{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n6QHvU2dfpFz"
      },
      "source": [
        "# 500 Species Bird Classification\n",
        "by Daniel Glownia \n",
        "\n",
        "In this notebook I use transfer learning and supervised learning fit the 500 species bird classification dataset found on Kaggle. The data set is around 80k images. I use MobileNetV3Small and MobileNetV3Large as my base model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lEzJXgjDf5y8"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from datetime import datetime\n",
        "from pathlib import Path\n",
        "import os.path\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import itertools\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers,models\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.layers import Dense, Dropout\n",
        "from tensorflow.keras.callbacks import Callback, EarlyStopping,ModelCheckpoint\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.applications import MobileNetV3Small\n",
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras.layers.experimental import preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uNgC9gY2VUb8",
        "outputId": "850805ec-cb12-48c1-9a49-ee3bfced64c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "archive\n",
            "archive.zip\n",
            "history_2023-03-08_19:01:49.007284.csv\n",
            "model_bird_2023-03-05_18:47:00.865455.h5\n",
            "model_bird_2023-03-05_20:21:09.026340.h5\n",
            "model_bird_2023-03-05_21:23:56.674497.h5\n",
            "model_bird_2023-03-06_02:48:10.768076.h5\n",
            "model_bird_2023-03-06_05:39:41.448853.h5\n",
            "model_bird_2023-03-06_05:53:27.594264.h5\n",
            "model_bird_2023-03-06_09:00:23.666948.h5\n",
            "model_bird_2023-03-06_15:21:31.478167.h5\n",
            "model_bird_2023-03-08_19:01:49.007284.h5\n",
            "model_saves\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "path = \"/content/drive/My Drive/ml_final/\"\n",
        "!ls \"/content/drive/My Drive/ml_final\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xs0mEKMxWcYb"
      },
      "outputs": [],
      "source": [
        "!unzip -q \"/content/drive/My Drive/ml_final/archive.zip\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "smr7sYk2XyHd",
        "outputId": "b9b82902-4476-4d48-b571-3ce550c95526"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "archive\n",
            "archive.zip\n",
            "history_2023-03-08_19:01:49.007284.csv\n",
            "model_bird_2023-03-05_18:47:00.865455.h5\n",
            "model_bird_2023-03-05_20:21:09.026340.h5\n",
            "model_bird_2023-03-05_21:23:56.674497.h5\n",
            "model_bird_2023-03-06_02:48:10.768076.h5\n",
            "model_bird_2023-03-06_05:39:41.448853.h5\n",
            "model_bird_2023-03-06_05:53:27.594264.h5\n",
            "model_bird_2023-03-06_09:00:23.666948.h5\n",
            "model_bird_2023-03-06_15:21:31.478167.h5\n",
            "model_bird_2023-03-08_19:01:49.007284.h5\n",
            "model_saves\n"
          ]
        }
      ],
      "source": [
        "!ls \"/content/drive/My Drive/ml_final\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8M5NPt-AX0Pn",
        "outputId": "612391c8-4337-49e9-84e7-bd17ee7163c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " birds.csv\t      info.txt\t\t\t\t\t    test\n",
            " drive\t\t     'mobilenetv3-small-500-(224 X 224)-98.68.h5'   train\n",
            "'images to predict'   sample_data\t\t\t\t    valid\n"
          ]
        }
      ],
      "source": [
        "!ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s14XOEp01m_s"
      },
      "outputs": [],
      "source": [
        "\n",
        "image_dir = Path('./test')\n",
        "filepaths = list(image_dir.glob(r'**/*.JPG')) + list(image_dir.glob(r'**/*.jpg')) + list(image_dir.glob(r'**/*.png')) + list(image_dir.glob(r'**/*.png'))\n",
        "labels = list(map(lambda x: os.path.split(os.path.split(x)[0])[1], filepaths))\n",
        "filepaths = pd.Series(filepaths, name='Filepath').astype(str)\n",
        "labels = pd.Series(labels, name='Label')\n",
        "\n",
        "test_df = pd.concat([filepaths, labels], axis=1)\n",
        "\n",
        "image_dir = Path('./train')\n",
        "filepaths = list(image_dir.glob(r'**/*.JPG')) + list(image_dir.glob(r'**/*.jpg')) + list(image_dir.glob(r'**/*.png')) + list(image_dir.glob(r'**/*.png'))\n",
        "labels = list(map(lambda x: os.path.split(os.path.split(x)[0])[1], filepaths))\n",
        "filepaths = pd.Series(filepaths, name='Filepath').astype(str)\n",
        "labels = pd.Series(labels, name='Label')\n",
        "\n",
        "train_df = pd.concat([filepaths, labels], axis=1)\n",
        "\n",
        "image_dir = Path('./valid')\n",
        "filepaths = list(image_dir.glob(r'**/*.JPG')) + list(image_dir.glob(r'**/*.jpg')) + list(image_dir.glob(r'**/*.png')) + list(image_dir.glob(r'**/*.png'))\n",
        "labels = list(map(lambda x: os.path.split(os.path.split(x)[0])[1], filepaths))\n",
        "filepaths = pd.Series(filepaths, name='Filepath').astype(str)\n",
        "labels = pd.Series(labels, name='Label')\n",
        "\n",
        "valid_df = pd.concat([filepaths, labels], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d3-uoP4n1oqK",
        "outputId": "af50c311-d1b4-42d6-c2ef-2c787626736a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2500, 2)\n",
            "(2500, 2)\n",
            "(80085, 2)\n"
          ]
        }
      ],
      "source": [
        "print(valid_df.shape)\n",
        "print(test_df.shape)\n",
        "print(train_df.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JrYcgASVYVn4"
      },
      "outputs": [],
      "source": [
        "# Transform Data and load into image data object "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3puUVDwl2Mcz"
      },
      "outputs": [],
      "source": [
        "train_generator = ImageDataGenerator(\n",
        "    preprocessing_function=tf.keras.applications.mobilenet_v3.preprocess_input\n",
        ")\n",
        "\n",
        "test_generator = ImageDataGenerator(\n",
        "    preprocessing_function=tf.keras.applications.mobilenet_v3.preprocess_input\n",
        ")\n",
        "\n",
        "valid_generator = ImageDataGenerator(\n",
        "    preprocessing_function=tf.keras.applications.mobilenet_v3.preprocess_input\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CsftNShQ2PaK",
        "outputId": "51837d2d-b90f-4ef1-fc6a-795a0cac23a9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 80085 validated image filenames belonging to 500 classes.\n",
            "Found 2500 validated image filenames belonging to 500 classes.\n",
            "Found 2500 validated image filenames belonging to 500 classes.\n"
          ]
        }
      ],
      "source": [
        "train_images = train_generator.flow_from_dataframe(\n",
        "    dataframe=train_df,\n",
        "    x_col='Filepath',\n",
        "    y_col='Label',\n",
        "    target_size=(224, 224),\n",
        "    color_mode='rgb',\n",
        "    class_mode='categorical',\n",
        "    batch_size=32,\n",
        "    shuffle=True,\n",
        "    seed=42\n",
        ")\n",
        "\n",
        "test_images = test_generator.flow_from_dataframe(\n",
        "    dataframe=test_df,\n",
        "    x_col='Filepath',\n",
        "    y_col='Label',\n",
        "    target_size=(224, 224),\n",
        "    color_mode='rgb',\n",
        "    class_mode='categorical',\n",
        "    batch_size=32,\n",
        "    shuffle=False,\n",
        "    seed=42\n",
        ")\n",
        "\n",
        "valid_images = valid_generator.flow_from_dataframe(\n",
        "    dataframe=valid_df,\n",
        "    x_col='Filepath',\n",
        "    y_col='Label',\n",
        "    target_size=(224, 224),\n",
        "    color_mode='rgb',\n",
        "    class_mode='categorical',\n",
        "    batch_size=32,\n",
        "    shuffle=False\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1VLFcri4YnPE"
      },
      "outputs": [],
      "source": [
        "#Fit the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1xn6j_La2Y2u"
      },
      "outputs": [],
      "source": [
        "# Create checkpoint callback\n",
        "checkpoint_path = \"/content/drive/My Drive/ml_final/model_saves/bird_final_at_{epoch}.keras\"\n",
        "checkpoint_callback = ModelCheckpoint(checkpoint_path,\n",
        "                                      save_weights_only=True,\n",
        "                                      monitor=\"val_accuracy\",\n",
        "                                      save_best_only=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sLbR4WtD2RPg"
      },
      "outputs": [],
      "source": [
        "# Resize Layer\n",
        "color_correction = tf.keras.Sequential([\n",
        "  layers.RandomFlip(),\n",
        "  layers.RandomBrightness(factor=0.3,value_range=(0, 1)),\n",
        "  layers.RandomContrast(factor=0.3),\n",
        "  layers.RandomRotation(factor=0.25),\n",
        "  layers.RandomZoom(height_factor=(-0.2, 0.2))\n",
        "])\n",
        "\n",
        "processing_layers = tf.keras.Sequential([\n",
        "  layers.experimental.preprocessing.Resizing(224,224),\n",
        "  layers.experimental.preprocessing.Rescaling(1./255),\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hq6JxrDdi371"
      },
      "outputs": [],
      "source": [
        "# Read in Dataset to see the images (not used for training)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IuhjRx-_NVLR",
        "outputId": "b1d86bd7-db5d-4cba-ad5e-f0b142f0d26e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2500 files belonging to 500 classes.\n",
            "Using 2000 files for training.\n",
            "Using 500 files for validation.\n"
          ]
        }
      ],
      "source": [
        "image_size = (224, 224)\n",
        "\n",
        "train_ds, val_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "    \"./test\",\n",
        "    validation_split=0.2,\n",
        "    subset=\"both\",\n",
        "    seed=1337,\n",
        "    image_size=image_size,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "Eao069SnTHXJ",
        "outputId": "d8805323-03c7-47ff-c208-5cd2e0d2ad35"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fec7863b3d0>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "image, label = next(iter(train_ds))\n",
        "plt.imshow(image[0].numpy().astype(\"uint8\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "XH3bRtzmTTye",
        "outputId": "8534850c-8e69-41fb-9ad8-fd33c2dc6627"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "for i in range(9):\n",
        "    augmented_image = processing_layers(image)\n",
        "    ax = plt.subplot(3, 3, i + 1)\n",
        "    plt.imshow(augmented_image[0])\n",
        "    plt.axis(\"off\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z4VI_UxV2Wp2"
      },
      "outputs": [],
      "source": [
        "# Load the pretained model\n",
        "pretrained_model = tf.keras.applications.MobileNetV3Small(\n",
        "    input_shape=(224, 224, 3),\n",
        "    include_top=False,\n",
        "    weights='imagenet',\n",
        "    pooling='avg'\n",
        ")\n",
        "\n",
        "pretrained_model.trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g7dtKUESZLV2"
      },
      "outputs": [],
      "source": [
        "# Model with Many Layers "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LxXsjn01oC2p",
        "outputId": "ed5bb872-9d54-441f-cb1c-26ad9e671243"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformFullIntV2 cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomGetKeyCounter cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/2\n",
            "2503/2503 [==============================] - 334s 131ms/step - loss: 6.1873 - accuracy: 0.0031 - val_loss: 6.0688 - val_accuracy: 0.0048\n",
            "Epoch 2/2\n",
            "2503/2503 [==============================] - 337s 134ms/step - loss: 6.0405 - accuracy: 0.0048 - val_loss: 5.9897 - val_accuracy: 0.0060\n",
            "    Test Loss: 5.98212\n",
            "Test Accuracy: 0.80%\n",
            "    Validation Set Loss: 5.98972\n",
            "Validation Set Accuracy: 0.60%\n"
          ]
        }
      ],
      "source": [
        "epochs = 2\n",
        "batch_size = 256\n",
        "\n",
        "inputs = pretrained_model.input\n",
        "x = processing_layers(inputs)\n",
        "\n",
        "x = layers.Conv2D(128, 3, strides=2, padding=\"same\")(x)\n",
        "x = layers.BatchNormalization()(x)\n",
        "x = layers.Activation(\"relu\")(x)\n",
        "\n",
        "previous_block_activation = x  # Set aside residual\n",
        "\n",
        "for size in [256, 512, 728]:\n",
        "    x = layers.Activation(\"relu\")(x)\n",
        "    x = layers.SeparableConv2D(size, 3, padding=\"same\")(x)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "\n",
        "    x = layers.Activation(\"relu\")(x)\n",
        "    x = layers.SeparableConv2D(size, 3, padding=\"same\")(x)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "\n",
        "    x = layers.MaxPooling2D(3, strides=2, padding=\"same\")(x)\n",
        "\n",
        "        # Project residual\n",
        "    residual = layers.Conv2D(size, 1, strides=2, padding=\"same\")(\n",
        "            previous_block_activation\n",
        "        )\n",
        "    x = layers.add([x, residual])  # Add back residual\n",
        "    previous_block_activation = x  # Set aside next residual\n",
        "\n",
        "x = layers.SeparableConv2D(1024, 3, padding=\"same\")(x)\n",
        "x = layers.BatchNormalization()(x)\n",
        "\n",
        "x = layers.Conv2DTranspose(8, kernel_size=(3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(x)\n",
        "x = layers.Conv2D(32, kernel_size=(3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(x)\n",
        "x = layers.BatchNormalization()(x)\n",
        "\n",
        "x = layers.Conv2DTranspose(32, kernel_size=(3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(x)\n",
        "x = layers.Conv2D(8, kernel_size=(3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(x)\n",
        "x = layers.BatchNormalization()(x)\n",
        "\n",
        "x = layers.Activation(\"relu\")(x)\n",
        "\n",
        "x = layers.GlobalAveragePooling2D()(x)\n",
        "x = layers.Dropout(0.5)(x)\n",
        "\n",
        "x = Dense(256, activation='relu')(pretrained_model.output)\n",
        "x = Dropout(0.2)(x)\n",
        "x = Dense(128, activation='relu')(x)\n",
        "x = Dropout(0.2)(x)\n",
        "x = Dense(64, activation='relu')(x)\n",
        "x = Dropout(0.2)(x)\n",
        "\n",
        "outputs = Dense(500, activation='softmax')(x)\n",
        "\n",
        "model = Model(inputs=inputs, outputs=outputs)\n",
        "\n",
        "model.compile(\n",
        "    optimizer=keras.optimizers.Adam(1e-3),\n",
        "    loss=keras.losses.CategoricalCrossentropy(),\n",
        "    metrics=[\"accuracy\"],\n",
        ")\n",
        "\n",
        "history = model.fit(\n",
        "    train_images,\n",
        "    batch_size = batch_size,\n",
        "    validation_data=valid_images,\n",
        "    epochs=epochs,\n",
        "    callbacks=[\n",
        "        checkpoint_callback,\n",
        "    ],\n",
        "    use_multiprocessing=True\n",
        ")\n",
        "\n",
        "time_stamp_name = str(datetime.now()).replace(' ', '_')\n",
        "model.save(f'/content/drive/My Drive/ml_final/model_bird_{time_stamp_name}.h5')\n",
        "loaded_model = tf.keras.models.load_model(f'/content/drive/My Drive/ml_final/model_bird_{time_stamp_name}.h5')\n",
        "\n",
        "results_test = loaded_model.evaluate(test_images, verbose=0)\n",
        "\n",
        "print(\"    Test Loss: {:.5f}\".format(results_test[0]))\n",
        "print(\"Test Accuracy: {:.2f}%\".format(results_test[1] * 100))\n",
        "\n",
        "results_valid = loaded_model.evaluate(valid_images, verbose=0)\n",
        "\n",
        "print(\"    Validation Set Loss: {:.5f}\".format(results_valid[0]))\n",
        "print(\"Validation Set Accuracy: {:.2f}%\".format(results_valid[1] * 100))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Uv3DlLkN3ha8"
      },
      "outputs": [],
      "source": [
        "#Model with few layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p29UPONTZQQu",
        "outputId": "df5495a7-38e4-4aa7-cc66-b1c6516cbb92"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "2503/2503 [==============================] - 322s 127ms/step - loss: 3.3844 - accuracy: 0.2393 - val_loss: 1.3711 - val_accuracy: 0.6416\n",
            "Epoch 2/100\n",
            "2503/2503 [==============================] - 308s 123ms/step - loss: 2.0283 - accuracy: 0.4704 - val_loss: 0.9661 - val_accuracy: 0.7420\n",
            "Epoch 3/100\n",
            "2503/2503 [==============================] - 313s 125ms/step - loss: 1.7224 - accuracy: 0.5433 - val_loss: 0.8075 - val_accuracy: 0.7780\n",
            "Epoch 4/100\n",
            "2503/2503 [==============================] - 306s 122ms/step - loss: 1.5638 - accuracy: 0.5831 - val_loss: 0.7247 - val_accuracy: 0.7984\n",
            "Epoch 5/100\n",
            "2503/2503 [==============================] - 297s 118ms/step - loss: 1.4634 - accuracy: 0.6055 - val_loss: 0.6588 - val_accuracy: 0.8172\n",
            "Epoch 6/100\n",
            "2503/2503 [==============================] - 303s 121ms/step - loss: 1.3873 - accuracy: 0.6254 - val_loss: 0.6397 - val_accuracy: 0.8188\n",
            "Epoch 7/100\n",
            "2503/2503 [==============================] - 304s 121ms/step - loss: 1.3317 - accuracy: 0.6389 - val_loss: 0.6149 - val_accuracy: 0.8240\n",
            "Epoch 8/100\n",
            "2503/2503 [==============================] - 302s 120ms/step - loss: 1.2922 - accuracy: 0.6503 - val_loss: 0.5932 - val_accuracy: 0.8324\n",
            "Epoch 9/100\n",
            "2503/2503 [==============================] - 278s 110ms/step - loss: 1.2441 - accuracy: 0.6601 - val_loss: 0.5664 - val_accuracy: 0.8416\n",
            "Epoch 10/100\n",
            "2503/2503 [==============================] - 303s 121ms/step - loss: 1.2220 - accuracy: 0.6659 - val_loss: 0.5530 - val_accuracy: 0.8428\n",
            "Epoch 11/100\n",
            "2503/2503 [==============================] - 302s 120ms/step - loss: 1.1929 - accuracy: 0.6746 - val_loss: 0.5385 - val_accuracy: 0.8484\n",
            "Epoch 12/100\n",
            "2503/2503 [==============================] - 300s 120ms/step - loss: 1.1677 - accuracy: 0.6812 - val_loss: 0.5178 - val_accuracy: 0.8624\n",
            "Epoch 13/100\n",
            "2503/2503 [==============================] - 320s 128ms/step - loss: 1.1484 - accuracy: 0.6851 - val_loss: 0.5070 - val_accuracy: 0.8632\n",
            "Epoch 14/100\n",
            "2503/2503 [==============================] - 310s 123ms/step - loss: 1.1294 - accuracy: 0.6911 - val_loss: 0.4902 - val_accuracy: 0.8688\n",
            "Epoch 15/100\n",
            "2503/2503 [==============================] - 299s 119ms/step - loss: 1.1021 - accuracy: 0.6967 - val_loss: 0.5038 - val_accuracy: 0.8620\n",
            "Epoch 16/100\n",
            "2503/2503 [==============================] - 272s 108ms/step - loss: 1.1070 - accuracy: 0.6977 - val_loss: 0.5019 - val_accuracy: 0.8584\n",
            "Epoch 17/100\n",
            "2503/2503 [==============================] - 314s 125ms/step - loss: 1.0822 - accuracy: 0.7040 - val_loss: 0.4977 - val_accuracy: 0.8644\n",
            "Epoch 18/100\n",
            "2503/2503 [==============================] - 314s 125ms/step - loss: 1.0769 - accuracy: 0.7056 - val_loss: 0.4774 - val_accuracy: 0.8688\n",
            "Epoch 19/100\n",
            "2503/2503 [==============================] - 275s 110ms/step - loss: 1.0646 - accuracy: 0.7073 - val_loss: 0.4895 - val_accuracy: 0.8628\n",
            "Epoch 20/100\n",
            "2503/2503 [==============================] - 300s 120ms/step - loss: 1.0555 - accuracy: 0.7093 - val_loss: 0.4732 - val_accuracy: 0.8664\n",
            "Epoch 21/100\n",
            "2503/2503 [==============================] - 295s 117ms/step - loss: 1.0439 - accuracy: 0.7148 - val_loss: 0.4889 - val_accuracy: 0.8676\n",
            "Epoch 22/100\n",
            "2503/2503 [==============================] - 273s 108ms/step - loss: 1.0278 - accuracy: 0.7169 - val_loss: 0.4769 - val_accuracy: 0.8664\n",
            "Epoch 23/100\n",
            "2503/2503 [==============================] - 320s 127ms/step - loss: 1.0219 - accuracy: 0.7210 - val_loss: 0.4531 - val_accuracy: 0.8656\n",
            "Epoch 24/100\n",
            "2503/2503 [==============================] - 308s 123ms/step - loss: 1.0234 - accuracy: 0.7197 - val_loss: 0.4598 - val_accuracy: 0.8728\n",
            "Epoch 25/100\n",
            "2503/2503 [==============================] - 307s 122ms/step - loss: 1.0061 - accuracy: 0.7237 - val_loss: 0.4454 - val_accuracy: 0.8748\n",
