IndianFabric_classification_new.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyOyKWsn6UK8uAmqHnv5aLPU",
      "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/DS-1090/Indian_Pattern_Classifier/blob/main/IndianFabric_classification_new.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Downloading Dataset"
      ],
      "metadata": {
        "id": "j4XtgGILIXSl"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ji2RxHO9Ya_6",
        "outputId": "e1cfe2c3-d661-47af-8f54-146be94f1a75"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting opendatasets\n",
            "  Downloading opendatasets-0.1.22-py3-none-any.whl.metadata (9.2 kB)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from opendatasets) (4.67.1)\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (from opendatasets) (1.6.17)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from opendatasets) (8.1.7)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (1.17.0)\n",
            "Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2024.12.14)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.32.3)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (8.0.4)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.2.3)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (6.2.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle->opendatasets) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle->opendatasets) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle->opendatasets) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle->opendatasets) (3.10)\n",
            "Downloading opendatasets-0.1.22-py3-none-any.whl (15 kB)\n",
            "Installing collected packages: opendatasets\n",
            "Successfully installed opendatasets-0.1.22\n"
          ]
        }
      ],
      "source": [
        "pip install opendatasets\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import opendatasets as od\n",
        "od.download(\"https://www.kaggle.com/datasets/div456/indian-fabrics-3-classes\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NRTZoLXGZAZI",
        "outputId": "38d00a77-2dc6-47a7-ed4b-39bc83ead645"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
            "Your Kaggle username: div456\n",
            "Your Kaggle Key: ··········\n",
            "Dataset URL: https://www.kaggle.com/datasets/div456/indian-fabrics-3-classes\n",
            "Downloading indian-fabrics-3-classes.zip to ./indian-fabrics-3-classes\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 88.4M/88.4M [00:05<00:00, 18.1MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing Libraries\n"
      ],
      "metadata": {
        "id": "MlfVKex6IbyX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow import keras\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
        "# from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.layers import BatchNormalization"
      ],
      "metadata": {
        "id": "4jRoZbwfZLuO"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating Train, Valid, Test Datasets\n"
      ],
      "metadata": {
        "id": "laWBr_tfIhmT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pathlib\n",
        "\n",
        "data_dir = pathlib.Path('/content/indian-fabrics-3-classes/train')"
      ],
      "metadata": {
        "id": "QLEpwYCse4Kx"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "  data_dir,\n",
        "  seed=123,\n",
        "  image_size=(320, 320),\n",
        "  batch_size=64,\n",
        "  color_mode='rgb',\n",
        "  labels='inferred',\n",
        "  label_mode='int'\n",
        "  )\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H05XlfL0eqgl",
        "outputId": "ba671676-06ce-4915-b051-c4a4569b9456"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1068 files belonging to 3 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = pathlib.Path('/content/indian-fabrics-3-classes/valid')\n",
        "valid_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "  data_dir,\n",
        "  seed=123,\n",
        "  image_size=(320, 320),\n",
        "  batch_size=64,\n",
        "  color_mode='rgb',\n",
        "  labels='inferred',\n",
        "  label_mode='int'\n",
        "  )\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qaWXhlPif6p9",
        "outputId": "aaf339e9-ed9e-49f0-b101-3f4c945f285f"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 71 files belonging to 3 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = pathlib.Path('/content/indian-fabrics-3-classes/test')\n",
        "test_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "  data_dir,\n",
        "  seed=123,\n",
        "  image_size=(320, 320),\n",
        "  batch_size=64,\n",
        "  color_mode='rgb',\n",
        "  labels='inferred',\n",
        "  label_mode='int'\n",
        "  )\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ha0FMA_pgE4e",
        "outputId": "e18480cd-520f-4936-fc75-e7f7b564860a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 49 files belonging to 3 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 64\n",
