From 3fa1b6a4afc1f07bbcb50998a16137312ce5d96c Mon Sep 17 00:00:00 2001
From: Yash Patel <57082017+yashpatel301@users.noreply.github.com>
Date: Sat, 17 Apr 2021 17:15:00 +0530
Subject: [PATCH 01/17] Update Readme.md

---
 Assignment 2 & 3/Readme.md | 22 +++++++++++++++++++++-
 1 file changed, 21 insertions(+), 1 deletion(-)

diff --git a/Assignment 2 & 3/Readme.md b/Assignment 2 & 3/Readme.md
index 4730d02..81057e2 100644
--- a/Assignment 2 & 3/Readme.md	
+++ b/Assignment 2 & 3/Readme.md	
@@ -1 +1,21 @@
-Please add the assignment-2 & 3 details here
+## **Introduction:**
+</br> Face recognition has been a topic of research since 1961 and there were many algorithms developed to improve the recognition rate. In earlier times PCA was used for face classification. There has been a great transformation for feature extraction when N. Dalal and B. Triggs have introduced the concept of Histograms of Oriented Gradient for human detection in [6]. In PCA we use eigen vectors as features while in HoG we will use feature vectors which will be extracted with the help of oriented gradients and all the mathematical analysis has been greatly explained in article 'Improved Face Recognition Rate Using HOG Features and SVM Classifier' [5]. There has been a detailed and intuitive article on HoG feature extraction to get strong intuition on how image matrix will get converted to HoG feature vector in [2]. After extracting features we will use Support Vector Machine with different image size and K-Nearest Neighbor model to predict the class of the face. 
+</br>
+## **Approach:**
+We have followed the basic image classification approach with the help of 2 classifiers and then comapred the results based on the accuracy we get and the time model takes to get learn. The flow of image classification starts with partitioning the dataset (Dataset: Fetch lfw people) into training and testing folders and then carry out the feature vectors for each image in training as well as in testing folder and terminate the process by classifying the image [5].
+</br>
+We will now discuss the approach taken to extract our feature vectors using HoG. The HoG feature discriptor focuses on the shape and edges of the image, along with that it also focus on the direction and orientation of the edge and because of this it is not similar to other edge detectors. The key idea behind this is to divide the image into small portions and for each portion we will calculate gradient and orientation. After getting small region we will calculate gradient of each pixel by simply subtracting the neighbor pixels in up-down and side-ways manner. After that we need to calculate the magnitude by the gradients in x and y directions using formula <img src="https://latex.codecogs.com/svg.latex?magnitude=\sqrt{(G_x)^2+(G_y)^2}" title="\Large magnitude=\sqrt{(G_x)^2+(G_y)^2}" />
+and orientation can be found using <img src="https://latex.codecogs.com/svg.latex?tan(\phi)=\frac{G_y}{G_x}" title="\Large tan(\phi)=\frac{G_y}{G_x}" />
+[1],where Gx is the gradient in horizontal direction and Gy is the gradient in the vertical direction [2]. 
+</br> After getting the magnitude and orientation will be add to the histogram with scale = 20 and we will get 9 elements from one feature vector of one 8x8 image region which will be return in bin variable if we use HoG inbuilt function [2]. With the help of these feature vectors we will train our classifier and test on the testing data. After testing the model we will compare the values of predicted and ground truth to get the accuracy of our model. 
+</br>
+## **Results:**
+
+
+## **References:**
+1. https://stackoverflow.com/questions/11256433/how-to-show-math-equations-in-general-githubs-markdownnot-githubs-blog
+2. https://www.analyticsvidhya.com/blog/2019/09/feature-engineering-images-introduction-hog-feature-descriptor/
+3. https://scikit-learn.org/stable/modules/svm.html
+4. https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
+5. Dadi, H. and Pillutla, G., 2021. Improved Face Recognition Rate Using HOG Features and SVM Classifier. [online] www.iosrjournals.org. Available at: <https://www.researchgate.net/profile/Pg-Mohan/publication/305709603_Improved_Face_Recognition_Rate_Using_HOG_Features_and_SVM_Classifier/links/57aedbba08ae95f9d8f11b57/Improved-Face-Recognition-Rate-Using-HOG-Features-and-SVM-Classifier.pdf> [Accessed 17 April 2021].
+6. Dalal, N. and Triggs, B., 2021. Histograms of oriented gradients for human detection. [online] Ieeexplore.ieee.org. Available at: <https://ieeexplore.ieee.org/abstract/document/1467360> [Accessed 17 April 2021].