            "Epoch 26/100\n",
            "2503/2503 [==============================] - 304s 121ms/step - loss: 1.0050 - accuracy: 0.7237 - val_loss: 0.4515 - val_accuracy: 0.8712\n",
            "Epoch 27/100\n",
            "2503/2503 [==============================] - 304s 121ms/step - loss: 0.9912 - accuracy: 0.7280 - val_loss: 0.4413 - val_accuracy: 0.8784\n",
            "Epoch 28/100\n",
            "2503/2503 [==============================] - 303s 121ms/step - loss: 0.9873 - accuracy: 0.7306 - val_loss: 0.4441 - val_accuracy: 0.8756\n",
            "Epoch 29/100\n",
            "2503/2503 [==============================] - 294s 117ms/step - loss: 0.9776 - accuracy: 0.7306 - val_loss: 0.4527 - val_accuracy: 0.8716\n",
            "Epoch 30/100\n",
            "2503/2503 [==============================] - 301s 120ms/step - loss: 0.9792 - accuracy: 0.7294 - val_loss: 0.4749 - val_accuracy: 0.8704\n",
            "Epoch 31/100\n",
            "2503/2503 [==============================] - 306s 122ms/step - loss: 0.9740 - accuracy: 0.7319 - val_loss: 0.4612 - val_accuracy: 0.8696\n",
            "Epoch 32/100\n",
            "1495/2503 [================>.............] - ETA: 1:56 - loss: 0.9525 - accuracy: 0.7347"
          ]
        }
      ],
      "source": [
        "epochs = 100\n",
        "batch_size = 256\n",
        "\n",
        "inputs = pretrained_model.input\n",
        "x = processing_layers(inputs)\n",
        "\n",
        "x = Dense(256, activation='relu')(pretrained_model.output)\n",
        "x = Dropout(0.2)(x)\n",
        "x = Dense(128, activation='relu')(x)\n",
        "x = Dropout(0.2)(x)\n",
        "x = Dense(64, activation='relu')(x)\n",
        "x = Dropout(0.2)(x)\n",
        "\n",
        "\n",
        "outputs = Dense(500, activation='softmax')(x)\n",
        "\n",
        "model = Model(inputs=inputs, outputs=outputs)\n",
        "\n",
        "model.compile(\n",
        "    optimizer=keras.optimizers.Adam(1e-3),\n",
        "    loss=keras.losses.CategoricalCrossentropy(),\n",
        "    metrics=[\"accuracy\"],\n",
        ")\n",
        "\n",
        "history = model.fit(\n",
        "    train_images,\n",
        "    batch_size = batch_size,\n",
        "    validation_data=valid_images,\n",
        "    epochs=epochs,\n",
        "    callbacks=[\n",
        "        checkpoint_callback,\n",
        "    ],\n",
        "    use_multiprocessing=True\n",
        ")\n",
        "\n",
        "time_stamp_name = str(datetime.now()).replace(' ', '_')\n",
        "model.save(f'/content/drive/My Drive/ml_final/model_bird_{time_stamp_name}.h5')\n",
        "loaded_model = tf.keras.models.load_model(f'/content/drive/My Drive/ml_final/model_bird_{time_stamp_name}.h5')\n",
        "\n",
        "results_test = loaded_model.evaluate(test_images, verbose=0)\n",
        "\n",
        "print(\"    Test Loss: {:.5f}\".format(results_test[0]))\n",
        "print(\"Test Accuracy: {:.2f}%\".format(results_test[1] * 100))\n",
        "\n",
        "results_valid = loaded_model.evaluate(valid_images, verbose=0)\n",
        "\n",
        "print(\"    Validation Set Loss: {:.5f}\".format(results_valid[0]))\n",
        "print(\"Validation Set Accuracy: {:.2f}%\".format(results_valid[1] * 100))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FC_xm-DsNSrz"
      },
      "outputs": [],
      "source": [
        "keras.utils.plot_model(model, show_shapes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HFBIpesXcgRK",
        "outputId": "c8981e72-4f1a-4b5d-9fc8-9f975e2ebb33"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "    Test Loss: 0.23907\n",
            "Test Accuracy: 93.40%\n",
            "    Validation Set Loss: 0.30329\n",
            "Validation Set Accuracy: 92.20%\n"
          ]
        }
      ],
      "source": [
        "#results_train = loaded_model.evaluate(train_images, verbose=0)\n",
        "\n",
        "\n",
        "#print(\"    Train Loss: {:.5f}\".format(results_train[0]))\n",
        "#print(\"Train Accuracy: {:.2f}%\".format(results_train[1] * 100))\n",
        "\n",
        "results_test = loaded_model.evaluate(test_images, verbose=0)\n",
        "\n",
        "print(\"    Test Loss: {:.5f}\".format(results_test[0]))\n",
        "print(\"Test Accuracy: {:.2f}%\".format(results_test[1] * 100))\n",
        "\n",
        "results_valid = loaded_model.evaluate(valid_images, verbose=0)\n",
        "\n",
        "print(\"    Validation Set Loss: {:.5f}\".format(results_valid[0]))\n",
        "print(\"Validation Set Accuracy: {:.2f}%\".format(results_valid[1] * 100))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b0sGVeVC3zZj"
      },
      "outputs": [],
      "source": [
        "#Large Model with more params\n",
        "\n",
        "# Load the pretained model\n",
        "pretrained_model = tf.keras.applications.MobileNetV3Large(\n",
        "    input_shape=(224, 224, 3),\n",
        "    include_top=False,\n",
        "    weights='imagenet',\n",
        "    pooling='avg'\n",
        ")\n",
        "\n",
        "pretrained_model.trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for x in test_baseline:\n",
        "  type(x)"
      ],
      "metadata": {
        "id": "kWnhkYqyFQ6X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inception_net.save('/content/drive/My Drive/ml_final/model.pb')\n",
        "converter = tf.lite.TFLiteConverter.from_saved_model('./model.pb') # path to the SavedModel directory\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "# Save the model.\n",
        "with open('model.tflite', 'wb') as f:\n",
        "  f.write(tflite_model)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Csz4b_O-8Awq",
        "outputId": "645c03b1-f7f0-4fd9-892d-1b5a65cc20be"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 63). These functions will not be directly callable after loading.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inception_net = tf.keras.models.load_model('/content/drive/My Drive/ml_final/model_bird_2023-03-08_19:01:49.007284.h5')\n",
        "image_size = (224, 224)\n",
        "\n",
        "test_baseline = tf.keras.utils.image_dataset_from_directory(\n",
        "    \"./test\",\n",
        "    seed=42,\n",
        "    image_size=image_size,\n",
        ")\n",
        "predictions = inception_net.predict(test_baseline)\n",
        "\n",
        "y_classes = predictions.argmax(axis=-1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j_2DhdnIpVqn",
        "outputId": "d31a961c-5253-4dc7-fcb4-6333984622eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2500 files belonging to 500 classes.\n",
            "79/79 [==============================] - 11s 118ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_classes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wOm85yTGqh7s",
        "outputId": "03c4f737-2541-426d-af66-d7b938821199"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  8, 307, 148, ..., 304, 219, 175])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inception_net = tf.keras.applications.MobileNetV3Large()\n",
        "\n",
        "image_size = (224, 224)\n",
        "\n",
        "test_baseline = tf.keras.utils.image_dataset_from_directory(\n",
        "    \"./test\",\n",
        "    seed=42,\n",
        "    image_size=image_size,\n",
        ")\n",
        "\n",
        "predictions = inception_net.predict(test_baseline)\n",
        "\n",
        "y_classes = predictions.argmax(axis=-1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xqmb3MIJAnwH",
        "outputId": "fdbb1acb-cde1-4147-d3d8-202b7a46299c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not 224. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2500 files belonging to 500 classes.\n",
            "79/79 [==============================] - 14s 161ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.applications import imagenet_utils\n",
        "\n",
        "preds = inception_net.predict(test_baseline)\n",
        "# decode the results into a list of tuples (class, description, probability)\n",
        "# (one such list for each sample in the batch)\n",
        "#print('Predicted:', decode_predictions(preds, top=3)[0])\n",
        "\n",
        "x = imagenet_utils.decode_predictions(preds, top=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "URlMr6fGIWcr",
        "outputId": "b56afcfe-077e-4e58-dc25-cf7f2515aa6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "79/79 [==============================] - 12s 155ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "sYuigzgd3yMN",
        "outputId": "98980ab8-3a7e-49d4-b3c4-2e5e1bd41333"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "2503/2503 [==============================] - 487s 192ms/step - loss: 2.8675 - accuracy: 0.3330 - val_loss: 0.8927 - val_accuracy: 0.7672\n",
            "Epoch 2/100\n",
            "2503/2503 [==============================] - 464s 185ms/step - loss: 1.5304 - accuracy: 0.5839 - val_loss: 0.6107 - val_accuracy: 0.8300\n",
            "Epoch 3/100\n",
            "2503/2503 [==============================] - 453s 181ms/step - loss: 1.2606 - accuracy: 0.6543 - val_loss: 0.5230 - val_accuracy: 0.8548\n",
            "Epoch 4/100\n",
            "2503/2503 [==============================] - 449s 179ms/step - loss: 1.1153 - accuracy: 0.6900 - val_loss: 0.4641 - val_accuracy: 0.8696\n",
            "Epoch 5/100\n",
            "2503/2503 [==============================] - 483s 192ms/step - loss: 1.0301 - accuracy: 0.7125 - val_loss: 0.3889 - val_accuracy: 0.8868\n",
            "Epoch 6/100\n",
            "2503/2503 [==============================] - 417s 166ms/step - loss: 0.9710 - accuracy: 0.7304 - val_loss: 0.4060 - val_accuracy: 0.8820\n",
            "Epoch 7/100\n",
            "2503/2503 [==============================] - 443s 176ms/step - loss: 0.9175 - accuracy: 0.7444 - val_loss: 0.3747 - val_accuracy: 0.8920\n",
            "Epoch 8/100\n",
            "2503/2503 [==============================] - 445s 177ms/step - loss: 0.8844 - accuracy: 0.7513 - val_loss: 0.3631 - val_accuracy: 0.8916\n",
            "Epoch 9/100\n",
            "2503/2503 [==============================] - 439s 175ms/step - loss: 0.8463 - accuracy: 0.7624 - val_loss: 0.3251 - val_accuracy: 0.9056\n",
            "Epoch 10/100\n",
            "2503/2503 [==============================] - 402s 160ms/step - loss: 0.8212 - accuracy: 0.7688 - val_loss: 0.3463 - val_accuracy: 0.9012\n",
            "Epoch 11/100\n",
            "2503/2503 [==============================] - 409s 163ms/step - loss: 0.8052 - accuracy: 0.7715 - val_loss: 0.3414 - val_accuracy: 0.9076\n",
            "Epoch 12/100\n",
            "2503/2503 [==============================] - 439s 175ms/step - loss: 0.7693 - accuracy: 0.7832 - val_loss: 0.3215 - val_accuracy: 0.9060\n",
            "Epoch 13/100\n",
            "2503/2503 [==============================] - 405s 161ms/step - loss: 0.7604 - accuracy: 0.7859 - val_loss: 0.3373 - val_accuracy: 0.9016\n",
            "Epoch 14/100\n",
            "2503/2503 [==============================] - 407s 162ms/step - loss: 0.7523 - accuracy: 0.7888 - val_loss: 0.3168 - val_accuracy: 0.9096\n",
            "Epoch 15/100\n",
            "2503/2503 [==============================] - 407s 162ms/step - loss: 0.7301 - accuracy: 0.7954 - val_loss: 0.3114 - val_accuracy: 0.9104\n",
            "Epoch 16/100\n",
            "2503/2503 [==============================] - 446s 178ms/step - loss: 0.7198 - accuracy: 0.7969 - val_loss: 0.3196 - val_accuracy: 0.9068\n",
            "Epoch 17/100\n",
            "2503/2503 [==============================] - 430s 171ms/step - loss: 0.7070 - accuracy: 0.8004 - val_loss: 0.2913 - val_accuracy: 0.9212\n",
            "Epoch 18/100\n",
            "2503/2503 [==============================] - 418s 166ms/step - loss: 0.6943 - accuracy: 0.8042 - val_loss: 0.3009 - val_accuracy: 0.9160\n",
            "Epoch 19/100\n",
            "2503/2503 [==============================] - 413s 164ms/step - loss: 0.6972 - accuracy: 0.8031 - val_loss: 0.3097 - val_accuracy: 0.9180\n",
            "Epoch 20/100\n",
            "2503/2503 [==============================] - 434s 173ms/step - loss: 0.6828 - accuracy: 0.8090 - val_loss: 0.2907 - val_accuracy: 0.9216\n",
            "Epoch 21/100\n",
            "2503/2503 [==============================] - 429s 171ms/step - loss: 0.6632 - accuracy: 0.8126 - val_loss: 0.2990 - val_accuracy: 0.9176\n",
            "Epoch 22/100\n",
            "2503/2503 [==============================] - 472s 188ms/step - loss: 0.6717 - accuracy: 0.8122 - val_loss: 0.3093 - val_accuracy: 0.9100\n",
            "Epoch 23/100\n",
            "2503/2503 [==============================] - 466s 185ms/step - loss: 0.6546 - accuracy: 0.8144 - val_loss: 0.3194 - val_accuracy: 0.9124\n",
            "Epoch 24/100\n",
            "2503/2503 [==============================] - 475s 189ms/step - loss: 0.6506 - accuracy: 0.8163 - val_loss: 0.3112 - val_accuracy: 0.9088\n",
            "Epoch 25/100\n",
            "2503/2503 [==============================] - 476s 190ms/step - loss: 0.6481 - accuracy: 0.8177 - val_loss: 0.2894 - val_accuracy: 0.9236\n",
            "Epoch 26/100\n",
            "2503/2503 [==============================] - 473s 188ms/step - loss: 0.6395 - accuracy: 0.8191 - val_loss: 0.2959 - val_accuracy: 0.9196\n",
            "Epoch 27/100\n",
            "2503/2503 [==============================] - 465s 185ms/step - loss: 0.6344 - accuracy: 0.8226 - val_loss: 0.3044 - val_accuracy: 0.9204\n",
            "Epoch 28/100\n",
            "2503/2503 [==============================] - 468s 186ms/step - loss: 0.6338 - accuracy: 0.8209 - val_loss: 0.3252 - val_accuracy: 0.9144\n",
            "Epoch 29/100\n",
            "2503/2503 [==============================] - 474s 189ms/step - loss: 0.6330 - accuracy: 0.8218 - val_loss: 0.2854 - val_accuracy: 0.9220\n",
            "Epoch 30/100\n",
            "2503/2503 [==============================] - 473s 189ms/step - loss: 0.6220 - accuracy: 0.8245 - val_loss: 0.2828 - val_accuracy: 0.9232\n",
            "Epoch 31/100\n",
            "2503/2503 [==============================] - 477s 190ms/step - loss: 0.6201 - accuracy: 0.8258 - val_loss: 0.3323 - val_accuracy: 0.9104\n",
            "Epoch 32/100\n",
            "2503/2503 [==============================] - 477s 190ms/step - loss: 0.6179 - accuracy: 0.8260 - val_loss: 0.3095 - val_accuracy: 0.9180\n",
            "Epoch 33/100\n",
            "2503/2503 [==============================] - 484s 193ms/step - loss: 0.6141 - accuracy: 0.8276 - val_loss: 0.3096 - val_accuracy: 0.9200\n",
            "Epoch 34/100\n",
            "2503/2503 [==============================] - 488s 195ms/step - loss: 0.6054 - accuracy: 0.8303 - val_loss: 0.2898 - val_accuracy: 0.9176\n",
            "Epoch 35/100\n",
            "2503/2503 [==============================] - 472s 188ms/step - loss: 0.5995 - accuracy: 0.8313 - val_loss: 0.3092 - val_accuracy: 0.9180\n",
            "Epoch 36/100\n",
            "2503/2503 [==============================] - 474s 189ms/step - loss: 0.6092 - accuracy: 0.8301 - val_loss: 0.3072 - val_accuracy: 0.9200\n",
            "Epoch 37/100\n",
            "2503/2503 [==============================] - 494s 197ms/step - loss: 0.5992 - accuracy: 0.8301 - val_loss: 0.3061 - val_accuracy: 0.9200\n",
            "Epoch 38/100\n",
            "2503/2503 [==============================] - 487s 194ms/step - loss: 0.5949 - accuracy: 0.8337 - val_loss: 0.3062 - val_accuracy: 0.9260\n",
            "Epoch 39/100\n",
            "2503/2503 [==============================] - 477s 190ms/step - loss: 0.5978 - accuracy: 0.8322 - val_loss: 0.2967 - val_accuracy: 0.9256\n",
            "Epoch 40/100\n",
            "2503/2503 [==============================] - 483s 193ms/step - loss: 0.5960 - accuracy: 0.8331 - val_loss: 0.3002 - val_accuracy: 0.9216\n",
            "Epoch 41/100\n",
            "2503/2503 [==============================] - 499s 199ms/step - loss: 0.5961 - accuracy: 0.8339 - val_loss: 0.3098 - val_accuracy: 0.9260\n",
            "Epoch 42/100\n",
            "2503/2503 [==============================] - 493s 196ms/step - loss: 0.5939 - accuracy: 0.8344 - val_loss: 0.2994 - val_accuracy: 0.9200\n",
            "Epoch 43/100\n",
            "2503/2503 [==============================] - 477s 190ms/step - loss: 0.5852 - accuracy: 0.8372 - val_loss: 0.2981 - val_accuracy: 0.9236\n",
            "Epoch 44/100\n",
            "2503/2503 [==============================] - 498s 198ms/step - loss: 0.5906 - accuracy: 0.8344 - val_loss: 0.2809 - val_accuracy: 0.9264\n",
            "Epoch 45/100\n",
            "2503/2503 [==============================] - 471s 188ms/step - loss: 0.5899 - accuracy: 0.8366 - val_loss: 0.2872 - val_accuracy: 0.9228\n",
            "Epoch 46/100\n",
            "2503/2503 [==============================] - 459s 183ms/step - loss: 0.5801 - accuracy: 0.8376 - val_loss: 0.3169 - val_accuracy: 0.9248\n",
            "Epoch 47/100\n",
            "2503/2503 [==============================] - 483s 192ms/step - loss: 0.5815 - accuracy: 0.8373 - val_loss: 0.2949 - val_accuracy: 0.9232\n",
            "Epoch 48/100\n",
            "2503/2503 [==============================] - 451s 180ms/step - loss: 0.5836 - accuracy: 0.8370 - val_loss: 0.2936 - val_accuracy: 0.9220\n",
            "Epoch 49/100\n",
            "2503/2503 [==============================] - 465s 185ms/step - loss: 0.5830 - accuracy: 0.8385 - val_loss: 0.3084 - val_accuracy: 0.9216\n",
            "Epoch 50/100\n",
            "2503/2503 [==============================] - 446s 178ms/step - loss: 0.5784 - accuracy: 0.8403 - val_loss: 0.3000 - val_accuracy: 0.9224\n",
            "Epoch 51/100\n",
            "2503/2503 [==============================] - 459s 183ms/step - loss: 0.5748 - accuracy: 0.8404 - val_loss: 0.3116 - val_accuracy: 0.9168\n",
            "Epoch 52/100\n",
            "2503/2503 [==============================] - 481s 192ms/step - loss: 0.5821 - accuracy: 0.8391 - val_loss: 0.2968 - val_accuracy: 0.9256\n",
            "Epoch 53/100\n",
            "2503/2503 [==============================] - 455s 181ms/step - loss: 0.5850 - accuracy: 0.8381 - val_loss: 0.2946 - val_accuracy: 0.9252\n",
            "Epoch 54/100\n",
            "2503/2503 [==============================] - 488s 194ms/step - loss: 0.5779 - accuracy: 0.8397 - val_loss: 0.3053 - val_accuracy: 0.9220\n",
            "Epoch 55/100\n",
            "2503/2503 [==============================] - 467s 186ms/step - loss: 0.5820 - accuracy: 0.8393 - val_loss: 0.3105 - val_accuracy: 0.9160\n",
            "Epoch 56/100\n",
            "2503/2503 [==============================] - 455s 181ms/step - loss: 0.5729 - accuracy: 0.8419 - val_loss: 0.2919 - val_accuracy: 0.9276\n",
            "Epoch 57/100\n",
            "2503/2503 [==============================] - 474s 189ms/step - loss: 0.5759 - accuracy: 0.8406 - val_loss: 0.2941 - val_accuracy: 0.9256\n",
            "Epoch 58/100\n",
            "2503/2503 [==============================] - 452s 180ms/step - loss: 0.5687 - accuracy: 0.8432 - val_loss: 0.2921 - val_accuracy: 0.9180\n",
            "Epoch 59/100\n",
            "2503/2503 [==============================] - 435s 173ms/step - loss: 0.5743 - accuracy: 0.8415 - val_loss: 0.3000 - val_accuracy: 0.9200\n",
            "Epoch 60/100\n",
            "2503/2503 [==============================] - 464s 185ms/step - loss: 0.5665 - accuracy: 0.8423 - val_loss: 0.3052 - val_accuracy: 0.9196\n",
            "Epoch 61/100\n",
            "2503/2503 [==============================] - 445s 177ms/step - loss: 0.5686 - accuracy: 0.8432 - val_loss: 0.3146 - val_accuracy: 0.9160\n",
            "Epoch 62/100\n",
            "2503/2503 [==============================] - 448s 178ms/step - loss: 0.5610 - accuracy: 0.8457 - val_loss: 0.3006 - val_accuracy: 0.9176\n",
            "Epoch 63/100\n",
            "2503/2503 [==============================] - 485s 194ms/step - loss: 0.5766 - accuracy: 0.8426 - val_loss: 0.3134 - val_accuracy: 0.9132\n",
            "Epoch 64/100\n",
            "2503/2503 [==============================] - 456s 182ms/step - loss: 0.5672 - accuracy: 0.8445 - val_loss: 0.3096 - val_accuracy: 0.9240\n",
            "Epoch 65/100\n",
            "2503/2503 [==============================] - 451s 180ms/step - loss: 0.5742 - accuracy: 0.8436 - val_loss: 0.2967 - val_accuracy: 0.9312\n",
            "Epoch 66/100\n",
            "2503/2503 [==============================] - 465s 185ms/step - loss: 0.5642 - accuracy: 0.8451 - val_loss: 0.2972 - val_accuracy: 0.9208\n",
            "Epoch 67/100\n",
            "2503/2503 [==============================] - 466s 186ms/step - loss: 0.5699 - accuracy: 0.8452 - val_loss: 0.2814 - val_accuracy: 0.9300\n",
            "Epoch 68/100\n",
            "2503/2503 [==============================] - 456s 182ms/step - loss: 0.5721 - accuracy: 0.8445 - val_loss: 0.3050 - val_accuracy: 0.9248\n",
            "Epoch 69/100\n",
            "2503/2503 [==============================] - 427s 170ms/step - loss: 0.5632 - accuracy: 0.8451 - val_loss: 0.2932 - val_accuracy: 0.9320\n",
