transfer_learning_with_inceptionV3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyM2k3rN8jPw5J5b/6OG03Up",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/devanshkasaudhan/ML_projects/blob/main/transfer_learning_with_inceptionV3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Youtube video link\n",
        "\n",
        "https://youtu.be/chQNuV9B-Rw?si=r-gGCHLVBxEWuoqG"
      ],
      "metadata": {
        "id": "7L0JhXy2VpB1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Downloading Kaggle dataset\n",
        "\n",
        "https://www.kaggle.com/datasets/noulam/tomato"
      ],
      "metadata": {
        "id": "QIK8fdnrV2AE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!mv kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "t61NUnF_CD9X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download noulam/tomato\n",
        "!unzip -q tomato.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oG0VQG3XDWPi",
        "outputId": "63559695-0df2-4b69-b15a-1ae97a154fcc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading tomato.zip to /content\n",
            "100% 742M/743M [00:22<00:00, 36.7MB/s]\n",
            "100% 743M/743M [00:22<00:00, 35.0MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R4cWrS09DhWL",
        "outputId": "b464b985-81c8-4dc6-ace1-07888b2896a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Oct 20 12:40:46 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   33C    P8    10W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing Libraries"
      ],
      "metadata": {
        "id": "POxSgrggWTPs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Input,Lambda, Dense, Flatten\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras.models import Sequential\n",
        "import numpy as np\n",
        "from glob import glob"
      ],
      "metadata": {
        "id": "8UFJ_oHcIQ5N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "IMAGE_SIZE =[224, 224]\n",
        "\n",
        "train_path ='/content/New Plant Diseases Dataset(Augmented)/train'\n",
        "valid_path ='/content/New Plant Diseases Dataset(Augmented)/valid'"
      ],
      "metadata": {
        "id": "KMLrJ_RwItvt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## creating an instance of the InceptionV3 model"
      ],
      "metadata": {
        "id": "vWuj9jRVWYKr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inception = InceptionV3(input_shape=IMAGE_SIZE+[3], weights='imagenet', include_top=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mmVxJbcDJxCt",
        "outputId": "d7a3b36b-7195-4702-a1dd-2a201e89b36f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "87910968/87910968 [==============================] - 3s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for layer in inception.layers:\n",
        "  layer.trainable=False"
      ],
      "metadata": {
        "id": "pYqIPJu7KJ7N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "folders = glob('/content/New Plant Diseases Dataset(Augmented)/train/*')\n",
        "folders"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sau61lC4Kb6Z",
        "outputId": "d5cbbf9e-81fa-4816-95c8-1e47aa8ade99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/New Plant Diseases Dataset(Augmented)/train/Tomato___Early_blight',\n",
              " '/content/New Plant Diseases Dataset(Augmented)/train/Tomato___Late_blight',\n",
              " '/content/New Plant Diseases Dataset(Augmented)/train/Tomato___healthy',\n",
              " '/content/New Plant Diseases Dataset(Augmented)/train/Tomato___Target_Spot',\n",
              " '/content/New Plant Diseases Dataset(Augmented)/train/Tomato___Spider_mites Two-spotted_spider_mite',\n",
              " '/content/New Plant Diseases Dataset(Augmented)/train/Tomato___Tomato_Yellow_Leaf_Curl_Virus',\n",
              " '/content/New Plant Diseases Dataset(Augmented)/train/Tomato___Tomato_mosaic_virus',\n",
              " '/content/New Plant Diseases Dataset(Augmented)/train/Tomato___Bacterial_spot',\n",
              " '/content/New Plant Diseases Dataset(Augmented)/train/Tomato___Septoria_leaf_spot',\n",
              " '/content/New Plant Diseases Dataset(Augmented)/train/Tomato___Leaf_Mold']"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = Flatten()(inception.output)"
      ],
      "metadata": {
        "id": "6K6o-nDUKvMx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Making Custum Output Layer"
      ],
      "metadata": {
        "id": "P1l2wl_EWi08"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prediction = Dense(len(folders), activation='softmax')(x)\n",
        "model = Model(inputs= inception.input, outputs=prediction)"
      ],
      "metadata": {
        "id": "Y5_v99lkK-tc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "klmRcpVpLg4A",
        "outputId": "f7ceb7f4-54bd-4a23-a799-17107f36de83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)        [(None, 224, 224, 3)]        0         []                            \n",
            "                                                                                                  \n",
            " conv2d (Conv2D)             (None, 111, 111, 32)         864       ['input_1[0][0]']             \n",
            "                                                                                                  \n",
            " batch_normalization (Batch  (None, 111, 111, 32)         96        ['conv2d[0][0]']              \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation (Activation)     (None, 111, 111, 32)         0         ['batch_normalization[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_1 (Conv2D)           (None, 109, 109, 32)         9216      ['activation[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_1 (Bat  (None, 109, 109, 32)         96        ['conv2d_1[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_1 (Activation)   (None, 109, 109, 32)         0         ['batch_normalization_1[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_2 (Conv2D)           (None, 109, 109, 64)         18432     ['activation_1[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_2 (Bat  (None, 109, 109, 64)         192       ['conv2d_2[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_2 (Activation)   (None, 109, 109, 64)         0         ['batch_normalization_2[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " max_pooling2d (MaxPooling2  (None, 54, 54, 64)           0         ['activation_2[0][0]']        \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2d_3 (Conv2D)           (None, 54, 54, 80)           5120      ['max_pooling2d[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_3 (Bat  (None, 54, 54, 80)           240       ['conv2d_3[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_3 (Activation)   (None, 54, 54, 80)           0         ['batch_normalization_3[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_4 (Conv2D)           (None, 52, 52, 192)          138240    ['activation_3[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_4 (Bat  (None, 52, 52, 192)          576       ['conv2d_4[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_4 (Activation)   (None, 52, 52, 192)          0         ['batch_normalization_4[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " max_pooling2d_1 (MaxPoolin  (None, 25, 25, 192)          0         ['activation_4[0][0]']        \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv2d_8 (Conv2D)           (None, 25, 25, 64)           12288     ['max_pooling2d_1[0][0]']     \n",