        "img_height = 320\n",
        "img_width = 320"
      ],
      "metadata": {
        "id": "zY_EebMJb1Hx"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create & Train the model"
      ],
      "metadata": {
        "id": "RMKbw-qpIsQO"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PzK5NtynCQoK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()"
      ],
      "metadata": {
        "id": "htSliKfubRjy"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)))"
      ],
      "metadata": {
        "id": "vfYUZ5Iwbrv8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d638fef2-a3d1-4955-88ed-fb8e7a6ff279"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/preprocessing/tf_data_layer.py:19: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model.add(layers.RandomFlip(\"horizontal\",input_shape=(img_height,img_width,3)))\n",
        "# model.add(layers.RandomRotation(0.1, fill_mode='nearest'))\n",
        "# model.add(layers.RandomZoom(0.1))\n",
        "# model.add(layers.RandomContrast(0.1))\n",
        "# model.add(layers.RandomBrightness(0.04))"
      ],
      "metadata": {
        "id": "Po7pvWiBoRlk"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# model.add(Conv2D(64, 3, padding='same', activation='relu'))\n",
        "# model.add(BatchNormalization())\n",
        "\n",
        "model.add(Conv2D(32, 3, padding='same', activation='relu'))\n",
        "\n",
        "model.add(Conv2D(16, 3, padding='same', activation='relu'))\n",
        "model.add(MaxPooling2D())\n",
        "\n",
        "# model.add(Conv2D(128, 3, padding='same', activation='relu'))\n",
        "# model.add(BatchNormalization())\n",
        "# model.add(MaxPooling2D())\n",
        "\n",
        "model.add(Flatten())\n",
        "# model.add(Dense(256, activation='relu'))\n",
        "# model.add(Dropout(0.3))\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dropout(0.1))\n",
        "model.add(Dense(32, activation='relu'))\n",
        "model.add(Dense(3, activation='softmax'))\n",
        "\n",
        "\n",
        "# model.add(Conv2D(32, 3, padding='same', activation='relu'))\n",
        "\n",
        "# model.add(Conv2D(64, 3, padding='same', activation='relu'))\n",
        "# model.add(MaxPooling2D())\n",
        "\n",
        "# model.add(Conv2D(128, 3, padding='same', activation='relu'))\n",
        "# model.add(MaxPooling2D())\n",
        "\n",
        "# model.add(Flatten())\n",
        "\n",
        "# model.add(Dense(64, activation='relu'))\n",
        "# model.add(Dropout(0.2))\n",
        "# model.add(Dense(3, activation='softmax'))\n"
      ],
      "metadata": {
        "id": "GXgm74YmcV3V"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "              metrics=['accuracy'],\n",
        "              run_eagerly=True\n",
        "               )\n"
      ],
      "metadata": {
        "id": "6JcTtQEVdzS_"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "ZvSESoY1eZQD",
        "outputId": "c5d48788-f5c5-4e29-eb99-a385622d7a7d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ]
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            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ rescaling (\u001b[38;5;33mRescaling\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m320\u001b[0m, \u001b[38;5;34m320\u001b[0m, \u001b[38;5;34m3\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m320\u001b[0m, \u001b[38;5;34m320\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │             \u001b[38;5;34m896\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m320\u001b[0m, \u001b[38;5;34m320\u001b[0m, \u001b[38;5;34m16\u001b[0m)        │           \u001b[38;5;34m4,624\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m160\u001b[0m, \u001b[38;5;34m160\u001b[0m, \u001b[38;5;34m16\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m409600\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │      \u001b[38;5;34m26,214,464\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │           \u001b[38;5;34m2,080\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)                   │              \u001b[38;5;34m99\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ rescaling (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Rescaling</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)        │           <span style=\"color: #00af00; text-decoration-color: #00af00\">4,624</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">409600</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │      <span style=\"color: #00af00; text-decoration-color: #00af00\">26,214,464</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,080</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">99</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m26,222,163\u001b[0m (100.03 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">26,222,163</span> (100.03 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