            "Epoch 70/100\n",
            "2503/2503 [==============================] - 451s 180ms/step - loss: 0.5600 - accuracy: 0.8453 - val_loss: 0.2879 - val_accuracy: 0.9240\n",
            "Epoch 71/100\n",
            "2503/2503 [==============================] - 435s 173ms/step - loss: 0.5570 - accuracy: 0.8475 - val_loss: 0.2872 - val_accuracy: 0.9276\n",
            "Epoch 72/100\n",
            "2503/2503 [==============================] - 444s 177ms/step - loss: 0.5687 - accuracy: 0.8451 - val_loss: 0.3067 - val_accuracy: 0.9228\n",
            "Epoch 73/100\n",
            "2503/2503 [==============================] - 456s 182ms/step - loss: 0.5611 - accuracy: 0.8474 - val_loss: 0.3188 - val_accuracy: 0.9236\n",
            "Epoch 74/100\n",
            "2503/2503 [==============================] - 452s 180ms/step - loss: 0.5654 - accuracy: 0.8468 - val_loss: 0.3114 - val_accuracy: 0.9208\n",
            "Epoch 75/100\n",
            "2503/2503 [==============================] - 446s 178ms/step - loss: 0.5707 - accuracy: 0.8455 - val_loss: 0.2994 - val_accuracy: 0.9208\n",
            "Epoch 76/100\n",
            "2503/2503 [==============================] - 421s 168ms/step - loss: 0.5730 - accuracy: 0.8455 - val_loss: 0.2960 - val_accuracy: 0.9252\n",
            "Epoch 77/100\n",
            "2503/2503 [==============================] - 412s 164ms/step - loss: 0.5608 - accuracy: 0.8473 - val_loss: 0.3509 - val_accuracy: 0.9148\n",
            "Epoch 78/100\n",
            "2503/2503 [==============================] - 426s 170ms/step - loss: 0.5771 - accuracy: 0.8449 - val_loss: 0.2983 - val_accuracy: 0.9200\n",
            "Epoch 79/100\n",
            "2503/2503 [==============================] - 420s 167ms/step - loss: 0.5720 - accuracy: 0.8465 - val_loss: 0.3049 - val_accuracy: 0.9232\n",
            "Epoch 80/100\n",
            "2503/2503 [==============================] - 445s 177ms/step - loss: 0.5645 - accuracy: 0.8468 - val_loss: 0.3342 - val_accuracy: 0.9236\n",
            "Epoch 81/100\n",
            "2503/2503 [==============================] - 437s 174ms/step - loss: 0.5627 - accuracy: 0.8473 - val_loss: 0.3160 - val_accuracy: 0.9264\n",
            "Epoch 82/100\n",
            "2503/2503 [==============================] - 454s 181ms/step - loss: 0.5662 - accuracy: 0.8467 - val_loss: 0.3328 - val_accuracy: 0.9248\n",
            "Epoch 83/100\n",
            "2503/2503 [==============================] - 465s 185ms/step - loss: 0.5688 - accuracy: 0.8456 - val_loss: 0.3391 - val_accuracy: 0.9256\n",
            "Epoch 84/100\n",
            "2503/2503 [==============================] - 444s 177ms/step - loss: 0.5656 - accuracy: 0.8469 - val_loss: 0.3324 - val_accuracy: 0.9180\n",
            "Epoch 85/100\n",
            "2503/2503 [==============================] - 413s 164ms/step - loss: 0.5636 - accuracy: 0.8481 - val_loss: 0.3105 - val_accuracy: 0.9300\n",
            "Epoch 86/100\n",
            "2503/2503 [==============================] - 447s 178ms/step - loss: 0.5610 - accuracy: 0.8496 - val_loss: 0.3101 - val_accuracy: 0.9212\n",
            "Epoch 87/100\n",
            "2503/2503 [==============================] - 463s 185ms/step - loss: 0.5718 - accuracy: 0.8473 - val_loss: 0.2971 - val_accuracy: 0.9284\n",
            "Epoch 88/100\n",
            "2503/2503 [==============================] - 441s 176ms/step - loss: 0.5639 - accuracy: 0.8482 - val_loss: 0.3129 - val_accuracy: 0.9256\n",
            "Epoch 89/100\n",
            "2503/2503 [==============================] - 425s 169ms/step - loss: 0.5702 - accuracy: 0.8472 - val_loss: 0.3168 - val_accuracy: 0.9244\n",
            "Epoch 90/100\n",
            "2503/2503 [==============================] - 438s 174ms/step - loss: 0.5604 - accuracy: 0.8498 - val_loss: 0.3308 - val_accuracy: 0.9192\n",
            "Epoch 91/100\n",
            "2503/2503 [==============================] - 426s 170ms/step - loss: 0.5766 - accuracy: 0.8460 - val_loss: 0.3126 - val_accuracy: 0.9256\n",
            "Epoch 92/100\n",
            "2503/2503 [==============================] - 424s 169ms/step - loss: 0.5743 - accuracy: 0.8480 - val_loss: 0.3329 - val_accuracy: 0.9188\n",
            "Epoch 93/100\n",
            "2503/2503 [==============================] - 434s 173ms/step - loss: 0.5703 - accuracy: 0.8477 - val_loss: 0.3501 - val_accuracy: 0.9172\n",
            "Epoch 94/100\n",
            "2503/2503 [==============================] - 433s 173ms/step - loss: 0.5656 - accuracy: 0.8488 - val_loss: 0.3331 - val_accuracy: 0.9200\n",
            "Epoch 95/100\n",
            "2503/2503 [==============================] - 474s 189ms/step - loss: 0.5697 - accuracy: 0.8484 - val_loss: 0.3305 - val_accuracy: 0.9260\n",
            "Epoch 96/100\n",
            "2503/2503 [==============================] - 461s 184ms/step - loss: 0.5654 - accuracy: 0.8489 - val_loss: 0.3295 - val_accuracy: 0.9184\n",
            "Epoch 97/100\n",
            "2503/2503 [==============================] - 405s 161ms/step - loss: 0.5783 - accuracy: 0.8457 - val_loss: 0.3478 - val_accuracy: 0.9220\n",
            "Epoch 98/100\n",
            "2503/2503 [==============================] - 417s 166ms/step - loss: 0.5640 - accuracy: 0.8493 - val_loss: 0.3398 - val_accuracy: 0.9172\n",
            "Epoch 99/100\n",
            "2503/2503 [==============================] - 416s 165ms/step - loss: 0.5646 - accuracy: 0.8484 - val_loss: 0.3270 - val_accuracy: 0.9236\n",
            "Epoch 100/100\n",
            "2503/2503 [==============================] - 413s 164ms/step - loss: 0.5686 - accuracy: 0.8487 - val_loss: 0.3374 - val_accuracy: 0.9268\n",
            "    Test Loss: 0.28045\n",
            "Test Accuracy: 93.36%\n",
            "    Validation Set Loss: 0.33737\n",
            "Validation Set Accuracy: 92.68%\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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BlwG3Oed+BOQnr1jJ1RsE6kIqIjL0NoIWM/sqvtvoWWaWxlFcub6nsVj3GxIRGeoZwZVAF/56gh1AJfD9/b3BzO4xsxozWzHI8HPMrMnMlgU/3ziokh+GnAwfBC2dsZGapYjIEWtIQRDs/O8FCs3sYqDTOXegNoKfARccYJy/OufmBD/fGUpZhsOk0hwANtW3j9QsRUSOWEO9xcSHgFeBDwIfAhaZ2RX7e49z7gWg/rBLmARj87PIjkbYUNuW6qKIiKTcUNsI/hk42TlXA2Bm5cBTwIOHOf/TzOwNYBvwpeCK5X2Y2bXAtQCTJk06zFlCWppRVZbLhl2thz0tEZGj3VDbCNJ6QyBQdxDvHcxSYLJz7iTgf4BHBxvROXeXc26Bc25BeXn5Yc7Wm1qey4ZdOiMQERnqzvzPZvaEmV1jZtcAfwQeP5wZO+eanXOtwd+PA1EzKzucaR6MqWW5bGnoUBdSEQm9oTYWfxm4C5gd/NzlnPvK4czYzMaZmQV/nxKUpe5wpnkwppTlEk84NqvBWERCbqhtBDjnHgIeGur4ZnYfcA5QZmbVwDcJrj1wzt0BXAF83sxiQAfw4eCitRExpSwXgA272jhmTN5IzVZE5Iiz3yAwsxZgoJ2zAc45VzDYe51zH9nftJ1ztwG3DaWQybAnCFqBsakqhohIyu03CJxzR+1tJA6kKCeDktwMNRiLSOiF8pnFvaaW5bJe1xKISMiFOgimlKkLqYhIuIOgPJeali5au3TPIREJr1AHwdTeBmNVD4lIiIU6CKaU+W6j63WrCREJsVAHweTSHMxQO4GIhFqogyArGqGiKFtBICKhFuogAPUcEhEJfRBMLctlQ20bI3h3CxGRI0rog2BKWS4tXTF2tXanuigiIimhICgPeg7VqueQiIRT6INg5nh/37wlmxtSXBIRkdQIfRCU52cyY3wBL7xTm+qiiIikROiDAODsY8tYsqmBNt1qQkRCSEEAnD29nJ644+V1I/aANBGRI4aCAFhQVUx2NMILa1Q9JCLhoyAAMtMjLJxaonYCEQklBUHg7GPL2VjXzuY6PcxeRMJFQRA4+9hyAFUPiUjoKAgCU8tyqSjKVvWQiISOgiBgZpx9bDkvraujJ55IdXFEREaMgqCPs6eX0doVY+kmXWUsIuGhIOjjjOll5GRE+M1rW1JdFBGREaMg6KMgK8pHTpnEY29sY0u9eg+JSDgoCPr5h7OmkGbwk7+uT3VRRERGhIKgn/GF2XxgbiW/fW0LtS1dqS6OiEjSKQgG8Nl3TaU7nuB/X9yQ6qKIiCSdgmAAU8vzuGjWeH758iaaO3tSXRwRkaRKWhCY2T1mVmNmKwYZbmZ2q5mtNbM3zWxesspyKD5/zjRaumLc8dy6VBdFRCSpknlG8DPggv0MvxCYHvxcC9yexLIctFkVhVw+r5I7X1jPsi2NqS6OiEjSJC0InHMvAPX7GeUy4BfOewUoMrPxySrPofjGJTMZk5/JTfcvo7MnnuriiIgkRSrbCCqAvlduVQev7cPMrjWzxWa2uLZ25O4FVJgd5ftXnMS62jb+84m3R2y+IiIj6ahoLHbO3eWcW+CcW1BeXj6i8z5zehlXL5zM3S9u4JX1eoKZiIw+qQyCrcDEPv9XBq8dcb560fFMLsnhht8so65V1xaIyOiSyiB4DPh40HtoIdDknNuewvIMKicjnds+Oo/69m7+z/1vkEi4VBdJRGTYJLP76H3Ay8BxZlZtZp82s8+Z2eeCUR4H1gNrgZ8AX0hWWYbDrIpCvnHxTJ5/p5bbn1eXUhEZPdKTNWHn3EcOMNwB1yVr/slw1amTeGV9Hf/3ybeZO7GI048pS3WRREQO21HRWHykMDNuuXw2U8pyueZnr/HI69WpLpKIyGELTxBsXQq/+0do39+lDQeWl5nOA587nXmTirjxt2/wb39aRVxtBiJyFAtPELTVwuu/hLq1hz2pktwMfvnpU7nq1Enc+fx6Pv+rJbrgTESOWuEJgpKp/nfd8DT0RiNpfO/9J/KNi2fy5Fs7+cwvFtPeHRuWaYuIjKTwBEHRZLA0qB/eB8586swpfP+K2by4dhdX3/0qTR26W6mIHF3CEwTpGVA4EeqHv+vnBxdM5LaPzuPN6kbe/6MXeXmdrkAWkaNHeIIAoHTasJ8R9LroxPH84lOnEks4PvKTV7jp/jd0FbKIHBXCFQQlU6FuPbjk9PI5bVopT954Ntf93TR+t2wrZ/z7M3zpgTdYsqkBl6R5iogcrpAFwTToajrsLqT7kxWN8OXzj+fPN5zF++dW8qfl27n89pe4/PaXqGnpTNp8RUQOVciCIOg5lIR2gv6OGZPPv33gRBb983l8932zWL2jhQ/8+CXW1bYmfd4iIgcjXEFQOs3/TlI7wUDyMtP52MLJ3PeZhXR0x7ni9pd4dUO9qopE5IiRtHsNHZF6u5AO07UEB+OkiUU8/IXT+fg9r/KhO19mTH4m8yYVM3dSEbMrizihooCCrOiIl0tEJFxBsLsL6cidEfQ1uTSXR79wBr9/cxtLNzWwZHMDf165Y/fw48bm84W/m8YlsyeQlmYpKaOIhE+4ggB8O8EItBEMpjg3g4+fVsXHT6sCoK61i+Vbm1he3cTjK3Zw/W+WcfffNnDzBcezcGqpAkFEki58QVA6Dd58wHchtdTvZEvzMjnnuDGcc9wYrvu7Y3jk9a3855Nv89GfLqI0N4OF00o5fVop7z5+DOMLs1NdXBEZhcIXBCVT93QhzS1NdWn2kpZmXD6/kvfOHs8f3tzOS+t28dLaOv74pn9w20kTizj/hLGcf8I4ppXnpbi0IjJahDAI+vQcOsKCoFdWNMIV8yu5Yn4lzjnW1bbyxMqdPLlyB//x57f5jz+/zTFj8jhvxlhyMiI0tHfT3BFjVkUBF8+eQHl+ZqoXQUSOIna0dWNcsGCBW7x48aFPoPYd+NHJ8P474aQPD1/BRsi2xg6eXLmDJ1bu5NWN9cQTjrzMdLIzItS2dBFJM848poyrTp3EeTPGqo1BRAAwsyXOuQUDDQvfGUFxcu5COlImFGVzzRlTuOaMKXR0x4mkGRnp/nKQd3a28OjrW3n09a1c+8slHD8un3989zFUlebyRnUjb25pojg3g6tOncTEkpwUL4mIHCnCd0YA8MMTofIUuOLu4SnUESYWT/DYG9u47dm1rK9t2/16UU6Uls4YCec49/gxXDhrPOOLshhbkEVZbiZ5WelEdAYhMirpjKC/kuTdhfRIkB5J4wPzKrlsTgXPrK6hsyfOnIlFVBZns7O5i3sXbeK+Vzfz1Kqavd5n5q+EHpOfyezKIuZMLGJWRSGTSnIoy8vAjoBeViIy/MJ5RvDHm2D5A/CVTUdEF9JU6I4l2NLQzs7mTmpbutjV2k1zRw9NHT1UN3TwRnUjtS17bqOdFU1jXEEW0UgakTQjPWJE0tKIphnZGRHmTy7mrOnlnFRZSHokXHcuETka6Iygv5Kp0NkEHQ2QU5Lq0qRERnoa08rzBu2G6pxje1Mnq7Y3s6W+neqGDna2dBGLJ4gnHLHen3iC+rZubn16DT98ag25GRHK8jPJyUgnNyNCXlY6eZnp5GelU5qbSXm+/5k+xs9bjdkiqRfSIAi6kO5YDlPfldqyHKHMjAlF2UwoGtpFbI3t3by4to5XN9TR2NFDW1ectq4Y9W3dbK5rp7kzRkN7N/HEnjPQ/Mx0TppYxPHj8plSnsuU0lzq2rp5bWM9r230z3A4ZUoJp04pZXZlIWMLsnY3jAPEE47uWIKsaJqqrUQOQzirhrpa4QcnwJSz4MpfDU/B5IASCUdDezc7m7t4a3szr29uYNmWRtbWtNIVS+weLycjwrxJxZjB4o0NdPTEdw8ry8sgJyOdpo4emjt7dl8gnhONkBmNAP5sJs2M3Ex/NlKUE+WECQXMnVTMjPEFPpzq29jW2Eki4YhEjGhaGseNy2f+5GJyM4f/+KgnOJPKCsooMtL2VzUUziAAePr/h7/+X/jHxVB2zOFPTw5ZIuHY0dzJxl1t5Gamc8KEgt3tDD3xBMu3NvHOjhZ2NHeys7mT9u44RdlRCnMyyI5G6OiJ094Vo6MnjhkYRsI52rpitHbFqG3tZtX2Zrr7hM1gImnGrAkFFGRH6eyJ09mToK07RmtnjLauGHHnSE/z7SQF2emML8xmQmEWYwuzKM/LpCwvk554gtU7Wli1vZlNde00dfTQ2hUDoLI4m+PHFVBZnE19Wzc7mjtp6Ywxc3wB8ycXM6uigO5Ygob2Hlq7ephUksOxY/PJz4rinKO2tYst9R2MLcikoij7gGdC8YTjxbW7aO7sYf7k4t23KYknHFvq2+noiXPs2PwBe4s55+joiRNPOPKTcGfcWDxBS2eMls4YRbnRve6+m0g4XlpXx7raViqKsqksyWZicU5SQrpXTzxBepoN6ezSOcerG+p5p6aV/D4HHGPysxhTkDnkwI/FEyzaUM+rG+qZP7mY06eVJq2NTUEwkNYa+MEsmPNRuOSHhz89OaJ1xxKs2t7M2ztbKMvLYFJJDpXFOaSnGbGEo7MnzvKtTSxaX8/iTfV09iTIjkbIiqaRk5lOfmY6uZnpu8ePxRM0dfSwrbGTrY0d1LR00hPf813KiqZx3Nh8ppbnUZyTQVFOFOfgnZoW3t7RwvbGDsryMxlbkEV2NMKKrU3UtXUPWv7xhVk0dfTQ3r3n7Kj3TCc7GqGl04deaV4mM8cXMHNCAWt3tvDAkmq2N+15Ml5FUTYluRmsqWmhs8cHY35WOqdOKWFqeR5b6tvZWNfOtsYOWjp76K3Jm1iSzbxJxcyuLGJcQRbFuVFKczMpzolSlJNBJM1YtqWR596u4aV1dRTnRJk5oZCZ4/OJJ9gd4tUN7Wyp72BzvQ/IXulpxilTSjhvxli64wnue3Uzm+ra9/kcqkpzmDG+gGPG5JGflU52RjrRNKOurZtdrV3Ut3XTFHR6aO+Kk5+VTkluBqV5GeQF67C3zaogK0pOZjortjbx4tpdLN7UQGZ6GidMKOCECYXEE/6q/vW1beRkRHw15dRSalu6+PWiTazr0zW7v4KsdCYUZVNZnE1hdga7WrvY2dxJc0fP7vWeFY3w1zW1NLbv+RzK8jK56MRxlOdlEks44glHXVs3Nc2d1LR08f65FXzqzCn72dIHpyAYzO+vh2X3wY0rIG/M8ExTQsk5R3NHjNrWLsygqjT3oK7JcM6xqa6d1Tuayc5Ipyg7Sm5mhI272nl7Zwtra1opyolSVZpLZXE225s6WbmtiZXbmonFHXlZvnF+Z3MXa2pa6Ik7zOCs6eVcuWAilcXZLNnUwOJN9bR0xjh2bD7Hjc0nmm68uqGeV9bXU93QzsSSHKpKc6koyqYwO0peVjoJ51he3cTSzQ3sbO4asPzRiNETd0TSjNmVhbR0xlhf20qfJiEyImlUFmczsSSHiSXZjMnPIj/oTLB+VxtPvbWTNTX+CX6nVJXw0VMnsXBqKTuCAFlf28aq7c28FZxp9ZebEaE0L5OiHH92kZMRobmzh/q2burbemgLzhoHMmN8AadPK6U7lmDFtiZWbW8mzYxp5XlMLc+lqaOHxRsbdp/ZzZlYxFWnTuLM6WV0dMdpCdrAalu6qGnpYkdTJ9saO9ja2EFTRw/l+ZmMyc+iIDudXa3d7GzqpLmzh4VTSzn/hHGcNrWUl9fv4tHXt/HM6hq64z6k0wxKcjMoz89ibEEml540gQ/MqxzydtWXgmAwu9bCbQvg7C/Bu/9leKYpkmJdsThra1opyc04qDvWOuf2Wy3inNvryLu+rZuG9h4a27pp7Yoxq6KQs6eXU5jjq3g6uuO8s7OFaCSNcYVZFOdED1jtsrmunYRzVJXl7ne8RMJXW7V1x+iJO0pyMsjOOHB1TDzhaOv21VHNHT20dMaYUpa7z/254glHmrFXeWPxBKu2t5AZTePYsfkHnNehisUTOCBiNqy96lIWBGZ2AfDfQAT4qXPuln7DrwG+D2wNXrrNOffT/U1zWIMA4DdXwca/wY0rIVN39BSR0Wl/QZC0K3/MLAL8CLgQmAl8xMxmDjDqb51zc4Kf/YZAUpxxA3Q2wgv/MeKzFhE5EiTzEtBTgLXOufXOuW7gN8BlSZzfoZl4Msz7BLx4K2xelOrSiIiMuGQGQQWwpc//1cFr/V1uZm+a2YNmNnGgCZnZtWa22MwW19bWDn9Jz/8eFE2ERz4L3YP3BBARGY1SfVOY3wNVzrnZwF+Anw80knPuLufcAufcgvLy8uEvRWY+vO92aNgIf/nG8E9fROQIlswg2Ar0PcKvZE+jMADOuTrnXG9/tJ8C85NYnv2rOhMWfgFe+ylseCFlxRARGWnJDILXgOlmNsXMMoAPA4/1HcHMxvf591JgVRLLc2Dnfh0KJ8GTX4fEga9CFREZDZIWBM65GPCPwBP4Hfz9zrmVZvYdM7s0GO2LZrbSzN4Avghck6zyDEk0G/7ua7B9Gbz1aEqLIiIyUsJ9QdlAEnG440yIdcJ1r0Jk+O+xIiIy0lJyHcFRKy0C537TP8FsadB2He+Bdc9A8/bUlk1EJAnC+TyCAzn2fJh0Gjz379BWB4vvgdYdkF0Ml/8Ujjkv1SUUERk2OiMYiBmc921oq4Hn/hXGngDvvwvyJ8CvroDn/0ONySIyauiMYDCTToWPPQxFk/c8r2DGxfCHG+HZ78Eb98H082H630PVWZCekdryiogcIjUWHyzn/IPv37wfNv7VNyqPmw1XPwK5Zakrl8hoEOuGRA9k7P/uo0e0RAJiHUfcMujh9cPJDGZ/yP90t8PqP8Bj/x/874Xw8d9BwQRo3AwvfB9qVkFBBRRWwvg5cML79u6FlEhAT5u/slkk7GLd/nvUWgPXPge5pYc2nUTcfwebqv2tYwonQdoI1IJ3NsGyX8OiO/38Z1wCp37WtzcOdvvt5m2w6A5Y/pDfd4w/CSbMhWnvhoLx/cYNOqv0f30Y6IxgOGx8EX59JeSUwPT3BL2NDCaeAi3b/QYZ6/TVTGfdBFPO9mcVr//SD5v9YXjXl6FkaqqXZGgScWjctP/ydjTAH7/kl3Xux3xvLJH9efJf4KX/gbR0v91c9eC+20085u8UvOYvcOIHYd7V/kCqbRcs/QWsfBh2rfHft17RHCg/Hk7+NJz0kUPfFmvfhhUP+/fPv2bPw6xaa+HFH8KSn0F3K0xcCONn+1qDzkYonuIPBrMKIbPAVyNHMnyZVz0GLuH3G53NsONNPw2AigVw7AV+H7LhBahbA2feCOd965CKrwfTjIStS+BXl/uVOfcqeNdX/MoHX5205kl47hbYtnTPe6aeAyXTYNm9vovqiVf4o4HiKsgpg5q3/PiNm2HhdTB9P72Vep/ivj/O+S/Jxr/6ZzD0dMCEOX6elSf7IDvQ+1f9Hp79V6hd5TfIM2/cd7x4DH79Qd/lFmDsLH9jv0mn7xkn2W0qifihfeFjXfDK7b6DwDHnHfgz7a+rxU9jqNWEw1WN0LQVql+FvLF+R9R7BOwc7FwBrTv9sLyxfttK9hFyaw2884TfBjJyYdyJfjuYMBcycvYdf81TcO/lsOBTvqr1DzfAOV+Dc76yZ5zGzfDQP8CWRVB6DNSthcxCmLQQ1j8L8W6/jVXOh7Jj/fevcQvUrvbb+443YcxMv91OmAeW5j+HrKJ913PNaqhZCS07/I543XOwczkQjBfJgJOu9Dv31+72wTPrCjjtC34ZwdcYLH/Afw4dDf6MoavZlzPW5bfPEz8ECz8PxZP9exIJ/916+3FY9Qd/cWtGHkw+3Yfj9PdA+XGHtEoUBCOlaauv3yyuGni4c7D2Kdi5EmZeBiXBs0dbdsDffuBPK7ua935PVqHfEJq3winXwt9/x18B3bwNNr8M1Yuh+jXYsdxv3Gfe6BuwzfxGVb8ONr0IG/7qA6B1p59u/gR/JLXrHcD5o6bTroPTvwhZBXvm39nsN8atS2DlI7D9DSid7pdx7V/g3G/4s5y+eo/sLrnVz+Opb/ovcV8TT4V3/RNMO9eH4Ju/hRf/24+XkesfEhTN8V+49Ex/lNi7rWYV+rOtyaf7L3xHA7TVQv0G2PwSbHrZ30BwwhyY8i4/Xnaxn05mvj8zG2gHv2stPPhJv8MA/8X7++/s+WKD38EtfxCW3w9drVAxDyrm+y/3mif9vBMxH/InfQSOu9DP08wv59alsP45vzNr2AhNW/zrMy72z8aoHPB7ukdHg1/X9ev9+xs2QvUSaOrz+RZUwKwPBMH92L6ffXYxHHeRr7rorbZIxP12Fe33RLN1z8DKR325pr8H8scNXrZEAt75k1/3m18BHOSP959Ne50fJz3Lr5Njz/efXW65n/dP3u2PsD/zjB/nkc/5beKSH/rlqF3tO2gkEv61E6/w237vvGZeCif/w+A7Sefgrd/B09/2n11fxVP8+497r98JL/n53gdskUwfZCdeASe836/3V37kv6/xbn9mcvY/7elUMpza6vz3cRgubFUQHC2c81+Yho1+hzPmeL+Rxrr8BvzKj/eETMNG/zs92++oxs6Et/8MzdX+yCunBLa9AV1Nfry8sX7HVnWm7+VUMtXvALpa/c598d2w4iHIKYWZ7/M7qNq3g51IsI2UHw9nXO+PYgAe/Zw/4jnnqzDnKsgugtWPwyPX+tC66Pt+vJ5OePM3/lQY/NHTsl/7cJswzy9rc7U/Epx6jr8VeHerP2PpPXpKxIKdt/ng3PX2wJ9hVpHf8ZdM9QFZvRhcv+fUjpvt625nXQ5pUV/NteF5f4+pSBQuvc0H7fO3+PVRUOlDJJLhg9PF/WeeP8EHZOsOP90xM/1ZRHqm34n17oDToj4MYl2+TQjz66h0mj8SdAlY+ktfjVAx34/b1eI/h/Qs/396pj+ba9y0ZznS0qFwoq+GmHQaVJ7id3IrHvQHHJbmP8/jL/aB2VYDLTth62K/rfRuG70imX6HOO/j/mj6ya/7NrBIJsS79nx25cf57bBwog/rtIgv76I7/VF0cZXfHo69wO9AwR9Vb3/TH7m//ae9l6N3O772Ob/Ng1/2n5zrd8wA0Vzfk++9/7XnAOpQxLph9e+hvd5/32IdsP55v/4T/nnElM+A+Z/wgZU/zgfnQAcO7fV+Wy6YcOjlGUEKgtFi3TPwzPf8xjn5DJh8mt+h9B4txHv8jnnRHYD5I64J8/zRd9n0A1dzbF0KT30Ltrzqd6Tlx8GYGX4aFfP2rTpKxOHRL/idfF9VZ/leVPs7iol1+TB45ce+quKs/3NwVTFtdf6ounGTD6/cMn8kXDp972qPrhYfdN3t/kvbvM3X5daugox8v4OLd/txJ53uLxgsDB6b0dkMr97lzzRinf6nbLpv0+ndYYGfpnN73gf+yHXzy766prPZl8PSoOoM//n0/yy7Wn2b0fIH/A4+I89XocS6/LCedr+DnTDH74zLjvU7oMGqvzqb/PwG64gQ6/b1zjUrwSJ+OnXr/JlOZxAQ0Vz/PO+FX/DVMO/82b+nYYNv23L9rqUpOxbO+pIP2Mh++qE45wO1bq0/OGjfBZPP9Dv6vjoa/ZF56TE+jJNZndVe70OqcKKvJj3YKsGjgIJADs5Q2ht6JeKw9ml/xNfZ6HcO8z5x4PaGVHLOV5Mtf9CfxZQd638q5qtRu6fDtwPVrvZVLYMd7cZ7/DrvPVsD/xmG/fM7gikIRERCTjedExGRQSkIRERCTkEgIhJyCgIRkZBTEIiIhJyCQEQk5BQEIiIhpyAQEQm5o+6CMjOrBTYdcMSBlQG7hrE4R4swLncYlxnCudxhXGY4+OWe7JwrH2jAURcEh8PMFg92Zd1oFsblDuMyQziXO4zLDMO73KoaEhEJOQWBiEjIhS0I7kp1AVIkjMsdxmWGcC53GJcZhnG5Q9VGICIi+wrbGYGIiPSjIBARCbnQBIGZXWBmb5vZWjO7OdXlSQYzm2hmz5rZW2a20syuD14vMbO/mNma4HdxqsuaDGYWMbPXzewPwf9TzGxRsM5/a2YZqS7jcDKzIjN70MxWm9kqMzstDOvazG4Mtu8VZnafmWWNxnVtZveYWY2Zrejz2oDr17xbg+V/08zmHcy8QhEEZhYBfgRcCMwEPmJmM1NbqqSIATc552YCC4HrguW8GXjaOTcdeDr4fzS6HljV5/9/B37gnDsGaAA+nZJSJc9/A392zh0PnIRf9lG9rs2sAvgisMA5NwuIAB9mdK7rnwEX9HttsPV7ITA9+LkWuP1gZhSKIABOAdY659Y757qB3wCXpbhMw845t905tzT4uwW/Y6jAL+vPg9F+DrwvJQVMIjOrBN4L/DT434B3Aw8Go4yq5TazQuBs4G4A51y3c66REKxrIB3INrN0IAfYzihc1865F4D6fi8Ptn4vA37hvFeAIjMbP9R5hSUIKoAtff6vDl4btcysCpgLLALGOue2B4N2AGNTVa4k+iHwT0Ai+L8UaHTOBU9WH3XrfApQC/xvUB32UzPLZZSva+fcVuA/gc34AGgCljC613Vfg63fw9rHhSUIQsXM8oCHgBucc819hznfX3hU9Rk2s4uBGufcklSXZQSlA/OA251zc4E2+lUDjdJ1XYw/+p0CTABy2bf6JBSGc/2GJQi2AhP7/F8ZvDbqmFkUHwL3OuceDl7e2XuaGPyuSVX5kuQM4FIz24iv9ns3vv68KKg+gNG3zquBaufcouD/B/HBMNrX9XnABudcrXOuB3gYv/5H87rua7D1e1j7uLAEwWvA9KBnQQa+cemxFJdp2AX14ncDq5xz/9Vn0GPAJ4K/PwH8bqTLlkzOua865yqdc1X4dfuMc+4q4FngimC0UbXczrkdwBYzOy546VzgLUb5usZXCS00s5xge+9d7lG7rvsZbP0+Bnw86D20EGjqU4V0YM65UPwAFwHvAOuAf051eZK0jGfiTxXfBJYFPxfh68ufBtYATwElqS5rEj+Dc4A/BH9PBV4F1gIPAJmpLt8wL+scYHGwvh8FisOwroFvA6uBFcAvgczRuK6B+/DtID34M8BPD7Z+AcP3jFwHLMf3qhryvHSLCRGRkAtL1ZCIiAxCQSAiEnIKAhGRkFMQiIiEnIJARCTkFAQiI8jMzum9O6rIkUJBICIScgoCkQGY2cfM7FUzW2ZmdwbPOmg1sx8E98J/2szKg3HnmNkrwX3gH+lzj/hjzOwpM3vDzJaa2bRg8nl9niNwb3CFrEjKKAhE+jGzGcCVwBnOuTlAHLgKf4Ozxc65E4DngW8Gb/kF8BXn3Gz8VZ29r98L/Mg5dxJwOv4qUfB3hb0B/2yMqfh75YikTPqBRxEJnXOB+cBrwcF6Nv7mXgngt8E4vwIeDp4LUOScez54/efAA2aWD1Q45x4BcM51AgTTe9U5Vx38vwyoAv6W9KUSGYSCQGRfBvzcOffVvV40+3q/8Q71/ixdff6Oo++hpJiqhkT29TRwhZmNgd3PiZ2M/7703uHyo8DfnHNNQIOZnRW8fjXwvPNPiKs2s/cF08g0s5yRXAiRodKRiEg/zrm3zOxfgCfNLA1/98fr8A9/OSUYVoNvRwB/O+A7gh39euCTwetXA3ea2XeCaXxwBBdDZMh091GRITKzVudcXqrLITLcVDUkIhJyOiMQEQk5nRGIiIScgkBEJOQUBCIiIacgEBEJOQWBiEjI/T9PhVt0n/C6JAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "epochs = 100\n",
        "batch_size = 256\n",
        "\n",
        "inputs = pretrained_model.input\n",
        "x = processing_layers(inputs)\n",
        "\n",
        "x = Dense(256, activation='relu')(pretrained_model.output)\n",
        "x = Dropout(0.2)(x)\n",
        "x = Dense(128, activation='relu')(x)\n",
        "x = Dropout(0.2)(x)\n",
        "x = Dense(64, activation='relu')(x)\n",
        "x = Dropout(0.2)(x)\n",
        "\n",
        "\n",
        "outputs = Dense(500, activation='softmax')(x)\n",
        "\n",
        "model = Model(inputs=inputs, outputs=outputs)\n",
        "\n",
        "model.compile(\n",
        "    optimizer=keras.optimizers.Adam(1e-3),\n",
        "    loss=keras.losses.CategoricalCrossentropy(),\n",
        "    metrics=[\"accuracy\"],\n",
        ")\n",
        "\n",
        "history = model.fit(\n",
        "    train_images,\n",
        "    batch_size = batch_size,\n",
        "    validation_data=valid_images,\n",
        "    epochs=epochs,\n",
        "    callbacks=[\n",
        "        checkpoint_callback,\n",
        "    ],\n",
        "    use_multiprocessing=True\n",
        ")\n",
        "\n",
        "time_stamp_name = str(datetime.now()).replace(' ', '_')\n",
        "model.save(f'/content/drive/My Drive/ml_final/model_bird_{time_stamp_name}.h5')\n",
        "loaded_model = tf.keras.models.load_model(f'/content/drive/My Drive/ml_final/model_bird_{time_stamp_name}.h5')\n",
        "\n",
        "results_test = loaded_model.evaluate(test_images, verbose=0)\n",
        "\n",
        "print(\"    Test Loss: {:.5f}\".format(results_test[0]))\n",
        "print(\"Test Accuracy: {:.2f}%\".format(results_test[1] * 100))\n",
        "\n",
        "results_valid = loaded_model.evaluate(valid_images, verbose=0)\n",
        "\n",
        "print(\"    Validation Set Loss: {:.5f}\".format(results_valid[0]))\n",
        "print(\"Validation Set Accuracy: {:.2f}%\".format(results_valid[1] * 100))\n",
        "\n",
        "hist_df = pd.DataFrame(history.history) \n",
        "hist_df.to_csv(f'/content/drive/My Drive/ml_final/history_{time_stamp_name}.csv')\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kd_1cXDa-VVm",
        "outputId": "1e26933f-84d8-442b-c4af-65d732a0b1dd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "79/79 [==============================] - 10s 116ms/step\n",
            "The first 5 predictions: ['INDIGO BUNTING', 'INDIGO BUNTING', 'INDIGO BUNTING', 'INDIGO BUNTING', 'INDIGO BUNTING']\n"
          ]
        }
      ],
      "source": [
        "# Predict the label of the test_images\n",
        "pred = loaded_model.predict(test_images)\n",
        "pred = np.argmax(pred,axis=1)\n",
        "\n",
        "# Map the label\n",
        "labels = (train_images.class_indices)\n",
        "labels = dict((v,k) for k,v in labels.items())\n",
        "pred = [labels[k] for k in pred]\n",
        "\n",
        "# Display the result\n",
        "print(f'The first 5 predictions: {pred[:5]}')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classes = list(labels.values())\n",
        "\n",
        "results_dict = {'test': 0, 'bob': 1}"
      ],
      "metadata": {
        "id": "FUwONdBYtZnD"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "{k: v for k, v in results_dict.items() if v == 1}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6bUmkMV8M-MK",
        "outputId": "477e4e2a-c0aa-4790-ba53-d94df09201e1"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bob': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d8Q6z_UsF0-K",
