Task_2_Kiran_Gyan_Inference_Engine.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Task_2_Kiran_Gyan_Inference_Engine.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1kqaW33VAWNksAR0tJCqNW1DeXtvovNJn",
      "authorship_tag": "ABX9TyMiCy55gK8O7BGPA5BzWIoe",
      "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/Kiran-Venkatesh/KV/blob/master/Task_2_Kiran_Gyan_Inference_Engine.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YKS_X0Yvo3uW"
      },
      "outputs": [],
      "source": [
        "from keras.models import load_model\n",
        "from google.colab import files\n",
        "import pandas as pd\n",
        "import cv2\n",
        "import numpy as np\n",
        "from numpy import expand_dims"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Loading 100% Validation accuracy model with the help of Data Augmentation,Checkpoints etc.\n",
        "model = load_model('/content/drive/MyDrive/Colab Notebooks/Gyan Data datasets/ships_classify_VGG.h5')\n",
        "labels = pd.read_pickle(\"/content/drive/MyDrive/Colab Notebooks/Gyan Data datasets/encoder_ship.pkl\")"
      ],
      "metadata": {
        "id": "LwRfWPO_prQn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "uploaded = files.upload()\n",
        "filename=list(uploaded.keys())[0]\n",
        "print(filename)\n",
        "image = cv2.imdecode(np.frombuffer(uploaded[filename], np.uint8),cv2.IMREAD_UNCHANGED)\n",
        "image = cv2.resize(image,(224,224))                     \n",
        "image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  \n",
        "sample_image = expand_dims(image, 0)\n",
        "prediction=model.predict(sample_image,verbose=1)\n",
        "index=list(np.where(prediction[0] == prediction[0].max())[0])[0]\n",
        "print(\"Probabilities of each Category:\",prediction)\n",
        "print(\"Maximum Probability class:\",prediction[0].max())\n",
        "print(\"Predicted_Label:\",index)\n",
        "print(\"Predicted_Class:\",labels.classes_[index])"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 177
        },
        "id": "U_VSkhnDqzHg",
        "outputId": "78729b5a-8bc9-4b82-cc15-23990bfb70f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-9d19f696-5109-4d95-b21f-a6dc236b787a\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-9d19f696-5109-4d95-b21f-a6dc236b787a\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving test_submarine.jpg to test_submarine (2).jpg\n",
            "test_submarine.jpg\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "Probabilities of each Category: [[0. 0. 0. 1. 0.]]\n",
            "Maximum Probability class: 1.0\n",
            "Predicted_Label: 3\n",
            "Predicted_Class: sub\n"
          ]
        }
      ]
    }
  ]
}