ResNet50AndVGG16.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "0YBEz670Nnmv"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import PIL\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.python.keras.layers import Dense, Flatten\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.optimizers import Adam"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PHnTPoBoa7Mu"
      },
      "source": [
        "import pathlib\n",
        "dataset_url = \"https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz\"\n",
        "data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)\n",
        "data_dir = pathlib.Path(data_dir)"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Divide dataset into training and testing"
      ],
      "metadata": {
        "id": "Ty-36AJWDKXx"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P68Vb_6lcqKG",
        "outputId": "88a875f1-eb76-4f48-f9c2-7540ce0dab33"
      },
      "source": [
        "batch_size=32\n",
        "train_dataset = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "  data_dir,\n",
        "  validation_split=0.2,\n",
        "  subset=\"training\",\n",
        "  seed=123,\n",
        "  image_size=(165,165),\n",
        "  batch_size=batch_size)\n"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 3670 files belonging to 5 classes.\n",
            "Using 2936 files for training.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VA-1Jqlscs-Y",
        "outputId": "fe4b2072-e802-4540-f741-55578f37e76c"
      },
      "source": [
        "testing_dataset = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "  data_dir,\n",
        "  validation_split=0.2,\n",
        "  subset=\"validation\",\n",
        "  seed=123,\n",
        "  image_size=(165,165),\n",
        "  batch_size=batch_size)\n"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 3670 files belonging to 5 classes.\n",
            "Using 734 files for validation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Number of possible labels"
      ],
      "metadata": {
        "id": "s-xxLedmDRrw"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ytv1zV7UcvPY",
        "outputId": "250edecf-a6db-4413-c3a4-2e128df12f8e"
      },
      "source": [
        "class_names = train_dataset.class_names\n",
        "print(class_names)"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jK5tHnEpggbu"
      },
      "source": [
        "# Training The ResNet50 Model and another CNN model\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Resnet50"
      ],
      "metadata": {
        "id": "E6KH6H3nIATD"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0AzdT8-Ya_lO"
      },
      "source": [
        "resnet50_model = Sequential()\n",
        "\n",
        "pretrained_model= tf.keras.applications.ResNet50(include_top=False,\n",
        "                   input_shape=(165,165,3),\n",
        "                   pooling='avg',classes=5,\n",
        "                   weights='imagenet')\n",
        "for layer in pretrained_model.layers:\n",
        "        layer.trainable=False\n",
        "\n",
        "resnet50_model.add(pretrained_model)\n",
        "resnet50_model.add(Flatten())\n",
        "resnet50_model.add(Dense(512, activation='relu'))\n",
        "resnet50_model.add(Dense(5, activation='softmax'))"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zAgSK9fAbp6y",
        "outputId": "91867c5d-04b8-469b-f311-3e38ed2a58ac"
      },
      "source": [
        "resnet50_model.summary()"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " resnet50 (Functional)       (None, 2048)              23587712  \n",
            "                                                                 \n",
            " module_wrapper_12 (ModuleWr  (None, 2048)             0         \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " module_wrapper_13 (ModuleWr  (None, 512)              1049088   \n",
            " apper)                                                          \n",
            "                                                                 \n",
            " module_wrapper_14 (ModuleWr  (None, 5)                2565      \n",
            " apper)                                                          \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 24,639,365\n",
            "Trainable params: 1,051,653\n",
            "Non-trainable params: 23,587,712\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vPCJsk68dm40",
        "outputId": "b064ba09-2dd0-4d8d-c7c4-90be4532813c"
      },
      "source": [
        "resnet50_model.compile(optimizer=Adam(lr=0.001),loss='sparse_categorical_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:`lr` is deprecated, please use `learning_rate` instead, or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "wXEYsGTaeFi4",
        "outputId": "51fbb954-10a4-494f-f5d5-3b243b3c6427"
      },
      "source": [
        "history_resnet50 = resnet50_model.fit(train_dataset, validation_data=testing_dataset,epochs=10)\n"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "92/92 [==============================] - 14s 106ms/step - loss: 0.7479 - accuracy: 0.7585 - val_loss: 0.4413 - val_accuracy: 0.8447\n",
            "Epoch 2/10\n",