From b1695c00c3ba41f857fe3a32b63ebbf93fae5f16 Mon Sep 17 00:00:00 2001
From: Yash Patel <57082017+yashpatel301@users.noreply.github.com>
Date: Sat, 17 Apr 2021 17:33:37 +0530
Subject: [PATCH 02/17] Update Readme.md

---
 Assignment 2 & 3/Readme.md | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)

diff --git a/Assignment 2 & 3/Readme.md b/Assignment 2 & 3/Readme.md
index 81057e2..e0e481b 100644
--- a/Assignment 2 & 3/Readme.md	
+++ b/Assignment 2 & 3/Readme.md	
@@ -10,7 +10,24 @@ and orientation can be found using <img src="https://latex.codecogs.com/svg.late
 </br> After getting the magnitude and orientation will be add to the histogram with scale = 20 and we will get 9 elements from one feature vector of one 8x8 image region which will be return in bin variable if we use HoG inbuilt function [2]. With the help of these feature vectors we will train our classifier and test on the testing data. After testing the model we will compare the values of predicted and ground truth to get the accuracy of our model. 
 </br>
 ## **Results:**
+HoG implementation on two faces: </br>
+Image 1 </br>
+![Image 1](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Face%20Recognition/Results/HOG_image11.png)
 
+</br>HoG image 1 </br>
+![HoG image 1](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Face%20Recognition/Results/HOG_image_12.png)
+
+</br> Image 2 </br>
+![Image 2](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Face%20Recognition/Results/HOG_image_21.png)
+
+</br>HoG image 2 </br>
+![HoG image 2](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Face%20Recognition/Results/HOG_image_22.png)
+
+</br> Results of classfiers used with different input image size (Comparing with respect to Accuracy of model and Time taken to train)
+![Accuracy of Classifiers](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Face%20Recognition/Results/Accuracy_SVM_vs_KNN.png)
+
+## **Installation guidelines and platform details:**
+</br> This assigment is performed on Google colab only. All the libraries are mentioned at the beginning of the code and are imported with any installment. All the libraries can be used by only importing in colab. For Anaconda environment requirement.txt can be used which is available in this folder only. 
 
 ## **References:**
 1. https://stackoverflow.com/questions/11256433/how-to-show-math-equations-in-general-githubs-markdownnot-githubs-blog

From 127e6431225ac6038637f2d95f36e110725d343c Mon Sep 17 00:00:00 2001
From: Yash Patel <57082017+yashpatel301@users.noreply.github.com>
Date: Sat, 17 Apr 2021 17:35:02 +0530
Subject: [PATCH 03/17] Add files via upload

---
 Assignment 2 & 3/Face_recognition.ipynb | 1226 +++++++++++++++++++++++
 1 file changed, 1226 insertions(+)
 create mode 100644 Assignment 2 & 3/Face_recognition.ipynb