        "outputId": "7756116e-b77b-4612-e877-78754580f599"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ],
      "source": [
        "y_test = list(test_df.Label)\n",
        "report = classification_report(y_test, pred, output_dict=True)\n",
        "\n",
        "df_report = pd.DataFrame(report).transpose()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "xfhU-i6yF53c",
        "outputId": "ed122d94-b160-43d4-f0c6-72cdb2f136ea"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-8c40b4ee-7da8-4802-9e6d-0fdf55af7123\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ABBOTTS BABBLER</th>\n",
              "      <td>0.833333</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.909091</td>\n",
              "      <td>5.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ABBOTTS BOOBY</th>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.800</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>5.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ABYSSINIAN GROUND HORNBILL</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AFRICAN CROWNED CRANE</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AFRICAN EMERALD CUCKOO</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.800</td>\n",
              "      <td>0.888889</td>\n",
              "      <td>5.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YELLOW CACIQUE</th>\n",
              "      <td>0.714286</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>5.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YELLOW HEADED BLACKBIRD</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.934000</td>\n",
              "      <td>0.934</td>\n",
              "      <td>0.934000</td>\n",
              "      <td>0.934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macro avg</th>\n",
              "      <td>0.944591</td>\n",
              "      <td>0.934</td>\n",
              "      <td>0.931093</td>\n",
              "      <td>2500.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>0.944591</td>\n",
              "      <td>0.934</td>\n",
              "      <td>0.931093</td>\n",
              "      <td>2500.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>503 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c40b4ee-7da8-4802-9e6d-0fdf55af7123')\"\n",
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              "\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                            precision  recall  f1-score   support\n",
              "ABBOTTS BABBLER              0.833333   1.000  0.909091     5.000\n",
              "ABBOTTS BOOBY                0.800000   0.800  0.800000     5.000\n",
              "ABYSSINIAN GROUND HORNBILL   1.000000   1.000  1.000000     5.000\n",
              "AFRICAN CROWNED CRANE        1.000000   1.000  1.000000     5.000\n",
              "AFRICAN EMERALD CUCKOO       1.000000   0.800  0.888889     5.000\n",
              "...                               ...     ...       ...       ...\n",
              "YELLOW CACIQUE               0.714286   1.000  0.833333     5.000\n",
              "YELLOW HEADED BLACKBIRD      1.000000   1.000  1.000000     5.000\n",
              "accuracy                     0.934000   0.934  0.934000     0.934\n",
              "macro avg                    0.944591   0.934  0.931093  2500.000\n",
              "weighted avg                 0.944591   0.934  0.931093  2500.000\n",
              "\n",
              "[503 rows x 4 columns]"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2xJev9h1Z4ZR"
      },
      "outputs": [],
      "source": [
        "df_pr = df_report.drop(df_report.tail(3).index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "rELcJf_PaUrt",
        "outputId": "325e13fa-0ba9-4d58-c0e3-c494a46fda68"
      },
      "outputs": [
        {
          "data": {
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              "      <th>precision</th>\n",
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              "      <td>5.0</td>\n",
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              "      <th>std</th>\n",
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            "text/plain": [
              "        precision      recall    f1-score  support\n",
              "count  500.000000  500.000000  500.000000    500.0\n",
              "mean     0.944591    0.934000    0.931093      5.0\n",
              "std      0.109776    0.138283    0.108805      0.0\n",
              "min      0.000000    0.000000    0.000000      5.0\n",
              "25%      1.000000    1.000000    0.888889      5.0\n",
              "50%      1.000000    1.000000    1.000000      5.0\n",
              "75%      1.000000    1.000000    1.000000      5.0\n",
              "max      1.000000    1.000000    1.000000      5.0"
            ]
          },
          "execution_count": 78,
          "metadata": {},
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        }
      ],
      "source": [
        "df_pr.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QGbXGry4agIp",
        "outputId": "f419a2a6-c176-492c-cc49-74b63c43a6ce"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model_2\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_3 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " rescaling_5 (Rescaling)        (None, 224, 224, 3)  0           ['input_3[0][0]']                \n",
            "                                                                                                  \n",
            " Conv (Conv2D)                  (None, 112, 112, 16  432         ['rescaling_5[0][0]']            \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " Conv/BatchNorm (BatchNormaliza  (None, 112, 112, 16  64         ['Conv[0][0]']                   \n",
            " tion)                          )                                                                 \n",
            "                                                                                                  \n",
            " tf.__operators__.add_54 (TFOpL  (None, 112, 112, 16  0          ['Conv/BatchNorm[0][0]']         \n",
            " ambda)                         )                                                                 \n",
            "                                                                                                  \n",
            " re_lu_64 (ReLU)                (None, 112, 112, 16  0           ['tf.__operators__.add_54[0][0]']\n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " tf.math.multiply_54 (TFOpLambd  (None, 112, 112, 16  0          ['re_lu_64[0][0]']               \n",
            " a)                             )                                                                 \n",
            "                                                                                                  \n",
            " multiply_36 (Multiply)         (None, 112, 112, 16  0           ['Conv/BatchNorm[0][0]',         \n",
            "                                )                                 'tf.math.multiply_54[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv/depthwise (Depth  (None, 112, 112, 16  144        ['multiply_36[0][0]']            \n",
            " wiseConv2D)                    )                                                                 \n",
            "                                                                                                  \n",
            " expanded_conv/depthwise/BatchN  (None, 112, 112, 16  64         ['expanded_conv/depthwise[0][0]']\n",
            " orm (BatchNormalization)       )                                                                 \n",
            "                                                                                                  \n",
            " re_lu_65 (ReLU)                (None, 112, 112, 16  0           ['expanded_conv/depthwise/BatchNo\n",
            "                                )                                rm[0][0]']                       \n",
            "                                                                                                  \n",
            " expanded_conv/project (Conv2D)  (None, 112, 112, 16  256        ['re_lu_65[0][0]']               \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " expanded_conv/project/BatchNor  (None, 112, 112, 16  64         ['expanded_conv/project[0][0]']  \n",
            " m (BatchNormalization)         )                                                                 \n",
            "                                                                                                  \n",
            " expanded_conv/Add (Add)        (None, 112, 112, 16  0           ['multiply_36[0][0]',            \n",
            "                                )                                 'expanded_conv/project/BatchNorm\n",
            "                                                                 [0][0]']                         \n",
            "                                                                                                  \n",
            " expanded_conv_1/expand (Conv2D  (None, 112, 112, 64  1024       ['expanded_conv/Add[0][0]']      \n",
            " )                              )                                                                 \n",
            "                                                                                                  \n",
            " expanded_conv_1/expand/BatchNo  (None, 112, 112, 64  256        ['expanded_conv_1/expand[0][0]'] \n",
            " rm (BatchNormalization)        )                                                                 \n",
            "                                                                                                  \n",
            " re_lu_66 (ReLU)                (None, 112, 112, 64  0           ['expanded_conv_1/expand/BatchNor\n",
            "                                )                                m[0][0]']                        \n",
            "                                                                                                  \n",
            " expanded_conv_1/depthwise/pad   (None, 113, 113, 64  0          ['re_lu_66[0][0]']               \n",
            " (ZeroPadding2D)                )                                                                 \n",
            "                                                                                                  \n",
            " expanded_conv_1/depthwise (Dep  (None, 56, 56, 64)  576         ['expanded_conv_1/depthwise/pad[0\n",
            " thwiseConv2D)                                                   ][0]']                           \n",
            "                                                                                                  \n",
            " expanded_conv_1/depthwise/Batc  (None, 56, 56, 64)  256         ['expanded_conv_1/depthwise[0][0]\n",
            " hNorm (BatchNormalization)                                      ']                               \n",
            "                                                                                                  \n",
            " re_lu_67 (ReLU)                (None, 56, 56, 64)   0           ['expanded_conv_1/depthwise/Batch\n",
            "                                                                 Norm[0][0]']                     \n",
            "                                                                                                  \n",
            " expanded_conv_1/project (Conv2  (None, 56, 56, 24)  1536        ['re_lu_67[0][0]']               \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " expanded_conv_1/project/BatchN  (None, 56, 56, 24)  96          ['expanded_conv_1/project[0][0]']\n",
            " orm (BatchNormalization)                                                                         \n",
            "                                                                                                  \n",
            " expanded_conv_2/expand (Conv2D  (None, 56, 56, 72)  1728        ['expanded_conv_1/project/BatchNo\n",
            " )                                                               rm[0][0]']                       \n",
            "                                                                                                  \n",
            " expanded_conv_2/expand/BatchNo  (None, 56, 56, 72)  288         ['expanded_conv_2/expand[0][0]'] \n",
            " rm (BatchNormalization)                                                                          \n",
            "                                                                                                  \n",
            " re_lu_68 (ReLU)                (None, 56, 56, 72)   0           ['expanded_conv_2/expand/BatchNor\n",
            "                                                                 m[0][0]']                        \n",
            "                                                                                                  \n",
            " expanded_conv_2/depthwise (Dep  (None, 56, 56, 72)  648         ['re_lu_68[0][0]']               \n",
            " thwiseConv2D)                                                                                    \n",
            "                                                                                                  \n",
            " expanded_conv_2/depthwise/Batc  (None, 56, 56, 72)  288         ['expanded_conv_2/depthwise[0][0]\n",
            " hNorm (BatchNormalization)                                      ']                               \n",
            "                                                                                                  \n",
            " re_lu_69 (ReLU)                (None, 56, 56, 72)   0           ['expanded_conv_2/depthwise/Batch\n",
            "                                                                 Norm[0][0]']                     \n",
            "                                                                                                  \n",
            " expanded_conv_2/project (Conv2  (None, 56, 56, 24)  1728        ['re_lu_69[0][0]']               \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " expanded_conv_2/project/BatchN  (None, 56, 56, 24)  96          ['expanded_conv_2/project[0][0]']\n",
            " orm (BatchNormalization)                                                                         \n",
            "                                                                                                  \n",
            " expanded_conv_2/Add (Add)      (None, 56, 56, 24)   0           ['expanded_conv_1/project/BatchNo\n",
            "                                                                 rm[0][0]',                       \n",
            "                                                                  'expanded_conv_2/project/BatchNo\n",
            "                                                                 rm[0][0]']                       \n",
            "                                                                                                  \n",
            " expanded_conv_3/expand (Conv2D  (None, 56, 56, 72)  1728        ['expanded_conv_2/Add[0][0]']    \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " expanded_conv_3/expand/BatchNo  (None, 56, 56, 72)  288         ['expanded_conv_3/expand[0][0]'] \n",
            " rm (BatchNormalization)                                                                          \n",
            "                                                                                                  \n",
            " re_lu_70 (ReLU)                (None, 56, 56, 72)   0           ['expanded_conv_3/expand/BatchNor\n",
            "                                                                 m[0][0]']                        \n",
            "                                                                                                  \n",
            " expanded_conv_3/depthwise/pad   (None, 59, 59, 72)  0           ['re_lu_70[0][0]']               \n",
            " (ZeroPadding2D)                                                                                  \n",
            "                                                                                                  \n",
            " expanded_conv_3/depthwise (Dep  (None, 28, 28, 72)  1800        ['expanded_conv_3/depthwise/pad[0\n",
            " thwiseConv2D)                                                   ][0]']                           \n",
            "                                                                                                  \n",
            " expanded_conv_3/depthwise/Batc  (None, 28, 28, 72)  288         ['expanded_conv_3/depthwise[0][0]\n",
            " hNorm (BatchNormalization)                                      ']                               \n",
            "                                                                                                  \n",
            " re_lu_71 (ReLU)                (None, 28, 28, 72)   0           ['expanded_conv_3/depthwise/Batch\n",
            "                                                                 Norm[0][0]']                     \n",
            "                                                                                                  \n",
            " expanded_conv_3/squeeze_excite  (None, 1, 1, 72)    0           ['re_lu_71[0][0]']               \n",
            " /AvgPool (GlobalAveragePooling                                                                   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " expanded_conv_3/squeeze_excite  (None, 1, 1, 24)    1752        ['expanded_conv_3/squeeze_excite/\n",
            " /Conv (Conv2D)                                                  AvgPool[0][0]']                  \n",
            "                                                                                                  \n",
            " expanded_conv_3/squeeze_excite  (None, 1, 1, 24)    0           ['expanded_conv_3/squeeze_excite/\n",
            " /Relu (ReLU)                                                    Conv[0][0]']                     \n",
            "                                                                                                  \n",
            " expanded_conv_3/squeeze_excite  (None, 1, 1, 72)    1800        ['expanded_conv_3/squeeze_excite/\n",
            " /Conv_1 (Conv2D)                                                Relu[0][0]']                     \n",
            "                                                                                                  \n",
            " tf.__operators__.add_55 (TFOpL  (None, 1, 1, 72)    0           ['expanded_conv_3/squeeze_excite/\n",
            " ambda)                                                          Conv_1[0][0]']                   \n",
            "                                                                                                  \n",
            " re_lu_72 (ReLU)                (None, 1, 1, 72)     0           ['tf.__operators__.add_55[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_55 (TFOpLambd  (None, 1, 1, 72)    0           ['re_lu_72[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " expanded_conv_3/squeeze_excite  (None, 28, 28, 72)  0           ['re_lu_71[0][0]',               \n",
            " /Mul (Multiply)                                                  'tf.math.multiply_55[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_3/project (Conv2  (None, 28, 28, 40)  2880        ['expanded_conv_3/squeeze_excite/\n",
            " D)                                                              Mul[0][0]']                      \n",
            "                                                                                                  \n",
            " expanded_conv_3/project/BatchN  (None, 28, 28, 40)  160         ['expanded_conv_3/project[0][0]']\n",
            " orm (BatchNormalization)                                                                         \n",
            "                                                                                                  \n",
            " expanded_conv_4/expand (Conv2D  (None, 28, 28, 120)  4800       ['expanded_conv_3/project/BatchNo\n",
            " )                                                               rm[0][0]']                       \n",
            "                                                                                                  \n",
            " expanded_conv_4/expand/BatchNo  (None, 28, 28, 120)  480        ['expanded_conv_4/expand[0][0]'] \n",
            " rm (BatchNormalization)                                                                          \n",
            "                                                                                                  \n",
            " re_lu_73 (ReLU)                (None, 28, 28, 120)  0           ['expanded_conv_4/expand/BatchNor\n",