            "                                                                                                  \n",
            " batch_normalization_8 (Bat  (None, 25, 25, 64)           192       ['conv2d_8[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_8 (Activation)   (None, 25, 25, 64)           0         ['batch_normalization_8[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_6 (Conv2D)           (None, 25, 25, 48)           9216      ['max_pooling2d_1[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_9 (Conv2D)           (None, 25, 25, 96)           55296     ['activation_8[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_6 (Bat  (None, 25, 25, 48)           144       ['conv2d_6[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_9 (Bat  (None, 25, 25, 96)           288       ['conv2d_9[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_6 (Activation)   (None, 25, 25, 48)           0         ['batch_normalization_6[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " activation_9 (Activation)   (None, 25, 25, 96)           0         ['batch_normalization_9[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " average_pooling2d (Average  (None, 25, 25, 192)          0         ['max_pooling2d_1[0][0]']     \n",
            " Pooling2D)                                                                                       \n",
            "                                                                                                  \n",
            " conv2d_5 (Conv2D)           (None, 25, 25, 64)           12288     ['max_pooling2d_1[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_7 (Conv2D)           (None, 25, 25, 64)           76800     ['activation_6[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_10 (Conv2D)          (None, 25, 25, 96)           82944     ['activation_9[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_11 (Conv2D)          (None, 25, 25, 32)           6144      ['average_pooling2d[0][0]']   \n",
            "                                                                                                  \n",
            " batch_normalization_5 (Bat  (None, 25, 25, 64)           192       ['conv2d_5[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_7 (Bat  (None, 25, 25, 64)           192       ['conv2d_7[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_10 (Ba  (None, 25, 25, 96)           288       ['conv2d_10[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_11 (Ba  (None, 25, 25, 32)           96        ['conv2d_11[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_5 (Activation)   (None, 25, 25, 64)           0         ['batch_normalization_5[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " activation_7 (Activation)   (None, 25, 25, 64)           0         ['batch_normalization_7[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " activation_10 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_10[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_11 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_11[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed0 (Concatenate)        (None, 25, 25, 256)          0         ['activation_5[0][0]',        \n",
            "                                                                     'activation_7[0][0]',        \n",
            "                                                                     'activation_10[0][0]',       \n",
            "                                                                     'activation_11[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_15 (Conv2D)          (None, 25, 25, 64)           16384     ['mixed0[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_15 (Ba  (None, 25, 25, 64)           192       ['conv2d_15[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_15 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_15[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_13 (Conv2D)          (None, 25, 25, 48)           12288     ['mixed0[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_16 (Conv2D)          (None, 25, 25, 96)           55296     ['activation_15[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_13 (Ba  (None, 25, 25, 48)           144       ['conv2d_13[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_16 (Ba  (None, 25, 25, 96)           288       ['conv2d_16[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_13 (Activation)  (None, 25, 25, 48)           0         ['batch_normalization_13[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_16 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_16[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_1 (Avera  (None, 25, 25, 256)          0         ['mixed0[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_12 (Conv2D)          (None, 25, 25, 64)           16384     ['mixed0[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_14 (Conv2D)          (None, 25, 25, 64)           76800     ['activation_13[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_17 (Conv2D)          (None, 25, 25, 96)           82944     ['activation_16[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_18 (Conv2D)          (None, 25, 25, 64)           16384     ['average_pooling2d_1[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_12 (Ba  (None, 25, 25, 64)           192       ['conv2d_12[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_14 (Ba  (None, 25, 25, 64)           192       ['conv2d_14[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_17 (Ba  (None, 25, 25, 96)           288       ['conv2d_17[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_18 (Ba  (None, 25, 25, 64)           192       ['conv2d_18[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_12 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_12[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_14 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_14[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_17 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_17[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_18 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_18[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed1 (Concatenate)        (None, 25, 25, 288)          0         ['activation_12[0][0]',       \n",