diff --git a/Assignment 2 & 3/Face_recognition.ipynb b/Assignment 2 & 3/Face_recognition.ipynb
new file mode 100644
index 0000000..3411b09
--- /dev/null
+++ b/Assignment 2 & 3/Face_recognition.ipynb	
@@ -0,0 +1,1226 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "HA2_CV.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rIrE6JGOjVeG"
+      },
+      "source": [
+        "# **Importing libraries and preprocessing shape**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "YrXVDRnn03Ud"
+      },
+      "source": [
+        "import cv2 \n",
+        "from skimage.io import imread, imshow\n",
+        "from skimage.transform import resize\n",
+        "from skimage.feature import hog\n",
+        "from skimage import exposure\n",
+        "import matplotlib.pyplot as plt\n",
+        "%matplotlib inline "
+      ],
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "18Rti9xM6CeQ",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f12ffe11-eb7f-47ea-b8ec-a2f5219abd71"
+      },
+      "source": [
+        "from sklearn.datasets import fetch_lfw_people\n",
+        "faces = fetch_lfw_people()"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Downloading LFW metadata: https://ndownloader.figshare.com/files/5976012\n",
+            "Downloading LFW metadata: https://ndownloader.figshare.com/files/5976009\n",
+            "Downloading LFW metadata: https://ndownloader.figshare.com/files/5976006\n",
+            "Downloading LFW data (~200MB): https://ndownloader.figshare.com/files/5976015\n"
+          ],
+          "name": "stderr"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "mE3SbjM8jClc"
+      },
+      "source": [
+        "face = faces.images"
+      ],
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gfUylEwojc4p",
+        "outputId": "2551cbd4-5d51-4361-bc8a-81d424a137a6"
+      },
+      "source": [
+        "face.shape"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(13233, 62, 47)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y9yLqpLDjmu8"
+      },
+      "source": [
+        "# **Implementing one example and image plotting in grayscale**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "id": "ZOhVxgmrkCY5",
+        "outputId": "3467810d-26fa-4943-9978-2f41e7bdc60b"
+      },
+      "source": [
+        "imshow(face[1])"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.6/dist-packages/skimage/io/_plugins/matplotlib_plugin.py:150: UserWarning: Float image out of standard range; displaying image with stretched contrast.\n",
+            "  lo, hi, cmap = _get_display_range(image)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.image.AxesImage at 0x7f303635bdd8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 386
+        },
+        "id": "zlrr9PaItzX5",
+        "outputId": "a0d53dff-e71f-434f-917d-118f0e98356b"
+      },
+      "source": [
+        "resized_img = resize(face[1],(128, 64))\n",
+        "fd_0, hog_img_0 = hog(resized_img, orientations=9, pixels_per_cell=(8,8),\n",
+        "                  cells_per_block=(2,2), visualize=True)\n",
+        "print(fd_0)\n",
+        "imshow(hog_img_0)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[0.20544315 0.24189511 0.24189511 ... 0.04941198 0.02591512 0.28776517]\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.6/dist-packages/skimage/io/_plugins/matplotlib_plugin.py:150: UserWarning: Float image out of standard range; displaying image with stretched contrast.\n",
+            "  lo, hi, cmap = _get_display_range(image)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.image.AxesImage at 0x7f302cb5a240>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BvdFD8GqkIRx"
+      },
+      "source": [
+        "# **Getting example converted into HOG**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fmQLAhCzkJPD",
+        "outputId": "f65ca989-c0d7-408d-e3bb-c6da4723585f"
+      },
+      "source": [
+        "resized_img = resize(face[2],(128, 64))\n",
+        "fd_1, hog_img = hog(resized_img, orientations=9, pixels_per_cell=(8,8),\n",
+        "                  cells_per_block=(2,2), visualize=True)\n",
+        "print(fd_1)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[0.39832236 0.39832236 0.00398809 ... 0.         0.         0.38671037]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wvJ9iVynPgkf",
+        "outputId": "7daedc58-bd3d-4384-8486-9f0fec261155"
+      },
+      "source": [
+        "fd_1.shape"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(3780,)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "id": "TP9IhtMZldyB",
+        "outputId": "ad81b6dd-5e8d-4110-d31a-b8aa848d0f56"
+      },
+      "source": [
+        "imshow(resized_img)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.6/dist-packages/skimage/io/_plugins/matplotlib_plugin.py:150: UserWarning: Float image out of standard range; displaying image with stretched contrast.\n",
