 75, 16)   0           ['batch_normalization_9[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_22 (Conv2D)             (None, 75, 75, 16)   2320        ['activation_9[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_23 (Conv2D)             (None, 75, 75, 16)   2320        ['activation_8[0][0]']           \n",
            "                                                                                                  \n",
            " add_4 (Add)                    (None, 75, 75, 16)   0           ['conv2d_22[0][0]',              \n",
            "                                                                  'conv2d_23[0][0]']              \n",
            "                                                                                                  \n",
            " max_pooling2d_6 (MaxPooling2D)  (None, 37, 37, 16)  0           ['add_4[0][0]']                  \n",
            "                                                                                                  \n",
            " dropout_11 (Dropout)           (None, 37, 37, 16)   0           ['max_pooling2d_6[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_10 (BatchN  (None, 37, 37, 16)  64          ['dropout_11[0][0]']             \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_10 (Activation)     (None, 37, 37, 16)   0           ['batch_normalization_10[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_24 (Conv2D)             (None, 37, 37, 64)   9280        ['activation_10[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_11 (BatchN  (None, 37, 37, 64)  256         ['conv2d_24[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_11 (Activation)     (None, 37, 37, 64)   0           ['batch_normalization_11[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_25 (Conv2D)             (None, 37, 37, 64)   36928       ['activation_11[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_26 (Conv2D)             (None, 37, 37, 64)   9280        ['activation_10[0][0]']          \n",
            "                                                                                                  \n",
            " add_5 (Add)                    (None, 37, 37, 64)   0           ['conv2d_25[0][0]',              \n",
            "                                                                  'conv2d_26[0][0]']              \n",
            "                                                                                                  \n",
            " dropout_12 (Dropout)           (None, 37, 37, 64)   0           ['add_5[0][0]']                  \n",
            "                                                                                                  \n",
            " batch_normalization_12 (BatchN  (None, 37, 37, 64)  256         ['dropout_12[0][0]']             \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_12 (Activation)     (None, 37, 37, 64)   0           ['batch_normalization_12[0][0]'] \n",
            "                                                                                                  \n",
            " flatten_3 (Flatten)            (None, 87616)        0           ['activation_12[0][0]']          \n",
            "                                                                                                  \n",
            " dropout_13 (Dropout)           (None, 87616)        0           ['flatten_3[0][0]']              \n",
            "                                                                                                  \n",
            " dense_5 (Dense)                (None, 256)          22429952    ['dropout_13[0][0]']             \n",
            "                                                                                                  \n",
            " dropout_14 (Dropout)           (None, 256)          0           ['dense_5[0][0]']                \n",
            "                                                                                                  \n",
            " dense_6 (Dense)                (None, 3)            771         ['dropout_14[0][0]']             \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 22,505,707\n",
            "Trainable params: 22,505,195\n",
            "Non-trainable params: 512\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ph0kldXONRrF"
      },
      "source": [
        "resnet_model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "               metrics=['acc', f1_m, precision_m, recall_m])"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MsIHOUJANRtp",
        "outputId": "1fcadd37-d44c-48ea-f982-a849ae569f02"
      },
      "source": [
        "history_resnet = resnet_model.fit(train_ds,\n",
        "                    validation_data=val_ds,\n",
        "                    callbacks=[callback],\n",
        "                    epochs=epochs)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "36/36 [==============================] - 198s 5s/step - loss: 6.9904 - acc: 0.8311 - f1_m: 0.7104 - precision_m: 0.5193 - recall_m: 1.1801 - val_loss: 39.1390 - val_acc: 0.4982 - val_f1_m: 0.6751 - val_precision_m: 0.4589 - val_recall_m: 1.3136\n",
            "Epoch 2/100\n",
            "36/36 [==============================] - 195s 5s/step - loss: 0.1425 - acc: 0.9831 - f1_m: 0.7543 - precision_m: 0.5614 - recall_m: 1.1640 - val_loss: 0.2609 - val_acc: 0.8897 - val_f1_m: 0.3120 - val_precision_m: 0.2826 - val_recall_m: 0.3761\n",
            "Epoch 3/100\n",
            "36/36 [==============================] - 199s 6s/step - loss: 0.1443 - acc: 0.9849 - f1_m: 0.7362 - precision_m: 0.5541 - recall_m: 1.1269 - val_loss: 0.6981 - val_acc: 0.5480 - val_f1_m: 0.2444 - val_precision_m: 0.2296 - val_recall_m: 0.2666\n",
            "Epoch 4/100\n",
            "36/36 [==============================] - 195s 5s/step - loss: 0.1010 - acc: 0.9813 - f1_m: 0.7365 - precision_m: 0.5581 - recall_m: 1.0953 - val_loss: 0.8229 - val_acc: 0.5196 - val_f1_m: 0.2443 - val_precision_m: 0.2189 - val_recall_m: 0.2837\n",
            "Epoch 5/100\n",
            "36/36 [==============================] - 196s 5s/step - loss: 0.0439 - acc: 0.9902 - f1_m: 0.7800 - precision_m: 0.5851 - recall_m: 1.1902 - val_loss: 1.1637 - val_acc: 0.4982 - val_f1_m: 0.2523 - val_precision_m: 0.2257 - val_recall_m: 0.2932\n",
            "Epoch 6/100\n",
            "36/36 [==============================] - 196s 5s/step - loss: 0.0770 - acc: 0.9867 - f1_m: 0.7597 - precision_m: 0.5773 - recall_m: 1.1365 - val_loss: 1.1624 - val_acc: 0.4982 - val_f1_m: 0.2529 - val_precision_m: 0.2329 - val_recall_m: 0.2856\n",
            "Epoch 7/100\n",
            "36/36 [==============================] - 200s 6s/step - loss: 0.0634 - acc: 0.9876 - f1_m: 0.7380 - precision_m: 0.5687 - recall_m: 1.0798 - val_loss: 1.4561 - val_acc: 0.4982 - val_f1_m: 0.2535 - val_precision_m: 0.2284 - val_recall_m: 0.2921\n",
            "Epoch 8/100\n",
            "36/36 [==============================] - 196s 5s/step - loss: 0.0874 - acc: 0.9840 - f1_m: 0.7825 - precision_m: 0.5965 - recall_m: 1.1458 - val_loss: 1.0082 - val_acc: 0.4982 - val_f1_m: 0.2551 - val_precision_m: 0.2297 - val_recall_m: 0.2919\n",
            "Epoch 9/100\n",
            "36/36 [==============================] - 197s 5s/step - loss: 0.0496 - acc: 0.9876 - f1_m: 0.7468 - precision_m: 0.5798 - recall_m: 1.0789 - val_loss: 1.1648 - val_acc: 0.4982 - val_f1_m: 0.2555 - val_precision_m: 0.2321 - val_recall_m: 0.2965\n",
            "Epoch 10/100\n",
            "36/36 [==============================] - 195s 5s/step - loss: 0.0477 - acc: 0.9902 - f1_m: 0.5642 - precision_m: 0.4886 - recall_m: 0.6957 - val_loss: 0.9649 - val_acc: 0.4982 - val_f1_m: 0.2623 - val_precision_m: 0.7130 - val_recall_m: 0.1707\n",
            "Epoch 11/100\n",
            "36/36 [==============================] - 196s 5s/step - loss: 0.0504 - acc: 0.9831 - f1_m: 0.5745 - precision_m: 0.5084 - recall_m: 0.6938 - val_loss: 0.9584 - val_acc: 0.4982 - val_f1_m: 0.3863 - val_precision_m: 0.8059 - val_recall_m: 0.2722\n",
            "Epoch 12/100\n",
            "36/36 [==============================] - 196s 5s/step - loss: 0.0415 - acc: 0.9876 - f1_m: 0.6524 - precision_m: 0.5411 - recall_m: 0.8386 - val_loss: 1.0546 - val_acc: 0.4982 - val_f1_m: 0.4220 - val_precision_m: 1.8222 - val_recall_m: 0.2571\n",
            "Epoch 13/100\n",
            "36/36 [==============================] - 196s 5s/step - loss: 0.0428 - acc: 0.9867 - f1_m: 0.6148 - precision_m: 0.5162 - recall_m: 0.7832 - val_loss: 1.0095 - val_acc: 0.4982 - val_f1_m: 0.4181 - val_precision_m: 0.9530 - val_recall_m: 0.2763\n",
            "Epoch 14/100\n",
            "36/36 [==============================] - 195s 5s/step - loss: 0.0380 - acc: 0.9867 - f1_m: 0.5733 - precision_m: 0.4920 - recall_m: 0.7034 - val_loss: 1.1775 - val_acc: 0.4982 - val_f1_m: 0.2710 - val_precision_m: 0.2604 - val_recall_m: 0.2877\n",
            "Epoch 15/100\n",
            "36/36 [==============================] - 195s 5s/step - loss: 0.0475 - acc: 0.9858 - f1_m: 0.6286 - precision_m: 0.5180 - recall_m: 0.8229 - val_loss: 1.0034 - val_acc: 0.4982 - val_f1_m: 0.2662 - val_precision_m: 0.2630 - val_recall_m: 0.2761\n",
            "Epoch 16/100\n",
            "36/36 [==============================] - 196s 5s/step - loss: 0.0578 - acc: 0.9858 - f1_m: 0.4942 - precision_m: 0.4451 - recall_m: 0.5961 - val_loss: 0.8093 - val_acc: 0.4982 - val_f1_m: 0.2035 - val_precision_m: 0.2612 - val_recall_m: 0.1778\n",
            "Epoch 17/100\n",
            "36/36 [==============================] - 196s 5s/step - loss: 0.0543 - acc: 0.9813 - f1_m: 0.3752 - precision_m: 0.3767 - recall_m: 0.3884 - val_loss: 0.8348 - val_acc: 0.4982 - val_f1_m: 0.0000e+00 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
            "Epoch 18/100\n",
            "36/36 [==============================] - 195s 5s/step - loss: 0.0587 - acc: 0.9867 - f1_m: 0.5930 - precision_m: 0.5071 - recall_m: 0.7332 - val_loss: 0.8014 - val_acc: 0.6157 - val_f1_m: 0.6798 - val_precision_m: 0.5306 - val_recall_m: 0.9529\n",
            "Epoch 19/100\n",
            "36/36 [==============================] - 196s 5s/step - loss: 0.0258 - acc: 0.9902 - f1_m: 0.3523 - precision_m: 0.3361 - recall_m: 0.3784 - val_loss: 0.8594 - val_acc: 0.9181 - val_f1_m: 0.0000e+00 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
            "Epoch 20/100\n",
            "36/36 [==============================] - 196s 5s/step - loss: 0.0342 - acc: 0.9902 - f1_m: 0.4162 - precision_m: 0.3851 - recall_m: 0.4711 - val_loss: 0.8764 - val_acc: 0.6263 - val_f1_m: 0.0000e+00 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
            "Epoch 21/100\n",
            "36/36 [==============================] - 196s 5s/step - loss: 0.0351 - acc: 0.9893 - f1_m: 0.3666 - precision_m: 0.3528 - recall_m: 0.3939 - val_loss: 0.8151 - val_acc: 0.9004 - val_f1_m: 0.0000e+00 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
            "Epoch 22/100\n",
            "36/36 [==============================] - 195s 5s/step - loss: 0.0264 - acc: 0.9893 - f1_m: 0.4158 - precision_m: 0.3921 - recall_m: 0.4529 - val_loss: 0.6864 - val_acc: 0.9075 - val_f1_m: 0.0000e+00 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
            "Epoch 23/100\n",
            "36/36 [==============================] - 195s 5s/step - loss: 0.0329 - acc: 0.9876 - f1_m: 0.3395 - precision_m: 0.3449 - recall_m: 0.3533 - val_loss: 0.7953 - val_acc: 0.9253 - val_f1_m: 0.0000e+00 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
            "Epoch 24/100\n",
            "36/36 [==============================] - 195s 5s/step - loss: 0.0345 - acc: 0.9911 - f1_m: 0.4846 - precision_m: 0.4353 - recall_m: 0.5756 - val_loss: 1.0975 - val_acc: 0.2598 - val_f1_m: 0.7618 - val_precision_m: 56666668.0000 - val_recall_m: 0.3809\n",
            "Epoch 25/100\n",
            "36/36 [==============================] - 195s 5s/step - loss: 0.0362 - acc: 0.9867 - f1_m: 0.5685 - precision_m: 0.4967 - recall_m: 0.6878 - val_loss: 1.0358 - val_acc: 0.3096 - val_f1_m: 0.2229 - val_precision_m: 16666667.0000 - val_recall_m: 0.1114\n",
            "Epoch 26/100\n",
            "36/36 [==============================] - 195s 5s/step - loss: 0.0437 - acc: 0.9884 - f1_m: 0.5156 - precision_m: 0.4664 - recall_m: 0.5969 - val_loss: 0.8725 - val_acc: 0.6584 - val_f1_m: 0.1083 - val_precision_m: 8888889.0000 - val_recall_m: 0.0551\n",
            "Epoch 27/100\n",
            "36/36 [==============================] - 195s 5s/step - loss: 0.0377 - acc: 0.9884 - f1_m: 0.4461 - precision_m: 0.4134 - recall_m: 0.4959 - val_loss: 0.9213 - val_acc: 0.3701 - val_f1_m: 0.6530 - val_precision_m: 2222224.5000 - val_recall_m: 0.3846\n",
            "Epoch 28/100\n",
            "36/36 [==============================] - 195s 5s/step - loss: 0.0378 - acc: 0.9938 - f1_m: 0.4019 - precision_m: 0.3862 - recall_m: 0.4340 - val_loss: 1.0197 - val_acc: 0.3132 - val_f1_m: 1.0789 - val_precision_m: 86666664.0000 - val_recall_m: 0.5395\n",
            "Epoch 29/100\n",
            "36/36 [==============================] - 196s 5s/step - loss: 0.0266 - acc: 0.9929 - f1_m: 0.3827 - precision_m: 0.3672 - recall_m: 0.4104 - val_loss: 0.9981 - val_acc: 0.3238 - val_f1_m: 1.0329 - val_precision_m: 0.8745 - val_recall_m: 1.2798\n",
            "Epoch 30/100\n",
            "36/36 [==============================] - 209s 6s/step - loss: 0.0311 - acc: 0.9911 - f1_m: 0.5417 - precision_m: 0.4875 - recall_m: 0.6349 - val_loss: 0.8578 - val_acc: 0.6014 - val_f1_m: 0.0000e+00 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
            "Epoch 31/100\n",
            "36/36 [==============================] - 198s 6s/step - loss: 0.0308 - acc: 0.9902 - f1_m: 0.4182 - precision_m: 0.3975 - recall_m: 0.4609 - val_loss: 0.8936 - val_acc: 0.6085 - val_f1_m: 0.0000e+00 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
            "Epoch 32/100\n",
            "36/36 [==============================] - 195s 5s/step - loss: 0.0416 - acc: 0.9849 - f1_m: 0.3663 - precision_m: 0.3659 - recall_m: 0.3879 - val_loss: 0.8032 - val_acc: 0.7580 - val_f1_m: 0.0000e+00 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