            "71/92 [======================>.......] - ETA: 1s - loss: 0.3125 - accuracy: 0.8829"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-89-c44d858220b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistory_resnet50\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresnet50_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtesting_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.8/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     63\u001b[0m         \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     66\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;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\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/usr/local/lib/python3.8/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1648\u001b[0m                         ):\n\u001b[1;32m   1649\u001b[0m                             \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1650\u001b[0;31m                             \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\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   1651\u001b[0m                             \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1652\u001b[0m                                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\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/python3.8/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    151\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;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\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/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    878\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    879\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\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--> 880\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\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    881\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    882\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\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/python3.8/dist-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    910\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    911\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 912\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_no_variable_creation_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    913\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable_creation_fn\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[1;32m    914\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\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/python3.8/dist-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    132\u001b[0m       (concrete_function,\n\u001b[1;32m    133\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m--> 134\u001b[0;31m     return concrete_function._call_flat(\n\u001b[0m\u001b[1;32m    135\u001b[0m         filtered_flat_args, captured_inputs=concrete_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[1;32m    136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1743\u001b[0m         and executing_eagerly):\n\u001b[1;32m   1744\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1745\u001b[0;31m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m   1746\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m   1747\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    376\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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    377\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\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;32m--> 378\u001b[0;31m           outputs = execute.execute(\n\u001b[0m\u001b[1;32m    379\u001b[0m               \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\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    380\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\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/python3.8/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     50\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\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[0;32m---> 52\u001b[0;31m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m     53\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[1;32m     54\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[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "VGG 16 Model"
      ],
      "metadata": {
        "id": "Lc6-Zy-UICnf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vgg16_model = Sequential()\n",
        "\n",
        "pretrained_model= tf.keras.applications.VGG19(include_top=False,\n",
        "                   input_shape=(165,165,3),\n",
        "                   pooling='avg',classes=5,\n",
        "                   weights='imagenet')\n",
        "for layer in pretrained_model.layers:\n",
        "        layer.trainable=False\n",
        "\n",
        "vgg16_model.add(pretrained_model)\n",
        "vgg16_model.add(Flatten())\n",
        "vgg16_model.add(Dense(512, activation='relu'))\n",
        "vgg16_model.add(Dense(5, activation='softmax'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QfhfSfZSH-TN",
        "outputId": "75dcb8f7-11d9-4a91-b341-a2b3a74df8dc"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "80134624/80134624 [==============================] - 5s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vgg16_model.summary()"
      ],
      "metadata": {