+            "  lo, hi, cmap = _get_display_range(image)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.image.AxesImage at 0x7f302d622908>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "id": "q5iJzEdglilC",
+        "outputId": "6718c30e-422f-4e45-bfec-fb246f8f5fb4"
+      },
+      "source": [
+        "imshow(hog_img)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.6/dist-packages/skimage/io/_plugins/matplotlib_plugin.py:150: UserWarning: Float image out of standard range; displaying image with stretched contrast.\n",
+            "  lo, hi, cmap = _get_display_range(image)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.image.AxesImage at 0x7f302d5584e0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 9
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KzxHzDjpkPO1"
+      },
+      "source": [
+        "# **Partition into train and test data and then passing both the arrays from HOG feature extraction**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tsu2rPDmyisw"
+      },
+      "source": [
+        "from sklearn.model_selection import train_test_split\n"
+      ],
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "73XgG9bfzd2-"
+      },
+      "source": [
+        "data = []\n",
+        "for i in range(0,13233):\n",
+        "  resized_img = resize(face[i],(64,32))\n",
+        "  fd_data, hog_img=hog(resized_img, orientations=9, pixels_per_cell=(8,8),\n",
+        "                  cells_per_block=(2,2), visualize=True)\n",
+        "  data.append(fd_data)\n",
+        "  "
+      ],
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tZ8BT8xKgc4L"
+      },
+      "source": [
+        "data_high_dimension = []\r\n",
+        "for i in range(0,13233):\r\n",
+        "  resized_img1 = resize(face[i],(128, 64))\r\n",
+        "  fd_data1, hog_img1=hog(resized_img1, orientations=9, pixels_per_cell=(8,8),\r\n",
+        "                  cells_per_block=(2,2), visualize=True)\r\n",
+        "  data_high_dimension.append(fd_data1)"
+      ],
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "gRO0AMSL8Eq3"
+      },
+      "source": [
+        "import numpy as np\n",
+        "data = np.array(data)"
+      ],
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "DztiAJChg9yk"
+      },
+      "source": [
+        "data_high_dimension = np.array(data_high_dimension)"
+      ],
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qWVXNuaV0kHK",
+        "outputId": "c8debf03-1443-47d4-a4f6-c6b331f012f9"
+      },
+      "source": [
+        "X_train, X_test, y_train, y_test = train_test_split(data, faces.target, test_size=0.3)\n",
+        "print(X_train.shape)\n",
+        "print(X_test.shape)\n",
+        "print(y_train.shape)\n",
+        "print(y_test.shape)"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(9263, 756)\n",
+            "(3970, 756)\n",
+            "(9263,)\n",
+            "(3970,)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lzmPlcLSg-li",
+        "outputId": "1115a5de-998d-4036-d5c7-f450c5df185e"
+      },
+      "source": [
+        "X_train1, X_test1, y_train1, y_test1 = train_test_split(data_high_dimension, faces.target, test_size=0.3)\r\n",
+        "print(X_train1.shape)\r\n",
+        "print(X_test1.shape)\r\n",
+        "print(y_train1.shape)\r\n",
+        "print(y_test1.shape)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(9263, 3780)\n",
+            "(3970, 3780)\n",
+            "(9263,)\n",
+            "(3970,)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wy-E8DKP00R-"
+      },
+      "source": [
+        "#X_train = X_train.reshape(9263,512)\n",
+        "#X_test = X_test.reshape(3970, 512)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wEk_8tr5kuN0"
+      },
+      "source": [
+        "# **Working on training and testing on SVM classifier with 64x32 image size**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "kdaa5G8I5fIS"
+      },
+      "source": [
+        "from sklearn.svm import LinearSVC\n",
+        "model = LinearSVC()"
+      ],
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Lv_OJg915fFI",
+        "outputId": "75fd64e8-a1d1-44f0-a011-94ad4f609f21"
+      },
+      "source": [
+        "from sklearn.metrics import accuracy_score\n",
+        "model.fit(X_train, y_train)\n",
+        "predicted = model.predict(X_test)\n",
+        "score = accuracy_score(y_test, predicted)\n",
+        "print(score)"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "0.19068010075566752\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "txXVwPrZk6l6"
+      },
+      "source": [
+        "# **Working on training and testing on SVM classifier with 128x64 image size**: "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AR-BMX9hhDQj",
+        "outputId": "938d4d5b-5353-4af6-a0f7-115c2a90bf28"
+      },
+      "source": [
+        "model1 = LinearSVC()\r\n",
+        "from sklearn.metrics import accuracy_score\r\n",