            "Epoch 33/100\n",
            "36/36 [==============================] - 195s 5s/step - loss: 0.0303 - acc: 0.9876 - f1_m: 0.4170 - precision_m: 0.3932 - recall_m: 0.4585 - val_loss: 0.8490 - val_acc: 0.5516 - val_f1_m: 0.0000e+00 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qimzZUTAGk-f",
        "outputId": "76033217-6c8c-4558-bef9-93b1a61d0fac"
      },
      "source": [
        "score = resnet_model.evaluate(test_ds, verbose=0)\n",
        "print(\"Test loss:\", score[0])\n",
        "print(\"Test accuracy:\", score[1])\n",
        "\n",
        "\n",
        "# Save the model\n",
        "filepath = './model_resnet'\n",
        "save_model(model, filepath)\n",
        "\n",
        "# Load the model\n",
        "model = load_model(filepath, compile = False)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 0.8280189633369446\n",
            "Test accuracy: 0.5267857313156128\n",
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
            "INFO:tensorflow:Assets written to: ./model_resnet/assets\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I2MIrRIFsu7F"
      },
      "source": [
        "acc = history_resnet.history['f1_m']\n",
        "val_acc = history_resnet.history['val_f1_m']\n",
        "\n",
        "loss = history_resnet.history['loss']\n",
        "val_loss = history_resnet.history['val_loss']\n",
        "\n",
        "epochs_range = range(len(acc))"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "juVkGZgBsvCJ",
        "outputId": "3bee9150-e2fa-4125-be17-f4a6de770adf"
      },
      "source": [
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs_range, loss, label='Training Loss')\n",
        "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.show()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Ew5kdV9svIL",
        "outputId": "224ac6b5-e2c3-48a7-ff44-13939d65f68a"
      },
      "source": [
        "predictions = resnet_model.predict(test_ds, steps=test_steps_per_epoch)\n",
        "# Get most likely class\n",
        "predicted_classes = numpy.argmax(predictions, axis=1)\n",
        "predicted_classes"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 0, 2, 0, 0, 0, 2, 2, 0, 0, 0, 2,\n",
              "       0, 0, 0, 2, 2, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 2, 2, 0, 0, 2, 0, 0,\n",
              "       2, 2, 0, 2, 0, 0, 0, 0, 0, 2, 2, 0, 2, 0, 2, 2, 0, 2, 2, 0, 0, 2,\n",
              "       2, 2, 0, 2, 2, 2, 2, 0, 2, 0, 0, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 0,\n",
              "       0, 0, 0, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9sFQXxxRs9OX",
        "outputId": "f6cd7f8c-e57b-481d-c1a5-60ae029d57c1"
      },
      "source": [
        "true_classes = test_generator.classes #true_classes = test_ds.class_names\n",
        "true_classes"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kAwDc0Jas9V6",
        "outputId": "4014b80c-417e-48b1-d202-c6ef38c52008"
      },
      "source": [
        "class_labels = list(test_generator.class_indices.keys())\n",
        "class_labels"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Danube', 'Dnieper', 'UnknownOrigin']"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        },
        "id": "abJ41UE-s9Zb",
        "outputId": "d3737836-d3f2-460f-b003-7e12c8ba98e4"
      },
      "source": [
        "report = classification_report(true_classes, predicted_classes, target_names=class_labels, zero_division=0)\n",
        "print(report)\n",
        "cmrforest = confusion_matrix(true_classes, predicted_classes)\n",
        "plt.figure(figsize=(10,7))\n",
        "sns.heatmap(cmrforest ,annot=True , fmt = 'd')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')\n",
        "plt.title('Confusion Report')\n",
        "plt.show()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               precision    recall  f1-score   support\n",
            "\n",
            "       Danube       0.63      1.00      0.77       109\n",
            "      Dnieper       0.00      0.00      0.00        78\n",
            "UnknownOrigin       0.18      0.24      0.21        37\n",
            "\n",
            "     accuracy                           0.53       224\n",
            "    macro avg       0.27      0.41      0.33       224\n",
            " weighted avg       0.33      0.53      0.41       224\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "edLwUcxm0Bad"
      },
      "source": [
        "# Transfer learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8sSs69tQAfXn"
      },
      "source": [
        "## ResNet50"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wb006oPsAjqi"
      },
      "source": [
        "### Initialization and Summary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VmCa5UV4tquS",
        "outputId": "faf8e9f3-4d07-429b-eb4a-7c920f7b0043"
      },
      "source": [
        "# create the base pre-trained model\n",
        "base_model = ResNet50(weights='imagenet', include_top=False)\n",
        "base_model.trainable = False\n",
        "\n",
        "# add a global spatial average pooling layer\n",
        "x = base_model.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "# add a fully-connected layer\n",
        "x = Dense(1024, activation='relu')(x)\n",
        "# add a dropout layer\n",
        "x = Dropout(0.2)(x)\n",
        "# and a logistic layer -- we have 3 classes\n",
        "predictions = Dense(3, activation='softmax')(x)\n",
        "\n",
        "# this is the model we will train\n",
        "model = Model(inputs=base_model.input, outputs=predictions)\n",
        "\n",
        "# compile the model (should be done *after* setting layers to non-trainable)\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model_3\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_4 (InputLayer)            [(None, None, None,  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1_pad (ZeroPadding2D)       (None, None, None, 3 0           input_4[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv1_conv (Conv2D)             (None, None, None, 6 9472        conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv1_bn (BatchNormalization)   (None, None, None, 6 256         conv1_conv[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1_relu (Activation)         (None, None, None, 6 0           conv1_bn[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pad (ZeroPadding2D)       (None, None, None, 6 0           conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pool (MaxPooling2D)       (None, None, None, 6 0           pool1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_conv (Conv2D)    (None, None, None, 6 4160        pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_bn (BatchNormali (None, None, None, 6 256         conv2_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_relu (Activation (None, None, None, 6 0           conv2_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_conv (Conv2D)    (None, None, None, 6 36928       conv2_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_bn (BatchNormali (None, None, None, 6 256         conv2_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_relu (Activation (None, None, None, 6 0           conv2_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_conv (Conv2D)    (None, None, None, 2 16640       pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_conv (Conv2D)    (None, None, None, 2 16640       conv2_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_bn (BatchNormali (None, None, None, 2 1024        conv2_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_bn (BatchNormali (None, None, None, 2 1024        conv2_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_add (Add)          (None, None, None, 2 0           conv2_block1_0_bn[0][0]          \n",
            "                                                                 conv2_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_out (Activation)   (None, None, None, 2 0           conv2_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_conv (Conv2D)    (None, None, None, 6 16448       conv2_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_bn (BatchNormali (None, None, None, 6 256         conv2_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_relu (Activation (None, None, None, 6 0           conv2_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_conv (Conv2D)    (None, None, None, 6 36928       conv2_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_bn (BatchNormali (None, None, None, 6 256         conv2_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_relu (Activation (None, None, None, 6 0           conv2_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_conv (Conv2D)    (None, None, None, 2 16640       conv2_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_bn (BatchNormali (None, None, None, 2 1024        conv2_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_add (Add)          (None, None, None, 2 0           conv2_block1_out[0][0]           \n",
            "                                                                 conv2_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_out (Activation)   (None, None, None, 2 0           conv2_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_conv (Conv2D)    (None, None, None, 6 16448       conv2_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_bn (BatchNormali (None, None, None, 6 256         conv2_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_relu (Activation (None, None, None, 6 0           conv2_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_conv (Conv2D)    (None, None, None, 6 36928       conv2_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_bn (BatchNormali (None, None, None, 6 256         conv2_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_relu (Activation (None, None, None, 6 0           conv2_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_conv (Conv2D)    (None, None, None, 2 16640       conv2_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_bn (BatchNormali (None, None, None, 2 1024        conv2_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_add (Add)          (None, None, None, 2 0           conv2_block2_out[0][0]           \n",
            "                                                                 conv2_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_out (Activation)   (None, None, None, 2 0           conv2_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_conv (Conv2D)    (None, None, None, 1 32896       conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_bn (BatchNormali (None, None, None, 1 512         conv3_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_relu (Activation (None, None, None, 1 0           conv3_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_conv (Conv2D)    (None, None, None, 1 147584      conv3_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_bn (BatchNormali (None, None, None, 1 512         conv3_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_relu (Activation (None, None, None, 1 0           conv3_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_conv (Conv2D)    (None, None, None, 5 131584      conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_conv (Conv2D)    (None, None, None, 5 66048       conv3_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_bn (BatchNormali (None, None, None, 5 2048        conv3_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_bn (BatchNormali (None, None, None, 5 2048        conv3_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_add (Add)          (None, None, None, 5 0           conv3_block1_0_bn[0][0]          \n",
            "                                                                 conv3_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_out (Activation)   (None, None, None, 5 0           conv3_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_conv (Conv2D)    (None, None, None, 1 65664       conv3_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_bn (BatchNormali (None, None, None, 1 512         conv3_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_relu (Activation (None, None, None, 1 0           conv3_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_conv (Conv2D)    (None, None, None, 1 147584      conv3_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_bn (BatchNormali (None, None, None, 1 512         conv3_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_relu (Activation (None, None, None, 1 0           conv3_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_conv (Conv2D)    (None, None, None, 5 66048       conv3_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_bn (BatchNormali (None, None, None, 5 2048        conv3_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_add (Add)          (None, None, None, 5 0           conv3_block1_out[0][0]           \n",
            "                                                                 conv3_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_out (Activation)   (None, None, None, 5 0           conv3_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_conv (Conv2D)    (None, None, None, 1 65664       conv3_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_bn (BatchNormali (None, None, None, 1 512         conv3_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_relu (Activation (None, None, None, 1 0           conv3_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_conv (Conv2D)    (None, None, None, 1 147584      conv3_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_bn (BatchNormali (None, None, None, 1 512         conv3_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_relu (Activation (None, None, None, 1 0           conv3_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_conv (Conv2D)    (None, None, None, 5 66048       conv3_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_bn (BatchNormali (None, None, None, 5 2048        conv3_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_add (Add)          (None, None, None, 5 0           conv3_block2_out[0][0]           \n",
