CNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8rlvf3wz3V6r"
   },
   "source": [
    "# <font color = blue>**1. What is CNN?**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vfXeZikcYE5k"
   },
   "source": [
    "CNN which is called as Convolutional Neural Network is majorly used for image classification. It is basically ANN but with some additional layers at the beginning. Some other domains where CNN is used are Scene labelling, object detection, face recognition, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_hhFiq1TZydw"
   },
   "source": [
    "# <font color = blue>**2. Structure of CNN?**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LJi_97owZ3JB"
   },
   "source": [
    "CNN is made up of 4 layers namely:\n",
    "\n",
    "\n",
    "1.   Input layer\n",
    "2.   Convolution layer\n",
    "3.   Pooling layer\n",
    "4.   Fully connected layer\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XPVOwhrfhzE_"
   },
   "source": [
    "# <font color = blue>**3. How does CNN work?**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L8Hd3xgEapWg"
   },
   "source": [
    "Image is made up of pixelated planes stacked on to each other. There are 3 planes Red, Green & Blue which are stacked together in a 3 dimensional space. So one plane has numerous amount of pixels depending upon the image dimensions. Each pixel cell carries a value from 0 to 255, 255 being the brightest of the colour plane and 0 being the darkest. One thing to remember here is that, Image being an unstructured data, is always passed as an array to the model.\n",
    "\n",
    "The thing is, such pixelated Images at times carry pixels that are not important as well leading to a very slow building of the model. Hence in CNN, we have added two extra layers before the input layer namely Convolutional layer & Pooling layer. These additional layers extract only the essential pixels from the image.\n",
    "\n",
    "Look at the images below to understand the concept more better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "4Olyd9Cij3v1"
   },
   "outputs": [],
   "source": [
    "from google.colab import files\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73
    },
    "id": "TPO5_gI1j6QM",
    "outputId": "9f5c3854-5e94-4835-f751-65e3071171f2"
   },
   "outputs": [
    {
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       "        style=\"border:none\" />\n",
       "     <output id=\"result-ecb0d311-411c-4159-a51f-0debad982394\">\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>// Copyright 2017 Google LLC\n",
       "//\n",
       "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
       "// you may not use this file except in compliance with the License.\n",
       "// You may obtain a copy of the License at\n",
       "//\n",
       "//      http://www.apache.org/licenses/LICENSE-2.0\n",
       "//\n",
       "// Unless required by applicable law or agreed to in writing, software\n",
       "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
       "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
       "// See the License for the specific language governing permissions and\n",
       "// limitations under the License.\n",
       "\n",
       "/**\n",
       " * @fileoverview Helpers for google.colab Python module.\n",
       " */\n",
       "(function(scope) {\n",
       "function span(text, styleAttributes = {}) {\n",
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       "  return element;\n",
       "}\n",
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       "// Max number of bytes which will be uploaded at a time.\n",
       "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
       "\n",
       "function _uploadFiles(inputId, outputId) {\n",
       "  const steps = uploadFilesStep(inputId, outputId);\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  // Cache steps on the outputElement to make it available for the next call\n",
       "  // to uploadFilesContinue from Python.\n",
       "  outputElement.steps = steps;\n",
       "\n",
       "  return _uploadFilesContinue(outputId);\n",
       "}\n",
       "\n",
       "// This is roughly an async generator (not supported in the browser yet),\n",
       "// where there are multiple asynchronous steps and the Python side is going\n",
       "// to poll for completion of each step.\n",
       "// This uses a Promise to block the python side on completion of each step,\n",
       "// then passes the result of the previous step as the input to the next step.\n",
       "function _uploadFilesContinue(outputId) {\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  const steps = outputElement.steps;\n",
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       "  const next = steps.next(outputElement.lastPromiseValue);\n",
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       "    // step of the generator.\n",
       "    outputElement.lastPromiseValue = value;\n",
       "    return next.value.response;\n",
       "  });\n",
       "}\n",
       "\n",
       "/**\n",
       " * Generator function which is called between each async step of the upload\n",
       " * process.\n",
       " * @param {string} inputId Element ID of the input file picker element.\n",
       " * @param {string} outputId Element ID of the output display.\n",
       " * @return {!Iterable<!Object>} Iterable of next steps.\n",
       " */\n",
       "function* uploadFilesStep(inputId, outputId) {\n",
       "  const inputElement = document.getElementById(inputId);\n",
       "  inputElement.disabled = false;\n",
       "\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  outputElement.innerHTML = '';\n",
       "\n",
       "  const pickedPromise = new Promise((resolve) => {\n",
       "    inputElement.addEventListener('change', (e) => {\n",
       "      resolve(e.target.files);\n",
       "    });\n",
       "  });\n",
       "\n",
       "  const cancel = document.createElement('button');\n",
       "  inputElement.parentElement.appendChild(cancel);\n",
       "  cancel.textContent = 'Cancel upload';\n",
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       "\n",
       "  // Wait for the user to pick the files.\n",
       "  const files = yield {\n",
       "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
       "    response: {\n",
       "      action: 'starting',\n",
       "    }\n",
       "  };\n",
       "\n",
       "  cancel.remove();\n",
       "\n",
       "  // Disable the input element since further picks are not allowed.\n",
       "  inputElement.disabled = true;\n",
       "\n",
       "  if (!files) {\n",
       "    return {\n",
       "      response: {\n",
       "        action: 'complete',\n",
       "      }\n",
       "    };\n",
       "  }\n",
       "\n",
       "  for (const file of files) {\n",
       "    const li = document.createElement('li');\n",
       "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
       "    li.append(span(\n",
       "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
       "        `last modified: ${\n",
       "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
       "                                    'n/a'} - `));\n",
       "    const percent = span('0% done');\n",
       "    li.appendChild(percent);\n",
       "\n",
       "    outputElement.appendChild(li);\n",
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       "        resolve(e.target.result);\n",
       "      };\n",
       "      reader.readAsArrayBuffer(file);\n",
       "    });\n",
       "    // Wait for the data to be ready.\n",
       "    let fileData = yield {\n",
       "      promise: fileDataPromise,\n",
       "      response: {\n",
       "        action: 'continue',\n",
       "      }\n",
       "    };\n",
       "\n",
       "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
       "    let position = 0;\n",
       "    do {\n",
       "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
       "      const chunk = new Uint8Array(fileData, position, length);\n",
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       "      yield {\n",
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       "        },\n",
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       "      let percentDone = fileData.byteLength === 0 ?\n",
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       "          Math.round((position / fileData.byteLength) * 100);\n",
       "      percent.textContent = `${percentDone}% done`;\n",
       "\n",
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       "    }\n",
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       "}\n",
       "\n",
       "scope.google = scope.google || {};\n",
       "scope.google.colab = scope.google.colab || {};\n",
       "scope.google.colab._files = {\n",
       "  _uploadFiles,\n",
       "  _uploadFilesContinue,\n",
       "};\n",
       "})(self);\n",
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving RGB_plane.jpg to RGB_plane.jpg\n"
     ]
    }
   ],
   "source": [
    "uploaded = files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "id": "LqOUOrSwkGvJ",
    "outputId": "588daafe-bea8-4555-aec3-d003a37c3690"
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('RGB_plane.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73
    },
    "id": "4IrbYXqlkOFJ",
    "outputId": "8c3588e9-f5c3-4478-cc00-4717ec3ffb51"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-bb358399-47ae-458d-b9ff-37d12ba438e9\" name=\"files[]\" multiple disabled\n",
       "        style=\"border:none\" />\n",
       "     <output id=\"result-bb358399-47ae-458d-b9ff-37d12ba438e9\">\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>// Copyright 2017 Google LLC\n",
       "//\n",
       "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
