python_notebooks/ML_model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "76l_dKipuhgy"
      },
      "source": [
        "# Upgrading pip\n",
        "\n",
        "Although this is not nessasry it speeds up the process and imports newer libraries which are more up to date.\n",
        "\n",
        "```\n",
        "# !pip install --upgrade pip\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G54_Hf6j28ab"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UwOqn57q23n4"
      },
      "outputs": [],
      "source": [
        "skipped = pd.read_csv(\"/content/empty_list.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u5BhtaBz2_iH",
        "outputId": "9847aee8-49dc-47ce-d8e1-8fd55a341a51"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "114949"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(skipped)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pG0mCxlQm20k"
      },
      "outputs": [],
      "source": [
        "!pip install --upgrade pip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zTvPyq6mvtGi"
      },
      "source": [
        "# Installing Tensor Flow Lite\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K-nLMXYEkCdR"
      },
      "outputs": [],
      "source": [
        "!pip install tflite_model_maker"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "54IUECSmv1tn"
      },
      "source": [
        "# Importing required libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vO_7PnLDn9vb"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "assert tf.__version__.startswith('2')\n",
        "import os\n",
        "import shutil\n",
        "\n",
        "from tflite_model_maker import model_spec\n",
        "from tflite_model_maker import image_classifier\n",
        "from tflite_model_maker.config import ExportFormat\n",
        "from tflite_model_maker.config import QuantizationConfig\n",
        "from tflite_model_maker.image_classifier import DataLoader\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r9T26fr3v7r9"
      },
      "source": [
        "# Importing Google Drive files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u7WaNdx3oSEf",
        "outputId": "fde9561c-ef08-4a37-9039-dac1c5e21ecb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive', force_remount=True)\n",
        "\n",
        "#!unzip \"/content/gdrive/MyDrive/skunk_image_training_data/training.zip\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h6EVFjLuwShu"
      },
      "source": [
        "# Setting image path\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mF62RDJ5oMC9",
        "outputId": "9da502a0-71dd-474c-9352-f5b3e00c26e6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/gdrive/MyDrive/skunk_image_training_data/training_images_select\n"
          ]
        }
      ],
      "source": [
        "image_path = '/content/gdrive/MyDrive/skunk_image_training_data/training_images_select'\n",
        "\n",
        "print(image_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pu7ywuoOHOtY"
      },
      "source": [
        "# File cleaning and preparation\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FbHySjhSyU5T"
      },
      "outputs": [],
      "source": [
        "trainging_path = image_path"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ib1iMSzut3zS"
      },
      "outputs": [],
      "source": [
        "!find \"/content/gdrive/MyDrive/skunk_image_training_data/training_images_select/\" -type f -name '*.jpg' -print0 | xargs -0 rename 's/.jpg$/.jpeg/'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R7rvuFEl0ZM_",
        "outputId": "4b79f006-58ce-4de5-9277-76caaf17d62c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'sudo apt autoremove' to remove it.\n",
            "The following NEW packages will be installed:\n",
            "  jpeginfo\n",
            "0 upgraded, 1 newly installed, 0 to remove and 22 not upgraded.\n",
            "Need to get 12.5 kB of archives.\n",
            "After this operation, 34.8 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 jpeginfo amd64 1.6.0-6build1 [12.5 kB]\n",
            "Fetched 12.5 kB in 0s (147 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package jpeginfo.\n",
            "(Reading database ... 123941 files and directories currently installed.)\n",
            "Preparing to unpack .../jpeginfo_1.6.0-6build1_amd64.deb ...\n",
            "Unpacking jpeginfo (1.6.0-6build1) ...\n",
            "Setting up jpeginfo (1.6.0-6build1) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n"
          ]
        }
      ],
      "source": [
        "!sudo apt install jpeginfo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "btUUmL4PqIVU",
        "outputId": "787f9463-81d0-44ba-bd2f-e31ae9b56a9c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "jpeg\n",
            "jpeg\n"
          ]
        }
      ],
      "source": [
        "!find /content/gdrive/MyDrive/skunk_image_training_data/training_images_select -type f | perl -ne 'print $1 if m/\\.([^.\\/]+)$/' | sort -u\n",
        "\n",