            "                                                                 conv3_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_out (Activation)   (None, None, None, 5 0           conv3_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_conv (Conv2D)    (None, None, None, 1 65664       conv3_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_bn (BatchNormali (None, None, None, 1 512         conv3_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_relu (Activation (None, None, None, 1 0           conv3_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_conv (Conv2D)    (None, None, None, 1 147584      conv3_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_bn (BatchNormali (None, None, None, 1 512         conv3_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_relu (Activation (None, None, None, 1 0           conv3_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_conv (Conv2D)    (None, None, None, 5 66048       conv3_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_bn (BatchNormali (None, None, None, 5 2048        conv3_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_add (Add)          (None, None, None, 5 0           conv3_block3_out[0][0]           \n",
            "                                                                 conv3_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_out (Activation)   (None, None, None, 5 0           conv3_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_conv (Conv2D)    (None, None, None, 2 131328      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_relu (Activation (None, None, None, 2 0           conv4_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_relu (Activation (None, None, None, 2 0           conv4_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_conv (Conv2D)    (None, None, None, 1 525312      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_bn (BatchNormali (None, None, None, 1 4096        conv4_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_add (Add)          (None, None, None, 1 0           conv4_block1_0_bn[0][0]          \n",
            "                                                                 conv4_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_out (Activation)   (None, None, None, 1 0           conv4_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_relu (Activation (None, None, None, 2 0           conv4_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_relu (Activation (None, None, None, 2 0           conv4_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_add (Add)          (None, None, None, 1 0           conv4_block1_out[0][0]           \n",
            "                                                                 conv4_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_out (Activation)   (None, None, None, 1 0           conv4_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_relu (Activation (None, None, None, 2 0           conv4_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_relu (Activation (None, None, None, 2 0           conv4_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_add (Add)          (None, None, None, 1 0           conv4_block2_out[0][0]           \n",
            "                                                                 conv4_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_out (Activation)   (None, None, None, 1 0           conv4_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_relu (Activation (None, None, None, 2 0           conv4_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_relu (Activation (None, None, None, 2 0           conv4_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_add (Add)          (None, None, None, 1 0           conv4_block3_out[0][0]           \n",
            "                                                                 conv4_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_out (Activation)   (None, None, None, 1 0           conv4_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block5_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_relu (Activation (None, None, None, 2 0           conv4_block5_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block5_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block5_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_relu (Activation (None, None, None, 2 0           conv4_block5_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block5_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block5_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_add (Add)          (None, None, None, 1 0           conv4_block4_out[0][0]           \n",
            "                                                                 conv4_block5_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_out (Activation)   (None, None, None, 1 0           conv4_block5_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block5_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block6_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_relu (Activation (None, None, None, 2 0           conv4_block6_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block6_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block6_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_relu (Activation (None, None, None, 2 0           conv4_block6_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block6_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block6_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_add (Add)          (None, None, None, 1 0           conv4_block5_out[0][0]           \n",
            "                                                                 conv4_block6_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_out (Activation)   (None, None, None, 1 0           conv4_block6_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_conv (Conv2D)    (None, None, None, 5 524800      conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_bn (BatchNormali (None, None, None, 5 2048        conv5_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_relu (Activation (None, None, None, 5 0           conv5_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_conv (Conv2D)    (None, None, None, 5 2359808     conv5_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_bn (BatchNormali (None, None, None, 5 2048        conv5_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_relu (Activation (None, None, None, 5 0           conv5_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_conv (Conv2D)    (None, None, None, 2 2099200     conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_conv (Conv2D)    (None, None, None, 2 1050624     conv5_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_bn (BatchNormali (None, None, None, 2 8192        conv5_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_bn (BatchNormali (None, None, None, 2 8192        conv5_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_add (Add)          (None, None, None, 2 0           conv5_block1_0_bn[0][0]          \n",
            "                                                                 conv5_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_out (Activation)   (None, None, None, 2 0           conv5_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_conv (Conv2D)    (None, None, None, 5 1049088     conv5_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_bn (BatchNormali (None, None, None, 5 2048        conv5_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_relu (Activation (None, None, None, 5 0           conv5_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_conv (Conv2D)    (None, None, None, 5 2359808     conv5_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_bn (BatchNormali (None, None, None, 5 2048        conv5_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_relu (Activation (None, None, None, 5 0           conv5_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_conv (Conv2D)    (None, None, None, 2 1050624     conv5_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_bn (BatchNormali (None, None, None, 2 8192        conv5_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_add (Add)          (None, None, None, 2 0           conv5_block1_out[0][0]           \n",
            "                                                                 conv5_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_out (Activation)   (None, None, None, 2 0           conv5_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_conv (Conv2D)    (None, None, None, 5 1049088     conv5_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_bn (BatchNormali (None, None, None, 5 2048        conv5_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_relu (Activation (None, None, None, 5 0           conv5_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_conv (Conv2D)    (None, None, None, 5 2359808     conv5_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_bn (BatchNormali (None, None, None, 5 2048        conv5_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_relu (Activation (None, None, None, 5 0           conv5_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_conv (Conv2D)    (None, None, None, 2 1050624     conv5_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_bn (BatchNormali (None, None, None, 2 8192        conv5_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_add (Add)          (None, None, None, 2 0           conv5_block2_out[0][0]           \n",
            "                                                                 conv5_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_out (Activation)   (None, None, None, 2 0           conv5_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_3 (Glo (None, 2048)         0           conv5_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "dense_6 (Dense)                 (None, 1024)         2098176     global_average_pooling2d_3[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "dropout_3 (Dropout)             (None, 1024)         0           dense_6[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_7 (Dense)                 (None, 3)            3075        dropout_3[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 25,688,963\n",
            "Trainable params: 2,101,251\n",
            "Non-trainable params: 23,587,712\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UckqmxTMCa6n"
      },
      "source": [
        "### Model Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "07OlYETI9bnw",
        "outputId": "c3b843b9-3f40-4348-9242-4976e1644b20"
      },
      "source": [
        "model.fit(\n",
        "    train_generator,\n",
        "    validation_data=validation_generator,\n",
        "    steps_per_epoch=1126//32,\n",
        "    epochs=14,\n",
        "    validation_steps=280//32)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/14\n",
            "35/35 [==============================] - 30s 790ms/step - loss: 1.0122 - accuracy: 0.5338 - val_loss: 0.8317 - val_accuracy: 0.6367\n",
            "Epoch 2/14\n",
            "35/35 [==============================] - 26s 748ms/step - loss: 0.7628 - accuracy: 0.7349 - val_loss: 0.7381 - val_accuracy: 0.6094\n",
            "Epoch 3/14\n",
            "35/35 [==============================] - 26s 744ms/step - loss: 0.6284 - accuracy: 0.8035 - val_loss: 0.5965 - val_accuracy: 0.9102\n",
            "Epoch 4/14\n",
            "35/35 [==============================] - 26s 740ms/step - loss: 0.4988 - accuracy: 0.8565 - val_loss: 0.4244 - val_accuracy: 0.8672\n",
            "Epoch 5/14\n",
            "35/35 [==============================] - 26s 744ms/step - loss: 0.4386 - accuracy: 0.8565 - val_loss: 0.3724 - val_accuracy: 0.9258\n",
            "Epoch 6/14\n",
            "35/35 [==============================] - 26s 742ms/step - loss: 0.3496 - accuracy: 0.8967 - val_loss: 0.3009 - val_accuracy: 0.8906\n",
            "Epoch 7/14\n",
            "35/35 [==============================] - 26s 749ms/step - loss: 0.3159 - accuracy: 0.9113 - val_loss: 0.2576 - val_accuracy: 0.9258\n",
            "Epoch 8/14\n",
            "35/35 [==============================] - 26s 743ms/step - loss: 0.2759 - accuracy: 0.9168 - val_loss: 0.3165 - val_accuracy: 0.9141\n",
            "Epoch 9/14\n",
            "35/35 [==============================] - 26s 743ms/step - loss: 0.2585 - accuracy: 0.9232 - val_loss: 0.1777 - val_accuracy: 0.9688\n",
            "Epoch 10/14\n",
            "35/35 [==============================] - 26s 740ms/step - loss: 0.2352 - accuracy: 0.9324 - val_loss: 0.1647 - val_accuracy: 0.9648\n",
            "Epoch 11/14\n",
            "35/35 [==============================] - 26s 756ms/step - loss: 0.2363 - accuracy: 0.9351 - val_loss: 0.1913 - val_accuracy: 0.9727\n",
            "Epoch 12/14\n",
            "35/35 [==============================] - 26s 747ms/step - loss: 0.2170 - accuracy: 0.9360 - val_loss: 0.3334 - val_accuracy: 0.8984\n",
            "Epoch 13/14\n",
            "35/35 [==============================] - 26s 753ms/step - loss: 0.2338 - accuracy: 0.9214 - val_loss: 0.1834 - val_accuracy: 0.9766\n",
            "Epoch 14/14\n",
            "35/35 [==============================] - 26s 746ms/step - loss: 0.2275 - accuracy: 0.9250 - val_loss: 0.1252 - val_accuracy: 0.9688\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f61842de310>"
            ]
          },
          "execution_count": 21,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iLO2rZn9ItGp"
      },
      "source": [
        "### Model Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ocCyHDzJIrfy",
        "outputId": "42bd7974-c983-4685-a700-1cf21c612fc5"
      },
      "source": [
        "score = model.evaluate(test_generator, verbose=0)\n",
        "print(\"Test loss:\", score[0])\n",
        "print(\"Test accuracy:\", score[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test loss: 0.2832943797111511\n",
            "Test accuracy: 0.8616071343421936\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b2O4bhCPCcMp"
      },
      "source": [
        "### Classification Report and Confusion Matrix Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "vn6eUMPa_Yit",
        "outputId": "a031ce62-0152-439b-bc0a-dc59f6f2994d"
      },
      "source": [
        "predictions = model.predict(test_generator, steps=test_steps_per_epoch)\n",
        "# Get most likely class\n",