            "                                                                 m[0][0]']                        \n",
            "                                                                                                  \n",
            " expanded_conv_4/depthwise (Dep  (None, 28, 28, 120)  3000       ['re_lu_73[0][0]']               \n",
            " thwiseConv2D)                                                                                    \n",
            "                                                                                                  \n",
            " expanded_conv_4/depthwise/Batc  (None, 28, 28, 120)  480        ['expanded_conv_4/depthwise[0][0]\n",
            " hNorm (BatchNormalization)                                      ']                               \n",
            "                                                                                                  \n",
            " re_lu_74 (ReLU)                (None, 28, 28, 120)  0           ['expanded_conv_4/depthwise/Batch\n",
            "                                                                 Norm[0][0]']                     \n",
            "                                                                                                  \n",
            " expanded_conv_4/squeeze_excite  (None, 1, 1, 120)   0           ['re_lu_74[0][0]']               \n",
            " /AvgPool (GlobalAveragePooling                                                                   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " expanded_conv_4/squeeze_excite  (None, 1, 1, 32)    3872        ['expanded_conv_4/squeeze_excite/\n",
            " /Conv (Conv2D)                                                  AvgPool[0][0]']                  \n",
            "                                                                                                  \n",
            " expanded_conv_4/squeeze_excite  (None, 1, 1, 32)    0           ['expanded_conv_4/squeeze_excite/\n",
            " /Relu (ReLU)                                                    Conv[0][0]']                     \n",
            "                                                                                                  \n",
            " expanded_conv_4/squeeze_excite  (None, 1, 1, 120)   3960        ['expanded_conv_4/squeeze_excite/\n",
            " /Conv_1 (Conv2D)                                                Relu[0][0]']                     \n",
            "                                                                                                  \n",
            " tf.__operators__.add_56 (TFOpL  (None, 1, 1, 120)   0           ['expanded_conv_4/squeeze_excite/\n",
            " ambda)                                                          Conv_1[0][0]']                   \n",
            "                                                                                                  \n",
            " re_lu_75 (ReLU)                (None, 1, 1, 120)    0           ['tf.__operators__.add_56[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_56 (TFOpLambd  (None, 1, 1, 120)   0           ['re_lu_75[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " expanded_conv_4/squeeze_excite  (None, 28, 28, 120)  0          ['re_lu_74[0][0]',               \n",
            " /Mul (Multiply)                                                  'tf.math.multiply_56[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_4/project (Conv2  (None, 28, 28, 40)  4800        ['expanded_conv_4/squeeze_excite/\n",
            " D)                                                              Mul[0][0]']                      \n",
            "                                                                                                  \n",
            " expanded_conv_4/project/BatchN  (None, 28, 28, 40)  160         ['expanded_conv_4/project[0][0]']\n",
            " orm (BatchNormalization)                                                                         \n",
            "                                                                                                  \n",
            " expanded_conv_4/Add (Add)      (None, 28, 28, 40)   0           ['expanded_conv_3/project/BatchNo\n",
            "                                                                 rm[0][0]',                       \n",
            "                                                                  'expanded_conv_4/project/BatchNo\n",
            "                                                                 rm[0][0]']                       \n",
            "                                                                                                  \n",
            " expanded_conv_5/expand (Conv2D  (None, 28, 28, 120)  4800       ['expanded_conv_4/Add[0][0]']    \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " expanded_conv_5/expand/BatchNo  (None, 28, 28, 120)  480        ['expanded_conv_5/expand[0][0]'] \n",
            " rm (BatchNormalization)                                                                          \n",
            "                                                                                                  \n",
            " re_lu_76 (ReLU)                (None, 28, 28, 120)  0           ['expanded_conv_5/expand/BatchNor\n",
            "                                                                 m[0][0]']                        \n",
            "                                                                                                  \n",
            " expanded_conv_5/depthwise (Dep  (None, 28, 28, 120)  3000       ['re_lu_76[0][0]']               \n",
            " thwiseConv2D)                                                                                    \n",
            "                                                                                                  \n",
            " expanded_conv_5/depthwise/Batc  (None, 28, 28, 120)  480        ['expanded_conv_5/depthwise[0][0]\n",
            " hNorm (BatchNormalization)                                      ']                               \n",
            "                                                                                                  \n",
            " re_lu_77 (ReLU)                (None, 28, 28, 120)  0           ['expanded_conv_5/depthwise/Batch\n",
            "                                                                 Norm[0][0]']                     \n",
            "                                                                                                  \n",
            " expanded_conv_5/squeeze_excite  (None, 1, 1, 120)   0           ['re_lu_77[0][0]']               \n",
            " /AvgPool (GlobalAveragePooling                                                                   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " expanded_conv_5/squeeze_excite  (None, 1, 1, 32)    3872        ['expanded_conv_5/squeeze_excite/\n",
            " /Conv (Conv2D)                                                  AvgPool[0][0]']                  \n",
            "                                                                                                  \n",
            " expanded_conv_5/squeeze_excite  (None, 1, 1, 32)    0           ['expanded_conv_5/squeeze_excite/\n",
            " /Relu (ReLU)                                                    Conv[0][0]']                     \n",
            "                                                                                                  \n",
            " expanded_conv_5/squeeze_excite  (None, 1, 1, 120)   3960        ['expanded_conv_5/squeeze_excite/\n",
            " /Conv_1 (Conv2D)                                                Relu[0][0]']                     \n",
            "                                                                                                  \n",
            " tf.__operators__.add_57 (TFOpL  (None, 1, 1, 120)   0           ['expanded_conv_5/squeeze_excite/\n",
            " ambda)                                                          Conv_1[0][0]']                   \n",
            "                                                                                                  \n",
            " re_lu_78 (ReLU)                (None, 1, 1, 120)    0           ['tf.__operators__.add_57[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_57 (TFOpLambd  (None, 1, 1, 120)   0           ['re_lu_78[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " expanded_conv_5/squeeze_excite  (None, 28, 28, 120)  0          ['re_lu_77[0][0]',               \n",
            " /Mul (Multiply)                                                  'tf.math.multiply_57[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_5/project (Conv2  (None, 28, 28, 40)  4800        ['expanded_conv_5/squeeze_excite/\n",
            " D)                                                              Mul[0][0]']                      \n",
            "                                                                                                  \n",
            " expanded_conv_5/project/BatchN  (None, 28, 28, 40)  160         ['expanded_conv_5/project[0][0]']\n",
            " orm (BatchNormalization)                                                                         \n",
            "                                                                                                  \n",
            " expanded_conv_5/Add (Add)      (None, 28, 28, 40)   0           ['expanded_conv_4/Add[0][0]',    \n",
            "                                                                  'expanded_conv_5/project/BatchNo\n",
            "                                                                 rm[0][0]']                       \n",
            "                                                                                                  \n",
            " expanded_conv_6/expand (Conv2D  (None, 28, 28, 240)  9600       ['expanded_conv_5/Add[0][0]']    \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " expanded_conv_6/expand/BatchNo  (None, 28, 28, 240)  960        ['expanded_conv_6/expand[0][0]'] \n",
            " rm (BatchNormalization)                                                                          \n",
            "                                                                                                  \n",
            " tf.__operators__.add_58 (TFOpL  (None, 28, 28, 240)  0          ['expanded_conv_6/expand/BatchNor\n",
            " ambda)                                                          m[0][0]']                        \n",
            "                                                                                                  \n",
            " re_lu_79 (ReLU)                (None, 28, 28, 240)  0           ['tf.__operators__.add_58[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_58 (TFOpLambd  (None, 28, 28, 240)  0          ['re_lu_79[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " multiply_37 (Multiply)         (None, 28, 28, 240)  0           ['expanded_conv_6/expand/BatchNor\n",
            "                                                                 m[0][0]',                        \n",
            "                                                                  'tf.math.multiply_58[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_6/depthwise/pad   (None, 29, 29, 240)  0          ['multiply_37[0][0]']            \n",
            " (ZeroPadding2D)                                                                                  \n",
            "                                                                                                  \n",
            " expanded_conv_6/depthwise (Dep  (None, 14, 14, 240)  2160       ['expanded_conv_6/depthwise/pad[0\n",
            " thwiseConv2D)                                                   ][0]']                           \n",
            "                                                                                                  \n",
            " expanded_conv_6/depthwise/Batc  (None, 14, 14, 240)  960        ['expanded_conv_6/depthwise[0][0]\n",
            " hNorm (BatchNormalization)                                      ']                               \n",
            "                                                                                                  \n",
            " tf.__operators__.add_59 (TFOpL  (None, 14, 14, 240)  0          ['expanded_conv_6/depthwise/Batch\n",
            " ambda)                                                          Norm[0][0]']                     \n",
            "                                                                                                  \n",
            " re_lu_80 (ReLU)                (None, 14, 14, 240)  0           ['tf.__operators__.add_59[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_59 (TFOpLambd  (None, 14, 14, 240)  0          ['re_lu_80[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " multiply_38 (Multiply)         (None, 14, 14, 240)  0           ['expanded_conv_6/depthwise/Batch\n",
            "                                                                 Norm[0][0]',                     \n",
            "                                                                  'tf.math.multiply_59[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_6/project (Conv2  (None, 14, 14, 80)  19200       ['multiply_38[0][0]']            \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " expanded_conv_6/project/BatchN  (None, 14, 14, 80)  320         ['expanded_conv_6/project[0][0]']\n",
            " orm (BatchNormalization)                                                                         \n",
            "                                                                                                  \n",
            " expanded_conv_7/expand (Conv2D  (None, 14, 14, 200)  16000      ['expanded_conv_6/project/BatchNo\n",
            " )                                                               rm[0][0]']                       \n",
            "                                                                                                  \n",
            " expanded_conv_7/expand/BatchNo  (None, 14, 14, 200)  800        ['expanded_conv_7/expand[0][0]'] \n",
            " rm (BatchNormalization)                                                                          \n",
            "                                                                                                  \n",
            " tf.__operators__.add_60 (TFOpL  (None, 14, 14, 200)  0          ['expanded_conv_7/expand/BatchNor\n",
            " ambda)                                                          m[0][0]']                        \n",
            "                                                                                                  \n",
            " re_lu_81 (ReLU)                (None, 14, 14, 200)  0           ['tf.__operators__.add_60[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_60 (TFOpLambd  (None, 14, 14, 200)  0          ['re_lu_81[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " multiply_39 (Multiply)         (None, 14, 14, 200)  0           ['expanded_conv_7/expand/BatchNor\n",
            "                                                                 m[0][0]',                        \n",
            "                                                                  'tf.math.multiply_60[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_7/depthwise (Dep  (None, 14, 14, 200)  1800       ['multiply_39[0][0]']            \n",
            " thwiseConv2D)                                                                                    \n",
            "                                                                                                  \n",
            " expanded_conv_7/depthwise/Batc  (None, 14, 14, 200)  800        ['expanded_conv_7/depthwise[0][0]\n",
            " hNorm (BatchNormalization)                                      ']                               \n",
            "                                                                                                  \n",
            " tf.__operators__.add_61 (TFOpL  (None, 14, 14, 200)  0          ['expanded_conv_7/depthwise/Batch\n",
            " ambda)                                                          Norm[0][0]']                     \n",
            "                                                                                                  \n",
            " re_lu_82 (ReLU)                (None, 14, 14, 200)  0           ['tf.__operators__.add_61[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_61 (TFOpLambd  (None, 14, 14, 200)  0          ['re_lu_82[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " multiply_40 (Multiply)         (None, 14, 14, 200)  0           ['expanded_conv_7/depthwise/Batch\n",
            "                                                                 Norm[0][0]',                     \n",
            "                                                                  'tf.math.multiply_61[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_7/project (Conv2  (None, 14, 14, 80)  16000       ['multiply_40[0][0]']            \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " expanded_conv_7/project/BatchN  (None, 14, 14, 80)  320         ['expanded_conv_7/project[0][0]']\n",
            " orm (BatchNormalization)                                                                         \n",
            "                                                                                                  \n",
            " expanded_conv_7/Add (Add)      (None, 14, 14, 80)   0           ['expanded_conv_6/project/BatchNo\n",
            "                                                                 rm[0][0]',                       \n",
            "                                                                  'expanded_conv_7/project/BatchNo\n",
            "                                                                 rm[0][0]']                       \n",
            "                                                                                                  \n",
            " expanded_conv_8/expand (Conv2D  (None, 14, 14, 184)  14720      ['expanded_conv_7/Add[0][0]']    \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " expanded_conv_8/expand/BatchNo  (None, 14, 14, 184)  736        ['expanded_conv_8/expand[0][0]'] \n",
            " rm (BatchNormalization)                                                                          \n",
            "                                                                                                  \n",
            " tf.__operators__.add_62 (TFOpL  (None, 14, 14, 184)  0          ['expanded_conv_8/expand/BatchNor\n",
            " ambda)                                                          m[0][0]']                        \n",
            "                                                                                                  \n",
            " re_lu_83 (ReLU)                (None, 14, 14, 184)  0           ['tf.__operators__.add_62[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_62 (TFOpLambd  (None, 14, 14, 184)  0          ['re_lu_83[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " multiply_41 (Multiply)         (None, 14, 14, 184)  0           ['expanded_conv_8/expand/BatchNor\n",
            "                                                                 m[0][0]',                        \n",
            "                                                                  'tf.math.multiply_62[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_8/depthwise (Dep  (None, 14, 14, 184)  1656       ['multiply_41[0][0]']            \n",
            " thwiseConv2D)                                                                                    \n",
            "                                                                                                  \n",
            " expanded_conv_8/depthwise/Batc  (None, 14, 14, 184)  736        ['expanded_conv_8/depthwise[0][0]\n",
            " hNorm (BatchNormalization)                                      ']                               \n",
            "                                                                                                  \n",
            " tf.__operators__.add_63 (TFOpL  (None, 14, 14, 184)  0          ['expanded_conv_8/depthwise/Batch\n",
            " ambda)                                                          Norm[0][0]']                     \n",
            "                                                                                                  \n",
            " re_lu_84 (ReLU)                (None, 14, 14, 184)  0           ['tf.__operators__.add_63[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_63 (TFOpLambd  (None, 14, 14, 184)  0          ['re_lu_84[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " multiply_42 (Multiply)         (None, 14, 14, 184)  0           ['expanded_conv_8/depthwise/Batch\n",
            "                                                                 Norm[0][0]',                     \n",