       "// you may not use this file except in compliance with the License.\n",
       "// You may obtain a copy of the License at\n",
       "//\n",
       "//      http://www.apache.org/licenses/LICENSE-2.0\n",
       "//\n",
       "// Unless required by applicable law or agreed to in writing, software\n",
       "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
       "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
       "// See the License for the specific language governing permissions and\n",
       "// limitations under the License.\n",
       "\n",
       "/**\n",
       " * @fileoverview Helpers for google.colab Python module.\n",
       " */\n",
       "(function(scope) {\n",
       "function span(text, styleAttributes = {}) {\n",
       "  const element = document.createElement('span');\n",
       "  element.textContent = text;\n",
       "  for (const key of Object.keys(styleAttributes)) {\n",
       "    element.style[key] = styleAttributes[key];\n",
       "  }\n",
       "  return element;\n",
       "}\n",
       "\n",
       "// Max number of bytes which will be uploaded at a time.\n",
       "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
       "\n",
       "function _uploadFiles(inputId, outputId) {\n",
       "  const steps = uploadFilesStep(inputId, outputId);\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  // Cache steps on the outputElement to make it available for the next call\n",
       "  // to uploadFilesContinue from Python.\n",
       "  outputElement.steps = steps;\n",
       "\n",
       "  return _uploadFilesContinue(outputId);\n",
       "}\n",
       "\n",
       "// This is roughly an async generator (not supported in the browser yet),\n",
       "// where there are multiple asynchronous steps and the Python side is going\n",
       "// to poll for completion of each step.\n",
       "// This uses a Promise to block the python side on completion of each step,\n",
       "// then passes the result of the previous step as the input to the next step.\n",
       "function _uploadFilesContinue(outputId) {\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  const steps = outputElement.steps;\n",
       "\n",
       "  const next = steps.next(outputElement.lastPromiseValue);\n",
       "  return Promise.resolve(next.value.promise).then((value) => {\n",
       "    // Cache the last promise value to make it available to the next\n",
       "    // step of the generator.\n",
       "    outputElement.lastPromiseValue = value;\n",
       "    return next.value.response;\n",
       "  });\n",
       "}\n",
       "\n",
       "/**\n",
       " * Generator function which is called between each async step of the upload\n",
       " * process.\n",
       " * @param {string} inputId Element ID of the input file picker element.\n",
       " * @param {string} outputId Element ID of the output display.\n",
       " * @return {!Iterable<!Object>} Iterable of next steps.\n",
       " */\n",
       "function* uploadFilesStep(inputId, outputId) {\n",
       "  const inputElement = document.getElementById(inputId);\n",
       "  inputElement.disabled = false;\n",
       "\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  outputElement.innerHTML = '';\n",
       "\n",
       "  const pickedPromise = new Promise((resolve) => {\n",
       "    inputElement.addEventListener('change', (e) => {\n",
       "      resolve(e.target.files);\n",
       "    });\n",
       "  });\n",
       "\n",
       "  const cancel = document.createElement('button');\n",
       "  inputElement.parentElement.appendChild(cancel);\n",
       "  cancel.textContent = 'Cancel upload';\n",
       "  const cancelPromise = new Promise((resolve) => {\n",
       "    cancel.onclick = () => {\n",
       "      resolve(null);\n",
       "    };\n",
       "  });\n",
       "\n",
       "  // Wait for the user to pick the files.\n",
       "  const files = yield {\n",
       "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
       "    response: {\n",
       "      action: 'starting',\n",
       "    }\n",
       "  };\n",
       "\n",
       "  cancel.remove();\n",
       "\n",
       "  // Disable the input element since further picks are not allowed.\n",
       "  inputElement.disabled = true;\n",
       "\n",
       "  if (!files) {\n",
       "    return {\n",
       "      response: {\n",
       "        action: 'complete',\n",
       "      }\n",
       "    };\n",
       "  }\n",
       "\n",
       "  for (const file of files) {\n",
       "    const li = document.createElement('li');\n",
       "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
       "    li.append(span(\n",
       "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
       "        `last modified: ${\n",
       "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
       "                                    'n/a'} - `));\n",
       "    const percent = span('0% done');\n",
       "    li.appendChild(percent);\n",
       "\n",
       "    outputElement.appendChild(li);\n",
       "\n",
       "    const fileDataPromise = new Promise((resolve) => {\n",
       "      const reader = new FileReader();\n",
       "      reader.onload = (e) => {\n",
       "        resolve(e.target.result);\n",
       "      };\n",
       "      reader.readAsArrayBuffer(file);\n",
       "    });\n",
       "    // Wait for the data to be ready.\n",
       "    let fileData = yield {\n",
       "      promise: fileDataPromise,\n",
       "      response: {\n",
       "        action: 'continue',\n",
       "      }\n",
       "    };\n",
       "\n",
       "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
       "    let position = 0;\n",
       "    do {\n",
       "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
       "      const chunk = new Uint8Array(fileData, position, length);\n",
       "      position += length;\n",
       "\n",
       "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
       "      yield {\n",
       "        response: {\n",
       "          action: 'append',\n",
       "          file: file.name,\n",
       "          data: base64,\n",
       "        },\n",
       "      };\n",
       "\n",
       "      let percentDone = fileData.byteLength === 0 ?\n",
       "          100 :\n",
       "          Math.round((position / fileData.byteLength) * 100);\n",
       "      percent.textContent = `${percentDone}% done`;\n",
       "\n",
       "    } while (position < fileData.byteLength);\n",
       "  }\n",
       "\n",
       "  // All done.\n",
       "  yield {\n",
       "    response: {\n",
       "      action: 'complete',\n",
       "    }\n",
       "  };\n",
       "}\n",
       "\n",
       "scope.google = scope.google || {};\n",
       "scope.google.colab = scope.google.colab || {};\n",
       "scope.google.colab._files = {\n",
       "  _uploadFiles,\n",
       "  _uploadFilesContinue,\n",
       "};\n",
       "})(self);\n",
       "</script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving CNN_structure_2.png to CNN_structure_2.png\n"
     ]
    }
   ],
   "source": [
    "uploaded = files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 467
    },
    "id": "PJ5XcJ7Plh58",
    "outputId": "c5857c16-e446-4857-addd-5d8528537897"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "image/png": {
       "width": 800
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('CNN_structure_2.png', width = 800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "osaCA0sFl1Lf"
   },
   "source": [
    "# <font color = blue>**4. How does Convolution layer work?**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hQnfBXP6mBmt"
   },
   "source": [
    "A Convolution layer make use of matrix multiplication to make a convoluted image from a pixelated array. The convoluted image is smaller in dimension and contains only important pixels.\n",
    "\n",
    "There's another concept called stride which one needs to know. So while doing matrix multiplication if the pixelated image is moving 1 pixel at a time then the stride= 1, if the pixelated image is moving 2 pixels at a time, then the stride = 2.\n",
    "\n",
    "So Stride here is a hyperparameter. If one keeps stride=2 (more) then the model will take less time to train, however the extracted information will be less. On the contrary if stride=1 (less) then the model will take more time to train but the information extracted will be more. So, it is important to use optimum amount of Stride."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73
    },
    "id": "oN1ZgLj02A-N",
    "outputId": "321df9ce-a06a-4b69-f235-a3576fb71a3e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-a61ea2d7-6e3c-4292-bf76-2cddb4bc4789\" name=\"files[]\" multiple disabled\n",
       "        style=\"border:none\" />\n",
       "     <output id=\"result-a61ea2d7-6e3c-4292-bf76-2cddb4bc4789\">\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>// Copyright 2017 Google LLC\n",
       "//\n",
       "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
       "// you may not use this file except in compliance with the License.\n",
       "// You may obtain a copy of the License at\n",
       "//\n",
       "//      http://www.apache.org/licenses/LICENSE-2.0\n",
       "//\n",
       "// Unless required by applicable law or agreed to in writing, software\n",
       "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
       "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
       "// See the License for the specific language governing permissions and\n",
       "// limitations under the License.\n",
       "\n",
       "/**\n",
       " * @fileoverview Helpers for google.colab Python module.\n",
       " */\n",
       "(function(scope) {\n",
       "function span(text, styleAttributes = {}) {\n",