        "predicted_classes = numpy.argmax(predictions, axis=1)\n",
        "\n",
        "true_classes = test_generator.classes\n",
        "class_labels = list(test_generator.class_indices.keys())   \n",
        "\n",
        "report = classification_report(true_classes, predicted_classes, target_names=class_labels, zero_division=0)\n",
        "print(report)\n",
        "cmrforest = confusion_matrix(true_classes, predicted_classes)\n",
        "plt.figure(figsize=(10,7))\n",
        "sns.heatmap(cmrforest ,annot=True , fmt = 'd')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')\n",
        "plt.title('Confusion Report')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "               precision    recall  f1-score   support\n",
            "\n",
            "       Danube       0.89      1.00      0.94       109\n",
            "      Dnieper       0.82      1.00      0.90        78\n",
            "UnknownOrigin       1.00      0.16      0.28        37\n",
            "\n",
            "     accuracy                           0.86       224\n",
            "    macro avg       0.90      0.72      0.71       224\n",
            " weighted avg       0.88      0.86      0.82       224\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yTIU2YsUChDj"
      },
      "source": [
        "## InceptionV3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QuTwwwIPCksS"
      },
      "source": [
        "### Initialization and Summary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "khlH3_RMzbfK",
        "outputId": "f8b9d2bf-be39-40a2-9b8c-1597c74f2c8f"
      },
      "source": [
        "base_model = InceptionV3(weights='imagenet', include_top=False)\n",
        "base_model.trainable = False\n",
        "\n",
        "# add a global spatial average pooling layer\n",
        "x = base_model.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "# add a fully-connected layer\n",
        "x = Dense(1024, activation='relu')(x)\n",
        "# add a dropout layer\n",
        "x = Dropout(0.2)(x)\n",
        "# and a logistic layer -- we have 3 classes\n",
        "predictions = Dense(3, activation='softmax')(x)\n",
        "\n",
        "# this is the model we will train\n",
        "model = Model(inputs=base_model.input, outputs=predictions)\n",
        "\n",
        "# compile the model (should be done *after* setting layers to non-trainable)\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "87916544/87910968 [==============================] - 0s 0us/step\n",
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            [(None, None, None,  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, None, None, 3 864         input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization (BatchNorma (None, None, None, 3 96          conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation (Activation)         (None, None, None, 3 0           batch_normalization[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, None, None, 3 9216        activation[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, None, None, 3 96          conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, None, None, 3 0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, None, None, 6 18432       activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, None, None, 6 192         conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, None, None, 6 0           batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D)    (None, None, None, 6 0           activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, None, None, 8 5120        max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, None, None, 8 240         conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, None, None, 8 0           batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, None, None, 1 138240      activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, None, None, 1 576         conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, None, None, 1 0           batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, None, None, 1 0           activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, None, None, 6 12288       max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, None, None, 6 192         conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, None, None, 6 0           batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, None, None, 4 9216        max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, None, None, 9 55296       activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, None, None, 4 144         conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, None, None, 9 288         conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, None, None, 4 0           batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, None, None, 9 0           batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d (AveragePooli (None, None, None, 1 0           max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, None, None, 6 12288       max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, None, None, 6 76800       activation_6[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, None, None, 9 82944       activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, None, None, 3 6144        average_pooling2d[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, None, None, 6 192         conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_7 (BatchNor (None, None, None, 6 192         conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, None, None, 9 288         conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, None, None, 3 96          conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, None, None, 6 0           batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, None, None, 6 0           batch_normalization_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, None, None, 9 0           batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, None, None, 3 0           batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed0 (Concatenate)            (None, None, None, 2 0           activation_5[0][0]               \n",
            "                                                                 activation_7[0][0]               \n",
            "                                                                 activation_10[0][0]              \n",
            "                                                                 activation_11[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, None, None, 6 16384       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, None, None, 6 192         conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, None, None, 6 0           batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, None, None, 4 12288       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, None, None, 9 55296       activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, None, None, 4 144         conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, None, None, 9 288         conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, None, None, 4 0           batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, None, None, 9 0           batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_1 (AveragePoo (None, None, None, 2 0           mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, None, None, 6 16384       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, None, None, 6 76800       activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, None, None, 9 82944       activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, None, None, 6 16384       average_pooling2d_1[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_12 (BatchNo (None, None, None, 6 192         conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, None, None, 6 192         conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, None, None, 9 288         conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_18 (BatchNo (None, None, None, 6 192         conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, None, None, 6 0           batch_normalization_12[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, None, None, 6 0           batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, None, None, 9 0           batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, None, None, 6 0           batch_normalization_18[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed1 (Concatenate)            (None, None, None, 2 0           activation_12[0][0]              \n",
            "                                                                 activation_14[0][0]              \n",
            "                                                                 activation_17[0][0]              \n",
            "                                                                 activation_18[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_22 (Conv2D)              (None, None, None, 6 18432       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_22 (BatchNo (None, None, None, 6 192         conv2d_22[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_22 (Activation)      (None, None, None, 6 0           batch_normalization_22[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, None, None, 4 13824       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_23 (Conv2D)              (None, None, None, 9 55296       activation_22[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_20 (BatchNo (None, None, None, 4 144         conv2d_20[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_23 (BatchNo (None, None, None, 9 288         conv2d_23[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_20 (Activation)      (None, None, None, 4 0           batch_normalization_20[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_23 (Activation)      (None, None, None, 9 0           batch_normalization_23[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_2 (AveragePoo (None, None, None, 2 0           mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, None, None, 6 18432       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_21 (Conv2D)              (None, None, None, 6 76800       activation_20[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_24 (Conv2D)              (None, None, None, 9 82944       activation_23[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_25 (Conv2D)              (None, None, None, 6 18432       average_pooling2d_2[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_19 (BatchNo (None, None, None, 6 192         conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_21 (BatchNo (None, None, None, 6 192         conv2d_21[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_24 (BatchNo (None, None, None, 9 288         conv2d_24[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_25 (BatchNo (None, None, None, 6 192         conv2d_25[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_19 (Activation)      (None, None, None, 6 0           batch_normalization_19[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_21 (Activation)      (None, None, None, 6 0           batch_normalization_21[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_24 (Activation)      (None, None, None, 9 0           batch_normalization_24[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_25 (Activation)      (None, None, None, 6 0           batch_normalization_25[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed2 (Concatenate)            (None, None, None, 2 0           activation_19[0][0]              \n",
            "                                                                 activation_21[0][0]              \n",
            "                                                                 activation_24[0][0]              \n",
            "                                                                 activation_25[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_27 (Conv2D)              (None, None, None, 6 18432       mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_27 (BatchNo (None, None, None, 6 192         conv2d_27[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_27 (Activation)      (None, None, None, 6 0           batch_normalization_27[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_28 (Conv2D)              (None, None, None, 9 55296       activation_27[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_28 (BatchNo (None, None, None, 9 288         conv2d_28[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_28 (Activation)      (None, None, None, 9 0           batch_normalization_28[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_26 (Conv2D)              (None, None, None, 3 995328      mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_29 (Conv2D)              (None, None, None, 9 82944       activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_26 (BatchNo (None, None, None, 3 1152        conv2d_26[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_29 (BatchNo (None, None, None, 9 288         conv2d_29[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_26 (Activation)      (None, None, None, 3 0           batch_normalization_26[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_29 (Activation)      (None, None, None, 9 0           batch_normalization_29[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, None, None, 2 0           mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "mixed3 (Concatenate)            (None, None, None, 7 0           activation_26[0][0]              \n",
            "                                                                 activation_29[0][0]              \n",
