Trash_Classification_CV_Neptune.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mohansameer1983/ComputerVisionProjects/blob/main/Trash_Classification_CV_Neptune.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eYtHaghPZIA3",
        "outputId": "034eafad-18e2-4dea-e0ce-826b66eb9b67"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "# Uncomment only for first run\n",
        "!pip install neptune-client neptune-tensorflow-keras tensorflow"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "hZj4c0xTeMwH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0cac6082-d2cc-45a0-d9f9-21e4b2b2f5b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TF version: 2.8.0\n",
            "Hub version: 0.12.0\n",
            "GPU is NOT AVAILABLE\n"
          ]
        }
      ],
      "source": [
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import datetime\n",
        "\n",
        "import neptune.new as neptune\n",
        "from neptune.new.integrations.tensorflow_keras import NeptuneCallback\n",
        "\n",
        "import cv2\n",
        "from google.colab.patches import cv2_imshow      # To show images using cv2 module\n",
        "\n",
        "import pathlib\n",
        "import os\n",
        "\n",
        "import tensorflow as tf\n",
        "import tensorflow.keras as keras\n",
        "from tensorflow.keras.layers import Dense, Input, Dropout\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "import tensorflow_hub as hub\n",
        "\n",
        "print(\"TF version:\", tf.__version__)\n",
        "print(\"Hub version:\", hub.__version__)\n",
        "print(\"GPU is\", \"available\" if tf.config.list_physical_devices('GPU') else \"NOT AVAILABLE\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mount Google drive so dataset can be accessed\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HcixSh2GcLys",
        "outputId": "c0340260-f16e-4144-dbfe-352e005baf55"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jI50JlncLBuf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa42e0b9-40ee-40fc-aeb7-a79deda2974e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "https://app.neptune.ai/mohansameer/Trash-Classification-Deep-Learning/e/TRAS-15\n",
            "Remember to stop your run once you’ve finished logging your metadata (https://docs.neptune.ai/api-reference/run#.stop). It will be stopped automatically only when the notebook kernel/interactive console is terminated.\n"
          ]
        }
      ],
      "source": [
        "run = neptune.init(\n",
        "    project=\"mohansameer/Trash-Classification-Deep-Learning\",\n",
        "    api_token=\"eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vYXBwLm5lcHR1bmUuYWkiLCJhcGlfdXJsIjoiaHR0cHM6Ly9hcHAubmVwdHVuZS5haSIsImFwaV9rZXkiOiI5ZjI5MDY4Yi0zMWVlLTQ3MmMtYjE4NS1iNDRjNGM0OTg4ZDkifQ==\",\n",
        ") "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QruPz2sHIoZC"
      },
      "source": [
        "# Model Selection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4LoocvGWInOZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25fd9efd-0f66-41c4-ee9f-2df9ebf68121"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected model: efficientnet_v2_50 : https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_b0/feature_vector/2\n",
            "Input size (224, 224)\n"
          ]
        }
      ],
      "source": [
        "  # Select base model  for transfer learning\n",
        "  model_name = \"efficientnet_v2_50\" # @param [ 'efficientnet_v2_50', 'resnet_v1_50', 'resnet_v1_101', 'resnet_v1_152', 'resnet_v2_50', 'resnet_v2_101', 'resnet_v2_152',  'mobilenet_v2_100_224', 'mobilenet_v2_130_224', 'mobilenet_v2_140_224', 'mobilenet_v3_small_100_224', 'mobilenet_v3_small_075_224', 'mobilenet_v3_large_100_224', 'mobilenet_v3_large_075_224']\n",
        "\n",
        "  model_handle_map = {\n",
        "    \"efficientnet_v2_50\": \"https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_b0/feature_vector/2\",   \n",
        "    \"resnet_v1_50\": \"https://tfhub.dev/google/imagenet/resnet_v1_50/feature-vector/4\",\n",
        "    \"resnet_v1_101\": \"https://tfhub.dev/google/imagenet/resnet_v1_101/feature-vector/4\",\n",
        "    \"resnet_v1_152\": \"https://tfhub.dev/google/imagenet/resnet_v1_152/feature-vector/4\",\n",
        "    \"resnet_v2_50\": \"https://tfhub.dev/google/imagenet/resnet_v2_50/feature-vector/4\",\n",
        "    \"resnet_v2_101\": \"https://tfhub.dev/google/imagenet/resnet_v2_101/feature_vector/5\",\n",
        "    \"resnet_v2_152\": \"https://tfhub.dev/google/imagenet/resnet_v2_152/feature-vector/4\",\n",
        "    \"nasnet_large\": \"https://tfhub.dev/google/imagenet/nasnet_large/feature_vector/4\",\n",
        "    \"nasnet_mobile\": \"https://tfhub.dev/google/imagenet/nasnet_mobile/feature_vector/4\",\n",
        "    \"pnasnet_large\": \"https://tfhub.dev/google/imagenet/pnasnet_large/feature_vector/4\",\n",
        "    \"mobilenet_v2_100_224\": \"https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4\",\n",