            "                                                                  'tf.math.multiply_63[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_8/project (Conv2  (None, 14, 14, 80)  14720       ['multiply_42[0][0]']            \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " expanded_conv_8/project/BatchN  (None, 14, 14, 80)  320         ['expanded_conv_8/project[0][0]']\n",
            " orm (BatchNormalization)                                                                         \n",
            "                                                                                                  \n",
            " expanded_conv_8/Add (Add)      (None, 14, 14, 80)   0           ['expanded_conv_7/Add[0][0]',    \n",
            "                                                                  'expanded_conv_8/project/BatchNo\n",
            "                                                                 rm[0][0]']                       \n",
            "                                                                                                  \n",
            " expanded_conv_9/expand (Conv2D  (None, 14, 14, 184)  14720      ['expanded_conv_8/Add[0][0]']    \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " expanded_conv_9/expand/BatchNo  (None, 14, 14, 184)  736        ['expanded_conv_9/expand[0][0]'] \n",
            " rm (BatchNormalization)                                                                          \n",
            "                                                                                                  \n",
            " tf.__operators__.add_64 (TFOpL  (None, 14, 14, 184)  0          ['expanded_conv_9/expand/BatchNor\n",
            " ambda)                                                          m[0][0]']                        \n",
            "                                                                                                  \n",
            " re_lu_85 (ReLU)                (None, 14, 14, 184)  0           ['tf.__operators__.add_64[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_64 (TFOpLambd  (None, 14, 14, 184)  0          ['re_lu_85[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " multiply_43 (Multiply)         (None, 14, 14, 184)  0           ['expanded_conv_9/expand/BatchNor\n",
            "                                                                 m[0][0]',                        \n",
            "                                                                  'tf.math.multiply_64[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_9/depthwise (Dep  (None, 14, 14, 184)  1656       ['multiply_43[0][0]']            \n",
            " thwiseConv2D)                                                                                    \n",
            "                                                                                                  \n",
            " expanded_conv_9/depthwise/Batc  (None, 14, 14, 184)  736        ['expanded_conv_9/depthwise[0][0]\n",
            " hNorm (BatchNormalization)                                      ']                               \n",
            "                                                                                                  \n",
            " tf.__operators__.add_65 (TFOpL  (None, 14, 14, 184)  0          ['expanded_conv_9/depthwise/Batch\n",
            " ambda)                                                          Norm[0][0]']                     \n",
            "                                                                                                  \n",
            " re_lu_86 (ReLU)                (None, 14, 14, 184)  0           ['tf.__operators__.add_65[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_65 (TFOpLambd  (None, 14, 14, 184)  0          ['re_lu_86[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " multiply_44 (Multiply)         (None, 14, 14, 184)  0           ['expanded_conv_9/depthwise/Batch\n",
            "                                                                 Norm[0][0]',                     \n",
            "                                                                  'tf.math.multiply_65[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_9/project (Conv2  (None, 14, 14, 80)  14720       ['multiply_44[0][0]']            \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " expanded_conv_9/project/BatchN  (None, 14, 14, 80)  320         ['expanded_conv_9/project[0][0]']\n",
            " orm (BatchNormalization)                                                                         \n",
            "                                                                                                  \n",
            " expanded_conv_9/Add (Add)      (None, 14, 14, 80)   0           ['expanded_conv_8/Add[0][0]',    \n",
            "                                                                  'expanded_conv_9/project/BatchNo\n",
            "                                                                 rm[0][0]']                       \n",
            "                                                                                                  \n",
            " expanded_conv_10/expand (Conv2  (None, 14, 14, 480)  38400      ['expanded_conv_9/Add[0][0]']    \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " expanded_conv_10/expand/BatchN  (None, 14, 14, 480)  1920       ['expanded_conv_10/expand[0][0]']\n",
            " orm (BatchNormalization)                                                                         \n",
            "                                                                                                  \n",
            " tf.__operators__.add_66 (TFOpL  (None, 14, 14, 480)  0          ['expanded_conv_10/expand/BatchNo\n",
            " ambda)                                                          rm[0][0]']                       \n",
            "                                                                                                  \n",
            " re_lu_87 (ReLU)                (None, 14, 14, 480)  0           ['tf.__operators__.add_66[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_66 (TFOpLambd  (None, 14, 14, 480)  0          ['re_lu_87[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " multiply_45 (Multiply)         (None, 14, 14, 480)  0           ['expanded_conv_10/expand/BatchNo\n",
            "                                                                 rm[0][0]',                       \n",
            "                                                                  'tf.math.multiply_66[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_10/depthwise (De  (None, 14, 14, 480)  4320       ['multiply_45[0][0]']            \n",
            " pthwiseConv2D)                                                                                   \n",
            "                                                                                                  \n",
            " expanded_conv_10/depthwise/Bat  (None, 14, 14, 480)  1920       ['expanded_conv_10/depthwise[0][0\n",
            " chNorm (BatchNormalization)                                     ]']                              \n",
            "                                                                                                  \n",
            " tf.__operators__.add_67 (TFOpL  (None, 14, 14, 480)  0          ['expanded_conv_10/depthwise/Batc\n",
            " ambda)                                                          hNorm[0][0]']                    \n",
            "                                                                                                  \n",
            " re_lu_88 (ReLU)                (None, 14, 14, 480)  0           ['tf.__operators__.add_67[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_67 (TFOpLambd  (None, 14, 14, 480)  0          ['re_lu_88[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " multiply_46 (Multiply)         (None, 14, 14, 480)  0           ['expanded_conv_10/depthwise/Batc\n",
            "                                                                 hNorm[0][0]',                    \n",
            "                                                                  'tf.math.multiply_67[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_10/squeeze_excit  (None, 1, 1, 480)   0           ['multiply_46[0][0]']            \n",
            " e/AvgPool (GlobalAveragePoolin                                                                   \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " expanded_conv_10/squeeze_excit  (None, 1, 1, 120)   57720       ['expanded_conv_10/squeeze_excite\n",
            " e/Conv (Conv2D)                                                 /AvgPool[0][0]']                 \n",
            "                                                                                                  \n",
            " expanded_conv_10/squeeze_excit  (None, 1, 1, 120)   0           ['expanded_conv_10/squeeze_excite\n",
            " e/Relu (ReLU)                                                   /Conv[0][0]']                    \n",
            "                                                                                                  \n",
            " expanded_conv_10/squeeze_excit  (None, 1, 1, 480)   58080       ['expanded_conv_10/squeeze_excite\n",
            " e/Conv_1 (Conv2D)                                               /Relu[0][0]']                    \n",
            "                                                                                                  \n",
            " tf.__operators__.add_68 (TFOpL  (None, 1, 1, 480)   0           ['expanded_conv_10/squeeze_excite\n",
            " ambda)                                                          /Conv_1[0][0]']                  \n",
            "                                                                                                  \n",
            " re_lu_89 (ReLU)                (None, 1, 1, 480)    0           ['tf.__operators__.add_68[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_68 (TFOpLambd  (None, 1, 1, 480)   0           ['re_lu_89[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " expanded_conv_10/squeeze_excit  (None, 14, 14, 480)  0          ['multiply_46[0][0]',            \n",
            " e/Mul (Multiply)                                                 'tf.math.multiply_68[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_10/project (Conv  (None, 14, 14, 112)  53760      ['expanded_conv_10/squeeze_excite\n",
            " 2D)                                                             /Mul[0][0]']                     \n",
            "                                                                                                  \n",
            " expanded_conv_10/project/Batch  (None, 14, 14, 112)  448        ['expanded_conv_10/project[0][0]'\n",
            " Norm (BatchNormalization)                                       ]                                \n",
            "                                                                                                  \n",
            " expanded_conv_11/expand (Conv2  (None, 14, 14, 672)  75264      ['expanded_conv_10/project/BatchN\n",
            " D)                                                              orm[0][0]']                      \n",
            "                                                                                                  \n",
            " expanded_conv_11/expand/BatchN  (None, 14, 14, 672)  2688       ['expanded_conv_11/expand[0][0]']\n",
            " orm (BatchNormalization)                                                                         \n",
            "                                                                                                  \n",
            " tf.__operators__.add_69 (TFOpL  (None, 14, 14, 672)  0          ['expanded_conv_11/expand/BatchNo\n",
            " ambda)                                                          rm[0][0]']                       \n",
            "                                                                                                  \n",
            " re_lu_90 (ReLU)                (None, 14, 14, 672)  0           ['tf.__operators__.add_69[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_69 (TFOpLambd  (None, 14, 14, 672)  0          ['re_lu_90[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " multiply_47 (Multiply)         (None, 14, 14, 672)  0           ['expanded_conv_11/expand/BatchNo\n",
            "                                                                 rm[0][0]',                       \n",
            "                                                                  'tf.math.multiply_69[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_11/depthwise (De  (None, 14, 14, 672)  6048       ['multiply_47[0][0]']            \n",
            " pthwiseConv2D)                                                                                   \n",
            "                                                                                                  \n",
            " expanded_conv_11/depthwise/Bat  (None, 14, 14, 672)  2688       ['expanded_conv_11/depthwise[0][0\n",
            " chNorm (BatchNormalization)                                     ]']                              \n",
            "                                                                                                  \n",
            " tf.__operators__.add_70 (TFOpL  (None, 14, 14, 672)  0          ['expanded_conv_11/depthwise/Batc\n",
            " ambda)                                                          hNorm[0][0]']                    \n",
            "                                                                                                  \n",
            " re_lu_91 (ReLU)                (None, 14, 14, 672)  0           ['tf.__operators__.add_70[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_70 (TFOpLambd  (None, 14, 14, 672)  0          ['re_lu_91[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " multiply_48 (Multiply)         (None, 14, 14, 672)  0           ['expanded_conv_11/depthwise/Batc\n",
            "                                                                 hNorm[0][0]',                    \n",
            "                                                                  'tf.math.multiply_70[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_11/squeeze_excit  (None, 1, 1, 672)   0           ['multiply_48[0][0]']            \n",
            " e/AvgPool (GlobalAveragePoolin                                                                   \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " expanded_conv_11/squeeze_excit  (None, 1, 1, 168)   113064      ['expanded_conv_11/squeeze_excite\n",
            " e/Conv (Conv2D)                                                 /AvgPool[0][0]']                 \n",
            "                                                                                                  \n",
            " expanded_conv_11/squeeze_excit  (None, 1, 1, 168)   0           ['expanded_conv_11/squeeze_excite\n",
            " e/Relu (ReLU)                                                   /Conv[0][0]']                    \n",
            "                                                                                                  \n",
            " expanded_conv_11/squeeze_excit  (None, 1, 1, 672)   113568      ['expanded_conv_11/squeeze_excite\n",
            " e/Conv_1 (Conv2D)                                               /Relu[0][0]']                    \n",
            "                                                                                                  \n",
            " tf.__operators__.add_71 (TFOpL  (None, 1, 1, 672)   0           ['expanded_conv_11/squeeze_excite\n",
            " ambda)                                                          /Conv_1[0][0]']                  \n",
            "                                                                                                  \n",
            " re_lu_92 (ReLU)                (None, 1, 1, 672)    0           ['tf.__operators__.add_71[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_71 (TFOpLambd  (None, 1, 1, 672)   0           ['re_lu_92[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " expanded_conv_11/squeeze_excit  (None, 14, 14, 672)  0          ['multiply_48[0][0]',            \n",
            " e/Mul (Multiply)                                                 'tf.math.multiply_71[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_11/project (Conv  (None, 14, 14, 112)  75264      ['expanded_conv_11/squeeze_excite\n",
            " 2D)                                                             /Mul[0][0]']                     \n",
            "                                                                                                  \n",
            " expanded_conv_11/project/Batch  (None, 14, 14, 112)  448        ['expanded_conv_11/project[0][0]'\n",
            " Norm (BatchNormalization)                                       ]                                \n",
            "                                                                                                  \n",
            " expanded_conv_11/Add (Add)     (None, 14, 14, 112)  0           ['expanded_conv_10/project/BatchN\n",
            "                                                                 orm[0][0]',                      \n",
            "                                                                  'expanded_conv_11/project/BatchN\n",
            "                                                                 orm[0][0]']                      \n",
            "                                                                                                  \n",
            " expanded_conv_12/expand (Conv2  (None, 14, 14, 672)  75264      ['expanded_conv_11/Add[0][0]']   \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " expanded_conv_12/expand/BatchN  (None, 14, 14, 672)  2688       ['expanded_conv_12/expand[0][0]']\n",
            " orm (BatchNormalization)                                                                         \n",
            "                                                                                                  \n",
            " tf.__operators__.add_72 (TFOpL  (None, 14, 14, 672)  0          ['expanded_conv_12/expand/BatchNo\n",
            " ambda)                                                          rm[0][0]']                       \n",
            "                                                                                                  \n",
            " re_lu_93 (ReLU)                (None, 14, 14, 672)  0           ['tf.__operators__.add_72[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_72 (TFOpLambd  (None, 14, 14, 672)  0          ['re_lu_93[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " multiply_49 (Multiply)         (None, 14, 14, 672)  0           ['expanded_conv_12/expand/BatchNo\n",
            "                                                                 rm[0][0]',                       \n",
            "                                                                  'tf.math.multiply_72[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_12/depthwise/pad  (None, 17, 17, 672)  0          ['multiply_49[0][0]']            \n",
            "  (ZeroPadding2D)                                                                                 \n",
            "                                                                                                  \n",
            " expanded_conv_12/depthwise (De  (None, 7, 7, 672)   16800       ['expanded_conv_12/depthwise/pad[\n",
            " pthwiseConv2D)                                                  0][0]']                          \n",
            "                                                                                                  \n",
            " expanded_conv_12/depthwise/Bat  (None, 7, 7, 672)   2688        ['expanded_conv_12/depthwise[0][0\n",
            " chNorm (BatchNormalization)                                     ]']                              \n",
            "                                                                                                  \n",
            " tf.__operators__.add_73 (TFOpL  (None, 7, 7, 672)   0           ['expanded_conv_12/depthwise/Batc\n",
            " ambda)                                                          hNorm[0][0]']                    \n",
            "                                                                                                  \n",
            " re_lu_94 (ReLU)                (None, 7, 7, 672)    0           ['tf.__operators__.add_73[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_73 (TFOpLambd  (None, 7, 7, 672)   0           ['re_lu_94[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " multiply_50 (Multiply)         (None, 7, 7, 672)    0           ['expanded_conv_12/depthwise/Batc\n",
            "                                                                 hNorm[0][0]',                    \n",