            "                                                                 max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_34 (Conv2D)              (None, None, None, 1 98304       mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_34 (BatchNo (None, None, None, 1 384         conv2d_34[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_34 (Activation)      (None, None, None, 1 0           batch_normalization_34[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_35 (Conv2D)              (None, None, None, 1 114688      activation_34[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_35 (BatchNo (None, None, None, 1 384         conv2d_35[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_35 (Activation)      (None, None, None, 1 0           batch_normalization_35[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_31 (Conv2D)              (None, None, None, 1 98304       mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_36 (Conv2D)              (None, None, None, 1 114688      activation_35[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_31 (BatchNo (None, None, None, 1 384         conv2d_31[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_36 (BatchNo (None, None, None, 1 384         conv2d_36[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_31 (Activation)      (None, None, None, 1 0           batch_normalization_31[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_36 (Activation)      (None, None, None, 1 0           batch_normalization_36[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_32 (Conv2D)              (None, None, None, 1 114688      activation_31[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_37 (Conv2D)              (None, None, None, 1 114688      activation_36[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_32 (BatchNo (None, None, None, 1 384         conv2d_32[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_37 (BatchNo (None, None, None, 1 384         conv2d_37[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_32 (Activation)      (None, None, None, 1 0           batch_normalization_32[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_37 (Activation)      (None, None, None, 1 0           batch_normalization_37[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_3 (AveragePoo (None, None, None, 7 0           mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_30 (Conv2D)              (None, None, None, 1 147456      mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_33 (Conv2D)              (None, None, None, 1 172032      activation_32[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_38 (Conv2D)              (None, None, None, 1 172032      activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_39 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_3[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_30 (BatchNo (None, None, None, 1 576         conv2d_30[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_33 (BatchNo (None, None, None, 1 576         conv2d_33[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_38 (BatchNo (None, None, None, 1 576         conv2d_38[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_39 (BatchNo (None, None, None, 1 576         conv2d_39[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_30 (Activation)      (None, None, None, 1 0           batch_normalization_30[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_33 (Activation)      (None, None, None, 1 0           batch_normalization_33[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_38 (Activation)      (None, None, None, 1 0           batch_normalization_38[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_39 (Activation)      (None, None, None, 1 0           batch_normalization_39[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed4 (Concatenate)            (None, None, None, 7 0           activation_30[0][0]              \n",
            "                                                                 activation_33[0][0]              \n",
            "                                                                 activation_38[0][0]              \n",
            "                                                                 activation_39[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_44 (Conv2D)              (None, None, None, 1 122880      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_44 (BatchNo (None, None, None, 1 480         conv2d_44[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_44 (Activation)      (None, None, None, 1 0           batch_normalization_44[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_45 (Conv2D)              (None, None, None, 1 179200      activation_44[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_45 (BatchNo (None, None, None, 1 480         conv2d_45[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_45 (Activation)      (None, None, None, 1 0           batch_normalization_45[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_41 (Conv2D)              (None, None, None, 1 122880      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_46 (Conv2D)              (None, None, None, 1 179200      activation_45[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_41 (BatchNo (None, None, None, 1 480         conv2d_41[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_46 (BatchNo (None, None, None, 1 480         conv2d_46[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_41 (Activation)      (None, None, None, 1 0           batch_normalization_41[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_46 (Activation)      (None, None, None, 1 0           batch_normalization_46[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_42 (Conv2D)              (None, None, None, 1 179200      activation_41[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_47 (Conv2D)              (None, None, None, 1 179200      activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_42 (BatchNo (None, None, None, 1 480         conv2d_42[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_47 (BatchNo (None, None, None, 1 480         conv2d_47[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_42 (Activation)      (None, None, None, 1 0           batch_normalization_42[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_47 (Activation)      (None, None, None, 1 0           batch_normalization_47[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_4 (AveragePoo (None, None, None, 7 0           mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_40 (Conv2D)              (None, None, None, 1 147456      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_43 (Conv2D)              (None, None, None, 1 215040      activation_42[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_48 (Conv2D)              (None, None, None, 1 215040      activation_47[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_49 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_4[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_40 (BatchNo (None, None, None, 1 576         conv2d_40[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_43 (BatchNo (None, None, None, 1 576         conv2d_43[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_48 (BatchNo (None, None, None, 1 576         conv2d_48[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_49 (BatchNo (None, None, None, 1 576         conv2d_49[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_40 (Activation)      (None, None, None, 1 0           batch_normalization_40[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_43 (Activation)      (None, None, None, 1 0           batch_normalization_43[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_48 (Activation)      (None, None, None, 1 0           batch_normalization_48[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_49 (Activation)      (None, None, None, 1 0           batch_normalization_49[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed5 (Concatenate)            (None, None, None, 7 0           activation_40[0][0]              \n",
            "                                                                 activation_43[0][0]              \n",
            "                                                                 activation_48[0][0]              \n",
            "                                                                 activation_49[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_54 (Conv2D)              (None, None, None, 1 122880      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_54 (BatchNo (None, None, None, 1 480         conv2d_54[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_54 (Activation)      (None, None, None, 1 0           batch_normalization_54[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_55 (Conv2D)              (None, None, None, 1 179200      activation_54[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_55 (BatchNo (None, None, None, 1 480         conv2d_55[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_55 (Activation)      (None, None, None, 1 0           batch_normalization_55[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_51 (Conv2D)              (None, None, None, 1 122880      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_56 (Conv2D)              (None, None, None, 1 179200      activation_55[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_51 (BatchNo (None, None, None, 1 480         conv2d_51[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_56 (BatchNo (None, None, None, 1 480         conv2d_56[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_51 (Activation)      (None, None, None, 1 0           batch_normalization_51[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_56 (Activation)      (None, None, None, 1 0           batch_normalization_56[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_52 (Conv2D)              (None, None, None, 1 179200      activation_51[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_57 (Conv2D)              (None, None, None, 1 179200      activation_56[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_52 (BatchNo (None, None, None, 1 480         conv2d_52[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_57 (BatchNo (None, None, None, 1 480         conv2d_57[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_52 (Activation)      (None, None, None, 1 0           batch_normalization_52[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_57 (Activation)      (None, None, None, 1 0           batch_normalization_57[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_5 (AveragePoo (None, None, None, 7 0           mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_50 (Conv2D)              (None, None, None, 1 147456      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_53 (Conv2D)              (None, None, None, 1 215040      activation_52[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_58 (Conv2D)              (None, None, None, 1 215040      activation_57[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_59 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_5[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_50 (BatchNo (None, None, None, 1 576         conv2d_50[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_53 (BatchNo (None, None, None, 1 576         conv2d_53[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_58 (BatchNo (None, None, None, 1 576         conv2d_58[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_59 (BatchNo (None, None, None, 1 576         conv2d_59[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_50 (Activation)      (None, None, None, 1 0           batch_normalization_50[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_53 (Activation)      (None, None, None, 1 0           batch_normalization_53[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_58 (Activation)      (None, None, None, 1 0           batch_normalization_58[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_59 (Activation)      (None, None, None, 1 0           batch_normalization_59[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed6 (Concatenate)            (None, None, None, 7 0           activation_50[0][0]              \n",
            "                                                                 activation_53[0][0]              \n",
            "                                                                 activation_58[0][0]              \n",
            "                                                                 activation_59[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_64 (Conv2D)              (None, None, None, 1 147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_64 (BatchNo (None, None, None, 1 576         conv2d_64[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_64 (Activation)      (None, None, None, 1 0           batch_normalization_64[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_65 (Conv2D)              (None, None, None, 1 258048      activation_64[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_65 (BatchNo (None, None, None, 1 576         conv2d_65[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_65 (Activation)      (None, None, None, 1 0           batch_normalization_65[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_61 (Conv2D)              (None, None, None, 1 147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_66 (Conv2D)              (None, None, None, 1 258048      activation_65[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_61 (BatchNo (None, None, None, 1 576         conv2d_61[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_66 (BatchNo (None, None, None, 1 576         conv2d_66[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_61 (Activation)      (None, None, None, 1 0           batch_normalization_61[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_66 (Activation)      (None, None, None, 1 0           batch_normalization_66[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_62 (Conv2D)              (None, None, None, 1 258048      activation_61[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_67 (Conv2D)              (None, None, None, 1 258048      activation_66[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_62 (BatchNo (None, None, None, 1 576         conv2d_62[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_67 (BatchNo (None, None, None, 1 576         conv2d_67[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_62 (Activation)      (None, None, None, 1 0           batch_normalization_62[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_67 (Activation)      (None, None, None, 1 0           batch_normalization_67[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_6 (AveragePoo (None, None, None, 7 0           mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_60 (Conv2D)              (None, None, None, 1 147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_63 (Conv2D)              (None, None, None, 1 258048      activation_62[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_68 (Conv2D)              (None, None, None, 1 258048      activation_67[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_69 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_6[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_60 (BatchNo (None, None, None, 1 576         conv2d_60[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_63 (BatchNo (None, None, None, 1 576         conv2d_63[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_68 (BatchNo (None, None, None, 1 576         conv2d_68[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_69 (BatchNo (None, None, None, 1 576         conv2d_69[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_60 (Activation)      (None, None, None, 1 0           batch_normalization_60[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_63 (Activation)      (None, None, None, 1 0           batch_normalization_63[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_68 (Activation)      (None, None, None, 1 0           batch_normalization_68[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_69 (Activation)      (None, None, None, 1 0           batch_normalization_69[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed7 (Concatenate)            (None, None, None, 7 0           activation_60[0][0]              \n",