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          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m26,222,163\u001b[0m (100.03 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">26,222,163</span> (100.03 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(model)"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 186
        },
        "id": "Te3Vr_3RjZeG",
        "outputId": "be54bea4-e696-4093-c80a-f216652749cd"
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      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "keras.src.models.sequential.Sequential"
            ],
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              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
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              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>keras.src.models.sequential.Sequential</b><br/>def error_handler(*args, **kwargs)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/keras/src/models/sequential.py</a>`Sequential` groups a linear stack of layers into a `Model`.\n",
              "\n",
              "Examples:\n",
              "\n",
              "```python\n",
              "model = keras.Sequential()\n",
              "model.add(keras.Input(shape=(16,)))\n",
              "model.add(keras.layers.Dense(8))\n",
              "\n",
              "# Note that you can also omit the initial `Input`.\n",
              "# In that case the model doesn&#x27;t have any weights until the first call\n",
              "# to a training/evaluation method (since it isn&#x27;t yet built):\n",
              "model = keras.Sequential()\n",
              "model.add(keras.layers.Dense(8))\n",
              "model.add(keras.layers.Dense(4))\n",
              "# model.weights not created yet\n",
              "\n",
              "# Whereas if you specify an `Input`, the model gets built\n",
              "# continuously as you are adding layers:\n",
              "model = keras.Sequential()\n",
              "model.add(keras.Input(shape=(16,)))\n",
              "model.add(keras.layers.Dense(8))\n",
              "len(model.weights)  # Returns &quot;2&quot;\n",
              "\n",
              "# When using the delayed-build pattern (no input shape specified), you can\n",
              "# choose to manually build your model by calling\n",
              "# `build(batch_input_shape)`:\n",
              "model = keras.Sequential()\n",
              "model.add(keras.layers.Dense(8))\n",
              "model.add(keras.layers.Dense(4))\n",
              "model.build((None, 16))\n",
              "len(model.weights)  # Returns &quot;4&quot;\n",
              "\n",
              "# Note that when using the delayed-build pattern (no input shape specified),\n",
              "# the model gets built the first time you call `fit`, `eval`, or `predict`,\n",
              "# or the first time you call the model on some input data.\n",
              "model = keras.Sequential()\n",
              "model.add(keras.layers.Dense(8))\n",
              "model.add(keras.layers.Dense(1))\n",
              "model.compile(optimizer=&#x27;sgd&#x27;, loss=&#x27;mse&#x27;)\n",
              "# This builds the model for the first time:\n",
              "model.fit(x, y, batch_size=32, epochs=10)\n",
              "```</pre>\n",
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          "metadata": {},
          "execution_count": 15
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    {
      "cell_type": "code",
      "source": [
        "h = model.fit(\n",
        "  train_ds,\n",
        "  validation_data=valid_ds,\n",
        "  epochs=10,\n",
        "  batch_size= 16\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5c1R-7POebuV",
        "outputId": "3d8e9fab-f508-4f5f-df22-3a2210cc465a"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m17/17\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 508ms/step - accuracy: 0.3425 - loss: 6.1181 - val_accuracy: 0.5070 - val_loss: 1.0379\n",
            "Epoch 2/10\n",
            "\u001b[1m17/17\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 323ms/step - accuracy: 0.5969 - loss: 0.8992 - val_accuracy: 0.7324 - val_loss: 0.8051\n",
            "Epoch 3/10\n",
            "\u001b[1m17/17\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 354ms/step - accuracy: 0.8703 - loss: 0.4542 - val_accuracy: 0.7183 - val_loss: 0.7537\n",
            "Epoch 4/10\n",
            "\u001b[1m17/17\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 329ms/step - accuracy: 0.9583 - loss: 0.1700 - val_accuracy: 0.6901 - val_loss: 0.7689\n",
            "Epoch 5/10\n",
            "\u001b[1m17/17\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 339ms/step - accuracy: 0.9881 - loss: 0.0821 - val_accuracy: 0.7465 - val_loss: 0.8012\n",
            "Epoch 6/10\n",
            "\u001b[1m17/17\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 331ms/step - accuracy: 0.9944 - loss: 0.0274 - val_accuracy: 0.7465 - val_loss: 0.7405\n",