       "  const element = document.createElement('span');\n",
       "  element.textContent = text;\n",
       "  for (const key of Object.keys(styleAttributes)) {\n",
       "    element.style[key] = styleAttributes[key];\n",
       "  }\n",
       "  return element;\n",
       "}\n",
       "\n",
       "// Max number of bytes which will be uploaded at a time.\n",
       "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
       "\n",
       "function _uploadFiles(inputId, outputId) {\n",
       "  const steps = uploadFilesStep(inputId, outputId);\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  // Cache steps on the outputElement to make it available for the next call\n",
       "  // to uploadFilesContinue from Python.\n",
       "  outputElement.steps = steps;\n",
       "\n",
       "  return _uploadFilesContinue(outputId);\n",
       "}\n",
       "\n",
       "// This is roughly an async generator (not supported in the browser yet),\n",
       "// where there are multiple asynchronous steps and the Python side is going\n",
       "// to poll for completion of each step.\n",
       "// This uses a Promise to block the python side on completion of each step,\n",
       "// then passes the result of the previous step as the input to the next step.\n",
       "function _uploadFilesContinue(outputId) {\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  const steps = outputElement.steps;\n",
       "\n",
       "  const next = steps.next(outputElement.lastPromiseValue);\n",
       "  return Promise.resolve(next.value.promise).then((value) => {\n",
       "    // Cache the last promise value to make it available to the next\n",
       "    // step of the generator.\n",
       "    outputElement.lastPromiseValue = value;\n",
       "    return next.value.response;\n",
       "  });\n",
       "}\n",
       "\n",
       "/**\n",
       " * Generator function which is called between each async step of the upload\n",
       " * process.\n",
       " * @param {string} inputId Element ID of the input file picker element.\n",
       " * @param {string} outputId Element ID of the output display.\n",
       " * @return {!Iterable<!Object>} Iterable of next steps.\n",
       " */\n",
       "function* uploadFilesStep(inputId, outputId) {\n",
       "  const inputElement = document.getElementById(inputId);\n",
       "  inputElement.disabled = false;\n",
       "\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  outputElement.innerHTML = '';\n",
       "\n",
       "  const pickedPromise = new Promise((resolve) => {\n",
       "    inputElement.addEventListener('change', (e) => {\n",
       "      resolve(e.target.files);\n",
       "    });\n",
       "  });\n",
       "\n",
       "  const cancel = document.createElement('button');\n",
       "  inputElement.parentElement.appendChild(cancel);\n",
       "  cancel.textContent = 'Cancel upload';\n",
       "  const cancelPromise = new Promise((resolve) => {\n",
       "    cancel.onclick = () => {\n",
       "      resolve(null);\n",
       "    };\n",
       "  });\n",
       "\n",
       "  // Wait for the user to pick the files.\n",
       "  const files = yield {\n",
       "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
       "    response: {\n",
       "      action: 'starting',\n",
       "    }\n",
       "  };\n",
       "\n",
       "  cancel.remove();\n",
       "\n",
       "  // Disable the input element since further picks are not allowed.\n",
       "  inputElement.disabled = true;\n",
       "\n",
       "  if (!files) {\n",
       "    return {\n",
       "      response: {\n",
       "        action: 'complete',\n",
       "      }\n",
       "    };\n",
       "  }\n",
       "\n",
       "  for (const file of files) {\n",
       "    const li = document.createElement('li');\n",
       "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
       "    li.append(span(\n",
       "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
       "        `last modified: ${\n",
       "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
       "                                    'n/a'} - `));\n",
       "    const percent = span('0% done');\n",
       "    li.appendChild(percent);\n",
       "\n",
       "    outputElement.appendChild(li);\n",
       "\n",
       "    const fileDataPromise = new Promise((resolve) => {\n",
       "      const reader = new FileReader();\n",
       "      reader.onload = (e) => {\n",
       "        resolve(e.target.result);\n",
       "      };\n",
       "      reader.readAsArrayBuffer(file);\n",
       "    });\n",
       "    // Wait for the data to be ready.\n",
       "    let fileData = yield {\n",
       "      promise: fileDataPromise,\n",
       "      response: {\n",
       "        action: 'continue',\n",
       "      }\n",
       "    };\n",
       "\n",
       "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
       "    let position = 0;\n",
       "    do {\n",
       "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
       "      const chunk = new Uint8Array(fileData, position, length);\n",
       "      position += length;\n",
       "\n",
       "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
       "      yield {\n",
       "        response: {\n",
       "          action: 'append',\n",
       "          file: file.name,\n",
       "          data: base64,\n",
       "        },\n",
       "      };\n",
       "\n",
       "      let percentDone = fileData.byteLength === 0 ?\n",
       "          100 :\n",
       "          Math.round((position / fileData.byteLength) * 100);\n",
       "      percent.textContent = `${percentDone}% done`;\n",
       "\n",
       "    } while (position < fileData.byteLength);\n",
       "  }\n",
       "\n",
       "  // All done.\n",
       "  yield {\n",
       "    response: {\n",
       "      action: 'complete',\n",
       "    }\n",
       "  };\n",
       "}\n",
       "\n",
       "scope.google = scope.google || {};\n",
       "scope.google.colab = scope.google.colab || {};\n",
       "scope.google.colab._files = {\n",
       "  _uploadFiles,\n",
       "  _uploadFilesContinue,\n",
       "};\n",
       "})(self);\n",
       "</script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving CNN_convolution.png to CNN_convolution.png\n"
     ]
    }
   ],
   "source": [
    "uploaded = files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 263
    },
    "id": "3UbSDMt72R_D",
    "outputId": "d5c3e00a-26f2-4677-b4ad-6d95060d2915"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "image/png": {
       "width": 600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('CNN_convolution.png', width = 600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "csBGpp-u3LmR"
   },
   "source": [
    "# <font color = blue>**5. How does Pooling layer work?**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oXvEC4MB3PTc"
   },
   "source": [
    "Pooling layer reduces the amount of information extracted in Convolution layer even further to keep only the most important pixels. So technically it reduces the feature size even further while retaining the most important information.\n",
    "The two common functions used in Pooling layer are:\n",
    "\n",
    "\n",
    "1.   Average pooling\n",
    "2.   Max pooling\n",
    "\n",
    "**In Average pooling we calculate the average/mean of each patch on the feature map.**\n",
    "\n",
    "**In Max pooling we calculate the maximum value for each patch on the feature map.**\n",
    "\n",
    "Look at the image below to understand it more better.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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     "height": 73
    },
    "id": "VFCHezrT6B1k",
    "outputId": "3ceadda6-4c80-4b86-c9e1-2cd30d283521"
   },
   "outputs": [
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      "Saving CNN_pooling.png to CNN_pooling.png\n"
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    "uploaded = files.upload()"
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 447
    },
    "id": "-F8vPdts6f1u",
    "outputId": "d447e506-5d8f-4d3d-d78d-f8001fa9c008"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "image/png": {
       "width": 600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('CNN_pooling.png', width = 600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zhhrbCmI7Yrr"
   },
   "source": [
    "# <font color = blue>**6. What is Fully connected layer?**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G76HkFhb7nnJ"
   },
   "source": [
    "A Fully connected layer is the layer where the output from pooling layer is flattened up into a vector array. It is then passed on to neurons in the i/p layer, followed by transformations in dense layer and then the classes are predicted on the output layer. To put it briefly, this is where our ANN starts. So, one can say CNN has 2 extra layers Convolution & Pooling layer prior to ANN.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73
    },
    "id": "x1DrVKcy9y0u",
    "outputId": "b0f5b338-e525-402d-ccdb-af767956f456"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-605afc84-d91c-4525-ae4f-5a757ff79188\" name=\"files[]\" multiple disabled\n",
       "        style=\"border:none\" />\n",
       "     <output id=\"result-605afc84-d91c-4525-ae4f-5a757ff79188\">\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>// Copyright 2017 Google LLC\n",
       "//\n",
       "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
       "// you may not use this file except in compliance with the License.\n",
       "// You may obtain a copy of the License at\n",
       "//\n",
       "//      http://www.apache.org/licenses/LICENSE-2.0\n",
       "//\n",
       "// Unless required by applicable law or agreed to in writing, software\n",