        "    \"mobilenet_v2_130_224\": \"https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/feature_vector/4\",\n",
        "    \"mobilenet_v2_140_224\": \"https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/feature_vector/4\",\n",
        "    \"mobilenet_v3_small_100_224\": \"https://tfhub.dev/google/imagenet/mobilenet_v3_small_100_224/feature_vector/5\",\n",
        "    \"mobilenet_v3_small_075_224\": \"https://tfhub.dev/google/imagenet/mobilenet_v3_small_075_224/feature_vector/5\",\n",
        "    \"mobilenet_v3_large_100_224\": \"https://tfhub.dev/google/imagenet/mobilenet_v3_large_100_224/feature_vector/5\",\n",
        "    \"mobilenet_v3_large_075_224\": \"https://tfhub.dev/google/imagenet/mobilenet_v3_large_075_224/feature_vector/5\",\n",
        "  }\n",
        "\n",
        "  PIXELS = 224#@param {type:\"integer\"}\n",
        "  BATCH_SIZE = 32#@param {type:\"integer\"}\n",
        "\n",
        "  model_handle = model_handle_map.get(model_name)\n",
        "\n",
        "  print(f\"Selected model: {model_name} : {model_handle}\")\n",
        "\n",
        "  IMAGE_SIZE = (PIXELS, PIXELS)\n",
        "  print(f\"Input size {IMAGE_SIZE}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gTV9nmxaLKci"
      },
      "outputs": [],
      "source": [
        "#Log to NeptuneAI\n",
        "run[\"model/transferlearning/model_name\"] = model_name\n",
        "run[\"model/transferlearning/model_url\"] = model_handle\n",
        "run[\"model/transferlearning/model_batch_size\"] = BATCH_SIZE\n",
        "run[\"model/parameters/image_size\"] = IMAGE_SIZE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jHvJJjnCRYF2"
      },
      "source": [
        "# Data Preparation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q3gYLKfEd0Hb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "192d8093-0cfa-4692-b595-1647662ddbfa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 288 images belonging to 28 classes.\n",
            "Found 1212 images belonging to 28 classes.\n"
          ]
        }
      ],
      "source": [
        "def load_data(save_dir=\"./\"):\n",
        "  data_dir = pathlib.Path('/content/drive/MyDrive/Colab Notebooks/data/trash') \n",
        "\n",
        "  datagen_kwargs = dict(rescale=1./255, validation_split=0.2)\n",
        "  dataflow_kwargs = dict(target_size=(PIXELS, PIXELS), \n",
        "                        batch_size=BATCH_SIZE,\n",
        "                        interpolation=\"nearest\")\n",
        "\n",
        "  valid_datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "      **datagen_kwargs)\n",
        "\n",
        "  valid_generator = valid_datagen.flow_from_directory(\n",
        "      data_dir, subset=\"validation\", \n",
        "      shuffle=False, **dataflow_kwargs)\n",
        "  \n",
        "  train_datagen = ImageDataGenerator(\n",
        "            horizontal_flip=True,\n",
        "            **datagen_kwargs)\n",
        "\n",
        "  train_generator = train_datagen.flow_from_directory(\n",
        "          data_dir, subset=\"training\",\n",
        "          class_mode='categorical',\n",
        "          **dataflow_kwargs)\n",
        "  return valid_generator, train_generator\n",
        "\n",
        "VAL_generator, TRAIN_generator = load_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-eWMH8Mi8ibt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80da031f-d04a-4f40-ed1c-2c1404dbf151"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_items([('Aluminium foil', 0), ('Battery', 1), ('Blister pack', 2), ('Bottle', 3), ('Bottle cap', 4), ('Broken glass', 5), ('Can', 6), ('Carton', 7), ('Cigarette', 8), ('Cup', 9), ('Food waste', 10), ('Glass jar', 11), ('Lid', 12), ('Other plastic', 13), ('Paper', 14), ('Paper bag', 15), ('Plastic bag & wrapper', 16), ('Plastic container', 17), ('Plastic glooves', 18), ('Plastic utensils', 19), ('Pop tab', 20), ('Rope & strings', 21), ('Scrap metal', 22), ('Shoe', 23), ('Squeezable tube', 24), ('Straw', 25), ('Styrofoam piece', 26), ('Unlabeled litter', 27)])\n"
          ]
        }
      ],
      "source": [
        "class_names = TRAIN_generator.class_indices.items()\n",
        "print(class_names)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i2USajN2MWjn"
      },
      "source": [
        "# Modelling\n",
        "\n",
        "## Build and Train Model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GZfFboF85rIe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a99c47db-7b4e-44c4-e5d0-2ba3c76628f5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Building model with https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_b0/feature_vector/2\n",
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " feature_extraction_layer (K  (None, 1280)             5919312   \n",
            " erasLayer)                                                      \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 128)               163968    \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 28)                3612      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 6,086,892\n",