            "                                                                 activation_63[0][0]              \n",
            "                                                                 activation_68[0][0]              \n",
            "                                                                 activation_69[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_72 (Conv2D)              (None, None, None, 1 147456      mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_72 (BatchNo (None, None, None, 1 576         conv2d_72[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_72 (Activation)      (None, None, None, 1 0           batch_normalization_72[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_73 (Conv2D)              (None, None, None, 1 258048      activation_72[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_73 (BatchNo (None, None, None, 1 576         conv2d_73[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_73 (Activation)      (None, None, None, 1 0           batch_normalization_73[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_70 (Conv2D)              (None, None, None, 1 147456      mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_74 (Conv2D)              (None, None, None, 1 258048      activation_73[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_70 (BatchNo (None, None, None, 1 576         conv2d_70[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_74 (BatchNo (None, None, None, 1 576         conv2d_74[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_70 (Activation)      (None, None, None, 1 0           batch_normalization_70[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_74 (Activation)      (None, None, None, 1 0           batch_normalization_74[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_71 (Conv2D)              (None, None, None, 3 552960      activation_70[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_75 (Conv2D)              (None, None, None, 1 331776      activation_74[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_71 (BatchNo (None, None, None, 3 960         conv2d_71[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_75 (BatchNo (None, None, None, 1 576         conv2d_75[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_71 (Activation)      (None, None, None, 3 0           batch_normalization_71[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_75 (Activation)      (None, None, None, 1 0           batch_normalization_75[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, None, None, 7 0           mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "mixed8 (Concatenate)            (None, None, None, 1 0           activation_71[0][0]              \n",
            "                                                                 activation_75[0][0]              \n",
            "                                                                 max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_80 (Conv2D)              (None, None, None, 4 573440      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_80 (BatchNo (None, None, None, 4 1344        conv2d_80[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_80 (Activation)      (None, None, None, 4 0           batch_normalization_80[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_77 (Conv2D)              (None, None, None, 3 491520      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_81 (Conv2D)              (None, None, None, 3 1548288     activation_80[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_77 (BatchNo (None, None, None, 3 1152        conv2d_77[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_81 (BatchNo (None, None, None, 3 1152        conv2d_81[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_77 (Activation)      (None, None, None, 3 0           batch_normalization_77[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_81 (Activation)      (None, None, None, 3 0           batch_normalization_81[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_78 (Conv2D)              (None, None, None, 3 442368      activation_77[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_79 (Conv2D)              (None, None, None, 3 442368      activation_77[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_82 (Conv2D)              (None, None, None, 3 442368      activation_81[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_83 (Conv2D)              (None, None, None, 3 442368      activation_81[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_7 (AveragePoo (None, None, None, 1 0           mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_76 (Conv2D)              (None, None, None, 3 409600      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_78 (BatchNo (None, None, None, 3 1152        conv2d_78[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_79 (BatchNo (None, None, None, 3 1152        conv2d_79[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_82 (BatchNo (None, None, None, 3 1152        conv2d_82[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_83 (BatchNo (None, None, None, 3 1152        conv2d_83[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_84 (Conv2D)              (None, None, None, 1 245760      average_pooling2d_7[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_76 (BatchNo (None, None, None, 3 960         conv2d_76[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_78 (Activation)      (None, None, None, 3 0           batch_normalization_78[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_79 (Activation)      (None, None, None, 3 0           batch_normalization_79[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_82 (Activation)      (None, None, None, 3 0           batch_normalization_82[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_83 (Activation)      (None, None, None, 3 0           batch_normalization_83[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_84 (BatchNo (None, None, None, 1 576         conv2d_84[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_76 (Activation)      (None, None, None, 3 0           batch_normalization_76[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed9_0 (Concatenate)          (None, None, None, 7 0           activation_78[0][0]              \n",
            "                                                                 activation_79[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, None, None, 7 0           activation_82[0][0]              \n",
            "                                                                 activation_83[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_84 (Activation)      (None, None, None, 1 0           batch_normalization_84[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed9 (Concatenate)            (None, None, None, 2 0           activation_76[0][0]              \n",
            "                                                                 mixed9_0[0][0]                   \n",
            "                                                                 concatenate[0][0]                \n",
            "                                                                 activation_84[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_89 (Conv2D)              (None, None, None, 4 917504      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_89 (BatchNo (None, None, None, 4 1344        conv2d_89[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_89 (Activation)      (None, None, None, 4 0           batch_normalization_89[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_86 (Conv2D)              (None, None, None, 3 786432      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_90 (Conv2D)              (None, None, None, 3 1548288     activation_89[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_86 (BatchNo (None, None, None, 3 1152        conv2d_86[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_90 (BatchNo (None, None, None, 3 1152        conv2d_90[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_86 (Activation)      (None, None, None, 3 0           batch_normalization_86[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_90 (Activation)      (None, None, None, 3 0           batch_normalization_90[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_87 (Conv2D)              (None, None, None, 3 442368      activation_86[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_88 (Conv2D)              (None, None, None, 3 442368      activation_86[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_91 (Conv2D)              (None, None, None, 3 442368      activation_90[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_92 (Conv2D)              (None, None, None, 3 442368      activation_90[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_8 (AveragePoo (None, None, None, 2 0           mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_85 (Conv2D)              (None, None, None, 3 655360      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_87 (BatchNo (None, None, None, 3 1152        conv2d_87[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_88 (BatchNo (None, None, None, 3 1152        conv2d_88[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_91 (BatchNo (None, None, None, 3 1152        conv2d_91[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_92 (BatchNo (None, None, None, 3 1152        conv2d_92[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_93 (Conv2D)              (None, None, None, 1 393216      average_pooling2d_8[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_85 (BatchNo (None, None, None, 3 960         conv2d_85[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_87 (Activation)      (None, None, None, 3 0           batch_normalization_87[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_88 (Activation)      (None, None, None, 3 0           batch_normalization_88[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_91 (Activation)      (None, None, None, 3 0           batch_normalization_91[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_92 (Activation)      (None, None, None, 3 0           batch_normalization_92[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_93 (BatchNo (None, None, None, 1 576         conv2d_93[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_85 (Activation)      (None, None, None, 3 0           batch_normalization_85[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed9_1 (Concatenate)          (None, None, None, 7 0           activation_87[0][0]              \n",
            "                                                                 activation_88[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, None, None, 7 0           activation_91[0][0]              \n",
            "                                                                 activation_92[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_93 (Activation)      (None, None, None, 1 0           batch_normalization_93[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed10 (Concatenate)           (None, None, None, 2 0           activation_85[0][0]              \n",
            "                                                                 mixed9_1[0][0]                   \n",
            "                                                                 concatenate_1[0][0]              \n",
            "                                                                 activation_93[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_1 (Glo (None, 2048)         0           mixed10[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 1024)         2098176     global_average_pooling2d_1[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "dropout_1 (Dropout)             (None, 1024)         0           dense_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_3 (Dense)                 (None, 3)            3075        dropout_1[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 23,904,035\n",
            "Trainable params: 2,101,251\n",
            "Non-trainable params: 21,802,784\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tFaIWvWICpYM"
      },
      "source": [
        "### Model Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9f8WNPlZzqCQ",
        "outputId": "31b31708-3959-496d-f60d-449eb3911b2a"
      },
      "source": [
        "model.fit(\n",
        "    train_generator,\n",
        "    validation_data=validation_generator,\n",
        "    steps_per_epoch=1126//32,\n",
        "    epochs=5,\n",