       "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
       "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
       "// See the License for the specific language governing permissions and\n",
       "// limitations under the License.\n",
       "\n",
       "/**\n",
       " * @fileoverview Helpers for google.colab Python module.\n",
       " */\n",
       "(function(scope) {\n",
       "function span(text, styleAttributes = {}) {\n",
       "  const element = document.createElement('span');\n",
       "  element.textContent = text;\n",
       "  for (const key of Object.keys(styleAttributes)) {\n",
       "    element.style[key] = styleAttributes[key];\n",
       "  }\n",
       "  return element;\n",
       "}\n",
       "\n",
       "// Max number of bytes which will be uploaded at a time.\n",
       "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
       "\n",
       "function _uploadFiles(inputId, outputId) {\n",
       "  const steps = uploadFilesStep(inputId, outputId);\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  // Cache steps on the outputElement to make it available for the next call\n",
       "  // to uploadFilesContinue from Python.\n",
       "  outputElement.steps = steps;\n",
       "\n",
       "  return _uploadFilesContinue(outputId);\n",
       "}\n",
       "\n",
       "// This is roughly an async generator (not supported in the browser yet),\n",
       "// where there are multiple asynchronous steps and the Python side is going\n",
       "// to poll for completion of each step.\n",
       "// This uses a Promise to block the python side on completion of each step,\n",
       "// then passes the result of the previous step as the input to the next step.\n",
       "function _uploadFilesContinue(outputId) {\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  const steps = outputElement.steps;\n",
       "\n",
       "  const next = steps.next(outputElement.lastPromiseValue);\n",
       "  return Promise.resolve(next.value.promise).then((value) => {\n",
       "    // Cache the last promise value to make it available to the next\n",
       "    // step of the generator.\n",
       "    outputElement.lastPromiseValue = value;\n",
       "    return next.value.response;\n",
       "  });\n",
       "}\n",
       "\n",
       "/**\n",
       " * Generator function which is called between each async step of the upload\n",
       " * process.\n",
       " * @param {string} inputId Element ID of the input file picker element.\n",
       " * @param {string} outputId Element ID of the output display.\n",
       " * @return {!Iterable<!Object>} Iterable of next steps.\n",
       " */\n",
       "function* uploadFilesStep(inputId, outputId) {\n",
       "  const inputElement = document.getElementById(inputId);\n",
       "  inputElement.disabled = false;\n",
       "\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  outputElement.innerHTML = '';\n",
       "\n",
       "  const pickedPromise = new Promise((resolve) => {\n",
       "    inputElement.addEventListener('change', (e) => {\n",
       "      resolve(e.target.files);\n",
       "    });\n",
       "  });\n",
       "\n",
       "  const cancel = document.createElement('button');\n",
       "  inputElement.parentElement.appendChild(cancel);\n",
       "  cancel.textContent = 'Cancel upload';\n",
       "  const cancelPromise = new Promise((resolve) => {\n",
       "    cancel.onclick = () => {\n",
       "      resolve(null);\n",
       "    };\n",
       "  });\n",
       "\n",
       "  // Wait for the user to pick the files.\n",
       "  const files = yield {\n",
       "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
       "    response: {\n",
       "      action: 'starting',\n",
       "    }\n",
       "  };\n",
       "\n",
       "  cancel.remove();\n",
       "\n",
       "  // Disable the input element since further picks are not allowed.\n",
       "  inputElement.disabled = true;\n",
       "\n",
       "  if (!files) {\n",
       "    return {\n",
       "      response: {\n",
       "        action: 'complete',\n",
       "      }\n",
       "    };\n",
       "  }\n",
       "\n",
       "  for (const file of files) {\n",
       "    const li = document.createElement('li');\n",
       "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
       "    li.append(span(\n",
       "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
       "        `last modified: ${\n",
       "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
       "                                    'n/a'} - `));\n",
       "    const percent = span('0% done');\n",
       "    li.appendChild(percent);\n",
       "\n",
       "    outputElement.appendChild(li);\n",
       "\n",
       "    const fileDataPromise = new Promise((resolve) => {\n",
       "      const reader = new FileReader();\n",
       "      reader.onload = (e) => {\n",
       "        resolve(e.target.result);\n",
       "      };\n",
       "      reader.readAsArrayBuffer(file);\n",
       "    });\n",
       "    // Wait for the data to be ready.\n",
       "    let fileData = yield {\n",
       "      promise: fileDataPromise,\n",
       "      response: {\n",
       "        action: 'continue',\n",
       "      }\n",
       "    };\n",
       "\n",
       "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
       "    let position = 0;\n",
       "    do {\n",
       "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
       "      const chunk = new Uint8Array(fileData, position, length);\n",
       "      position += length;\n",
       "\n",
       "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
       "      yield {\n",
       "        response: {\n",
       "          action: 'append',\n",
       "          file: file.name,\n",
       "          data: base64,\n",
       "        },\n",
       "      };\n",
       "\n",
       "      let percentDone = fileData.byteLength === 0 ?\n",
       "          100 :\n",
       "          Math.round((position / fileData.byteLength) * 100);\n",
       "      percent.textContent = `${percentDone}% done`;\n",
       "\n",
       "    } while (position < fileData.byteLength);\n",
       "  }\n",
       "\n",
       "  // All done.\n",
       "  yield {\n",
       "    response: {\n",
       "      action: 'complete',\n",
       "    }\n",
       "  };\n",
       "}\n",
       "\n",
       "scope.google = scope.google || {};\n",
       "scope.google.colab = scope.google.colab || {};\n",
       "scope.google.colab._files = {\n",
       "  _uploadFiles,\n",
       "  _uploadFilesContinue,\n",
       "};\n",
       "})(self);\n",
       "</script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving CNN_fully_connecting_layer.png to CNN_fully_connecting_layer.png\n"
     ]
    }
   ],
   "source": [
    "uploaded = files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 407
    },
    "id": "omnLxuW698ey",
    "outputId": "399e6955-d3e4-4dd7-fe6b-c9cb763e541a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "image/png": {
       "width": 600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('CNN_fully_connecting_layer.png', width = 600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QMW3Yf-JAQ3V"
   },
   "source": [
    "# <font color = Violet>**Code**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q4ARQhJ2CVGI"
   },
   "source": [
    "Since we are working on Google colab we have to mount the drive, locate the folder and extract the dataset first.\n",
    "\n",
    "While mounting the drive, Colab will ask for permissions to access the Google drive, so do grant it.\n",
    "\n",
    "To locate the folder, first you have to make a folder in the drive first. Once done, run this command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KWl_-4wc1Kbl",
    "outputId": "b60aaf07-aa98-4792-b78a-62876f9cd2b0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "# Mount the drive\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hY3IxS_51-yj"
   },
   "outputs": [],
   "source": [
    "# Locate the folder\n",
    "import os\n",
    "os.chdir('/content/drive/MyDrive/CNN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 261
    },
    "id": "QAWRkAil2RjO",
    "outputId": "27e77162-b5b7-4be5-8b80-60df16debf02"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting patool\n",
      "  Downloading patool-2.2.0-py2.py3-none-any.whl (96 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m96.0/96.0 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: patool\n",
      "Successfully installed patool-2.2.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO patool: Extracting dataset.rar ...\n",
      "INFO:patool:Extracting dataset.rar ...\n",
      "INFO patool: running /usr/bin/unrar x -- /content/drive/MyDrive/CNN/dataset.rar\n",
      "INFO:patool:running /usr/bin/unrar x -- /content/drive/MyDrive/CNN/dataset.rar\n",
      "INFO patool:     with cwd='./Unpack_nc0nh8ex', input=''\n",
      "INFO:patool:    with cwd='./Unpack_nc0nh8ex', input=''\n",
      "INFO patool: ... dataset.rar extracted to `dataset11' (local file exists).\n",
      "INFO:patool:... dataset.rar extracted to `dataset11' (local file exists).\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'dataset11'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting rar files\n",
    "! pip install patool\n",
    "import patoolib\n",
    "patoolib.extract_archive('dataset.rar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dyHl-AyEDPgs"
   },
   "source": [
    "Install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "k9U9Elsk3BAx",
    "outputId": "1099d05c-3bb7-4bfc-c798-0b35b8920736"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflow==2.11\n",
      "  Downloading tensorflow-2.11.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (588.3 MB)\n",