            "Trainable params: 167,580\n",
            "Non-trainable params: 5,919,312\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "def build_model(trainable=False, params={}):\n",
        "   print(\"Building model with\", model_handle)\n",
        "   # Download the pretrained model and save it as a Keras layer\n",
        "   feature_extractor_layer = hub.KerasLayer(model_handle,\n",
        "                                           trainable=trainable, # freeze the underlying patterns\n",
        "                                           name='feature_extraction_layer',\n",
        "                                           input_shape=IMAGE_SIZE+(3,)) # define the input image shape\n",
        "                                           \n",
        "   model = keras.Sequential([\n",
        "        feature_extractor_layer,\n",
        "        keras.layers.Dense(params['dense_units_1'],activation=params['dense_layer_activation_1']),\n",
        "        keras.layers.Dropout(params['dropout']),\n",
        "        keras.layers.Dense(TRAIN_generator.num_classes,\n",
        "                              kernel_regularizer=tf.keras.regularizers.l2(params['regularizer_rate']))])\n",
        "   model.build((None,)+IMAGE_SIZE+(3,))\n",
        "   model.summary()\n",
        "   return model\n",
        "\n",
        "params = {\n",
        "    \"dense_units_1\": 128,\n",
        "    \"dense_layer_activation_1\": \"relu\",\n",
        "    \"regularizer_rate\":0.005,\n",
        "    \"dropout\": 0.1,\n",
        "    \"learning_rate\": 0.01,\n",
        "    \"n_epochs\": 20,\n",
        "}\n",
        "run[\"model/parameters\"] = params\n",
        "model = build_model(params=params)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z3EyvQbSzu5m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c27da822-1302-441b-f70c-29ae4cdbeb39"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(SGD, self).__init__(name, **kwargs)\n"
          ]
        }
      ],
      "source": [
        "def compile_model(model):\n",
        "  model.compile(\n",
        "    optimizer=tf.keras.optimizers.SGD(lr=params['learning_rate'], momentum=0.8), \n",
        "  loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True, label_smoothing=0.1),\n",
        "  metrics=['accuracy'])\n",
        "  return model\n",
        "\n",
        "model = compile_model(model)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Add Callbacks\n",
        "\n",
        "# Model Checkpoint\n",
        "tl_checkpoint_1 = ModelCheckpoint(filepath = '/content/drive/MyDrive/Colab Notebooks/Models/garbageclassification_model_checkpoint/resnet_best_weights.hdf5', save_best_only = True, verbose = 0)\n",
        "\n",
        "# Early Stopping Checkpoint\n",
        "EarlyStoppingCallback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=2)"
      ],
      "metadata": {
        "id": "31UbexrwVlb4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Nz8kT3f8zykl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e2fa90b-a218-4134-ce4d-a60339fb4663"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "37/37 [==============================] - 188s 5s/step - loss: 3.1644 - accuracy: 0.2119 - val_loss: 2.9804 - val_accuracy: 0.2431\n",
            "Epoch 2/20\n",
            "37/37 [==============================] - 177s 5s/step - loss: 2.8743 - accuracy: 0.2780 - val_loss: 2.9440 - val_accuracy: 0.2569\n",
            "Epoch 3/20\n",
            "37/37 [==============================] - 176s 5s/step - loss: 2.7599 - accuracy: 0.3153 - val_loss: 2.9206 - val_accuracy: 0.2604\n",
            "Epoch 4/20\n",
            "37/37 [==============================] - 177s 5s/step - loss: 2.6890 - accuracy: 0.3364 - val_loss: 2.9050 - val_accuracy: 0.2778\n",
            "Epoch 5/20\n",
            "37/37 [==============================] - 177s 5s/step - loss: 2.6007 - accuracy: 0.3661 - val_loss: 2.9188 - val_accuracy: 0.2639\n",
            "Epoch 6/20\n",
            "37/37 [==============================] - 177s 5s/step - loss: 2.5285 - accuracy: 0.3907 - val_loss: 2.9115 - val_accuracy: 0.2812\n"
          ]
        }
      ],
      "source": [
        "def train_model(model, num_epochs):\n",
        "\n",
        "    steps_per_epoch = TRAIN_generator.samples // TRAIN_generator.batch_size\n",
        "    validation_steps = VAL_generator.samples // VAL_generator.batch_size\n",
        "\n",
        "    history = model.fit(\n",
        "      TRAIN_generator,\n",
        "      epochs=num_epochs, steps_per_epoch=steps_per_epoch,\n",
        "      validation_data = VAL_generator,\n",
        "      validation_steps = validation_steps,\n",
        "      callbacks=[NeptuneCallback(run=run), EarlyStoppingCallback]).history\n",
        "    \n",
        "    return model, history\n",
        "\n",
        "model, history = train_model(model, num_epochs=params['n_epochs'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U9JAgWLwHQbQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566
        },