        "id": "-EBkghLbIdIp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vgg16_model.compile(optimizer=Adam(learning_rate=0.001), loss='sparse_categorical_crossentropy',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "bhqM6OUaIfGK"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history_vgg16 = vgg16_model.fit(train_dataset, validation_data=testing_dataset,epochs=10)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1KJbhmLnJIyP",
        "outputId": "58d78575-7e7c-465a-90c8-9cbcac58b995"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "92/92 [==============================] - 19s 169ms/step - loss: 1.4097 - accuracy: 0.7234 - val_loss: 0.9543 - val_accuracy: 0.7861\n",
            "Epoch 2/10\n",
            "92/92 [==============================] - 12s 129ms/step - loss: 0.4129 - accuracy: 0.8733 - val_loss: 0.6633 - val_accuracy: 0.8256\n",
            "Epoch 3/10\n",
            "92/92 [==============================] - 13s 131ms/step - loss: 0.1881 - accuracy: 0.9319 - val_loss: 0.6143 - val_accuracy: 0.8406\n",
            "Epoch 4/10\n",
            "92/92 [==============================] - 12s 129ms/step - loss: 0.0789 - accuracy: 0.9789 - val_loss: 0.6406 - val_accuracy: 0.8365\n",
            "Epoch 5/10\n",
            "92/92 [==============================] - 12s 127ms/step - loss: 0.0400 - accuracy: 0.9905 - val_loss: 0.6514 - val_accuracy: 0.8311\n",
            "Epoch 6/10\n",
            "92/92 [==============================] - 12s 128ms/step - loss: 0.0204 - accuracy: 0.9980 - val_loss: 0.6672 - val_accuracy: 0.8324\n",
            "Epoch 7/10\n",
            "92/92 [==============================] - 12s 126ms/step - loss: 0.0108 - accuracy: 1.0000 - val_loss: 0.6866 - val_accuracy: 0.8297\n",
            "Epoch 8/10\n",
            "92/92 [==============================] - 12s 126ms/step - loss: 0.0071 - accuracy: 1.0000 - val_loss: 0.6994 - val_accuracy: 0.8379\n",
            "Epoch 9/10\n",
            "92/92 [==============================] - 12s 126ms/step - loss: 0.0056 - accuracy: 1.0000 - val_loss: 0.7057 - val_accuracy: 0.8365\n",
            "Epoch 10/10\n",
            "92/92 [==============================] - 14s 153ms/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 0.7158 - val_accuracy: 0.8379\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ggs5y5NcgcAH"
      },
      "source": [
        "# Evaluating The Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "vAjGo1SvePCE",
        "outputId": "a38dc051-3cd2-41cf-92cb-71ebc892d08a"
      },
      "source": [
        "plt.plot(history_resnet50.history['accuracy'])\n",
        "plt.plot(history_resnet50.history['val_accuracy'])\n",
        "plt.axis(ymin=0.4,ymax=1)\n",
        "plt.grid()\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.legend(['train', 'validation'])\n",
        "plt.show()"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history_vgg16.history['accuracy'])\n",
        "plt.plot(history_vgg16.history['val_accuracy'])\n",
        "plt.axis(ymin=0.4,ymax=1)\n",
        "plt.grid()\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.legend(['train', 'validation'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "onfqyRJIJz_t",
        "outputId": "a71e9b3a-2462-44f4-f931-3f06fda63219"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history_resnet50.history['loss'])\n",
        "plt.plot(history_resnet50.history['val_loss'])\n",
        "plt.grid()\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.legend(['train', 'validation'])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "oMeoU0_kKWBH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "rEUy8LNOeCVF",
        "outputId": "1516378c-22c2-42ba-9baa-7b8c1be2a488"
      },
      "source": [
        "plt.plot(history_vgg16.history['loss'])\n",
        "plt.plot(history_vgg16.history['val_loss'])\n",
        "plt.grid()\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.legend(['train', 'validation'])\n",
        "plt.show()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dE5MsVdegXVj"
      },
      "source": [
        "# Making Predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KCURoyOpemgA",
        "outputId": "1bb8add8-6460-4f48-efbf-654b4dd47efc"
      },
      "source": [
        "import cv2\n",
        "image=cv2.imread('sunflower.jpg')\n",
        "image_resized= cv2.resize(image, (200,200))\n",
        "image=np.expand_dims(image_resized,axis=0)\n",
        "print(image.shape)\n"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 200, 200, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G1pSzemJe19G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc268b26-c840-40ff-97ad-2e6a82ee7d62"
      },
      "source": [
        "pred=resnet50_model.predict(image)\n",
        "print(pred)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 26ms/step\n",
            "[[4.6048468e-04 3.4056646e-07 2.6270334e-04 9.9927336e-01 3.1835705e-06]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iudnVH8yfpFH",
        "outputId": "0eedf5e3-c6ed-4df4-c14e-52ae399d80b9"
      },
      "source": [
        "output_class=class_names[np.argmax(pred)]\n",
        "print(\"The predicted class is\", output_class)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The predicted class is sunflowers\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SLbvPRHXgHGO"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}