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      "\u001b[?25hCollecting tensorboard-plugin-wit>=1.6.0 (from tensorboard<2.12,>=2.11->tensorflow==2.11)\n",
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      "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow==2.11) (3.2.2)\n",
      "Installing collected packages: tensorboard-plugin-wit, tensorflow-estimator, tensorboard-data-server, protobuf, keras, gast, google-auth-oauthlib, tensorboard, tensorflow\n",
      "  Attempting uninstall: tensorflow-estimator\n",
      "    Found existing installation: tensorflow-estimator 2.15.0\n",
      "    Uninstalling tensorflow-estimator-2.15.0:\n",
      "      Successfully uninstalled tensorflow-estimator-2.15.0\n",
      "  Attempting uninstall: tensorboard-data-server\n",
      "    Found existing installation: tensorboard-data-server 0.7.2\n",
      "    Uninstalling tensorboard-data-server-0.7.2:\n",
      "      Successfully uninstalled tensorboard-data-server-0.7.2\n",
      "  Attempting uninstall: protobuf\n",
      "    Found existing installation: protobuf 3.20.3\n",
      "    Uninstalling protobuf-3.20.3:\n",
      "      Successfully uninstalled protobuf-3.20.3\n",
      "  Attempting uninstall: keras\n",
      "    Found existing installation: keras 2.15.0\n",
      "    Uninstalling keras-2.15.0:\n",
      "      Successfully uninstalled keras-2.15.0\n",
      "  Attempting uninstall: gast\n",
      "    Found existing installation: gast 0.5.4\n",
      "    Uninstalling gast-0.5.4:\n",
      "      Successfully uninstalled gast-0.5.4\n",
      "  Attempting uninstall: google-auth-oauthlib\n",
      "    Found existing installation: google-auth-oauthlib 1.2.0\n",
      "    Uninstalling google-auth-oauthlib-1.2.0:\n",
      "      Successfully uninstalled google-auth-oauthlib-1.2.0\n",
      "  Attempting uninstall: tensorboard\n",
      "    Found existing installation: tensorboard 2.15.2\n",
      "    Uninstalling tensorboard-2.15.2:\n",
      "      Successfully uninstalled tensorboard-2.15.2\n",
      "  Attempting uninstall: tensorflow\n",
      "    Found existing installation: tensorflow 2.15.0\n",
      "    Uninstalling tensorflow-2.15.0:\n",
      "      Successfully uninstalled tensorflow-2.15.0\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "pandas-gbq 0.19.2 requires google-auth-oauthlib>=0.7.0, but you have google-auth-oauthlib 0.4.6 which is incompatible.\n",
      "tensorflow-datasets 4.9.4 requires protobuf>=3.20, but you have protobuf 3.19.6 which is incompatible.\n",
      "tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 3.19.6 which is incompatible.\n",
      "tf-keras 2.15.1 requires tensorflow<2.16,>=2.15, but you have tensorflow 2.11.0 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed gast-0.4.0 google-auth-oauthlib-0.4.6 keras-2.11.0 protobuf-3.19.6 tensorboard-2.11.2 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 tensorflow-2.11.0 tensorflow-estimator-2.11.0\n"
     ]
    },
    {
     "data": {
      "application/vnd.colab-display-data+json": {
       "id": "c44ccf9fba294144ad748ea6af1f7c16",
       "pip_warning": {
        "packages": [
         "google"
        ]
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "! pip install tensorflow==2.11"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RxyGIYvuEeMU"
   },
   "source": [
    "## <font color = Red>**1. Importing the libraries**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NYkbj3_Y4Xjm"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models\n",
    "from tensorflow.keras. preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ixIxwbePFgB9"
   },
   "source": [
    "## <font color = Red>**2. Reading the image & defining it's dimensions**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nb5EKFoYFwje"
   },
   "source": [
    "In the code block below, we are extracting dataset and defining batch size as 32 and image dimensions as 128x128."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xP635uDO5ozL"
   },
   "outputs": [],
   "source": [
    "# Defining the dimensions of image\n",
    "train_data_dir = 'dataset'\n",
    "batch_size = 32\n",
    "img_size= (128,128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KQtQlPeEF_E-"
   },
   "source": [
    "## <font color = Red>**3. Generating train & validation (test) data**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GNjgUHvSGQ3q"
   },
   "source": [
    "We are using ImageDataGenerator function to rescale the image and dividing it into:\n",
    "\n",
    "train = 80 %\n",
    "\n",
    "validation (test) = 20% (validation_split = 0.2)\n",
    "\n",
    "where the batch size and target image size is same as defined above.\n",
    "\n",
    "'rescale=1./255' parameter is used to rescale the pixel values of the images. In this case, it is dividing all pixel values by 255. This operation scales the pixel values to the range [0, 1], which is typically done to normalize the input data. Normalization helps in improving the convergence of the training process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "m_cs-92r52N0",
    "outputId": "1f5cc9e5-4f30-4332-a1f8-e12b3b195ec2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1062 images belonging to 3 classes.\n"
     ]
    }
   ],
   "source": [
    "# Generating train data\n",
    "datagen = ImageDataGenerator(rescale=1./255, validation_split= 0.2)\n",
    "\n",
    "train_generator = datagen.flow_from_directory(\n",
    "      train_data_dir,\n",
    "      target_size = img_size,\n",
    "      batch_size = batch_size,\n",
    "      class_mode = 'categorical',\n",
    "      subset = 'training'\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IyK1U4Km6pN3",
    "outputId": "89dd3e36-a9cc-492c-f279-bc7455a81a4d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 264 images belonging to 3 classes.\n"
     ]
    }
   ],
   "source": [
    "# Generating validation data\n",
    "validation_generator = datagen.flow_from_directory(\n",
    "    train_data_dir,\n",
    "    target_size = img_size,\n",
    "    batch_size = batch_size,\n",
    "    class_mode = 'categorical',\n",
    "    subset = 'validation'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q0P4xWX-HFRF"
   },
   "source": [
    "So overall we have got 1062 images in the training set and 264 images in the validation set. The classes here are the 3 image classes from the dataset namely Sea, Building & Forest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ymsp5rHzVm_G"
   },
   "source": [
    "## <font color = Red>**4. Creating the model**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HBKqiTdbVuiS"
   },
   "source": [
    "So we start by creating a Sequential model. In this model we add 32, 3x3 convolution layers. We have to define the input dimensions '128x128x3' as well.\n",
    "\n",
    "It is then followed by adding 1, 2x2 pooling layer. I have created more such combinations of convolution & pooling layers to make sure the image is normalized even more.\n",
    "\n",
    "It is then followed by flattening the image. Once flattened, the image will be passed as a 1D array to the i/p layer. Here I have then passed the image to 128 dense layers to generate an output with 3 dense layers.\n",
    "\n",
    "There are 2 important things to note down here, the first being, since this is a classification model with more than 2 classes we are using activation function as 'softmax' or else if the classes were 2 we could have used 'sigmoid'.\n",
    "\n",
    "Second being, we have introduced a Dropout layer between the dense and the output layer to avoid any unnecessary Overfitting of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9QwaI-lO7via"
   },
   "outputs": [],
   "source": [
    "# Creating a Sequential model\n",
    "model = models.Sequential()\n",
    "\n",
    "# Adding  32 Convolution &  1 Pooling layer\n",
    "model.add(layers.Conv2D(32,(3,3),activation = 'relu',input_shape = (128,128,3)))\n",
    "model.add(layers.MaxPooling2D((2,2)))\n",
    "\n",
    "# Adding 64 Convolution layers & 1 Pooling layer\n",
    "model.add(layers.Conv2D(64,(3,3),activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2,2)))\n",
    "\n",
    "# Adding 128 Convolution layers & 1 Pooling layer\n",
    "model.add(layers.Conv2D(128,(3,3),activation = 'relu'))\n",
    "model.add(layers.MaxPooling2D((2,2)))\n",
    "\n",
    "# Flattening the layer to create a 1D array & passing it as an input.\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(128,activation = 'relu'))\n",
    "\n",
    "# Adding a Dropout layer is used to avoid Overfitting & defining o/p layer.\n",
    "model.add(layers.Dropout(0.5))\n",
    "model.add(layers.Dense(3,activation = 'softmax'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HR9mLafgaL0M"
   },
   "source": [
    "## <font color = Red>**5. Compiling the model**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p5BuCvKMaPWw"
   },
   "source": [
    "It is then followed by the traditional steps of ANN. Here, we compile the model on the optimizer 'adam', loss being 'categorical_crossentropy' (since we have a classification dataset) and metrics on which we compile is 'accuracy'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fuMVPOlW-A98"
   },
   "outputs": [],
   "source": [
    "# Compiling the model\n",
    "model.compile(optimizer = 'adam', loss='categorical_crossentropy',metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fEFADqFqk_Nz"
   },
   "source": [
    "## <font color = Red>**6. Training the model**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WT6OY2UMli9l"
   },
   "source": [