            "Epoch 7/10\n",
            "\u001b[1m17/17\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 337ms/step - accuracy: 1.0000 - loss: 0.0125 - val_accuracy: 0.7746 - val_loss: 1.0873\n",
            "Epoch 8/10\n",
            "\u001b[1m17/17\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 336ms/step - accuracy: 0.9965 - loss: 0.0083 - val_accuracy: 0.8028 - val_loss: 1.0090\n",
            "Epoch 9/10\n",
            "\u001b[1m17/17\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 339ms/step - accuracy: 1.0000 - loss: 0.0036 - val_accuracy: 0.7887 - val_loss: 1.1258\n",
            "Epoch 10/10\n",
            "\u001b[1m17/17\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 343ms/step - accuracy: 1.0000 - loss: 0.0019 - val_accuracy: 0.7887 - val_loss: 1.2217\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "h.history"
      ],
      "metadata": {
        "id": "uqDonPDVgrVJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba99d7af-d6b8-4eb5-c33d-a85f1fa2ca92"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': [0.4016853868961334,\n",
              "  0.6647940278053284,\n",
              "  0.882022500038147,\n",
              "  0.9625468254089355,\n",
              "  0.9887640476226807,\n",
              "  0.9962546825408936,\n",
              "  1.0,\n",
              "  0.9981273412704468,\n",
              "  1.0,\n",
              "  1.0],\n",
              " 'loss': [4.114221572875977,\n",
              "  0.8160333633422852,\n",
              "  0.39542537927627563,\n",
              "  0.14928284287452698,\n",
              "  0.06416001170873642,\n",
              "  0.025555623695254326,\n",
              "  0.009948845952749252,\n",
              "  0.006732339039444923,\n",
              "  0.0031930352561175823,\n",
              "  0.0024369319435209036],\n",
              " 'val_accuracy': [0.5070422291755676,\n",
              "  0.7323943376541138,\n",
              "  0.7183098793029785,\n",
              "  0.6901408433914185,\n",
              "  0.7464788556098938,\n",
              "  0.7464788556098938,\n",
              "  0.7746478915214539,\n",
              "  0.8028169274330139,\n",
              "  0.7887324094772339,\n",
              "  0.7887324094772339],\n",
              " 'val_loss': [1.0379111766815186,\n",
              "  0.8051376938819885,\n",
              "  0.7536662817001343,\n",
              "  0.7689419388771057,\n",
              "  0.8012498021125793,\n",
              "  0.7405144572257996,\n",
              "  1.0873422622680664,\n",
              "  1.0090079307556152,\n",
              "  1.1258260011672974,\n",
              "  1.2216641902923584]}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as mp\n",
        "\n",
        "mp.plot(h.history['accuracy'], color='blue')\n",
        "mp.plot(h.history['val_accuracy'], color='green')\n",
        "mp.xlabel('epoch')\n",
        "mp.ylabel('accuracy')\n",
        "mp.legend(['train', 'valid'])\n",
        "mp.title('Accuracy vs Epochs')"
      ],
      "metadata": {
        "id": "45PzaxtTmHfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "outputId": "a16422d9-0c48-4d75-a40f-6552b5dd925e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy vs Epochs')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as mp\n",
        "\n",
        "mp.plot(h.history['loss'], color='blue')\n",
        "mp.plot(h.history['val_loss'], color='green')\n",
        "mp.xlabel('epoch')\n",
        "mp.ylabel('loss')\n",
        "mp.legend(['train', 'valid'])\n",
        "mp.title('Loss vs Epochs')"
      ],
      "metadata": {
        "id": "ZdL-di4XmVj1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "outputId": "f5755dca-ea2c-4cb6-a355-1fc673634b91"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Loss vs Epochs')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Test the Model"
      ],
      "metadata": {
        "id": "VGUELo3PIzPk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss, accuracy = model.evaluate(test_ds)\n",
        "print(f'Test Accuracy: {accuracy:.2f}')\n",
        "print(f'Test Loss: {loss:.2f}')\n"
      ],
      "metadata": {
        "id": "8S1iEREarBsz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43cf9c56-f7f4-4aea-e236-f28651bd675f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - accuracy: 0.8163 - loss: 1.1647\n",
            "Test Accuracy: 0.82\n",
            "Test Loss: 1.16\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "Fmn190GBsmiG"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for images, labels in test_ds:\n",
        "    predictions = model.predict(images)\n",
        "    for i in range(len(images)):\n",
        "        image_path = test_ds.file_paths[i]  # Assuming test_ds contains file paths\n",
        "        index_pred = np.argmax(predictions[i])\n",
        "        class_names = ['Banarasi', 'Ikat', 'Pichwai']\n",
        "        predicted_class = class_names[index_pred]\n",
        "        print(\"Image Path:\", image_path)\n",