        "!find /content/gdrive/MyDrive/skunk_image_training_data/training_images_select -name '*DS_Store' -type f -delete\n",
        "\n",
        "!find /content/gdrive/MyDrive/skunk_image_training_data/training_images_select -type f | perl -ne 'print $1 if m/\\.([^.\\/]+)$/' | sort -u"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sh5Eoc7-ybNM",
        "outputId": "9241ea41-413b-47df-cb05-a263475d4d01"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/gdrive/MyDrive/skunk_image_training_data/training_images_select/Empty/loc-h500ee07133450-004-479.jpeg\n"
          ]
        }
      ],
      "source": [
        "\n",
        "!find /content/gdrive/MyDrive/skunk_image_training_data/training_images_select -type f \\\n",
        "\\( -iname \"*.jpg\" \\\n",
        " -o -iname \"*.jpeg\" \\) \\\n",
        "-exec jpeginfo -c {} \\; | \\\n",
        "grep -E \"WARNING|ERROR\" | \\\n",
        "cut -d \" \" -f 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ODBS8XdwAweR"
      },
      "outputs": [],
      "source": [
        "os.remove(\"/content/gdrive/MyDrive/skunk_image_training_data/training_images_select/Empty/loc-h500ee07133450-004-479.jpeg\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LbTKEpIuwoSH"
      },
      "source": [
        "# Creating train and rest data from images in image_path Directory"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RN9V4nvhr47r"
      },
      "outputs": [],
      "source": [
        "data = DataLoader.from_folder(image_path, \n",
        "                              shuffle = True)\n",
        "train_data, rest_data = data.split(0.8)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YnGc-I-1xHed"
      },
      "source": [
        "# Creating validation and test data from rest_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6T_O6Q8ir_yO"
      },
      "outputs": [],
      "source": [
        "validation_data, test_data = rest_data.split(0.5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3-657dwFxtZl"
      },
      "source": [
        "# Tensor Flow Lite Model\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "skeE1YC_xyDh"
      },
      "source": [
        "## Leaving variables outside for easy reading and updating"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9vweHI16sbV3"
      },
      "outputs": [],
      "source": [
        "num_epochs = 85\n",
        "learning_rate = 0.0001\n",
        "batch_size = 4\n",
        "shuffle = True\n",
        "augmentation = False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n8h30gV2x_lD"
      },
      "source": [
        "## Training "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "K2C1UzV8cEAA",
        "outputId": "b1d9833b-162b-444c-d3ce-604edfd38c9b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " hub_keras_layer_v1v2 (HubKe  (None, 1280)             3413024   \n",
            " rasLayerV1V2)                                                   \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 1280)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 4)                 5124      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,418,148\n",
            "Trainable params: 5,124\n",
            "Non-trainable params: 3,413,024\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/500\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(SGD, self).__init__(name, **kwargs)\n"
          ]
        },
        {
          "ename": "UnimplementedError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mUnimplementedError\u001b[0m                        Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-35-9a1b27a13f19>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m                                 \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m                                 \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m                                 use_augmentation= augmentation)\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_examples/lite/model_maker/core/task/image_classifier.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(cls, train_data, model_spec, validation_data, batch_size, epochs, steps_per_epoch, train_whole_model, dropout_rate, learning_rate, momentum, shuffle, use_augmentation, use_hub_library, warmup_steps, model_dir, do_train)\u001b[0m\n\u001b[1;32m    337\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdo_train\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    338\u001b[0m       \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mv1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Retraining the models...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 339\u001b[0;31m       \u001b[0mimage_classifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    340\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    341\u001b[0m       \u001b[0;31m# Used in evaluation.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_examples/lite/model_maker/core/task/image_classifier.