+        "model1.fit(X_train1, y_train1)"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
+              "          intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
+              "          multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
+              "          verbose=0)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VpXpe7rjiZCN",
+        "outputId": "dd648a75-32f7-4e3b-c421-9a98a26cac7d"
+      },
+      "source": [
+        "predicted1 = model1.predict(X_test1)\n",
+        "score1 = accuracy_score(y_test1, predicted1)\n",
+        "print(score1)"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "0.25642317380352647\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OnAqEe-mQ-kT"
+      },
+      "source": [
+        "# **Working on training and testing on KNN model with 128x64 image size**: "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qy0Wp15q5fAP",
+        "outputId": "494a125a-c32c-4bba-a37b-d897b2a47d75"
+      },
+      "source": [
+        "from sklearn.neighbors import KNeighborsClassifier\n",
+        "model_knn = KNeighborsClassifier()\n",
+        "model_knn.fit(X_train1, y_train1)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
+              "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
+              "                     weights='uniform')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4w0kcWGw00J2",
+        "outputId": "6e3d496d-0349-4771-d041-9175961a26ae"
+      },
+      "source": [
+        "from sklearn.metrics import accuracy_score\n",
+        "predicted_knn = model_knn.predict(X_test1)\n",
+        "score_knn = accuracy_score(y_test1, predicted_knn)\n",
+        "print(score_knn)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "0.1544080604534005\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mNoMz0p0RCr1"
+      },
+      "source": [
+        "# **Performance Metrics**: "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "vqzQn5TmNbkN"
+      },
+      "source": [
+        "from sklearn.metrics import roc_curve\r\n",
+        "from sklearn.metrics import roc_auc_score"
+      ],
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "UlI_EPdCnDeg",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "adaf2e5e-cf00-4767-a5c4-c81a687d016d"
+      },
+      "source": [
+        "print(predicted.shape)\r\n",
+        "print(predicted1.shape)\r\n",
+        "#print(predicted_knn.shape)"
+      ],
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(3970,)\n",
+            "(3970,)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tewFp3PLVsi_",
+        "outputId": "db585757-fe9f-4648-8005-0de634ae531d"
+      },
+      "source": [
+        "from nltk import FreqDist\r\n",
+        "f_d = FreqDist(y_test1)\r\n",
+        "for word, frequency in f_d.most_common(10):\r\n",
+        "  print(u'{}\\t{}'.format(word, frequency))"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "1871\t160\n",
+            "1047\t82\n",
+            "5458\t44\n",
+            "1892\t32\n",
+            "1404\t31\n",
+            "2175\t25\n",
+            "373\t22\n",
+            "2453\t21\n",
+            "385\t18\n",
+            "1933\t17\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "YObcPeYUOiAh"
+      },
+      "source": [
+        "TP_1871 = 0\r\n",
+        "FN_1871 = 0\r\n",
+        "FP_1871 = 0\r\n",
+        "TN_1871 = 0\r\n",
+        "for i in range(0,3969):\r\n",
+        "  if predicted1[i] == y_test1[i] and predicted1[i] == 1871:\r\n",
+        "    TP_1871 = TP_1871+1\r\n",
+        "  elif predicted1[i] == y_test1[i] and y_test1[i] != 1871:\r\n",
+        "    TN_1871 = TN_1871+1\r\n",
+        "  elif predicted1[i] != y_test1[i] and y_test1[i] == 1871:\r\n",
+        "    FN_1871 = FN_1871+1\r\n",
+        "  elif predicted1[i] == y_test1[i] and predicted1[i] != 1871:\r\n",
+        "    FP_1871 = FP_1871+1"
+      ],
+      "execution_count": 19,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Q-gbBH7ZOh-C",
+        "outputId": "2acc712f-a92b-4695-f717-cabe40da7f60"
+      },
+      "source": [
+        "FPR = FP_1871/(FP_1871+TN_1871)\r\n",
+        "TPR = TP_1871/(TP_1871+FN_1871)\r\n",
+        "print('FPR', FPR, 'TPR', TPR)"
+      ],
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "FPR 0.0 TPR 0.875\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1EZ9DRXROh63",
+        "outputId": "1d4a7145-1aaa-4820-a8a6-599f2f696b38"
+      },
+      "source": [
+        "print('TP_1871-',TP_1871,' | ', 'TP_1047-',TP_1047)\r\n",
+        "print('FN_1871-',FN_1871,'  | ', 'FN_1047-',FN_1047)\r\n",
+        "print('FP_1871-',FP_1871,'   | ', 'FP_1047-',FP_1047)\r\n",
+        "print('TN_1871-',TN_1871,' | ', 'TN_1047-',TN_1047)"
+      ],
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "TP_1871- 140  |  TP_1047- 70\n",
+            "FN_1871- 20   |  FN_1047- 12\n",