    "We then train & fit the model on training data (train_generator) & validation data (test data). We define the epoch steps as the ratio of train generator samples (1062) divided by batch size (32) which would be 33 steps per epoch. We then define the total number of epochs required (100) followed by validation data & validation steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "izYTzxTE-aac",
    "outputId": "644ab8a7-e7d3-4388-e647-8d5512cc48be"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "33/33 [==============================] - 203s 6s/step - loss: 0.8570 - accuracy: 0.6223 - val_loss: 0.5521 - val_accuracy: 0.7852\n",
      "Epoch 2/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.5350 - accuracy: 0.7903 - val_loss: 0.5641 - val_accuracy: 0.7656\n",
      "Epoch 3/100\n",
      "33/33 [==============================] - 36s 1s/step - loss: 0.4918 - accuracy: 0.8252 - val_loss: 0.4654 - val_accuracy: 0.8242\n",
      "Epoch 4/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.3999 - accuracy: 0.8592 - val_loss: 0.5350 - val_accuracy: 0.8086\n",
      "Epoch 5/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.3463 - accuracy: 0.8728 - val_loss: 0.4210 - val_accuracy: 0.8359\n",
      "Epoch 6/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.2917 - accuracy: 0.8961 - val_loss: 0.4136 - val_accuracy: 0.8594\n",
      "Epoch 7/100\n",
      "33/33 [==============================] - 37s 1s/step - loss: 0.2376 - accuracy: 0.9194 - val_loss: 0.4290 - val_accuracy: 0.8594\n",
      "Epoch 8/100\n",
      "33/33 [==============================] - 36s 1s/step - loss: 0.2893 - accuracy: 0.9029 - val_loss: 0.7253 - val_accuracy: 0.6680\n",
      "Epoch 9/100\n",
      "33/33 [==============================] - 37s 1s/step - loss: 0.3314 - accuracy: 0.8951 - val_loss: 0.4221 - val_accuracy: 0.8398\n",
      "Epoch 10/100\n",
      "33/33 [==============================] - 36s 1s/step - loss: 0.1984 - accuracy: 0.9272 - val_loss: 0.4135 - val_accuracy: 0.8789\n",
      "Epoch 11/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.1344 - accuracy: 0.9573 - val_loss: 0.4700 - val_accuracy: 0.8750\n",
      "Epoch 12/100\n",
      "33/33 [==============================] - 36s 1s/step - loss: 0.0963 - accuracy: 0.9689 - val_loss: 0.5111 - val_accuracy: 0.8789\n",
      "Epoch 13/100\n",
      "33/33 [==============================] - 38s 1s/step - loss: 0.0762 - accuracy: 0.9796 - val_loss: 0.5588 - val_accuracy: 0.8633\n",
      "Epoch 14/100\n",
      "33/33 [==============================] - 39s 1s/step - loss: 0.0749 - accuracy: 0.9786 - val_loss: 0.5830 - val_accuracy: 0.8828\n",
      "Epoch 15/100\n",
      "33/33 [==============================] - 38s 1s/step - loss: 0.0495 - accuracy: 0.9874 - val_loss: 0.5728 - val_accuracy: 0.8828\n",
      "Epoch 16/100\n",
      "33/33 [==============================] - 36s 1s/step - loss: 0.0423 - accuracy: 0.9893 - val_loss: 0.7370 - val_accuracy: 0.8477\n",
      "Epoch 17/100\n",
      "33/33 [==============================] - 37s 1s/step - loss: 0.0794 - accuracy: 0.9728 - val_loss: 0.6046 - val_accuracy: 0.8789\n",
      "Epoch 18/100\n",
      "33/33 [==============================] - 41s 1s/step - loss: 0.0427 - accuracy: 0.9886 - val_loss: 0.6062 - val_accuracy: 0.8633\n",
      "Epoch 19/100\n",
      "33/33 [==============================] - 38s 1s/step - loss: 0.0379 - accuracy: 0.9893 - val_loss: 0.6570 - val_accuracy: 0.8711\n",
      "Epoch 20/100\n",
      "33/33 [==============================] - 36s 1s/step - loss: 0.0355 - accuracy: 0.9854 - val_loss: 0.8719 - val_accuracy: 0.8398\n",
      "Epoch 21/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.0332 - accuracy: 0.9942 - val_loss: 0.7031 - val_accuracy: 0.8633\n",
      "Epoch 22/100\n",
      "33/33 [==============================] - 37s 1s/step - loss: 0.0391 - accuracy: 0.9913 - val_loss: 0.7501 - val_accuracy: 0.8594\n",
      "Epoch 23/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.1368 - accuracy: 0.9592 - val_loss: 0.7701 - val_accuracy: 0.8516\n",
      "Epoch 24/100\n",
      "33/33 [==============================] - 36s 1s/step - loss: 0.0484 - accuracy: 0.9874 - val_loss: 0.7230 - val_accuracy: 0.8594\n",
      "Epoch 25/100\n",
      "33/33 [==============================] - 39s 1s/step - loss: 0.0395 - accuracy: 0.9893 - val_loss: 0.6904 - val_accuracy: 0.8672\n",
      "Epoch 26/100\n",
      "33/33 [==============================] - 36s 1s/step - loss: 0.0414 - accuracy: 0.9874 - val_loss: 0.7505 - val_accuracy: 0.8633\n",
      "Epoch 27/100\n",
      "33/33 [==============================] - 36s 1s/step - loss: 0.0246 - accuracy: 0.9942 - val_loss: 0.7723 - val_accuracy: 0.8633\n",
      "Epoch 28/100\n",
      "33/33 [==============================] - 36s 1s/step - loss: 0.0374 - accuracy: 0.9903 - val_loss: 0.7391 - val_accuracy: 0.8555\n",
      "Epoch 29/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.0206 - accuracy: 0.9951 - val_loss: 0.8187 - val_accuracy: 0.8672\n",
      "Epoch 30/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0151 - accuracy: 0.9961 - val_loss: 0.9632 - val_accuracy: 0.8750\n",
      "Epoch 31/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0158 - accuracy: 0.9971 - val_loss: 0.9958 - val_accuracy: 0.8750\n",
      "Epoch 32/100\n",
      "33/33 [==============================] - 37s 1s/step - loss: 0.0133 - accuracy: 0.9961 - val_loss: 0.8316 - val_accuracy: 0.8750\n",
      "Epoch 33/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0134 - accuracy: 0.9961 - val_loss: 0.9012 - val_accuracy: 0.8789\n",
      "Epoch 34/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0089 - accuracy: 0.9990 - val_loss: 1.1643 - val_accuracy: 0.8633\n",
      "Epoch 35/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.0132 - accuracy: 0.9961 - val_loss: 0.8520 - val_accuracy: 0.8672\n",
      "Epoch 36/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0092 - accuracy: 0.9990 - val_loss: 0.9845 - val_accuracy: 0.8711\n",
      "Epoch 37/100\n",
      "33/33 [==============================] - 36s 1s/step - loss: 0.0058 - accuracy: 0.9981 - val_loss: 1.0334 - val_accuracy: 0.8750\n",
      "Epoch 38/100\n",
      "33/33 [==============================] - 36s 1s/step - loss: 0.0080 - accuracy: 0.9961 - val_loss: 1.0234 - val_accuracy: 0.8711\n",
      "Epoch 39/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.0046 - accuracy: 0.9990 - val_loss: 1.1749 - val_accuracy: 0.8672\n",
      "Epoch 40/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0050 - accuracy: 0.9981 - val_loss: 1.1510 - val_accuracy: 0.8711\n",
      "Epoch 41/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.0263 - accuracy: 0.9922 - val_loss: 0.7625 - val_accuracy: 0.8711\n",
      "Epoch 42/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0330 - accuracy: 0.9922 - val_loss: 1.1021 - val_accuracy: 0.8477\n",
      "Epoch 43/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0130 - accuracy: 0.9951 - val_loss: 0.9716 - val_accuracy: 0.8750\n",
      "Epoch 44/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0067 - accuracy: 0.9981 - val_loss: 1.1397 - val_accuracy: 0.8750\n",
      "Epoch 45/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.0061 - accuracy: 0.9971 - val_loss: 1.1139 - val_accuracy: 0.8672\n",
      "Epoch 46/100\n",
      "33/33 [==============================] - 37s 1s/step - loss: 0.0100 - accuracy: 0.9961 - val_loss: 1.0893 - val_accuracy: 0.8711\n",
      "Epoch 47/100\n",
      "33/33 [==============================] - 37s 1s/step - loss: 0.0049 - accuracy: 0.9990 - val_loss: 1.0318 - val_accuracy: 0.8789\n",
      "Epoch 48/100\n",
      "33/33 [==============================] - 36s 1s/step - loss: 0.0045 - accuracy: 0.9981 - val_loss: 1.2391 - val_accuracy: 0.8750\n",
      "Epoch 49/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.0034 - accuracy: 0.9981 - val_loss: 1.2515 - val_accuracy: 0.8750\n",
      "Epoch 50/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0090 - accuracy: 0.9961 - val_loss: 1.0946 - val_accuracy: 0.8828\n",
      "Epoch 51/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0138 - accuracy: 0.9942 - val_loss: 1.0728 - val_accuracy: 0.8711\n",
      "Epoch 52/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0086 - accuracy: 0.9981 - val_loss: 1.1442 - val_accuracy: 0.8828\n",
      "Epoch 53/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.0107 - accuracy: 0.9951 - val_loss: 1.1509 - val_accuracy: 0.8711\n",
      "Epoch 54/100\n",
      "33/33 [==============================] - 36s 1s/step - loss: 0.0094 - accuracy: 0.9971 - val_loss: 1.3337 - val_accuracy: 0.8320\n",
      "Epoch 55/100\n",
      "33/33 [==============================] - 36s 1s/step - loss: 0.0205 - accuracy: 0.9913 - val_loss: 1.2389 - val_accuracy: 0.8594\n",
      "Epoch 56/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0280 - accuracy: 0.9883 - val_loss: 1.1985 - val_accuracy: 0.8672\n",
      "Epoch 57/100\n",
      "33/33 [==============================] - 36s 1s/step - loss: 0.0487 - accuracy: 0.9806 - val_loss: 0.8661 - val_accuracy: 0.8594\n",
      "Epoch 58/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0418 - accuracy: 0.9858 - val_loss: 0.9846 - val_accuracy: 0.8398\n",
      "Epoch 59/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0285 - accuracy: 0.9903 - val_loss: 1.0757 - val_accuracy: 0.8633\n",
      "Epoch 60/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.0352 - accuracy: 0.9893 - val_loss: 1.4047 - val_accuracy: 0.8164\n",
      "Epoch 61/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0289 - accuracy: 0.9913 - val_loss: 0.8008 - val_accuracy: 0.8672\n",
      "Epoch 62/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.0107 - accuracy: 0.9981 - val_loss: 1.0821 - val_accuracy: 0.8672\n",
      "Epoch 63/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0041 - accuracy: 1.0000 - val_loss: 1.1114 - val_accuracy: 0.8711\n",