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, train_data, validation_data, hparams, steps_per_epoch)\u001b[0m\n\u001b[1;32m    194\u001b[0m         \u001b[0mtrain_ds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_ds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m         \u001b[0mvalidation_ds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_ds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m         steps_per_epoch=steps_per_epoch)\n\u001b[0m\u001b[1;32m    197\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m   def _export_tflite(self,\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow_examples/lite/model_maker/core/task/train_image_classifier_lib.py\u001b[0m in \u001b[0;36mhub_train_model\u001b[0;34m(model, hparams, train_ds, validation_ds, steps_per_epoch)\u001b[0m\n\u001b[1;32m    142\u001b[0m       \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhparams\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_epochs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m       \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 144\u001b[0;31m       validation_data=validation_ds)\n\u001b[0m\u001b[1;32m    145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 55\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     56\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mUnimplementedError\u001b[0m: Graph execution error:\n\nDetected at node 'efficientnet-lite0/model/stem/conv2d/Conv2D' defined at (most recent call last):\n    File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n      \"__main__\", mod_spec)\n    File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n      exec(code, run_globals)\n    File \"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py\", line 16, in <module>\n      app.launch_new_instance()\n    File \"/usr/local/lib/python3.7/dist-packages/traitlets/config/application.py\", line 846, in launch_instance\n      app.start()\n    File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelapp.py\", line 612, in start\n      self.io_loop.start()\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py\", line 132, in start\n      self.asyncio_loop.run_forever()\n    File \"/usr/lib/python3.7/asyncio/base_events.py\", line 541, in run_forever\n      self._run_once()\n    File \"/usr/lib/python3.7/asyncio/base_events.py\", line 1786, in _run_once\n      handle._run()\n    File \"/usr/lib/python3.7/asyncio/events.py\", line 88, in _run\n      self._context.run(self._callback, *self._args)\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/ioloop.py\", line 758, in _run_callback\n      ret = callback()\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py\", line 300, in null_wrapper\n      return fn(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/gen.py\", line 1233, in inner\n      self.run()\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/gen.py\", line 1147, in run\n      yielded = self.gen.send(value)\n    File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\", line 365, in process_one\n      yield gen.maybe_future(dispatch(*args))\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/gen.py\", line 326, in wrapper\n      yielded = next(result)\n    File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\", line 268, in dispatch_shell\n      yield gen.maybe_future(handler(stream, idents, msg))\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/gen.py\", line 326, in wrapper\n      yielded = next(result)\n    File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\", line 545, in execute_request\n      user_expressions, allow_stdin,\n    File \"/usr/local/lib/python3.7/dist-packages/tornado/gen.py\", line 326, in wrapper\n      yielded = next(result)\n    File \"/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py\", line 306, in do_execute\n      res = shell.run_cell(code, store_history=store_history, silent=silent)\n    File \"/usr/local/lib/python3.7/dist-packages/ipykernel/zmqshell.py\", line 536, in run_cell\n      return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n    File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 2855, in run_cell\n      raw_cell, store_history, silent, shell_futures)\n    File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 2881, in _run_cell\n      return runner(coro)\n    File \"/usr/local/lib/python3.7/dist-packages/IPython/core/async_helpers.py\", line 68, in _pseudo_sync_runner\n      coro.send(None)\n    File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 3058, in run_cell_async\n      interactivity=interactivity, compiler=compiler, result=result)\n    File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 3249, in run_ast_nodes\n      if (await self.run_code(code, result,  async_=asy)):\n    File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 3326, in run_code\n      exec(code_obj, self.user_global_ns, self.user_ns)\n    File \"<ipython-input-35-9a1b27a13f19>\", line 15, in <module>\n      use_augmentation= augmentation)\n    File \"/usr/local/lib/python3.7/dist-packages/tensorflow_examples/lite/model_maker/core/task/image_classifier.py\", line 339, in create\n      