            "                                                                     'activation_14[0][0]',       \n",
            "                                                                     'activation_17[0][0]',       \n",
            "                                                                     'activation_18[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_22 (Conv2D)          (None, 25, 25, 64)           18432     ['mixed1[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_22 (Ba  (None, 25, 25, 64)           192       ['conv2d_22[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_22 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_22[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_20 (Conv2D)          (None, 25, 25, 48)           13824     ['mixed1[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_23 (Conv2D)          (None, 25, 25, 96)           55296     ['activation_22[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_20 (Ba  (None, 25, 25, 48)           144       ['conv2d_20[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_23 (Ba  (None, 25, 25, 96)           288       ['conv2d_23[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_20 (Activation)  (None, 25, 25, 48)           0         ['batch_normalization_20[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_23 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_23[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_2 (Avera  (None, 25, 25, 288)          0         ['mixed1[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_19 (Conv2D)          (None, 25, 25, 64)           18432     ['mixed1[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_21 (Conv2D)          (None, 25, 25, 64)           76800     ['activation_20[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_24 (Conv2D)          (None, 25, 25, 96)           82944     ['activation_23[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_25 (Conv2D)          (None, 25, 25, 64)           18432     ['average_pooling2d_2[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_19 (Ba  (None, 25, 25, 64)           192       ['conv2d_19[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_21 (Ba  (None, 25, 25, 64)           192       ['conv2d_21[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_24 (Ba  (None, 25, 25, 96)           288       ['conv2d_24[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_25 (Ba  (None, 25, 25, 64)           192       ['conv2d_25[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_19 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_19[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_21 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_21[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_24 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_24[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_25 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_25[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed2 (Concatenate)        (None, 25, 25, 288)          0         ['activation_19[0][0]',       \n",
            "                                                                     'activation_21[0][0]',       \n",
            "                                                                     'activation_24[0][0]',       \n",
            "                                                                     'activation_25[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_27 (Conv2D)          (None, 25, 25, 64)           18432     ['mixed2[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_27 (Ba  (None, 25, 25, 64)           192       ['conv2d_27[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_27 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_27[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_28 (Conv2D)          (None, 25, 25, 96)           55296     ['activation_27[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_28 (Ba  (None, 25, 25, 96)           288       ['conv2d_28[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_28 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_28[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_26 (Conv2D)          (None, 12, 12, 384)          995328    ['mixed2[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_29 (Conv2D)          (None, 12, 12, 96)           82944     ['activation_28[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_26 (Ba  (None, 12, 12, 384)          1152      ['conv2d_26[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_29 (Ba  (None, 12, 12, 96)           288       ['conv2d_29[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_26 (Activation)  (None, 12, 12, 384)          0         ['batch_normalization_26[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_29 (Activation)  (None, 12, 12, 96)           0         ['batch_normalization_29[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " max_pooling2d_2 (MaxPoolin  (None, 12, 12, 288)          0         ['mixed2[0][0]']              \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " mixed3 (Concatenate)        (None, 12, 12, 768)          0         ['activation_26[0][0]',       \n",
            "                                                                     'activation_29[0][0]',       \n",
            "                                                                     'max_pooling2d_2[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_34 (Conv2D)          (None, 12, 12, 128)          98304     ['mixed3[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_34 (Ba  (None, 12, 12, 128)          384       ['conv2d_34[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_34 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_34[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_35 (Conv2D)          (None, 12, 12, 128)          114688    ['activation_34[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_35 (Ba  (None, 12, 12, 128)          384       ['conv2d_35[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_35 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_35[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_31 (Conv2D)          (None, 12, 12, 128)          98304     ['mixed3[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_36 (Conv2D)          (None, 12, 12, 128)          114688    ['activation_35[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_31 (Ba  (None, 12, 12, 128)          384       ['conv2d_31[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_36 (Ba  (None, 12, 12, 128)          384       ['conv2d_36[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_31 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_31[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_36 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_36[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_32 (Conv2D)          (None, 12, 12, 128)          114688    ['activation_31[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_37 (Conv2D)          (None, 12, 12, 128)          114688    ['activation_36[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_32 (Ba  (None, 12, 12, 128)          384       ['conv2d_32[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_37 (Ba  (None, 12, 12, 128)          384       ['conv2d_37[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_32 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_32[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_37 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_37[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_3 (Avera  (None, 12, 12, 768)          0         ['mixed3[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_30 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed3[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_33 (Conv2D)          (None, 12, 12, 192)          172032    ['activation_32[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_38 (Conv2D)          (None, 12, 12, 192)          172032    ['activation_37[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_39 (Conv2D)          (None, 12, 12, 192)          147456    ['average_pooling2d_3[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_30 (Ba  (None, 12, 12, 192)          576       ['conv2d_30[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_33 (Ba  (None, 12, 12, 192)          576       ['conv2d_33[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_38 (Ba  (None, 12, 12, 192)          576       ['conv2d_38[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_39 (Ba  (None, 12, 12, 192)          576       ['conv2d_39[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_30 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_30[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_33 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_33[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_38 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_38[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_39 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_39[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed4 (Concatenate)        (None, 12, 12, 768)          0         ['activation_30[0][0]',       \n",
            "                                                                     'activation_33[0][0]',       \n",
            "                                                                     'activation_38[0][0]',       \n",
            "                                                                     'activation_39[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_44 (Conv2D)          (None, 12, 12, 160)          122880    ['mixed4[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_44 (Ba  (None, 12, 12, 160)          480       ['conv2d_44[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_44 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_44[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_45 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_44[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_45 (Ba  (None, 12, 12, 160)          480       ['conv2d_45[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_45 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_45[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_41 (Conv2D)          (None, 12, 12, 160)          122880    ['mixed4[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_46 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_45[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_41 (Ba  (None, 12, 12, 160)          480       ['conv2d_41[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_46 (Ba  (None, 12, 12, 160)          480       ['conv2d_46[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_41 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_41[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_46 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_46[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_42 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_41[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_47 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_46[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_42 (Ba  (None, 12, 12, 160)          480       ['conv2d_42[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_47 (Ba  (None, 12, 12, 160)          480       ['conv2d_47[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_42 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_42[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_47 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_47[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_4 (Avera  (None, 12, 12, 768)          0         ['mixed4[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_40 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed4[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_43 (Conv2D)          (None, 12, 12, 192)          215040    ['activation_42[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_48 (Conv2D)          (None, 12, 12, 192)          215040    ['activation_47[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_49 (Conv2D)          (None, 12, 12, 192)          147456    ['average_pooling2d_4[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_40 (Ba  (None, 12, 12, 192)          576       ['conv2d_40[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_43 (Ba  (None, 12, 12, 192)          576       ['conv2d_43[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_48 (Ba  (None, 12, 12, 192)          576       ['conv2d_48[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_49 (Ba  (None, 12, 12, 192)          576       ['conv2d_49[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_40 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_40[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_43 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_43[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_48 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_48[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_49 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_49[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed5 (Concatenate)        (None, 12, 12, 768)          0         ['activation_40[0][0]',       \n",
            "                                                                     'activation_43[0][0]',       \n",
            "                                                                     'activation_48[0][0]',       \n",