        "        print(\"Predicted class:\", predicted_class)\n"
      ],
      "metadata": {
        "id": "mSCNko-PropD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "900757d5-fdef-4f65-8c1b-d5bc2fcc0fea"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 442ms/step\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Banarasi/images13_jpg.rf.f2a3b1720972de4bb75b7fcb062269c8.jpg\n",
            "Predicted class: Banarasi\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Banarasi/images33_jpg.rf.b224fd1647ca405f955e199d2b136f2b.jpg\n",
            "Predicted class: Pichwai\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Ikat/images33_jpg.rf.9e6ae9d131373c18cdbe6273089867f6.jpg\n",
            "Predicted class: Banarasi\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Pichwai/pastel-hand-painted-pure-moonga-silk-pichwai-blouse-fabric-1-mtr-714991_jpg.rf.11185ddc8f105f1a385559cb725f5cc7.jpg\n",
            "Predicted class: Pichwai\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Banarasi/red-traditonal-banarasi-silk-brocade-fabric-2018415488-kfz42azv_jpg.rf.db3ab1c289bebaecbcff8767f5644a8c.jpg\n",
            "Predicted class: Pichwai\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Banarasi/FKACJRD10028_2_1200x_jpg.rf.b6a9cf3e00dfa401ad208adce5ffac55.jpg\n",
            "Predicted class: Pichwai\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Pichwai/94235-3-202_jpg.rf.63093f3d3842db637c05c34ee2851af2.jpg\n",
            "Predicted class: Pichwai\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Banarasi/images165_jpg.rf.69c08ad29cd52eddbeb529e48397898d.jpg\n",
            "Predicted class: Pichwai\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Ikat/images2_jpg.rf.02a6b1ab9b6a85556c27609b6cdc69d2.jpg\n",
            "Predicted class: Banarasi\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Banarasi/images137_jpg.rf.0cc7bafc4b52a70afb5ec9e679a132e1.jpg\n",
            "Predicted class: Banarasi\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Ikat/ABB80897-FE29-4D7E-9FB3-438565872A39_1_201_a_1024x10242x_jpg.rf.4d4aa9bb60c2154ad8aad58ee8ef9173.jpg\n",
            "Predicted class: Pichwai\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Pichwai/images24_jpg.rf.82b0457b8451ba98639e5a9bf7dc18a8.jpg\n",
            "Predicted class: Banarasi\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Banarasi/images154_jpg.rf.af53dcd0bb1f8ca3a5f74f31393e3761.jpg\n",
            "Predicted class: Ikat\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Pichwai/DSC07221_Medium_87b37a73-5171-47f6-af65-9602d6206692_jpg.rf.017ebb2ce2785fa45994804834aca36e.jpg\n",
            "Predicted class: Banarasi\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Banarasi/image19_jpeg.rf.03c31350253eb513e405bd998f9fa9bf.jpg\n",
            "Predicted class: Banarasi\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Banarasi/IG_04011_jpg.rf.ab2e485681de61ac37a88ee374947f20.jpg\n",
            "Predicted class: Pichwai\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Banarasi/images102_jpg.rf.f420915e94eb4bc0244f4a49d92b50b0.jpg\n",
            "Predicted class: Ikat\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Ikat/images16_jpg.rf.72b40d91b758a8038c5198adc15924f5.jpg\n",
            "Predicted class: Pichwai\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Pichwai/image19_jpeg.rf.1e1ae4c2b3e979d84fa61e12951a9559.jpg\n",
            "Predicted class: Pichwai\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Ikat/f46-1_21128d53-1a33-419c-9643-58618f1013c8_jpg.rf.adac2c7ab9c5335cefbc3ba18cf6c4f8.jpg\n",
            "Predicted class: Ikat\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Ikat/images7_jpg.rf.74017171f37deb9d4c9ebf64652599d8.jpg\n",
            "Predicted class: Banarasi\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Banarasi/images42_jpg.rf.ccac6b7048d37352c69f409e7e669821.jpg\n",
            "Predicted class: Banarasi\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Pichwai/22003334-B_jpg.rf.74bce99ac3eebfb5a007f6c6311e0867.jpg\n",
            "Predicted class: Banarasi\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Ikat/images11_jpg.rf.79b597424656a2fabb74a3b5025f2bee.jpg\n",
            "Predicted class: Banarasi\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Banarasi/images117_jpg.rf.032c6291752f728f46eb588166856cd3.jpg\n",
            "Predicted class: Pichwai\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Ikat/-473Wx593H-469483210-multi-MODEL_jpg.rf.896ace1ca420f5f07b9c9df0a8466bf1.jpg\n",
            "Predicted class: Banarasi\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Pichwai/images2_jpg.rf.1e897de0921e30de0d787933ff7fe36e.jpg\n",
            "Predicted class: Ikat\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Banarasi/IMG_E9590_2796f3d7-9007-4e6c-923c-b46a3c378b4b_jpg.rf.a71486f377401a9e6acdd2966f21c9c2.jpg\n",