image_classifier.train(train_data, validation_data, steps_per_epoch)\n    File \"/usr/local/lib/python3.7/dist-packages/tensorflow_examples/lite/model_maker/core/task/image_classifier.py\", line 160, in train\n      self.create_model()\n    File \"/usr/local/lib/python3.7/dist-packages/tensorflow_examples/lite/model_maker/core/task/image_classifier.py\", line 130, in create_model\n      self.model_spec.uri, trainable=hparams.do_fine_tuning)\n    File \"/usr/local/lib/python3.7/dist-packages/tensorflow_hub/keras_layer.py\", line 153, in __init__\n      self._func = load_module(handle, tags, self._load_options)\n    File \"/usr/local/lib/python3.7/dist-packages/tensorflow_hub/keras_layer.py\", line 449, in load_module\n      return module_v2.load(handle, tags=tags, options=set_load_options)\n    File \"/usr/local/lib/python3.7/dist-packages/tensorflow_hub/module_v2.py\", line 106, in load\n      obj = tf.compat.v1.saved_model.load_v2(module_path, tags=tags)\nNode: 'efficientnet-lite0/model/stem/conv2d/Conv2D'\nDNN library is not found.\n\t [[{{node efficientnet-lite0/model/stem/conv2d/Conv2D}}]] [Op:__inference_train_function_11113]"
          ]
        }
      ],
      "source": [
        "# efficientnet_lite0'=\n",
        "num_epochs = 500\n",
        "learning_rate = 0.0001\n",
        "batch_size = 4\n",
        "shuffle = True\n",
        "augmentation = False\n",
        "\n",
        "model = image_classifier.create(train_data,\n",
        "                                validation_data=validation_data,\n",
        "                                model_spec=model_spec.get('efficientnet_lite0'), \n",
        "                                epochs =num_epochs, \n",
        "                                learning_rate= learning_rate,\n",
        "                                batch_size = batch_size, \n",
        "                                shuffle= shuffle, \n",
        "                                use_augmentation= augmentation)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-vL-_z25i-x1"
      },
      "outputs": [],
      "source": [
        "snum_epochs = 500\n",
        "learning_rate = 0.0001 #0.0001\n",
        "batch_size = 4\n",
        "shuffle = False #False\n",
        "augmentation = False\n",
        "\n",
        "model = image_classifier.create(train_data,\n",
        "                                validation_data=validation_data,\n",
        "                                model_spec=model_spec.get('efficientnet_lite2'), \n",
        "                                epochs = num_epochs, \n",
        "                                learning_rate = learning_rate,\n",
        "                                batch_size = batch_size, \n",
        "                                shuffle = shuffle, \n",
        "                                use_augmentation= augmentation)\n",
        "\n",
        "\n",
        "#77ms/step - loss: 0.3032 - accuracy: 1.0000 - val_loss: 0.3932 - val_accuracy: 0.9545"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "c6zhIwnseAu1",
        "outputId": "3f8eb3ef-5fc3-487b-eb57-7e96157e4473"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " hub_keras_layer_v1v2 (HubKe  (None, 1280)             6992768   \n",
            " rasLayerV1V2)                                                   \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 1280)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 4)                 5124      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 6,997,892\n",
            "Trainable params: 5,124\n",
            "Non-trainable params: 6,992,768\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/85\n",
            "1551/1551 [==============================] - 117s 68ms/step - loss: 0.8327 - accuracy: 0.7645 - val_loss: 0.6362 - val_accuracy: 0.8737\n",
            "Epoch 2/85\n",
            "1551/1551 [==============================] - 106s 68ms/step - loss: 0.6455 - accuracy: 0.8680 - val_loss: 0.5879 - val_accuracy: 0.8982\n",
            "Epoch 3/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.6044 - accuracy: 0.8828 - val_loss: 0.5693 - val_accuracy: 0.9072\n",
            "Epoch 4/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.5887 - accuracy: 0.8915 - val_loss: 0.5588 - val_accuracy: 0.9098\n",
            "Epoch 5/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.5754 - accuracy: 0.8981 - val_loss: 0.5513 - val_accuracy: 0.9162\n",
            "Epoch 6/85\n",
            "1551/1551 [==============================] - 105s 68ms/step - loss: 0.5688 - accuracy: 0.9007 - val_loss: 0.5450 - val_accuracy: 0.9188\n",
            "Epoch 7/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.5562 - accuracy: 0.9065 - val_loss: 0.5409 - val_accuracy: 0.9201\n",
            "Epoch 8/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.5526 - accuracy: 0.9091 - val_loss: 0.5372 - val_accuracy: 0.9240\n",
            "Epoch 9/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.5460 - accuracy: 0.9110 - val_loss: 0.5340 - val_accuracy: 0.9253\n",