            "                                                                     'activation_49[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_54 (Conv2D)          (None, 12, 12, 160)          122880    ['mixed5[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_54 (Ba  (None, 12, 12, 160)          480       ['conv2d_54[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_54 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_54[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_55 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_54[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_55 (Ba  (None, 12, 12, 160)          480       ['conv2d_55[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_55 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_55[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_51 (Conv2D)          (None, 12, 12, 160)          122880    ['mixed5[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_56 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_55[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_51 (Ba  (None, 12, 12, 160)          480       ['conv2d_51[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_56 (Ba  (None, 12, 12, 160)          480       ['conv2d_56[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_51 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_51[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_56 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_56[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_52 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_51[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_57 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_56[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_52 (Ba  (None, 12, 12, 160)          480       ['conv2d_52[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_57 (Ba  (None, 12, 12, 160)          480       ['conv2d_57[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_52 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_52[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_57 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_57[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_5 (Avera  (None, 12, 12, 768)          0         ['mixed5[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_50 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed5[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_53 (Conv2D)          (None, 12, 12, 192)          215040    ['activation_52[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_58 (Conv2D)          (None, 12, 12, 192)          215040    ['activation_57[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_59 (Conv2D)          (None, 12, 12, 192)          147456    ['average_pooling2d_5[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_50 (Ba  (None, 12, 12, 192)          576       ['conv2d_50[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_53 (Ba  (None, 12, 12, 192)          576       ['conv2d_53[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_58 (Ba  (None, 12, 12, 192)          576       ['conv2d_58[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_59 (Ba  (None, 12, 12, 192)          576       ['conv2d_59[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_50 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_50[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_53 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_53[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_58 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_58[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_59 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_59[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed6 (Concatenate)        (None, 12, 12, 768)          0         ['activation_50[0][0]',       \n",
            "                                                                     'activation_53[0][0]',       \n",
            "                                                                     'activation_58[0][0]',       \n",
            "                                                                     'activation_59[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_64 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed6[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_64 (Ba  (None, 12, 12, 192)          576       ['conv2d_64[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_64 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_64[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_65 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_64[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_65 (Ba  (None, 12, 12, 192)          576       ['conv2d_65[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_65 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_65[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_61 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed6[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_66 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_65[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_61 (Ba  (None, 12, 12, 192)          576       ['conv2d_61[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_66 (Ba  (None, 12, 12, 192)          576       ['conv2d_66[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_61 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_61[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_66 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_66[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_62 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_61[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_67 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_66[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_62 (Ba  (None, 12, 12, 192)          576       ['conv2d_62[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_67 (Ba  (None, 12, 12, 192)          576       ['conv2d_67[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_62 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_62[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_67 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_67[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_6 (Avera  (None, 12, 12, 768)          0         ['mixed6[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_60 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed6[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_63 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_62[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_68 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_67[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_69 (Conv2D)          (None, 12, 12, 192)          147456    ['average_pooling2d_6[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_60 (Ba  (None, 12, 12, 192)          576       ['conv2d_60[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_63 (Ba  (None, 12, 12, 192)          576       ['conv2d_63[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_68 (Ba  (None, 12, 12, 192)          576       ['conv2d_68[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_69 (Ba  (None, 12, 12, 192)          576       ['conv2d_69[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_60 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_60[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_63 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_63[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_68 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_68[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_69 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_69[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed7 (Concatenate)        (None, 12, 12, 768)          0         ['activation_60[0][0]',       \n",