        "    validation_steps=280//32)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "35/35 [==============================] - 31s 771ms/step - loss: 1.1153 - accuracy: 0.6746 - val_loss: 0.3353 - val_accuracy: 0.8945\n",
            "Epoch 2/5\n",
            "35/35 [==============================] - 26s 733ms/step - loss: 0.2571 - accuracy: 0.9333 - val_loss: 0.1917 - val_accuracy: 0.9414\n",
            "Epoch 3/5\n",
            "35/35 [==============================] - 26s 738ms/step - loss: 0.2115 - accuracy: 0.9287 - val_loss: 0.1302 - val_accuracy: 0.9570\n",
            "Epoch 4/5\n",
            "35/35 [==============================] - 25s 729ms/step - loss: 0.1600 - accuracy: 0.9543 - val_loss: 0.1688 - val_accuracy: 0.9492\n",
            "Epoch 5/5\n",
            "35/35 [==============================] - 26s 733ms/step - loss: 0.1605 - accuracy: 0.9506 - val_loss: 0.1190 - val_accuracy: 0.9570\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f618406e590>"
            ]
          },
          "execution_count": 13,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KvzI7YknJbJC"
      },
      "source": [
        "### Model Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "brX7sMVUJa10",
        "outputId": "fefdaef0-194d-48e1-99a6-ccd6a4af04b3"
      },
      "source": [
        "score = model.evaluate(test_generator, verbose=0)\n",
        "print(\"Test loss:\", score[0])\n",
        "print(\"Test accuracy:\", score[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test loss: 0.03664422780275345\n",
            "Test accuracy: 0.9910714030265808\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ir20KcmrCsqz"
      },
      "source": [
        "### Classification Report and Confusion Matrix Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "KZM_J_rvzrx9",
        "outputId": "729cfb42-4d64-4f96-818c-d66710c69a76"
      },
      "source": [
        "predictions = model.predict(test_generator, steps=test_steps_per_epoch)\n",
        "# Get most likely class\n",
        "predicted_classes = numpy.argmax(predictions, axis=1)\n",
        "\n",
        "true_classes = test_generator.classes\n",
        "class_labels = list(test_generator.class_indices.keys())   \n",
        "\n",
        "report = classification_report(true_classes, predicted_classes, target_names=class_labels, zero_division=0)\n",
        "print(report)\n",
        "cmrforest = confusion_matrix(true_classes, predicted_classes)\n",
        "plt.figure(figsize=(10,7))\n",
        "sns.heatmap(cmrforest ,annot=True , fmt = 'd')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')\n",
        "plt.title('Confusion Report')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "               precision    recall  f1-score   support\n",
            "\n",
            "       Danube       0.98      1.00      0.99       109\n",
            "      Dnieper       1.00      0.97      0.99        78\n",
            "UnknownOrigin       1.00      1.00      1.00        37\n",
            "\n",
            "     accuracy                           0.99       224\n",
            "    macro avg       0.99      0.99      0.99       224\n",
            " weighted avg       0.99      0.99      0.99       224\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b-9AUv_sCzHK"
      },
      "source": [
        "## MobileNetV2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MBk9xGPbC3Bn"
      },
      "source": [
        "### Initialization and Summary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kodvkg930LBd",
        "outputId": "0d1b4a65-738e-44de-820c-59ee56cba441"
      },
      "source": [
        "base_model = MobileNetV2(weights='imagenet', include_top=False)\n",
        "base_model.trainable = False\n",
        "\n",
        "# add a global spatial average pooling layer\n",
        "x = base_model.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "# add a fully-connected layer\n",
        "x = Dense(1024, activation='relu')(x)\n",
        "# add a dropout layer\n",
        "x = Dropout(0.2)(x)\n",
        "# and a logistic layer -- we have 3 classes\n",
        "predictions = Dense(3, activation='softmax')(x)\n",
        "\n",
        "# this is the model we will train\n",
        "model = Model(inputs=base_model.input, outputs=predictions)\n",
        "\n",
        "# compile the model (should be done *after* setting layers to non-trainable)\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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",
            "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",
            "Model: \"model_2\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_3 (InputLayer)            [(None, None, None,  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "Conv1 (Conv2D)                  (None, None, None, 3 864         input_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "bn_Conv1 (BatchNormalization)   (None, None, None, 3 128         Conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_relu (ReLU)               (None, None, None, 3 0           bn_Conv1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise (Depthw (None, None, None, 3 288         Conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_BN (Bat (None, None, None, 3 128         expanded_conv_depthwise[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_relu (R (None, None, None, 3 0           expanded_conv_depthwise_BN[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project (Conv2D)  (None, None, None, 1 512         expanded_conv_depthwise_relu[0][0\n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project_BN (Batch (None, None, None, 1 64          expanded_conv_project[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand (Conv2D)         (None, None, None, 9 1536        expanded_conv_project_BN[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_BN (BatchNormali (None, None, None, 9 384         block_1_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_relu (ReLU)      (None, None, None, 9 0           block_1_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_pad (ZeroPadding2D)     (None, None, None, 9 0           block_1_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise (DepthwiseCon (None, None, None, 9 864         block_1_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_BN (BatchNorm (None, None, None, 9 384         block_1_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_relu (ReLU)   (None, None, None, 9 0           block_1_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project (Conv2D)        (None, None, None, 2 2304        block_1_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project_BN (BatchNormal (None, None, None, 2 96          block_1_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand (Conv2D)         (None, None, None, 1 3456        block_1_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_BN (BatchNormali (None, None, None, 1 576         block_2_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_relu (ReLU)      (None, None, None, 1 0           block_2_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise (DepthwiseCon (None, None, None, 1 1296        block_2_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_BN (BatchNorm (None, None, None, 1 576         block_2_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_relu (ReLU)   (None, None, None, 1 0           block_2_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project (Conv2D)        (None, None, None, 2 3456        block_2_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project_BN (BatchNormal (None, None, None, 2 96          block_2_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_add (Add)               (None, None, None, 2 0           block_1_project_BN[0][0]         \n",
            "                                                                 block_2_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand (Conv2D)         (None, None, None, 1 3456        block_2_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_BN (BatchNormali (None, None, None, 1 576         block_3_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_relu (ReLU)      (None, None, None, 1 0           block_3_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_pad (ZeroPadding2D)     (None, None, None, 1 0           block_3_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise (DepthwiseCon (None, None, None, 1 1296        block_3_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_BN (BatchNorm (None, None, None, 1 576         block_3_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_relu (ReLU)   (None, None, None, 1 0           block_3_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project (Conv2D)        (None, None, None, 3 4608        block_3_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project_BN (BatchNormal (None, None, None, 3 128         block_3_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand (Conv2D)         (None, None, None, 1 6144        block_3_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_BN (BatchNormali (None, None, None, 1 768         block_4_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_relu (ReLU)      (None, None, None, 1 0           block_4_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise (DepthwiseCon (None, None, None, 1 1728        block_4_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_BN (BatchNorm (None, None, None, 1 768         block_4_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_relu (ReLU)   (None, None, None, 1 0           block_4_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project (Conv2D)        (None, None, None, 3 6144        block_4_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project_BN (BatchNormal (None, None, None, 3 128         block_4_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_add (Add)               (None, None, None, 3 0           block_3_project_BN[0][0]         \n",
            "                                                                 block_4_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand (Conv2D)         (None, None, None, 1 6144        block_4_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_BN (BatchNormali (None, None, None, 1 768         block_5_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_relu (ReLU)      (None, None, None, 1 0           block_5_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise (DepthwiseCon (None, None, None, 1 1728        block_5_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_BN (BatchNorm (None, None, None, 1 768         block_5_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_relu (ReLU)   (None, None, None, 1 0           block_5_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project (Conv2D)        (None, None, None, 3 6144        block_5_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project_BN (BatchNormal (None, None, None, 3 128         block_5_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_5_add (Add)               (None, None, None, 3 0           block_4_add[0][0]                \n",
            "                                                                 block_5_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand (Conv2D)         (None, None, None, 1 6144        block_5_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_BN (BatchNormali (None, None, None, 1 768         block_6_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_relu (ReLU)      (None, None, None, 1 0           block_6_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_pad (ZeroPadding2D)     (None, None, None, 1 0           block_6_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise (DepthwiseCon (None, None, None, 1 1728        block_6_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_BN (BatchNorm (None, None, None, 1 768         block_6_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_relu (ReLU)   (None, None, None, 1 0           block_6_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project (Conv2D)        (None, None, None, 6 12288       block_6_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project_BN (BatchNormal (None, None, None, 6 256         block_6_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand (Conv2D)         (None, None, None, 3 24576       block_6_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_BN (BatchNormali (None, None, None, 3 1536        block_7_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_relu (ReLU)      (None, None, None, 3 0           block_7_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise (DepthwiseCon (None, None, None, 3 3456        block_7_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_BN (BatchNorm (None, None, None, 3 1536        block_7_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_relu (ReLU)   (None, None, None, 3 0           block_7_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project (Conv2D)        (None, None, None, 6 24576       block_7_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project_BN (BatchNormal (None, None, None, 6 256         block_7_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_add (Add)               (None, None, None, 6 0           block_6_project_BN[0][0]         \n",
            "                                                                 block_7_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand (Conv2D)         (None, None, None, 3 24576       block_7_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_BN (BatchNormali (None, None, None, 3 1536        block_8_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_relu (ReLU)      (None, None, None, 3 0           block_8_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise (DepthwiseCon (None, None, None, 3 3456        block_8_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_BN (BatchNorm (None, None, None, 3 1536        block_8_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_relu (ReLU)   (None, None, None, 3 0           block_8_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project (Conv2D)        (None, None, None, 6 24576       block_8_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project_BN (BatchNormal (None, None, None, 6 256         block_8_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_8_add (Add)               (None, None, None, 6 0           block_7_add[0][0]                \n",