            "Epoch 10/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.5439 - accuracy: 0.9139 - val_loss: 0.5300 - val_accuracy: 0.9291\n",
            "Epoch 11/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.5416 - accuracy: 0.9180 - val_loss: 0.5287 - val_accuracy: 0.9265\n",
            "Epoch 12/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.5361 - accuracy: 0.9168 - val_loss: 0.5272 - val_accuracy: 0.9330\n",
            "Epoch 13/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.5320 - accuracy: 0.9199 - val_loss: 0.5250 - val_accuracy: 0.9317\n",
            "Epoch 14/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.5304 - accuracy: 0.9249 - val_loss: 0.5237 - val_accuracy: 0.9330\n",
            "Epoch 15/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.5294 - accuracy: 0.9230 - val_loss: 0.5221 - val_accuracy: 0.9330\n",
            "Epoch 16/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.5275 - accuracy: 0.9246 - val_loss: 0.5208 - val_accuracy: 0.9356\n",
            "Epoch 17/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.5247 - accuracy: 0.9230 - val_loss: 0.5199 - val_accuracy: 0.9330\n",
            "Epoch 18/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.5223 - accuracy: 0.9249 - val_loss: 0.5189 - val_accuracy: 0.9343\n",
            "Epoch 19/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.5204 - accuracy: 0.9263 - val_loss: 0.5180 - val_accuracy: 0.9330\n",
            "Epoch 20/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.5208 - accuracy: 0.9265 - val_loss: 0.5175 - val_accuracy: 0.9356\n",
            "Epoch 21/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.5152 - accuracy: 0.9313 - val_loss: 0.5167 - val_accuracy: 0.9330\n",
            "Epoch 22/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.5179 - accuracy: 0.9270 - val_loss: 0.5164 - val_accuracy: 0.9343\n",
            "Epoch 23/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.5174 - accuracy: 0.9254 - val_loss: 0.5147 - val_accuracy: 0.9343\n",
            "Epoch 24/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.5138 - accuracy: 0.9297 - val_loss: 0.5136 - val_accuracy: 0.9343\n",
            "Epoch 25/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.5119 - accuracy: 0.9313 - val_loss: 0.5129 - val_accuracy: 0.9381\n",
            "Epoch 26/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.5110 - accuracy: 0.9338 - val_loss: 0.5122 - val_accuracy: 0.9381\n",
            "Epoch 27/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.5123 - accuracy: 0.9328 - val_loss: 0.5120 - val_accuracy: 0.9369\n",
            "Epoch 28/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.5101 - accuracy: 0.9342 - val_loss: 0.5116 - val_accuracy: 0.9369\n",
            "Epoch 29/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.5128 - accuracy: 0.9331 - val_loss: 0.5120 - val_accuracy: 0.9343\n",
            "Epoch 30/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.5074 - accuracy: 0.9355 - val_loss: 0.5111 - val_accuracy: 0.9356\n",
            "Epoch 31/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.5072 - accuracy: 0.9342 - val_loss: 0.5104 - val_accuracy: 0.9356\n",
            "Epoch 32/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.5066 - accuracy: 0.9355 - val_loss: 0.5104 - val_accuracy: 0.9343\n",
            "Epoch 33/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.5048 - accuracy: 0.9355 - val_loss: 0.5096 - val_accuracy: 0.9356\n",
            "Epoch 34/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.5037 - accuracy: 0.9396 - val_loss: 0.5095 - val_accuracy: 0.9356\n",
            "Epoch 35/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.5023 - accuracy: 0.9405 - val_loss: 0.5099 - val_accuracy: 0.9343\n",
            "Epoch 36/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.5053 - accuracy: 0.9365 - val_loss: 0.5099 - val_accuracy: 0.9356\n",
            "Epoch 37/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.5049 - accuracy: 0.9346 - val_loss: 0.5088 - val_accuracy: 0.9343\n",
            "Epoch 38/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.5036 - accuracy: 0.9367 - val_loss: 0.5088 - val_accuracy: 0.9356\n",
            "Epoch 39/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.5014 - accuracy: 0.9386 - val_loss: 0.5081 - val_accuracy: 0.9369\n",
            "Epoch 40/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.5011 - accuracy: 0.9389 - val_loss: 0.5075 - val_accuracy: 0.9369\n",
            "Epoch 41/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.5000 - accuracy: 0.9400 - val_loss: 0.5073 - val_accuracy: 0.9356\n",
            "Epoch 42/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.5003 - accuracy: 0.9391 - val_loss: 0.5078 - val_accuracy: 0.9369\n",
            "Epoch 43/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.4990 - accuracy: 0.9426 - val_loss: 0.5081 - val_accuracy: 0.9369\n",
            "Epoch 44/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.5016 - accuracy: 0.9371 - val_loss: 0.5069 - val_accuracy: 0.9356\n",