            "                                                                  'tf.math.multiply_73[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_12/squeeze_excit  (None, 1, 1, 672)   0           ['multiply_50[0][0]']            \n",
            " e/AvgPool (GlobalAveragePoolin                                                                   \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " expanded_conv_12/squeeze_excit  (None, 1, 1, 168)   113064      ['expanded_conv_12/squeeze_excite\n",
            " e/Conv (Conv2D)                                                 /AvgPool[0][0]']                 \n",
            "                                                                                                  \n",
            " expanded_conv_12/squeeze_excit  (None, 1, 1, 168)   0           ['expanded_conv_12/squeeze_excite\n",
            " e/Relu (ReLU)                                                   /Conv[0][0]']                    \n",
            "                                                                                                  \n",
            " expanded_conv_12/squeeze_excit  (None, 1, 1, 672)   113568      ['expanded_conv_12/squeeze_excite\n",
            " e/Conv_1 (Conv2D)                                               /Relu[0][0]']                    \n",
            "                                                                                                  \n",
            " tf.__operators__.add_74 (TFOpL  (None, 1, 1, 672)   0           ['expanded_conv_12/squeeze_excite\n",
            " ambda)                                                          /Conv_1[0][0]']                  \n",
            "                                                                                                  \n",
            " re_lu_95 (ReLU)                (None, 1, 1, 672)    0           ['tf.__operators__.add_74[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_74 (TFOpLambd  (None, 1, 1, 672)   0           ['re_lu_95[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " expanded_conv_12/squeeze_excit  (None, 7, 7, 672)   0           ['multiply_50[0][0]',            \n",
            " e/Mul (Multiply)                                                 'tf.math.multiply_74[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_12/project (Conv  (None, 7, 7, 160)   107520      ['expanded_conv_12/squeeze_excite\n",
            " 2D)                                                             /Mul[0][0]']                     \n",
            "                                                                                                  \n",
            " expanded_conv_12/project/Batch  (None, 7, 7, 160)   640         ['expanded_conv_12/project[0][0]'\n",
            " Norm (BatchNormalization)                                       ]                                \n",
            "                                                                                                  \n",
            " expanded_conv_13/expand (Conv2  (None, 7, 7, 960)   153600      ['expanded_conv_12/project/BatchN\n",
            " D)                                                              orm[0][0]']                      \n",
            "                                                                                                  \n",
            " expanded_conv_13/expand/BatchN  (None, 7, 7, 960)   3840        ['expanded_conv_13/expand[0][0]']\n",
            " orm (BatchNormalization)                                                                         \n",
            "                                                                                                  \n",
            " tf.__operators__.add_75 (TFOpL  (None, 7, 7, 960)   0           ['expanded_conv_13/expand/BatchNo\n",
            " ambda)                                                          rm[0][0]']                       \n",
            "                                                                                                  \n",
            " re_lu_96 (ReLU)                (None, 7, 7, 960)    0           ['tf.__operators__.add_75[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_75 (TFOpLambd  (None, 7, 7, 960)   0           ['re_lu_96[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " multiply_51 (Multiply)         (None, 7, 7, 960)    0           ['expanded_conv_13/expand/BatchNo\n",
            "                                                                 rm[0][0]',                       \n",
            "                                                                  'tf.math.multiply_75[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_13/depthwise (De  (None, 7, 7, 960)   24000       ['multiply_51[0][0]']            \n",
            " pthwiseConv2D)                                                                                   \n",
            "                                                                                                  \n",
            " expanded_conv_13/depthwise/Bat  (None, 7, 7, 960)   3840        ['expanded_conv_13/depthwise[0][0\n",
            " chNorm (BatchNormalization)                                     ]']                              \n",
            "                                                                                                  \n",
            " tf.__operators__.add_76 (TFOpL  (None, 7, 7, 960)   0           ['expanded_conv_13/depthwise/Batc\n",
            " ambda)                                                          hNorm[0][0]']                    \n",
            "                                                                                                  \n",
            " re_lu_97 (ReLU)                (None, 7, 7, 960)    0           ['tf.__operators__.add_76[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_76 (TFOpLambd  (None, 7, 7, 960)   0           ['re_lu_97[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " multiply_52 (Multiply)         (None, 7, 7, 960)    0           ['expanded_conv_13/depthwise/Batc\n",
            "                                                                 hNorm[0][0]',                    \n",
            "                                                                  'tf.math.multiply_76[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_13/squeeze_excit  (None, 1, 1, 960)   0           ['multiply_52[0][0]']            \n",
            " e/AvgPool (GlobalAveragePoolin                                                                   \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " expanded_conv_13/squeeze_excit  (None, 1, 1, 240)   230640      ['expanded_conv_13/squeeze_excite\n",
            " e/Conv (Conv2D)                                                 /AvgPool[0][0]']                 \n",
            "                                                                                                  \n",
            " expanded_conv_13/squeeze_excit  (None, 1, 1, 240)   0           ['expanded_conv_13/squeeze_excite\n",
            " e/Relu (ReLU)                                                   /Conv[0][0]']                    \n",
            "                                                                                                  \n",
            " expanded_conv_13/squeeze_excit  (None, 1, 1, 960)   231360      ['expanded_conv_13/squeeze_excite\n",
            " e/Conv_1 (Conv2D)                                               /Relu[0][0]']                    \n",
            "                                                                                                  \n",
            " tf.__operators__.add_77 (TFOpL  (None, 1, 1, 960)   0           ['expanded_conv_13/squeeze_excite\n",
            " ambda)                                                          /Conv_1[0][0]']                  \n",
            "                                                                                                  \n",
            " re_lu_98 (ReLU)                (None, 1, 1, 960)    0           ['tf.__operators__.add_77[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_77 (TFOpLambd  (None, 1, 1, 960)   0           ['re_lu_98[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " expanded_conv_13/squeeze_excit  (None, 7, 7, 960)   0           ['multiply_52[0][0]',            \n",
            " e/Mul (Multiply)                                                 'tf.math.multiply_77[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_13/project (Conv  (None, 7, 7, 160)   153600      ['expanded_conv_13/squeeze_excite\n",
            " 2D)                                                             /Mul[0][0]']                     \n",
            "                                                                                                  \n",
            " expanded_conv_13/project/Batch  (None, 7, 7, 160)   640         ['expanded_conv_13/project[0][0]'\n",
            " Norm (BatchNormalization)                                       ]                                \n",
            "                                                                                                  \n",
            " expanded_conv_13/Add (Add)     (None, 7, 7, 160)    0           ['expanded_conv_12/project/BatchN\n",
            "                                                                 orm[0][0]',                      \n",
            "                                                                  'expanded_conv_13/project/BatchN\n",
            "                                                                 orm[0][0]']                      \n",
            "                                                                                                  \n",
            " expanded_conv_14/expand (Conv2  (None, 7, 7, 960)   153600      ['expanded_conv_13/Add[0][0]']   \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " expanded_conv_14/expand/BatchN  (None, 7, 7, 960)   3840        ['expanded_conv_14/expand[0][0]']\n",
            " orm (BatchNormalization)                                                                         \n",
            "                                                                                                  \n",
            " tf.__operators__.add_78 (TFOpL  (None, 7, 7, 960)   0           ['expanded_conv_14/expand/BatchNo\n",
            " ambda)                                                          rm[0][0]']                       \n",
            "                                                                                                  \n",
            " re_lu_99 (ReLU)                (None, 7, 7, 960)    0           ['tf.__operators__.add_78[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_78 (TFOpLambd  (None, 7, 7, 960)   0           ['re_lu_99[0][0]']               \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " multiply_53 (Multiply)         (None, 7, 7, 960)    0           ['expanded_conv_14/expand/BatchNo\n",
            "                                                                 rm[0][0]',                       \n",
            "                                                                  'tf.math.multiply_78[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_14/depthwise (De  (None, 7, 7, 960)   24000       ['multiply_53[0][0]']            \n",
            " pthwiseConv2D)                                                                                   \n",
            "                                                                                                  \n",
            " expanded_conv_14/depthwise/Bat  (None, 7, 7, 960)   3840        ['expanded_conv_14/depthwise[0][0\n",
            " chNorm (BatchNormalization)                                     ]']                              \n",
            "                                                                                                  \n",
            " tf.__operators__.add_79 (TFOpL  (None, 7, 7, 960)   0           ['expanded_conv_14/depthwise/Batc\n",
            " ambda)                                                          hNorm[0][0]']                    \n",
            "                                                                                                  \n",
            " re_lu_100 (ReLU)               (None, 7, 7, 960)    0           ['tf.__operators__.add_79[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_79 (TFOpLambd  (None, 7, 7, 960)   0           ['re_lu_100[0][0]']              \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " multiply_54 (Multiply)         (None, 7, 7, 960)    0           ['expanded_conv_14/depthwise/Batc\n",
            "                                                                 hNorm[0][0]',                    \n",
            "                                                                  'tf.math.multiply_79[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_14/squeeze_excit  (None, 1, 1, 960)   0           ['multiply_54[0][0]']            \n",
            " e/AvgPool (GlobalAveragePoolin                                                                   \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " expanded_conv_14/squeeze_excit  (None, 1, 1, 240)   230640      ['expanded_conv_14/squeeze_excite\n",
            " e/Conv (Conv2D)                                                 /AvgPool[0][0]']                 \n",
            "                                                                                                  \n",
            " expanded_conv_14/squeeze_excit  (None, 1, 1, 240)   0           ['expanded_conv_14/squeeze_excite\n",
            " e/Relu (ReLU)                                                   /Conv[0][0]']                    \n",
            "                                                                                                  \n",
            " expanded_conv_14/squeeze_excit  (None, 1, 1, 960)   231360      ['expanded_conv_14/squeeze_excite\n",
            " e/Conv_1 (Conv2D)                                               /Relu[0][0]']                    \n",
            "                                                                                                  \n",
            " tf.__operators__.add_80 (TFOpL  (None, 1, 1, 960)   0           ['expanded_conv_14/squeeze_excite\n",
            " ambda)                                                          /Conv_1[0][0]']                  \n",
            "                                                                                                  \n",
            " re_lu_101 (ReLU)               (None, 1, 1, 960)    0           ['tf.__operators__.add_80[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_80 (TFOpLambd  (None, 1, 1, 960)   0           ['re_lu_101[0][0]']              \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " expanded_conv_14/squeeze_excit  (None, 7, 7, 960)   0           ['multiply_54[0][0]',            \n",
            " e/Mul (Multiply)                                                 'tf.math.multiply_80[0][0]']    \n",
            "                                                                                                  \n",
            " expanded_conv_14/project (Conv  (None, 7, 7, 160)   153600      ['expanded_conv_14/squeeze_excite\n",
            " 2D)                                                             /Mul[0][0]']                     \n",
            "                                                                                                  \n",
            " expanded_conv_14/project/Batch  (None, 7, 7, 160)   640         ['expanded_conv_14/project[0][0]'\n",
            " Norm (BatchNormalization)                                       ]                                \n",
            "                                                                                                  \n",
            " expanded_conv_14/Add (Add)     (None, 7, 7, 160)    0           ['expanded_conv_13/Add[0][0]',   \n",
            "                                                                  'expanded_conv_14/project/BatchN\n",
            "                                                                 orm[0][0]']                      \n",
            "                                                                                                  \n",
            " Conv_1 (Conv2D)                (None, 7, 7, 960)    153600      ['expanded_conv_14/Add[0][0]']   \n",
            "                                                                                                  \n",
            " Conv_1/BatchNorm (BatchNormali  (None, 7, 7, 960)   3840        ['Conv_1[0][0]']                 \n",
            " zation)                                                                                          \n",
            "                                                                                                  \n",
            " tf.__operators__.add_81 (TFOpL  (None, 7, 7, 960)   0           ['Conv_1/BatchNorm[0][0]']       \n",
            " ambda)                                                                                           \n",
            "                                                                                                  \n",
            " re_lu_102 (ReLU)               (None, 7, 7, 960)    0           ['tf.__operators__.add_81[0][0]']\n",
            "                                                                                                  \n",
            " tf.math.multiply_81 (TFOpLambd  (None, 7, 7, 960)   0           ['re_lu_102[0][0]']              \n",
            " a)                                                                                               \n",
            "                                                                                                  \n",
            " multiply_55 (Multiply)         (None, 7, 7, 960)    0           ['Conv_1/BatchNorm[0][0]',       \n",
            "                                                                  'tf.math.multiply_81[0][0]']    \n",
            "                                                                                                  \n",
            " avg_pool (GlobalAveragePooling  (None, 960)         0           ['multiply_55[0][0]']            \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " dense_8 (Dense)                (None, 256)          246016      ['avg_pool[0][0]']               \n",
            "                                                                                                  \n",
            " dropout_6 (Dropout)            (None, 256)          0           ['dense_8[0][0]']                \n",
            "                                                                                                  \n",
            " dense_9 (Dense)                (None, 128)          32896       ['dropout_6[0][0]']              \n",
            "                                                                                                  \n",
            " dropout_7 (Dropout)            (None, 128)          0           ['dense_9[0][0]']                \n",
            "                                                                                                  \n",
            " dense_10 (Dense)               (None, 64)           8256        ['dropout_7[0][0]']              \n",
            "                                                                                                  \n",
            " dropout_8 (Dropout)            (None, 64)           0           ['dense_10[0][0]']               \n",
            "                                                                                                  \n",
            " dense_11 (Dense)               (None, 500)          32500       ['dropout_8[0][0]']              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 3,316,020\n",
            "Trainable params: 319,668\n",
            "Non-trainable params: 2,996,352\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "loaded_model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "uQeU--ntpkdE",
        "outputId": "c046baa0-6760-451f-c151-ac206b591ee2"
      },
      "outputs": [
        {
          "data": {
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              "      <th>Filepath</th>\n",
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              "      <td>test/PALILA/2.jpg</td>\n",
              "      <td>PALILA</td>\n",
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              "      <th>1</th>\n",
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              "      <td>PALILA</td>\n",
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            "text/plain": [
              "            Filepath   Label    pred\n",
              "0  test/PALILA/2.jpg  PALILA  PALILA\n",
              "1  test/PALILA/4.jpg  PALILA  PALILA\n",
              "2  test/PALILA/1.jpg  PALILA  PALILA\n",
              "3  test/PALILA/5.jpg  PALILA  PALILA\n",
              "4  test/PALILA/3.jpg  PALILA  PALILA"
            ]
          },
          "execution_count": 161,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_df['pred']=pred\n",