+            "FP_1871- 0    |  FP_1047- 0\n",
+            "TN_1871- 877  |  TN_1047- 947\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "H8Tl1yRROh3p",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b0d89678-f204-4005-912f-bb77b0d6a48a"
+      },
+      "source": [
+        "TP_1047 = 0\n",
+        "FN_1047 = 0\n",
+        "FP_1047 = 0\n",
+        "TN_1047 = 0\n",
+        "for i in range(0,3969):\n",
+        "  if predicted1[i] == y_test1[i] and predicted1[i] == 1047:\n",
+        "    TP_1047 = TP_1047+1\n",
+        "  elif predicted1[i] == y_test1[i] and y_test1[i] != 1047:\n",
+        "    TN_1047 = TN_1047+1\n",
+        "  elif predicted1[i] != y_test1[i] and y_test1[i] == 1047:\n",
+        "    FN_1047 = FN_1047+1\n",
+        "  elif predicted1[i] == y_test1[i] and predicted1[i] != 1047:\n",
+        "    FP_1047 = FP_1047+1\n",
+        "FPR = FP_1047/(FP_1047+TN_1047)\n",
+        "TPR = TP_1047/(TP_1047+FN_1047)\n",
+        "print('FPR', FPR, 'TPR', TPR)\n",
+        "print(TP_1047)\n",
+        "print(FN_1047)\n",
+        "print(FP_1047)\n",
+        "print(TN_1047)\n"
+      ],
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "FPR 0.0 TPR 0.8536585365853658\n",
+            "70\n",
+            "12\n",
+            "0\n",
+            "947\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "B83IfxWKOh0r"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FZ9uor9llyZ7"
+      },
+      "source": [
+        "# **HOG Implimentation**\r\n",
+        "\r\n",
+        "Here I have just tried an implentation of HOG example on House.jpg image for practise purpose\r\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 314
+        },
+        "id": "s-AdviMt2EiG",
+        "outputId": "fbbffa77-e161-4c0b-e99c-65cc37f6c8b1"
+      },
+      "source": [
+        "img = imread('House.jpeg')\n",
+        "imshow(img)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.image.AxesImage at 0x7f6d3cf81eb8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rwq77RIv2Jbv",
+        "outputId": "41be64fb-d6a0-42a9-aec1-6fbb4f652dc2"
+      },
+      "source": [
+        "img.shape"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(183, 275, 3)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 314
+        },
+        "id": "aYoStYGK3QF0",
+        "outputId": "511a58f8-f277-4751-edd0-c64f5137aee7"
+      },
+      "source": [
+        "img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
+        "imshow(img)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.image.AxesImage at 0x7f6d34712160>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "y9HXaQLj3e_x",
+        "outputId": "0d88e508-a2c4-4158-fa8b-dfe6cbd5ea80"
+      },
+      "source": [
+        "print(img.shape)\n",
+        "print(img)\n",
+        "img = img.astype(int)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(183, 275)\n",
+            "[[188 188 187 ... 179 179 179]\n",
+            " [188 188 187 ... 178 178 178]\n",
+            " [189 189 189 ... 179 179 179]\n",
+            " ...\n",
+            " [224 226 230 ... 202 202 202]\n",
+            " [233 235 238 ... 192 192 191]\n",
+            " [236 238 240 ... 192 192 191]]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IBZqgEAa2PXB"
+      },
+      "source": [
+        "img_fin = resize(img, (128,128))"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "id": "c6tpBK_g2XlA",
+        "outputId": "7fad161d-cf4f-4943-fade-0c084280108a"
+      },
+      "source": [
+        "imshow(img_fin)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.6/dist-packages/skimage/io/_plugins/matplotlib_plugin.py:150: UserWarning: Low image data range; displaying image with stretched contrast.\n",
+            "  lo, hi, cmap = _get_display_range(image)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.image.AxesImage at 0x7f6d32a363c8>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 11
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "a9-alA084whd"
+      },
+      "source": [
+        "fd, hog_image = hog(img_fin, orientations=30, pixels_per_cell=(8, 8),\n",
+        "cells_per_block=(2, 2), visualize=True)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369
+        },
+        "id": "7BHxiCEo58Ap",
+        "outputId": "a81b7911-ddfb-4b9b-bc14-519f2720e5bf"
+      },
+      "source": [
+        "imshow(hog_image)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.6/dist-packages/skimage/io/_plugins/matplotlib_plugin.py:150: UserWarning: Low image data range; displaying image with stretched contrast.\n",
+            "  lo, hi, cmap = _get_display_range(image)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.image.AxesImage at 0x7f6d32972da0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 13
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "vnLklCzxlwZN"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file