            "Epoch 45/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.4986 - accuracy: 0.9387 - val_loss: 0.5067 - val_accuracy: 0.9356\n",
            "Epoch 46/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.4977 - accuracy: 0.9415 - val_loss: 0.5061 - val_accuracy: 0.9356\n",
            "Epoch 47/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.4979 - accuracy: 0.9378 - val_loss: 0.5061 - val_accuracy: 0.9356\n",
            "Epoch 48/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.5000 - accuracy: 0.9392 - val_loss: 0.5056 - val_accuracy: 0.9369\n",
            "Epoch 49/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.4951 - accuracy: 0.9439 - val_loss: 0.5049 - val_accuracy: 0.9356\n",
            "Epoch 50/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.4977 - accuracy: 0.9392 - val_loss: 0.5049 - val_accuracy: 0.9356\n",
            "Epoch 51/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.4935 - accuracy: 0.9421 - val_loss: 0.5051 - val_accuracy: 0.9356\n",
            "Epoch 52/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.4972 - accuracy: 0.9396 - val_loss: 0.5055 - val_accuracy: 0.9369\n",
            "Epoch 53/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.4948 - accuracy: 0.9429 - val_loss: 0.5044 - val_accuracy: 0.9369\n",
            "Epoch 54/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.4955 - accuracy: 0.9417 - val_loss: 0.5050 - val_accuracy: 0.9369\n",
            "Epoch 55/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.4952 - accuracy: 0.9444 - val_loss: 0.5048 - val_accuracy: 0.9369\n",
            "Epoch 56/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.4939 - accuracy: 0.9436 - val_loss: 0.5043 - val_accuracy: 0.9369\n",
            "Epoch 57/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.4929 - accuracy: 0.9458 - val_loss: 0.5036 - val_accuracy: 0.9369\n",
            "Epoch 58/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.4963 - accuracy: 0.9423 - val_loss: 0.5038 - val_accuracy: 0.9369\n",
            "Epoch 59/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.4949 - accuracy: 0.9428 - val_loss: 0.5036 - val_accuracy: 0.9381\n",
            "Epoch 60/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.4916 - accuracy: 0.9460 - val_loss: 0.5033 - val_accuracy: 0.9381\n",
            "Epoch 61/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.4929 - accuracy: 0.9433 - val_loss: 0.5025 - val_accuracy: 0.9381\n",
            "Epoch 62/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.4920 - accuracy: 0.9442 - val_loss: 0.5031 - val_accuracy: 0.9381\n",
            "Epoch 63/85\n",
            "1551/1551 [==============================] - 103s 67ms/step - loss: 0.4931 - accuracy: 0.9458 - val_loss: 0.5044 - val_accuracy: 0.9381\n",
            "Epoch 64/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.4910 - accuracy: 0.9436 - val_loss: 0.5042 - val_accuracy: 0.9381\n",
            "Epoch 65/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.4915 - accuracy: 0.9431 - val_loss: 0.5034 - val_accuracy: 0.9369\n",
            "Epoch 66/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.4917 - accuracy: 0.9468 - val_loss: 0.5034 - val_accuracy: 0.9381\n",
            "Epoch 67/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.4893 - accuracy: 0.9466 - val_loss: 0.5036 - val_accuracy: 0.9381\n",
            "Epoch 68/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.4900 - accuracy: 0.9458 - val_loss: 0.5028 - val_accuracy: 0.9381\n",
            "Epoch 69/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.4887 - accuracy: 0.9468 - val_loss: 0.5026 - val_accuracy: 0.9394\n",
            "Epoch 70/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.4879 - accuracy: 0.9476 - val_loss: 0.5028 - val_accuracy: 0.9381\n",
            "Epoch 71/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.4894 - accuracy: 0.9452 - val_loss: 0.5032 - val_accuracy: 0.9369\n",
            "Epoch 72/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.4883 - accuracy: 0.9468 - val_loss: 0.5027 - val_accuracy: 0.9394\n",
            "Epoch 73/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.4917 - accuracy: 0.9428 - val_loss: 0.5027 - val_accuracy: 0.9381\n",
            "Epoch 74/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.4880 - accuracy: 0.9487 - val_loss: 0.5019 - val_accuracy: 0.9394\n",
            "Epoch 75/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.4902 - accuracy: 0.9476 - val_loss: 0.5024 - val_accuracy: 0.9369\n",
            "Epoch 76/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.4900 - accuracy: 0.9460 - val_loss: 0.5018 - val_accuracy: 0.9381\n",
            "Epoch 77/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.4885 - accuracy: 0.9483 - val_loss: 0.5019 - val_accuracy: 0.9394\n",
            "Epoch 78/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.4887 - accuracy: 0.9452 - val_loss: 0.5010 - val_accuracy: 0.9394\n",