            "                                                                     'activation_63[0][0]',       \n",
            "                                                                     'activation_68[0][0]',       \n",
            "                                                                     'activation_69[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_72 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed7[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_72 (Ba  (None, 12, 12, 192)          576       ['conv2d_72[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_72 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_72[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_73 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_72[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_73 (Ba  (None, 12, 12, 192)          576       ['conv2d_73[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_73 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_73[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_70 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed7[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_74 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_73[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_70 (Ba  (None, 12, 12, 192)          576       ['conv2d_70[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_74 (Ba  (None, 12, 12, 192)          576       ['conv2d_74[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_70 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_70[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_74 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_74[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_71 (Conv2D)          (None, 5, 5, 320)            552960    ['activation_70[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_75 (Conv2D)          (None, 5, 5, 192)            331776    ['activation_74[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_71 (Ba  (None, 5, 5, 320)            960       ['conv2d_71[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_75 (Ba  (None, 5, 5, 192)            576       ['conv2d_75[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_71 (Activation)  (None, 5, 5, 320)            0         ['batch_normalization_71[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_75 (Activation)  (None, 5, 5, 192)            0         ['batch_normalization_75[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " max_pooling2d_3 (MaxPoolin  (None, 5, 5, 768)            0         ['mixed7[0][0]']              \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " mixed8 (Concatenate)        (None, 5, 5, 1280)           0         ['activation_71[0][0]',       \n",
            "                                                                     'activation_75[0][0]',       \n",
            "                                                                     'max_pooling2d_3[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_80 (Conv2D)          (None, 5, 5, 448)            573440    ['mixed8[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_80 (Ba  (None, 5, 5, 448)            1344      ['conv2d_80[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_80 (Activation)  (None, 5, 5, 448)            0         ['batch_normalization_80[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_77 (Conv2D)          (None, 5, 5, 384)            491520    ['mixed8[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_81 (Conv2D)          (None, 5, 5, 384)            1548288   ['activation_80[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_77 (Ba  (None, 5, 5, 384)            1152      ['conv2d_77[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_81 (Ba  (None, 5, 5, 384)            1152      ['conv2d_81[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_77 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_77[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_81 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_81[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_78 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_77[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_79 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_77[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_82 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_81[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_83 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_81[0][0]']       \n",
            "                                                                                                  \n",
            " average_pooling2d_7 (Avera  (None, 5, 5, 1280)           0         ['mixed8[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_76 (Conv2D)          (None, 5, 5, 320)            409600    ['mixed8[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_78 (Ba  (None, 5, 5, 384)            1152      ['conv2d_78[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_79 (Ba  (None, 5, 5, 384)            1152      ['conv2d_79[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_82 (Ba  (None, 5, 5, 384)            1152      ['conv2d_82[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_83 (Ba  (None, 5, 5, 384)            1152      ['conv2d_83[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " conv2d_84 (Conv2D)          (None, 5, 5, 192)            245760    ['average_pooling2d_7[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_76 (Ba  (None, 5, 5, 320)            960       ['conv2d_76[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_78 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_78[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_79 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_79[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_82 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_82[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_83 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_83[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " batch_normalization_84 (Ba  (None, 5, 5, 192)            576       ['conv2d_84[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_76 (Activation)  (None, 5, 5, 320)            0         ['batch_normalization_76[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed9_0 (Concatenate)      (None, 5, 5, 768)            0         ['activation_78[0][0]',       \n",