      "Epoch 64/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.0558 - accuracy: 0.9854 - val_loss: 0.9792 - val_accuracy: 0.8398\n",
      "Epoch 65/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.0201 - accuracy: 0.9913 - val_loss: 0.9543 - val_accuracy: 0.8789\n",
      "Epoch 66/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.0297 - accuracy: 0.9922 - val_loss: 1.0051 - val_accuracy: 0.8828\n",
      "Epoch 67/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.0063 - accuracy: 0.9981 - val_loss: 1.0268 - val_accuracy: 0.8789\n",
      "Epoch 68/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0048 - accuracy: 0.9981 - val_loss: 1.1379 - val_accuracy: 0.8789\n",
      "Epoch 69/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.0049 - accuracy: 0.9981 - val_loss: 0.8495 - val_accuracy: 0.8828\n",
      "Epoch 70/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0440 - accuracy: 0.9835 - val_loss: 0.8514 - val_accuracy: 0.8828\n",
      "Epoch 71/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.0205 - accuracy: 0.9903 - val_loss: 0.9549 - val_accuracy: 0.8633\n",
      "Epoch 72/100\n",
      "33/33 [==============================] - 33s 1s/step - loss: 0.0204 - accuracy: 0.9922 - val_loss: 1.2612 - val_accuracy: 0.8633\n",
      "Epoch 73/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0056 - accuracy: 0.9981 - val_loss: 1.1077 - val_accuracy: 0.8828\n",
      "Epoch 74/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.0086 - accuracy: 0.9961 - val_loss: 1.1003 - val_accuracy: 0.8672\n",
      "Epoch 75/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.0057 - accuracy: 0.9971 - val_loss: 1.0998 - val_accuracy: 0.8789\n",
      "Epoch 76/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0032 - accuracy: 0.9990 - val_loss: 1.1269 - val_accuracy: 0.8828\n",
      "Epoch 77/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.0027 - accuracy: 0.9981 - val_loss: 1.1805 - val_accuracy: 0.8750\n",
      "Epoch 78/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0018 - accuracy: 0.9990 - val_loss: 1.1944 - val_accuracy: 0.8789\n",
      "Epoch 79/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0023 - accuracy: 0.9990 - val_loss: 1.1641 - val_accuracy: 0.8828\n",
      "Epoch 80/100\n",
      "33/33 [==============================] - 33s 1s/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.3168 - val_accuracy: 0.8789\n",
      "Epoch 81/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0027 - accuracy: 0.9990 - val_loss: 1.2230 - val_accuracy: 0.8828\n",
      "Epoch 82/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0053 - accuracy: 0.9971 - val_loss: 1.2347 - val_accuracy: 0.8750\n",
      "Epoch 83/100\n",
      "33/33 [==============================] - 33s 1s/step - loss: 0.0546 - accuracy: 0.9845 - val_loss: 1.3795 - val_accuracy: 0.8203\n",
      "Epoch 84/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.0179 - accuracy: 0.9951 - val_loss: 1.1289 - val_accuracy: 0.8828\n",
      "Epoch 85/100\n",
      "33/33 [==============================] - 33s 994ms/step - loss: 0.0063 - accuracy: 0.9990 - val_loss: 1.2115 - val_accuracy: 0.8750\n",
      "Epoch 86/100\n",
      "33/33 [==============================] - 36s 1s/step - loss: 0.0107 - accuracy: 0.9951 - val_loss: 1.5023 - val_accuracy: 0.8750\n",
      "Epoch 87/100\n",
      "33/33 [==============================] - 36s 1s/step - loss: 0.0202 - accuracy: 0.9932 - val_loss: 1.4046 - val_accuracy: 0.8750\n",
      "Epoch 88/100\n",
      "33/33 [==============================] - 36s 1s/step - loss: 0.0077 - accuracy: 0.9981 - val_loss: 1.2579 - val_accuracy: 0.8516\n",
      "Epoch 89/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0243 - accuracy: 0.9962 - val_loss: 1.3788 - val_accuracy: 0.8438\n",
      "Epoch 90/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0050 - accuracy: 0.9990 - val_loss: 1.1882 - val_accuracy: 0.8828\n",
      "Epoch 91/100\n",
      "33/33 [==============================] - 33s 993ms/step - loss: 0.0038 - accuracy: 0.9981 - val_loss: 1.2861 - val_accuracy: 0.8672\n",
      "Epoch 92/100\n",
      "33/33 [==============================] - 36s 1s/step - loss: 0.0035 - accuracy: 0.9981 - val_loss: 1.3903 - val_accuracy: 0.8789\n",
      "Epoch 93/100\n",
      "33/33 [==============================] - 37s 1s/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 1.4270 - val_accuracy: 0.8789\n",
      "Epoch 94/100\n",
      "33/33 [==============================] - 36s 1s/step - loss: 0.0034 - accuracy: 0.9981 - val_loss: 1.4776 - val_accuracy: 0.8750\n",
      "Epoch 95/100\n",
      "33/33 [==============================] - 33s 1s/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 1.4599 - val_accuracy: 0.8789\n",
      "Epoch 96/100\n",
      "33/33 [==============================] - 33s 988ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.4499 - val_accuracy: 0.8750\n",
      "Epoch 97/100\n",
      "33/33 [==============================] - 33s 995ms/step - loss: 0.0073 - accuracy: 0.9961 - val_loss: 1.5167 - val_accuracy: 0.8633\n",
      "Epoch 98/100\n",
      "33/33 [==============================] - 35s 1s/step - loss: 0.0055 - accuracy: 0.9981 - val_loss: 1.5414 - val_accuracy: 0.8633\n",
      "Epoch 99/100\n",
      "33/33 [==============================] - 34s 1s/step - loss: 0.0047 - accuracy: 0.9981 - val_loss: 1.4345 - val_accuracy: 0.8516\n",
      "Epoch 100/100\n",
      "33/33 [==============================] - 36s 1s/step - loss: 0.1136 - accuracy: 0.9728 - val_loss: 0.9722 - val_accuracy: 0.8359\n"
     ]
    }
   ],
   "source": [
    "# Training the model\n",
    "history = model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch = train_generator.samples // batch_size,\n",
    "    epochs = 100,\n",
    "    validation_data = validation_generator,\n",
    "    validation_steps = validation_generator.samples // batch_size\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8GKywBDY2gF1"
   },
   "source": [
    "## <font color = Red>**7. Saving the model**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R8GdZIlD2nXK"
   },
   "source": [
    "We then save the model as 'mymodel.h5' so that we can test it anytime in future. To test the model we are going to use UI from gradio, so do install that library as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hzUTACp4JWxF"
   },
   "outputs": [],
   "source": [
    "model.save('mymodel.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ADn-q_4zRJlI",
    "outputId": "9b7937ba-da6d-4b3d-e632-92b2c215f875"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting gradio\n",
      "  Downloading gradio-4.27.0-py3-none-any.whl (17.1 MB)\n",
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      "\u001b[?25hCollecting aiofiles<24.0,>=22.0 (from gradio)\n",
      "  Downloading aiofiles-23.2.1-py3-none-any.whl (15 kB)\n",
      "Requirement already satisfied: altair<6.0,>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (4.2.2)\n",
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      "\u001b[?25hCollecting ffmpy (from gradio)\n",
      "  Downloading ffmpy-0.3.2.tar.gz (5.5 kB)\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "Collecting gradio-client==0.15.1 (from gradio)\n",
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      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m313.6/313.6 kB\u001b[0m \u001b[31m29.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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      "Building wheels for collected packages: ffmpy\n",
      "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for ffmpy: filename=ffmpy-0.3.2-py3-none-any.whl size=5584 sha256=203a49a85f7c8535ab431260fa2f25d6a06b02c5e6c584240da1f7c57f557ec2\n",
      "  Stored in directory: /root/.cache/pip/wheels/bd/65/9a/671fc6dcde07d4418df0c592f8df512b26d7a0029c2a23dd81\n",
      "Successfully built ffmpy\n",
      "Installing collected packages: pydub, ffmpy, websockets, tomlkit, shellingham, semantic-version, ruff, python-multipart, orjson, h11, aiofiles, uvicorn, starlette, httpcore, typer, httpx, fastapi, gradio-client, gradio\n",
      "  Attempting uninstall: typer\n",
      "    Found existing installation: typer 0.9.4\n",
      "    Uninstalling typer-0.9.4:\n",
      "      Successfully uninstalled typer-0.9.4\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "spacy 3.7.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\n",
      "weasel 0.3.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed aiofiles-23.2.1 fastapi-0.110.2 ffmpy-0.3.2 gradio-4.27.0 gradio-client-0.15.1 h11-0.14.0 httpcore-1.0.5 httpx-0.27.0 orjson-3.10.1 pydub-0.25.1 python-multipart-0.0.9 ruff-0.4.1 semantic-version-2.10.0 shellingham-1.5.4 starlette-0.37.2 tomlkit-0.12.0 typer-0.12.3 uvicorn-0.29.0 websockets-11.0.3\n"
     ]
    }
   ],
   "source": [
    "! pip install gradio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U5vQBCcfFeny"
   },
   "source": [
    "## <font color = Red>**8. Testing the model**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MYndNrVIFrpp"
   },
   "source": [
    "I have written a sample gradio code to test whether the model is working fine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 645
    },
    "id": "bp37kKqhpjqF",
    "outputId": "c025ee93-7ee0-4052-c83e-618eb61a2344"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
      "\n",
      "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
      "Running on public URL: https://c3d7aec977b51eeca7.gradio.live\n",
      "\n",
      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"https://c3d7aec977b51eeca7.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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    {
     "data": {
      "text/plain": []
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gradio as gr\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "# Load the saved model\n",