            "Epoch 79/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.4891 - accuracy: 0.9462 - val_loss: 0.5019 - val_accuracy: 0.9381\n",
            "Epoch 80/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.4868 - accuracy: 0.9491 - val_loss: 0.5020 - val_accuracy: 0.9369\n",
            "Epoch 81/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.4895 - accuracy: 0.9449 - val_loss: 0.5017 - val_accuracy: 0.9381\n",
            "Epoch 82/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.4858 - accuracy: 0.9492 - val_loss: 0.5005 - val_accuracy: 0.9381\n",
            "Epoch 83/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.4868 - accuracy: 0.9516 - val_loss: 0.5017 - val_accuracy: 0.9394\n",
            "Epoch 84/85\n",
            "1551/1551 [==============================] - 103s 66ms/step - loss: 0.4861 - accuracy: 0.9492 - val_loss: 0.5020 - val_accuracy: 0.9394\n",
            "Epoch 85/85\n",
            "1551/1551 [==============================] - 104s 67ms/step - loss: 0.4873 - accuracy: 0.9487 - val_loss: 0.5015 - val_accuracy: 0.9381\n"
          ]
        }
      ],
      "source": [
        "num_epochs = 85\n",
        "learning_rate = 0.0001 #0.0001\n",
        "batch_size = 4\n",
        "shuffle = True #True\n",
        "augmentation = False\n",
        "\n",
        "model = image_classifier.create(train_data,\n",
        "                                validation_data=validation_data,\n",
        "                                model_spec=model_spec.get('efficientnet_lite3'), \n",
        "                                epochs = num_epochs, \n",
        "                                learning_rate = learning_rate,\n",
        "                                batch_size = batch_size, \n",
        "                                shuffle = shuffle, \n",
        "                                use_augmentation= augmentation)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m2aKyiG9uLx3"
      },
      "outputs": [],
      "source": [
        "# efficientnet_lite4' NO BUENO\n",
        "\n",
        "num_epochs = 500\n",
        "learning_rate = 0.0001\n",
        "batch_size = 4\n",
        "shuffle = True\n",
        "augmentation = True\n",
        "\n",
        "model = image_classifier.create(train_data,\n",
        "                                validation_data=validation_data,\n",
        "                                model_spec=model_spec.get('efficientnet_lite4'), \n",
        "                                epochs = num_epochs, \n",
        "                                learning_rate = learning_rate,\n",
        "                                batch_size = batch_size, \n",
        "                                shuffle = shuffle, \n",
        "                                use_augmentation= augmentation)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GAxnTY1qyDuo"
      },
      "source": [
        "# Ploting loss, val, accuracy, & val loss"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ox4R-BgWySfN"
      },
      "source": [
        "## Setting variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "EUG8KaK9r2of"
      },
      "outputs": [],
      "source": [
        "acc = model.history.history['accuracy']\n",
        "val_acc = model.history.history['val_accuracy']\n",
        "loss = model.history.history['loss']\n",
        "val_loss = model.history.history['val_loss']\n",
        "epochs_range = range(num_epochs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "peClktfVyUlT"
      },
      "source": [
        "## Plotting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "s1NtINJ4tahl",
        "outputId": "74614cb1-d6d5-4df8-e53b-20609c679f73"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(20, 10)) # width, height\n",
        "plt.subplot(1, 2, 1) #rows, columns\n",
        "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs_range, loss, label='Training Loss')\n",
        "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "lPPlhR5jYKs2",
        "outputId": "090ee0ed-6692-4e45-e37e-e88bf2d444c2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " hub_keras_layer_v1v2 (HubKe  (None, 1280)             6992768   \n",
            " rasLayerV1V2)                                                   \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 1280)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 4)                 5124      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 6,997,892\n",
            "Trainable params: 5,124\n",
            "Non-trainable params: 6,992,768\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mvEzdOsx1DmU"
      },
      "source": [
        "# Exporting trained model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rS6ALCgL1DBM"
      },
      "outputs": [],
      "source": [
        "newpath = r'/content/gdrive/MyDrive/SCI_Model/' \n",
        "if not os.path.exists(newpath):\n",
        "    os.makedirs(newpath)\n",
        "\n",
        "model.export(export_dir=\"/content/gdrive/MyDrive/SCI_Model/\")"
      ]
    }
  ],
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