        "outputId": "af3955a0-522e-4ba8-8327-d9d62d0dd01c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f3e1524ad10>]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure()\n",
        "plt.ylabel(\"Loss (training and validation)\")\n",
        "plt.xlabel(\"Training Steps\")\n",
        "plt.ylim([0,2])\n",
        "plt.plot(history[\"loss\"])\n",
        "plt.plot(history[\"val_loss\"])\n",
        "\n",
        "plt.figure()\n",
        "plt.ylabel(\"Accuracy (training and validation)\")\n",
        "plt.xlabel(\"Training Steps\")\n",
        "plt.ylim([0,1])\n",
        "plt.plot(history[\"accuracy\"])\n",
        "plt.plot(history[\"val_accuracy\"])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate predictions\n",
        "model.load_weights('/content/drive/MyDrive/Colab Notebooks/Models/garbageclassification_model_checkpoint/resnet_best_weights.hdf5') # initialize the best trained weights\n",
        "preds = model.predict(test_ds)\n",
        "pred_classes = np.argmax(preds, axis = 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "6hs6Z-xgWuIo",
        "outputId": "657d8855-4999-495e-d1c2-bf6fed38538a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "OSError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-37-6442a4859453>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Generate predictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/Colab Notebooks/Models/garbageclassification_model_checkpoint/resnet_best_weights.hdf5'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# initialize the best trained weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_ds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mpred_classes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\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/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/h5py/_hl/files.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, mode, driver, libver, userblock_size, swmr, rdcc_nslots, rdcc_nbytes, rdcc_w0, track_order, fs_strategy, fs_persist, fs_threshold, **kwds)\u001b[0m\n\u001b[1;32m    425\u001b[0m                                fapl, fcpl=make_fcpl(track_order=track_order, fs_strategy=fs_strategy,\n\u001b[1;32m    426\u001b[0m                                fs_persist=fs_persist, fs_threshold=fs_threshold),\n\u001b[0;32m--> 427\u001b[0;31m                                swmr=swmr)\n\u001b[0m\u001b[1;32m    428\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlibver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\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[0;32m/usr/local/lib/python3.7/dist-packages/h5py/_hl/files.py\u001b[0m in \u001b[0;36mmake_fid\u001b[0;34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[0m\n\u001b[1;32m    188\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mswmr\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mswmr_support\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m             \u001b[0mflags\u001b[0m \u001b[0;34m|=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mACC_SWMR_READ\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 190\u001b[0;31m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfapl\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    191\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'r+'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mACC_RDWR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfapl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mh5py/h5f.pyx\u001b[0m in \u001b[0;36mh5py.h5f.open\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mOSError\u001b[0m: Unable to open file (unable to open file: name = '/content/drive/MyDrive/Colab Notebooks/Models/garbageclassification_model_checkpoint/resnet_best_weights.hdf5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tLyG7AqZH2Q2"
      },
      "source": [
        "# Save Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "32IylFqtH1MQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bae4b87a-8654-4674-e4d4-919ac1b31404"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as restored_function_body, restored_function_body, restored_function_body, restored_function_body, restored_function_body while saving (showing 5 of 334). These functions will not be directly callable after loading.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: /content/drive/MyDrive/Colab Notebooks/Models/saved_garbageclassification_model/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: /content/drive/MyDrive/Colab Notebooks/Models/saved_garbageclassification_model/assets\n"
          ]
        }
      ],
      "source": [
        "saved_model_path = \"/content/drive/MyDrive/Colab Notebooks/Models/saved_garbageclassification_model\"\n",
        "tf.saved_model.save(model, saved_model_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fine Tuning"
      ],
      "metadata": {
        "id": "u7PqyGFsgjyd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "CsAx0dOzgjOg"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Trash_Classification_CV_Neptune.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}