From 73c63f3a3bed2e6398c30c416fd17c1f4d3432ad Mon Sep 17 00:00:00 2001
From: Yash Patel <57082017+yashpatel301@users.noreply.github.com>
Date: Sat, 17 Apr 2021 19:51:21 +0530
Subject: [PATCH 04/17] Add files via upload

---
 Assignment 2 & 3/requirements.txt | 35 +++++++++++++++++++++++++++++++
 1 file changed, 35 insertions(+)
 create mode 100644 Assignment 2 & 3/requirements.txt

diff --git a/Assignment 2 & 3/requirements.txt b/Assignment 2 & 3/requirements.txt
new file mode 100644
index 0000000..074f76e
--- /dev/null
+++ b/Assignment 2 & 3/requirements.txt	
@@ -0,0 +1,35 @@
+imageio==2.4.1
+Keras==2.4.3
+Keras-Preprocessing==1.1.2
+keras-vis==0.4.1
+matplotlib==3.2.2
+matplotlib-venn==0.11.6
+networkx==2.5.1
+nltk==3.2.5
+numpy==1.19.5
+opencv-contrib-python==4.1.2.30
+opencv-python==4.1.2.30
+pandas==1.1.5
+pandas-datareader==0.9.0
+pandas-gbq==0.13.3
+pandas-profiling==1.4.1
+pickleshare==0.7.5
+Pillow==7.1.2
+pip-tools==4.5.1
+pymc3==3.7
+regex==2019.12.20
+scikit-image==0.16.2
+scikit-learn==0.22.2.post1
+scipy==1.4.1
+seaborn==0.11.1
+sklearn==0.0
+sklearn-pandas==1.8.0
+tensorboard==2.4.1
+tensorboard-plugin-wit==1.8.0
+tensorflow==2.4.1
+tensorflow-datasets==4.0.1
+tensorflow-estimator==2.4.0
+tensorflow-gcs-config==2.4.0
+tensorflow-hub==0.12.0
+tensorflow-metadata==0.29.0
+tensorflow-probability==0.12.1