    "model = tf.keras.models.load_model('mymodel.h5')\n",
    "\n",
    "# Define the labels\n",
    "labels = ['Buildings', 'Forest', 'Sea']\n",
    "\n",
    "# Define the image classification function\n",
    "def classify_image(image):\n",
    "    # Preprocess the image\n",
    "    image = np.array(image)\n",
    "    image = tf.image.resize(image, (128, 128))  # Resize the image to match the input size of the model\n",
    "    image = tf.expand_dims(image, axis=0)  # Add batch dimension\n",
    "    image = tf.keras.applications.resnet50.preprocess_input(image)\n",
    "\n",
    "    # Predict the class\n",
    "    predictions = model.predict(image).flatten()\n",
    "\n",
    "    # Get the predicted class label\n",
    "    predicted_class = labels[np.argmax(predictions)]\n",
    "\n",
    "    return predicted_class\n",
    "\n",
    "# Create the Gradio interface directly\n",
    "interface = gr.Interface(classify_image, \"image\", \"label\", title=\"Image Classifier\")\n",
    "\n",
    "# Launch the interface\n",
    "interface.launch()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "12ZLfMEy3VN3"
   },
   "source": [
    "Since I have written the code in Colab the runtime won't make the UI available everytime on this notebook.\n",
    "I would recommend you to visit my Hugging face profile where the app  'Image Classifier' is running live.\n",
    "\n",
    "[Hugging face profile](https://huggingface.co/Ninad077)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oi4pnMma4SxX"
   },
   "source": [
    "Enclosed below is the image of how the Image Classifier UI looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73
    },
    "id": "qccpZ3nlbanP",
    "outputId": "ec0a2a6b-854e-4ccc-eca3-3362cb7eb68f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        style=\"border:none\" />\n",
       "     <output id=\"result-d70401f7-f78a-4329-b20b-c60fa0833a51\">\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>// Copyright 2017 Google LLC\n",
       "//\n",
       "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
       "// you may not use this file except in compliance with the License.\n",
       "// You may obtain a copy of the License at\n",
       "//\n",
       "//      http://www.apache.org/licenses/LICENSE-2.0\n",
       "//\n",
       "// Unless required by applicable law or agreed to in writing, software\n",
       "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
       "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
       "// See the License for the specific language governing permissions and\n",
       "// limitations under the License.\n",
       "\n",
       "/**\n",
       " * @fileoverview Helpers for google.colab Python module.\n",
       " */\n",
       "(function(scope) {\n",
       "function span(text, styleAttributes = {}) {\n",
       "  const element = document.createElement('span');\n",
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       "    element.style[key] = styleAttributes[key];\n",
       "  }\n",
       "  return element;\n",
       "}\n",
       "\n",
       "// Max number of bytes which will be uploaded at a time.\n",
       "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
       "\n",
       "function _uploadFiles(inputId, outputId) {\n",
       "  const steps = uploadFilesStep(inputId, outputId);\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  // Cache steps on the outputElement to make it available for the next call\n",
       "  // to uploadFilesContinue from Python.\n",
       "  outputElement.steps = steps;\n",
       "\n",
       "  return _uploadFilesContinue(outputId);\n",
       "}\n",
       "\n",
       "// This is roughly an async generator (not supported in the browser yet),\n",
       "// where there are multiple asynchronous steps and the Python side is going\n",
       "// to poll for completion of each step.\n",
       "// This uses a Promise to block the python side on completion of each step,\n",
       "// then passes the result of the previous step as the input to the next step.\n",
       "function _uploadFilesContinue(outputId) {\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  const steps = outputElement.steps;\n",
       "\n",
       "  const next = steps.next(outputElement.lastPromiseValue);\n",
       "  return Promise.resolve(next.value.promise).then((value) => {\n",
       "    // Cache the last promise value to make it available to the next\n",
       "    // step of the generator.\n",
       "    outputElement.lastPromiseValue = value;\n",
       "    return next.value.response;\n",
       "  });\n",
       "}\n",
       "\n",
       "/**\n",
       " * Generator function which is called between each async step of the upload\n",
       " * process.\n",
       " * @param {string} inputId Element ID of the input file picker element.\n",
       " * @param {string} outputId Element ID of the output display.\n",
       " * @return {!Iterable<!Object>} Iterable of next steps.\n",
       " */\n",
       "function* uploadFilesStep(inputId, outputId) {\n",
       "  const inputElement = document.getElementById(inputId);\n",
       "  inputElement.disabled = false;\n",
       "\n",
       "  const outputElement = document.getElementById(outputId);\n",
       "  outputElement.innerHTML = '';\n",
       "\n",
       "  const pickedPromise = new Promise((resolve) => {\n",
       "    inputElement.addEventListener('change', (e) => {\n",
       "      resolve(e.target.files);\n",
       "    });\n",
       "  });\n",
       "\n",
       "  const cancel = document.createElement('button');\n",
       "  inputElement.parentElement.appendChild(cancel);\n",
       "  cancel.textContent = 'Cancel upload';\n",
       "  const cancelPromise = new Promise((resolve) => {\n",
       "    cancel.onclick = () => {\n",
       "      resolve(null);\n",
       "    };\n",
       "  });\n",
       "\n",
       "  // Wait for the user to pick the files.\n",
       "  const files = yield {\n",
       "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
       "    response: {\n",
       "      action: 'starting',\n",
       "    }\n",
       "  };\n",
       "\n",
       "  cancel.remove();\n",
       "\n",
       "  // Disable the input element since further picks are not allowed.\n",
       "  inputElement.disabled = true;\n",
       "\n",
       "  if (!files) {\n",
       "    return {\n",
       "      response: {\n",
       "        action: 'complete',\n",
       "      }\n",
       "    };\n",
       "  }\n",
       "\n",
       "  for (const file of files) {\n",
       "    const li = document.createElement('li');\n",
       "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
       "    li.append(span(\n",
       "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
       "        `last modified: ${\n",
       "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
       "                                    'n/a'} - `));\n",
       "    const percent = span('0% done');\n",
       "    li.appendChild(percent);\n",
       "\n",
       "    outputElement.appendChild(li);\n",
       "\n",
       "    const fileDataPromise = new Promise((resolve) => {\n",
       "      const reader = new FileReader();\n",
       "      reader.onload = (e) => {\n",
       "        resolve(e.target.result);\n",
       "      };\n",
       "      reader.readAsArrayBuffer(file);\n",
       "    });\n",
       "    // Wait for the data to be ready.\n",
       "    let fileData = yield {\n",
       "      promise: fileDataPromise,\n",
       "      response: {\n",
       "        action: 'continue',\n",
       "      }\n",
       "    };\n",
       "\n",
       "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
       "    let position = 0;\n",
       "    do {\n",
       "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
       "      const chunk = new Uint8Array(fileData, position, length);\n",
       "      position += length;\n",
       "\n",
       "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
       "      yield {\n",
       "        response: {\n",
       "          action: 'append',\n",
       "          file: file.name,\n",
       "          data: base64,\n",
       "        },\n",
       "      };\n",
       "\n",
       "      let percentDone = fileData.byteLength === 0 ?\n",
       "          100 :\n",
       "          Math.round((position / fileData.byteLength) * 100);\n",
       "      percent.textContent = `${percentDone}% done`;\n",
       "\n",
       "    } while (position < fileData.byteLength);\n",
       "  }\n",
       "\n",
       "  // All done.\n",
       "  yield {\n",
       "    response: {\n",
       "      action: 'complete',\n",
       "    }\n",
       "  };\n",
       "}\n",
       "\n",
       "scope.google = scope.google || {};\n",
       "scope.google.colab = scope.google.colab || {};\n",
       "scope.google.colab._files = {\n",
       "  _uploadFiles,\n",
       "  _uploadFilesContinue,\n",
       "};\n",
       "})(self);\n",
       "</script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving Image_Classifier_UI_1.png to Image_Classifier_UI_1.png\n"
     ]
    }
   ],
   "source": [
    "uploaded = files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 325
    },
    "id": "MsZ7yNK-51ze",
    "outputId": "f3811110-d14d-41b9-c0b0-82386d69ea5a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('Image_Classifier_UI_1.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gbILS3HXF9_c"
   },
   "source": [
    "As one can clearly see from the flag window, our interface has correctly classified the image as forest. So, today we studied the core concepts of CNN and understood how one can use CNN to classify images correctly."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}