        "test_df.head()"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
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        },
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        {
          "data": {
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              "500"
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          "execution_count": 169,
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        "len(test_df.Label.unique())"
      ]
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      },
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[5 0 0 ... 0 0 0]\n",
            " [0 4 0 ... 0 0 0]\n",
            " [0 0 3 ... 0 0 0]\n",
            " ...\n",
            " [0 0 0 ... 5 0 0]\n",
            " [0 0 0 ... 0 4 0]\n",
            " [0 0 0 ... 0 0 5]]\n"
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              "      <th>PALILA</th>\n",
              "      <th>ANDEAN GOOSE</th>\n",
              "      <th>SANDHILL CRANE</th>\n",
              "      <th>PYGMY KINGFISHER</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PYGMY KINGFISHER</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CAMPO FLICKER</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AZURE JAY</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>STRIPPED SWALLOW</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EASTERN WIP POOR WILL</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AVADAVAT</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INLAND DOTTEREL</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 500 columns</p>\n",
              "</div>\n",
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              "  "
            ],
            "text/plain": [
              "                       PALILA  ANDEAN GOOSE  SANDHILL CRANE  PYGMY KINGFISHER  \\\n",
              "PALILA                      5             0               0                 0   \n",
              "ANDEAN GOOSE                0             4               0                 0   \n",
              "SANDHILL CRANE              0             0               3                 0   \n",
              "PYGMY KINGFISHER            0             0               0                 5   \n",
              "CAMPO FLICKER               0             0               0                 0   \n",
              "...                       ...           ...             ...               ...   \n",
              "AZURE JAY                   0             0               0                 0   \n",
              "STRIPPED SWALLOW            0             0               0                 0   \n",
              "EASTERN WIP POOR WILL       0             0               0                 0   \n",
              "AVADAVAT                    0             0               0                 0   \n",
              "INLAND DOTTEREL             0             0               0                 0   \n",
              "\n",
              "                       CAMPO FLICKER  AFRICAN OYSTER CATCHER  BLUE DACNIS  \\\n",
              "PALILA                             0                       0            0   \n",
              "ANDEAN GOOSE                       0                       0            0   \n",
              "SANDHILL CRANE                     0                       0            0   \n",
              "PYGMY KINGFISHER                   0                       0            0   \n",
              "CAMPO FLICKER                      5                       0            0   \n",
              "...                              ...                     ...          ...   \n",
              "AZURE JAY                          0                       0            0   \n",
              "STRIPPED SWALLOW                   0                       0            0   \n",
              "EASTERN WIP POOR WILL              0                       0            0   \n",
              "AVADAVAT                           0                       0            0   \n",
              "INLAND DOTTEREL                    0                       0            0   \n",
              "\n",
              "                       SAYS PHOEBE  ANTBIRD  ABBOTTS BOOBY  ...  \\\n",
              "PALILA                           0        0              0  ...   \n",
              "ANDEAN GOOSE                     0        0              0  ...   \n",
              "SANDHILL CRANE                   0        0              0  ...   \n",
              "PYGMY KINGFISHER                 0        0              0  ...   \n",
              "CAMPO FLICKER                    0        0              0  ...   \n",
              "...                            ...      ...            ...  ...   \n",
              "AZURE JAY                        0        0              0  ...   \n",
              "STRIPPED SWALLOW                 0        0              0  ...   \n",
              "EASTERN WIP POOR WILL            0        0              0  ...   \n",
              "AVADAVAT                         0        0              0  ...   \n",
              "INLAND DOTTEREL                  0        0              0  ...   \n",
              "\n",
              "                       JACOBIN PIGEON  HEPATIC TANAGER  RED BROWED FINCH  \\\n",
              "PALILA                              0                0                 0   \n",
              "ANDEAN GOOSE                        0                0                 0   \n",
              "SANDHILL CRANE                      0                0                 0   \n",
              "PYGMY KINGFISHER                    0                0                 0   \n",
              "CAMPO FLICKER                       0                0                 0   \n",
              "...                               ...              ...               ...   \n",
              "AZURE JAY                           0                0                 0   \n",
              "STRIPPED SWALLOW                    0                0                 0   \n",
              "EASTERN WIP POOR WILL               0                0                 0   \n",
              "AVADAVAT                            0                0                 0   \n",
              "INLAND DOTTEREL                     0                0                 0   \n",
              "\n",
              "                       NORTHERN GANNET  PARUS MAJOR  AZURE JAY  \\\n",
              "PALILA                               0            0          0   \n",
              "ANDEAN GOOSE                         0            0          0   \n",
              "SANDHILL CRANE                       0            0          0   \n",
              "PYGMY KINGFISHER                     0            0          0   \n",
              "CAMPO FLICKER                        0            0          0   \n",
              "...                                ...          ...        ...   \n",
              "AZURE JAY                            0            0          5   \n",
              "STRIPPED SWALLOW                     0            0          0   \n",
              "EASTERN WIP POOR WILL                0            0          0   \n",
              "AVADAVAT                             0            0          0   \n",
              "INLAND DOTTEREL                      0            0          0   \n",
              "\n",
              "                       STRIPPED SWALLOW  EASTERN WIP POOR WILL  AVADAVAT  \\\n",
              "PALILA                                0                      0         0   \n",
              "ANDEAN GOOSE                          0                      0         0   \n",
              "SANDHILL CRANE                        0                      0         0   \n",
              "PYGMY KINGFISHER                      0                      0         0   \n",
              "CAMPO FLICKER                         0                      0         0   \n",
              "...                                 ...                    ...       ...   \n",
              "AZURE JAY                             0                      0         0   \n",
              "STRIPPED SWALLOW                      5                      0         0   \n",
              "EASTERN WIP POOR WILL                 0                      5         0   \n",
              "AVADAVAT                              0                      0         4   \n",
              "INLAND DOTTEREL                       0                      0         0   \n",
              "\n",
              "                       INLAND DOTTEREL  \n",
              "PALILA                               0  \n",
              "ANDEAN GOOSE                         0  \n",
              "SANDHILL CRANE                       0  \n",
              "PYGMY KINGFISHER                     0  \n",
              "CAMPO FLICKER                        0  \n",
              "...                                ...  \n",
              "AZURE JAY                            0  \n",
              "STRIPPED SWALLOW                     0  \n",
              "EASTERN WIP POOR WILL                0  \n",
              "AVADAVAT                             0  \n",
              "INLAND DOTTEREL                      5  \n",
              "\n",
              "[500 rows x 500 columns]"
            ]
          },
          "execution_count": 173,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "labels=test_df.Label.unique()\n",
        "cf_matrix = confusion_matrix(test_df.Label, test_df.pred, labels=labels)\n",
        "print(cf_matrix)\n",
        "\n",
        "df_cm = pd.DataFrame(cf_matrix, columns=labels, index=labels)\n",
        "df_cm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oELmQV-Ll0_1"
      },
      "outputs": [],
      "source": [
        "df_cm_no_5 = df_cm.loc[~(df_cm == 5).any(),:][df_cm.loc[:, ~(df_cm == 5).any()].columns]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "A493SZZenj4J",
        "outputId": "9e8b9eba-6e51-415b-f8f6-5ffbb72e0e6a"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>ANDEAN GOOSE</th>\n",
              "      <th>SANDHILL CRANE</th>\n",
              "      <th>BLUE DACNIS</th>\n",
              "      <th>SAYS PHOEBE</th>\n",
              "      <th>ANTBIRD</th>\n",
              "      <th>ABBOTTS BOOBY</th>\n",
              "      <th>JANDAYA PARAKEET</th>\n",
              "      <th>CRESTED OROPENDOLA</th>\n",
              "      <th>RED LEGGED HONEYCREEPER</th>\n",
              "      <th>LUCIFER HUMMINGBIRD</th>\n",
              "      <th>...</th>\n",
              "      <th>EASTERN GOLDEN WEAVER</th>\n",
              "      <th>BAY-BREASTED WARBLER</th>\n",
              "      <th>NORTHERN GOSHAWK</th>\n",
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              "    </tr>\n",
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              "      <th>BLUE DACNIS</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ANTBIRD</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EUROPEAN GOLDFINCH</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DUSKY ROBIN</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JACOBIN PIGEON</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HEPATIC TANAGER</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AVADAVAT</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>117 rows × 117 columns</p>\n",
              "</div>\n",
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            ],
            "text/plain": [
              "                    ANDEAN GOOSE  SANDHILL CRANE  BLUE DACNIS  SAYS PHOEBE  \\\n",
              "ANDEAN GOOSE                   4               0            0            0   \n",
              "SANDHILL CRANE                 0               3            0            0   \n",
              "BLUE DACNIS                    0               0            4            0   \n",
              "SAYS PHOEBE                    0               0            0            4   \n",
              "ANTBIRD                        0               0            0            0   \n",
              "...                          ...             ...          ...          ...   \n",
              "EUROPEAN GOLDFINCH             0               0            0            0   \n",
              "DUSKY ROBIN                    0               0            0            0   \n",
              "JACOBIN PIGEON                 0               0            0            0   \n",
              "HEPATIC TANAGER                0               0            0            0   \n",
              "AVADAVAT                       0               0            0            0   \n",
              "\n",
              "                    ANTBIRD  ABBOTTS BOOBY  JANDAYA PARAKEET  \\\n",
              "ANDEAN GOOSE              0              0                 0   \n",
              "SANDHILL CRANE            0              0                 0   \n",
              "BLUE DACNIS               0              0                 0   \n",
              "SAYS PHOEBE               0              0                 0   \n",
              "ANTBIRD                   4              0                 0   \n",
              "...                     ...            ...               ...   \n",
              "EUROPEAN GOLDFINCH        0              0                 0   \n",
              "DUSKY ROBIN               0              0                 0   \n",
              "JACOBIN PIGEON            0              0                 0   \n",
              "HEPATIC TANAGER           0              0                 0   \n",
              "AVADAVAT                  0              0                 0   \n",
              "\n",
              "                    CRESTED OROPENDOLA  RED LEGGED HONEYCREEPER  \\\n",
              "ANDEAN GOOSE                         0                        0   \n",
              "SANDHILL CRANE                       0                        0   \n",
              "BLUE DACNIS                          0                        1   \n",
              "SAYS PHOEBE                          0                        0   \n",
              "ANTBIRD                              0                        0   \n",
              "...                                ...                      ...   \n",
              "EUROPEAN GOLDFINCH                   0                        0   \n",
              "DUSKY ROBIN                          0                        0   \n",
              "JACOBIN PIGEON                       0                        0   \n",
              "HEPATIC TANAGER                      0                        0   \n",
              "AVADAVAT                             0                        0   \n",
              "\n",
              "                    LUCIFER HUMMINGBIRD  ...  EASTERN GOLDEN WEAVER  \\\n",
              "ANDEAN GOOSE                          0  ...                      0   \n",
              "SANDHILL CRANE                        0  ...                      0   \n",
              "BLUE DACNIS                           0  ...                      0   \n",
              "SAYS PHOEBE                           0  ...                      0   \n",
              "ANTBIRD                               0  ...                      0   \n",
              "...                                 ...  ...                    ...   \n",
              "EUROPEAN GOLDFINCH                    0  ...                      0   \n",
              "DUSKY ROBIN                           0  ...                      0   \n",
              "JACOBIN PIGEON                        0  ...                      0   \n",
              "HEPATIC TANAGER                       0  ...                      0   \n",
              "AVADAVAT                              0  ...                      0   \n",
              "\n",
              "                    BAY-BREASTED WARBLER  NORTHERN GOSHAWK  \\\n",
              "ANDEAN GOOSE                           0                 0   \n",
              "SANDHILL CRANE                         0                 0   \n",
              "BLUE DACNIS                            0                 0   \n",
              "SAYS PHOEBE                            0                 0   \n",
              "ANTBIRD                                0                 0   \n",
              "...                                  ...               ...   \n",
              "EUROPEAN GOLDFINCH                     0                 0   \n",
              "DUSKY ROBIN                            0                 0   \n",
              "JACOBIN PIGEON                         0                 0   \n",
              "HEPATIC TANAGER                        0                 0   \n",
              "AVADAVAT                               0                 0   \n",
              "\n",
              "                    WHITE CHEEKED TURACO  CRESTED NUTHATCH  \\\n",
              "ANDEAN GOOSE                           0                 0   \n",
              "SANDHILL CRANE                         0                 0   \n",
              "BLUE DACNIS                            0                 0   \n",
              "SAYS PHOEBE                            0                 0   \n",
              "ANTBIRD                                0                 0   \n",
              "...                                  ...               ...   \n",
              "EUROPEAN GOLDFINCH                     0                 0   \n",
              "DUSKY ROBIN                            0                 0   \n",
              "JACOBIN PIGEON                         0                 0   \n",
              "HEPATIC TANAGER                        0                 0   \n",
              "AVADAVAT                               0                 0   \n",
              "\n",
              "                    EUROPEAN GOLDFINCH  DUSKY ROBIN  JACOBIN PIGEON  \\\n",
              "ANDEAN GOOSE                         0            0               0   \n",
              "SANDHILL CRANE                       0            0               0   \n",
              "BLUE DACNIS                          0            0               0   \n",
              "SAYS PHOEBE                          0            0               0   \n",
              "ANTBIRD                              0            0               0   \n",
              "...                                ...          ...             ...   \n",
              "EUROPEAN GOLDFINCH                   4            0               0   \n",
              "DUSKY ROBIN                          0            2               0   \n",
              "JACOBIN PIGEON                       0            0               3   \n",
              "HEPATIC TANAGER                      0            0               0   \n",
              "AVADAVAT                             0            0               0   \n",
              "\n",
              "                    HEPATIC TANAGER  AVADAVAT  \n",
              "ANDEAN GOOSE                      0         0  \n",
              "SANDHILL CRANE                    0         0  \n",
              "BLUE DACNIS                       0         0  \n",
              "SAYS PHOEBE                       0         0  \n",
              "ANTBIRD                           0         0  \n",
              "...                             ...       ...  \n",
              "EUROPEAN GOLDFINCH                0         0  \n",
              "DUSKY ROBIN                       0         0  \n",
              "JACOBIN PIGEON                    0         0  \n",
              "HEPATIC TANAGER                   4         0  \n",
              "AVADAVAT                          0         4  \n",
              "\n",
              "[117 rows x 117 columns]"
            ]
          },
          "execution_count": 175,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_cm_no_5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XmEMcaJHgsaH",
        "outputId": "d6e71572-39e1-4653-9260-304e8e688e6c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "count    500.0\n",
              "mean       5.0\n",
              "std        0.0\n",
              "min        5.0\n",
              "25%        5.0\n",
              "50%        5.0\n",
              "75%        5.0\n",
              "max        5.0\n",
              "dtype: float64"
            ]
          },
          "execution_count": 176,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_cm.sum(axis=1).describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 971
        },
        "id": "TyHnLu8Td2vY",
        "outputId": "6aa7736c-0470-42ac-e1b1-ae1380247769"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x1008 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize = (25,14))\n",
        "sns.heatmap(df_cm_no_5, annot=False, \n",
        "            cmap='Blues')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kjv2gdobeQiF"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "machine_shape": "hm",
      "provenance": []
    },
    "gpuClass": "premium",
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.6.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}