            "                                                                     'activation_79[0][0]']       \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)   (None, 5, 5, 768)            0         ['activation_82[0][0]',       \n",
            "                                                                     'activation_83[0][0]']       \n",
            "                                                                                                  \n",
            " activation_84 (Activation)  (None, 5, 5, 192)            0         ['batch_normalization_84[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed9 (Concatenate)        (None, 5, 5, 2048)           0         ['activation_76[0][0]',       \n",
            "                                                                     'mixed9_0[0][0]',            \n",
            "                                                                     'concatenate[0][0]',         \n",
            "                                                                     'activation_84[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_89 (Conv2D)          (None, 5, 5, 448)            917504    ['mixed9[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_89 (Ba  (None, 5, 5, 448)            1344      ['conv2d_89[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_89 (Activation)  (None, 5, 5, 448)            0         ['batch_normalization_89[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_86 (Conv2D)          (None, 5, 5, 384)            786432    ['mixed9[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_90 (Conv2D)          (None, 5, 5, 384)            1548288   ['activation_89[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_86 (Ba  (None, 5, 5, 384)            1152      ['conv2d_86[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_90 (Ba  (None, 5, 5, 384)            1152      ['conv2d_90[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_86 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_86[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_90 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_90[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_87 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_86[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_88 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_86[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_91 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_90[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_92 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_90[0][0]']       \n",
            "                                                                                                  \n",
            " average_pooling2d_8 (Avera  (None, 5, 5, 2048)           0         ['mixed9[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_85 (Conv2D)          (None, 5, 5, 320)            655360    ['mixed9[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_87 (Ba  (None, 5, 5, 384)            1152      ['conv2d_87[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_88 (Ba  (None, 5, 5, 384)            1152      ['conv2d_88[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_91 (Ba  (None, 5, 5, 384)            1152      ['conv2d_91[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_92 (Ba  (None, 5, 5, 384)            1152      ['conv2d_92[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " conv2d_93 (Conv2D)          (None, 5, 5, 192)            393216    ['average_pooling2d_8[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_85 (Ba  (None, 5, 5, 320)            960       ['conv2d_85[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_87 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_87[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_88 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_88[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_91 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_91[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_92 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_92[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " batch_normalization_93 (Ba  (None, 5, 5, 192)            576       ['conv2d_93[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_85 (Activation)  (None, 5, 5, 320)            0         ['batch_normalization_85[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed9_1 (Concatenate)      (None, 5, 5, 768)            0         ['activation_87[0][0]',       \n",
            "                                                                     'activation_88[0][0]']       \n",
            "                                                                                                  \n",
            " concatenate_1 (Concatenate  (None, 5, 5, 768)            0         ['activation_91[0][0]',       \n",
            " )                                                                   'activation_92[0][0]']       \n",
            "                                                                                                  \n",
            " activation_93 (Activation)  (None, 5, 5, 192)            0         ['batch_normalization_93[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed10 (Concatenate)       (None, 5, 5, 2048)           0         ['activation_85[0][0]',       \n",
            "                                                                     'mixed9_1[0][0]',            \n",
            "                                                                     'concatenate_1[0][0]',       \n",
            "                                                                     'activation_93[0][0]']       \n",
            "                                                                                                  \n",
            " flatten (Flatten)           (None, 51200)                0         ['mixed10[0][0]']             \n",
            "                                                                                                  \n",
            " dense (Dense)               (None, 10)                   512010    ['flatten[0][0]']             \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 22314794 (85.12 MB)\n",
            "Trainable params: 512010 (1.95 MB)\n",
            "Non-trainable params: 21802784 (83.17 MB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    loss='categorical_crossentropy',\n",
        "    optimizer='adam',\n",
        "    metrics=['accuracy']\n",
        "\n",
        ")"
      ],
      "metadata": {
        "id": "nMdE-4ASLjWM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train and test data Preprocessing"
      ],
      "metadata": {
        "id": "9yAW7kA4WpjR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "train_datagen = ImageDataGenerator(rescale=1./255,\n",