            "                                                                 block_8_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand (Conv2D)         (None, None, None, 3 24576       block_8_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_BN (BatchNormali (None, None, None, 3 1536        block_9_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_relu (ReLU)      (None, None, None, 3 0           block_9_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise (DepthwiseCon (None, None, None, 3 3456        block_9_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_BN (BatchNorm (None, None, None, 3 1536        block_9_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_relu (ReLU)   (None, None, None, 3 0           block_9_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project (Conv2D)        (None, None, None, 6 24576       block_9_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project_BN (BatchNormal (None, None, None, 6 256         block_9_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_9_add (Add)               (None, None, None, 6 0           block_8_add[0][0]                \n",
            "                                                                 block_9_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand (Conv2D)        (None, None, None, 3 24576       block_9_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_BN (BatchNormal (None, None, None, 3 1536        block_10_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_relu (ReLU)     (None, None, None, 3 0           block_10_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise (DepthwiseCo (None, None, None, 3 3456        block_10_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_BN (BatchNor (None, None, None, 3 1536        block_10_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_relu (ReLU)  (None, None, None, 3 0           block_10_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project (Conv2D)       (None, None, None, 9 36864       block_10_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project_BN (BatchNorma (None, None, None, 9 384         block_10_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand (Conv2D)        (None, None, None, 5 55296       block_10_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_BN (BatchNormal (None, None, None, 5 2304        block_11_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_relu (ReLU)     (None, None, None, 5 0           block_11_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise (DepthwiseCo (None, None, None, 5 5184        block_11_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_BN (BatchNor (None, None, None, 5 2304        block_11_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_relu (ReLU)  (None, None, None, 5 0           block_11_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project (Conv2D)       (None, None, None, 9 55296       block_11_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project_BN (BatchNorma (None, None, None, 9 384         block_11_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_add (Add)              (None, None, None, 9 0           block_10_project_BN[0][0]        \n",
            "                                                                 block_11_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand (Conv2D)        (None, None, None, 5 55296       block_11_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_BN (BatchNormal (None, None, None, 5 2304        block_12_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_relu (ReLU)     (None, None, None, 5 0           block_12_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise (DepthwiseCo (None, None, None, 5 5184        block_12_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_BN (BatchNor (None, None, None, 5 2304        block_12_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_relu (ReLU)  (None, None, None, 5 0           block_12_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project (Conv2D)       (None, None, None, 9 55296       block_12_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project_BN (BatchNorma (None, None, None, 9 384         block_12_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_12_add (Add)              (None, None, None, 9 0           block_11_add[0][0]               \n",
            "                                                                 block_12_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand (Conv2D)        (None, None, None, 5 55296       block_12_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_BN (BatchNormal (None, None, None, 5 2304        block_13_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_relu (ReLU)     (None, None, None, 5 0           block_13_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_pad (ZeroPadding2D)    (None, None, None, 5 0           block_13_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise (DepthwiseCo (None, None, None, 5 5184        block_13_pad[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_BN (BatchNor (None, None, None, 5 2304        block_13_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_relu (ReLU)  (None, None, None, 5 0           block_13_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project (Conv2D)       (None, None, None, 1 92160       block_13_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project_BN (BatchNorma (None, None, None, 1 640         block_13_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand (Conv2D)        (None, None, None, 9 153600      block_13_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_BN (BatchNormal (None, None, None, 9 3840        block_14_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_relu (ReLU)     (None, None, None, 9 0           block_14_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise (DepthwiseCo (None, None, None, 9 8640        block_14_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_BN (BatchNor (None, None, None, 9 3840        block_14_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_relu (ReLU)  (None, None, None, 9 0           block_14_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project (Conv2D)       (None, None, None, 1 153600      block_14_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project_BN (BatchNorma (None, None, None, 1 640         block_14_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_add (Add)              (None, None, None, 1 0           block_13_project_BN[0][0]        \n",
            "                                                                 block_14_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand (Conv2D)        (None, None, None, 9 153600      block_14_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_BN (BatchNormal (None, None, None, 9 3840        block_15_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_relu (ReLU)     (None, None, None, 9 0           block_15_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise (DepthwiseCo (None, None, None, 9 8640        block_15_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_BN (BatchNor (None, None, None, 9 3840        block_15_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_relu (ReLU)  (None, None, None, 9 0           block_15_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project (Conv2D)       (None, None, None, 1 153600      block_15_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project_BN (BatchNorma (None, None, None, 1 640         block_15_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_15_add (Add)              (None, None, None, 1 0           block_14_add[0][0]               \n",
            "                                                                 block_15_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand (Conv2D)        (None, None, None, 9 153600      block_15_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_BN (BatchNormal (None, None, None, 9 3840        block_16_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_relu (ReLU)     (None, None, None, 9 0           block_16_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise (DepthwiseCo (None, None, None, 9 8640        block_16_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_BN (BatchNor (None, None, None, 9 3840        block_16_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_relu (ReLU)  (None, None, None, 9 0           block_16_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project (Conv2D)       (None, None, None, 3 307200      block_16_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project_BN (BatchNorma (None, None, None, 3 1280        block_16_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1 (Conv2D)                 (None, None, None, 1 409600      block_16_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1_bn (BatchNormalization)  (None, None, None, 1 5120        Conv_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "out_relu (ReLU)                 (None, None, None, 1 0           Conv_1_bn[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_2 (Glo (None, 1280)         0           out_relu[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_4 (Dense)                 (None, 1024)         1311744     global_average_pooling2d_2[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "dropout_2 (Dropout)             (None, 1024)         0           dense_4[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_5 (Dense)                 (None, 3)            3075        dropout_2[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 3,572,803\n",
            "Trainable params: 1,314,819\n",
            "Non-trainable params: 2,257,984\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p57-N9aOC5Ex"
      },
      "source": [
        "### Model Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ck2TBSyM0WNK",
        "outputId": "d2841e63-c9c2-4d4e-987f-5e77907efe73"
      },
      "source": [
        "model.fit(\n",
        "    train_generator,\n",
        "    validation_data=validation_generator,\n",
        "    steps_per_epoch=1126//32,\n",
        "    epochs=9,\n",
        "    validation_steps=280//32)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/9\n",
            "35/35 [==============================] - 29s 763ms/step - loss: 0.4266 - accuracy: 0.8912 - val_loss: 0.0344 - val_accuracy: 0.9922\n",
            "Epoch 2/9\n",
            "35/35 [==============================] - 25s 724ms/step - loss: 0.1032 - accuracy: 0.9771 - val_loss: 0.1260 - val_accuracy: 0.9648\n",
            "Epoch 3/9\n",
            "35/35 [==============================] - 25s 711ms/step - loss: 0.1093 - accuracy: 0.9726 - val_loss: 0.0133 - val_accuracy: 0.9961\n",
            "Epoch 4/9\n",
            "35/35 [==============================] - 25s 713ms/step - loss: 0.0866 - accuracy: 0.9753 - val_loss: 0.2561 - val_accuracy: 0.8984\n",
            "Epoch 5/9\n",
            "35/35 [==============================] - 25s 711ms/step - loss: 0.0968 - accuracy: 0.9735 - val_loss: 0.1049 - val_accuracy: 0.9570\n",
            "Epoch 6/9\n",
            "35/35 [==============================] - 25s 713ms/step - loss: 0.0909 - accuracy: 0.9735 - val_loss: 0.0363 - val_accuracy: 0.9883\n",
            "Epoch 7/9\n",
            "35/35 [==============================] - 25s 714ms/step - loss: 0.0846 - accuracy: 0.9781 - val_loss: 0.0219 - val_accuracy: 0.9922\n",
            "Epoch 8/9\n",
            "35/35 [==============================] - 25s 714ms/step - loss: 0.0829 - accuracy: 0.9781 - val_loss: 0.0620 - val_accuracy: 0.9805\n",
            "Epoch 9/9\n",
            "35/35 [==============================] - 25s 719ms/step - loss: 0.0819 - accuracy: 0.9771 - val_loss: 0.0532 - val_accuracy: 0.9805\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f617f592050>"
            ]
          },
          "execution_count": 17,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ei0q1lvFJgEo"
      },
      "source": [
        "### Model Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WNUP1j1EJgab",
        "outputId": "6889d3d4-9958-4ee4-9a54-079a072f9ab9"
      },
      "source": [
        "score = model.evaluate(test_generator, verbose=0)\n",
        "print(\"Test loss:\", score[0])\n",
        "print(\"Test accuracy:\", score[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test loss: 0.20453548431396484\n",
            "Test accuracy: 0.9285714030265808\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VWR9jkUbDAqB"
      },
      "source": [
        "### Classification Report and Confusion Matrix Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "olzLMmaB0Xkt",
        "outputId": "bc2bf172-340c-43c1-e011-9014da2a476c"
      },
      "source": [
        "predictions = model.predict(test_generator, steps=test_steps_per_epoch)\n",
        "# Get most likely class\n",
        "predicted_classes = numpy.argmax(predictions, axis=1)\n",
        "\n",
        "true_classes = test_generator.classes\n",
        "class_labels = list(test_generator.class_indices.keys())   \n",
        "\n",
        "report = classification_report(true_classes, predicted_classes, target_names=class_labels, zero_division=0)\n",
        "print(report)\n",
        "cmrforest = confusion_matrix(true_classes, predicted_classes)\n",
        "plt.figure(figsize=(10,7))\n",
        "sns.heatmap(cmrforest ,annot=True , fmt = 'd')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')\n",
        "plt.title('Confusion Report')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "               precision    recall  f1-score   support\n",
            "\n",
            "       Danube       1.00      0.85      0.92       109\n",
            "      Dnieper       1.00      1.00      1.00        78\n",
            "UnknownOrigin       0.70      1.00      0.82        37\n",
            "\n",
            "     accuracy                           0.93       224\n",
            "    macro avg       0.90      0.95      0.91       224\n",
            " weighted avg       0.95      0.93      0.93       224\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ]
    }
  ]
}