            "Predicted class: Banarasi\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Ikat/e42f5dd20f355044d6cda9e30444b36c_jpg.rf.a3306754f8bdfcaa9410fbf3f2e37472.jpg\n",
            "Predicted class: Banarasi\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Pichwai/il_1140xN-3336549267_bg7z_jpg.rf.d7ef573c4417a24f995137729497129b.jpg\n",
            "Predicted class: Pichwai\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Banarasi/image10_jpeg.rf.3ea58bed195a6cf66993888488fc683c.jpg\n",
            "Predicted class: Pichwai\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Banarasi/images63_jpg.rf.34aa6a555a77b707800294929a181729.jpg\n",
            "Predicted class: Banarasi\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Pichwai/27042022040344_png.rf.35b6e139045bd0e9edf0356b2e77f5e1.jpg\n",
            "Predicted class: Ikat\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Banarasi/images34_jpg.rf.4eda2027e8a35ae5b22ca9331f9fd284.jpg\n",
            "Predicted class: Pichwai\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Pichwai/PICHWAI-100005-01_jpg.rf.78aca72d64b640652f3171dd31284757.jpg\n",
            "Predicted class: Banarasi\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Ikat/image_jpeg.rf.e61fe17efe6729c908868bd20dfb7ea0.jpg\n",
            "Predicted class: Pichwai\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Pichwai/48062-DSC_9739-JPG_jpg.rf.f0bbe31c8ade6c5c1991268b59cb2166.jpg\n",
            "Predicted class: Pichwai\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Pichwai/833_6772_1563199443_jpg.rf.87bdfd8b2e4c68273c3e1b7dfacdf72c.jpg\n",
            "Predicted class: Pichwai\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Banarasi/images139_jpg.rf.8e00005559a458c891ae4adacd8941dd.jpg\n",
            "Predicted class: Ikat\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Ikat/img_5849_3_1280x_jpg.rf.7c55911d0a2bc68ab0b339d377cd1f36.jpg\n",
            "Predicted class: Banarasi\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Ikat/whatsapp-image-2023-03-21-at-20-18-28_jpeg.rf.9a3419e13ce921947769c33b0ad3b21e.jpg\n",
            "Predicted class: Banarasi\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Ikat/Pochampally20Violet20Cotton20Ikat20Fabric-1000x1000_jpeg.rf.7718f9f18ee2f7b5213af34d3cbc41db.jpg\n",
            "Predicted class: Ikat\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Banarasi/sf40595-saroj-fabrics-purple-banarasi-fabric_jpg.rf.e25e831e8fc6c846a2ab5e8ef24bd165.jpg\n",
            "Predicted class: Ikat\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Banarasi/images93_jpg.rf.d1c511b77ef99040201d17d5dfda33c0.jpg\n",
            "Predicted class: Banarasi\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Pichwai/71VxsnD2t8L-_AC_UF8941000_QL80__jpg.rf.f8c61cc9601ae711b89798ae9f0ff186.jpg\n",
            "Predicted class: Banarasi\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Pichwai/0fd2df9315d8aba5cf9471c838a639aa_jpg.rf.8af9125e06a5d8518102a7b9efdae70a.jpg\n",
            "Predicted class: Ikat\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Ikat/images23_jpg.rf.1b4ca76eef45e3537528107f5b6e6c89.jpg\n",
            "Predicted class: Banarasi\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Banarasi/IG_09957_bf42d59b-be3e-45e3-a9b9-54ac921bcbcc_jpg.rf.af800d9117f90db4f19670bff8d18049.jpg\n",
            "Predicted class: Banarasi\n",
            "Image Path: /content/indian-fabrics-3-classes/test/Pichwai/image27_jpeg.rf.4b850f7338f54b53a643b330c2858bbd.jpg\n",
            "Predicted class: Banarasi\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing import image\n"
      ],
      "metadata": {
        "id": "hlmZaczDuFTK"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img = image.load_img('/content/indian-fabrics-3-classes/test/Ikat/images11_jpg.rf.79b597424656a2fabb74a3b5025f2bee.jpg', target_size=(320, 320))\n",
        "img_array = image.img_to_array(img)\n",
        "img_array = np.expand_dims(img_array, axis=0)\n",
        "print(img_array.shape)\n",
        "img_array /= 255.0\n",
        "a= model.predict(img_array)\n",
        "pred_class_ind = np.argmax(a)\n",
        "print(a)\n",
        "print(class_names[pred_class_ind])\n",
        "\n"
      ],
      "metadata": {
        "id": "hYn1tdtXttSv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b6bdeac1-5a47-4c38-fc06-ea6691376b40"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 320, 320, 3)\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
            "[[0.24739887 0.60566515 0.14693597]]\n",
            "Ikat\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = test_ds.class_names\n",
        "print(\"Class Names:\", class_names)\n"
      ],
      "metadata": {
        "id": "djrcCwPItlzL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b702d9cf-79cb-4b9b-e5a2-dffc3a8b0702"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class Names: ['Banarasi', 'Ikat', 'Pichwai']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8Rr9L__lDR3L"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}