        "                                   shear_range=0.2,\n",
        "                                   zoom_range=0.2,\n",
        "                                   horizontal_flip=True)\n",
        "\n",
        "test_datagen= ImageDataGenerator(rescale=1./255)"
      ],
      "metadata": {
        "id": "_mscCRBqNOfa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_set = train_datagen.flow_from_directory('/content/New Plant Diseases Dataset(Augmented)/train',\n",
        "                                                 target_size= (224,224),\n",
        "                                                 batch_size=16,\n",
        "                                                 class_mode='categorical')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_-S2hOpHNtak",
        "outputId": "ed123a59-a962-4162-8c25-d358818e15f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 18345 images belonging to 10 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_set = test_datagen.flow_from_directory('/content/New Plant Diseases Dataset(Augmented)/valid',\n",
        "                                                 target_size= (224,224),\n",
        "                                                 batch_size=16,\n",
        "                                                 class_mode='categorical')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "crWS6_g4OJr5",
        "outputId": "5e5a8e5d-84e6-4172-9086-18a4f1c5bb4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 4585 images belonging to 10 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fitting the model"
      ],
      "metadata": {
        "id": "ciqv9E9xWxWe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "r = model.fit_generator(\n",
        "    training_set,\n",
        "    validation_data= test_set,\n",
        "    epochs= 10,\n",
        "    steps_per_epoch=len(training_set),\n",
        "    validation_steps = len(test_set)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IK1zrRYWOcsk",
        "outputId": "4f96fdef-0e90-420e-f30d-a6de372a5ed9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-32-5f88b01ff5e3>:1: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  r = model.fit_generator(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1147/1147 [==============================] - 287s 238ms/step - loss: 4.1352 - accuracy: 0.6806 - val_loss: 2.6948 - val_accuracy: 0.7952\n",
            "Epoch 2/10\n",
            "1147/1147 [==============================] - 273s 238ms/step - loss: 3.6229 - accuracy: 0.7827 - val_loss: 2.5814 - val_accuracy: 0.8345\n",
            "Epoch 3/10\n",
            "1147/1147 [==============================] - 265s 231ms/step - loss: 3.1569 - accuracy: 0.8228 - val_loss: 3.6086 - val_accuracy: 0.8153\n",
            "Epoch 4/10\n",
            "1147/1147 [==============================] - 274s 239ms/step - loss: 3.1452 - accuracy: 0.8382 - val_loss: 4.2433 - val_accuracy: 0.8131\n",
            "Epoch 5/10\n",
            "1147/1147 [==============================] - 266s 232ms/step - loss: 2.9496 - accuracy: 0.8540 - val_loss: 5.0710 - val_accuracy: 0.7913\n",
            "Epoch 6/10\n",
            "1147/1147 [==============================] - 261s 227ms/step - loss: 2.9322 - accuracy: 0.8664 - val_loss: 3.4801 - val_accuracy: 0.8462\n",
            "Epoch 7/10\n",
            "1147/1147 [==============================] - 263s 229ms/step - loss: 2.8333 - accuracy: 0.8718 - val_loss: 4.2691 - val_accuracy: 0.8266\n",
            "Epoch 8/10\n",
            "1147/1147 [==============================] - 265s 231ms/step - loss: 2.7036 - accuracy: 0.8788 - val_loss: 4.6857 - val_accuracy: 0.8303\n",
            "Epoch 9/10\n",
            "1147/1147 [==============================] - 264s 230ms/step - loss: 2.6617 - accuracy: 0.8854 - val_loss: 3.4050 - val_accuracy: 0.8582\n",
            "Epoch 10/10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "7-27Ru1Aao0a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# plot the loss\n",
        "plt.plot(r.history['loss'], label='train loss')\n",
        "plt.plot(r.history['val_loss'], label='val loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "plt.savefig('LossVal_loss')\n",
        "\n",
        "# plot the accuracy\n",
        "plt.plot(r.history['accuracy'], label='train acc')\n",
        "plt.plot(r.history['val_accuracy'], label='val acc')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "plt.savefig('AccVal_acc')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 860
        },
        "id": "ya9CN-oMPCCe",
        "outputId": "0390c430-2523-4db1-fdcf-ecccb093f615"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the image path\n",
        "image_path = '/content/new plant diseases dataset(augmented)/New Plant Diseases Dataset(Augmented)/valid/Tomato___Spider_mites Two-spotted_spider_mite/002835d1-c18e-4471-aa6e-8d8c29585e9b___Com.G_SpM_FL 8584.JPG'\n",
        "\n",
        "# Load the image and resize it to match the input size of your model (224x224 in your case)\n",
        "img = image.load_img(image_path, target_size=(224, 224))\n",
        "\n",
        "# Convert the image to a NumPy array and preprocess it\n",
        "img = image.img_to_array(img)\n",
        "img = np.expand_dims(img, axis=0)\n",
        "img = img / 255.0  # Rescale pixel values to [0, 1]\n",
        "\n",
        "# Ensure that you are using the same preprocessing steps as you used during training\n"
      ],
      "metadata": {
        "id": "O90liYh7YZaU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(img)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9L8ixICabjOA",
        "outputId": "9d0c6fb2-b57c-4f72-d77d-802d322d28d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 27ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AgkvVQWacAQs",
        "outputId": "a88fb495-91b2-490b-d1bf-5ba0147aee11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.0000000e+00, 4.9681066e-15, 0.0000000e+00, 1.0918321e-21,\n",
              "        0.0000000e+00, 1.0000000e+00, 9.0572081e-13, 5.4355880e-20,\n",
              "        2.1986586e-34, 2.3163880e-20]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "custom_class_labels = folders\n",
        "\n",
        "# Assuming \"predictions\" contains the model's output\n",
        "top_class_index = predictions.argmax(axis=-1)[0]\n",
        "predicted_class = custom_class_labels[top_class_index]\n",
        "\n",
        "print(\"Predicted Class:\", predicted_class)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iCD9UryhcGng",
        "outputId": "22657f9a-333f-434c-ad6f-e3f82f4d798e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class: /content/New Plant Diseases Dataset(Augmented)/train/Tomato___Tomato_Yellow_Leaf_Curl_Virus\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gEUgGufMc_DA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}