From 900dc3ee526552fe3b5aff83d9f3e8cac84e1dc3 Mon Sep 17 00:00:00 2001
From: Yash Patel <57082017+yashpatel301@users.noreply.github.com>
Date: Sat, 17 Apr 2021 19:52:53 +0530
Subject: [PATCH 05/17] Add files via upload

---
 Assignment 4 & 5/requirements.txt | 35 +++++++++++++++++++++++++++++++
 1 file changed, 35 insertions(+)
 create mode 100644 Assignment 4 & 5/requirements.txt

diff --git a/Assignment 4 & 5/requirements.txt b/Assignment 4 & 5/requirements.txt
new file mode 100644
index 0000000..074f76e
--- /dev/null
+++ b/Assignment 4 & 5/requirements.txt	
@@ -0,0 +1,35 @@
+imageio==2.4.1
+Keras==2.4.3
+Keras-Preprocessing==1.1.2
+keras-vis==0.4.1
+matplotlib==3.2.2
+matplotlib-venn==0.11.6
+networkx==2.5.1
+nltk==3.2.5
+numpy==1.19.5
+opencv-contrib-python==4.1.2.30
+opencv-python==4.1.2.30
+pandas==1.1.5
+pandas-datareader==0.9.0
+pandas-gbq==0.13.3
+pandas-profiling==1.4.1
+pickleshare==0.7.5
+Pillow==7.1.2
+pip-tools==4.5.1
+pymc3==3.7
+regex==2019.12.20
+scikit-image==0.16.2
+scikit-learn==0.22.2.post1
+scipy==1.4.1
+seaborn==0.11.1
+sklearn==0.0
+sklearn-pandas==1.8.0
+tensorboard==2.4.1
+tensorboard-plugin-wit==1.8.0
+tensorflow==2.4.1
+tensorflow-datasets==4.0.1
+tensorflow-estimator==2.4.0
+tensorflow-gcs-config==2.4.0
+tensorflow-hub==0.12.0
+tensorflow-metadata==0.29.0
+tensorflow-probability==0.12.1

From 1d1eae892dabb460d965aac52379568fa73ab94b Mon Sep 17 00:00:00 2001
From: Yash Patel <57082017+yashpatel301@users.noreply.github.com>
Date: Sat, 17 Apr 2021 19:55:26 +0530
Subject: [PATCH 06/17] Add files via upload

---
 .../classification_of_citrus_leaves.ipynb     | 1192 +++++++++++++++++
 1 file changed, 1192 insertions(+)
 create mode 100644 Assignment 4 & 5/classification_of_citrus_leaves.ipynb

diff --git a/Assignment 4 & 5/classification_of_citrus_leaves.ipynb b/Assignment 4 & 5/classification_of_citrus_leaves.ipynb
new file mode 100644
index 0000000..85442fa
--- /dev/null
+++ b/Assignment 4 & 5/classification_of_citrus_leaves.ipynb	
@@ -0,0 +1,1192 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "HA3_CV.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hdWq98vVGLW4"
+      },
+      "source": [
+        "### **Necessary Imports**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "G0vEOKHsqE4j"
+      },
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "%matplotlib inline\n",
+        "import PIL"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9S-eBH0P9q2y",
+        "outputId": "ac8ff836-036a-43e2-8cb3-c68d6982c668"
+      },
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Mounted at /content/drive\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JPFvt09kqE12",
+        "outputId": "81a905be-8a5a-4c34-cc6e-49872dba84bb"
+      },
+      "source": [
+        "from PIL import Image\n",
+        "image1 = Image.open('/content/drive/MyDrive/HA3_CV_dataset/Leaves/Black spot/b (14).png')\n",
+        "image2 = Image.open('/content/drive/MyDrive/HA3_CV_dataset/Leaves/greening/g (102).png')\n",
+        "print('Image-1 shape: ',image1.size)\n",
+        "print('Image-2 shape: ',image2.size)\n",
+        "print('Image-1 mode: ', image1.mode)\n",
+        "print('Image-2 mode: ', image2.mode)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Image-1 shape:  (256, 256)\n",
+            "Image-2 shape:  (256, 256)\n",
+            "Image-1 mode:  RGB\n",
+            "Image-2 mode:  RGB\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 286
+        },
+        "id": "3cPSwcGf96eD",
+        "outputId": "45701be7-179b-4e23-e15a-4eef9129b690"
+      },
+      "source": [
+        "plt.imshow(image1)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.image.AxesImage at 0x7fcd4ec98750>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_v6b3X_asY8V"
+      },
+      "source": [
+        "Here both the images are of same size but what if any of the image is not of same size, hence we need to resize all images to 256 x 256"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "l_4SvTGYqEup"
+      },
+      "source": [
+        "img_w, img_h = 256, 256"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "33K2cTrGstq7"
+      },
+      "source": [
+        "### **For data-generation part**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "a026yVIPthQb"
+      },
+      "source": [
+        "from keras.preprocessing.image import ImageDataGenerator\n",
+        "from keras.preprocessing.image import img_to_array, array_to_img, load_img"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "g62gQmUofA24",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1bec9877-7625-425e-a565-f3c1ebe657eb"
+      },
+      "source": [
+        "train_data = ImageDataGenerator(rescale = 1./255,\n",
+        "                                shear_range = 0.2,\n",
+        "                                zoom_range = 0.2,\n",
+        "                                horizontal_flip=True,\n",
+        "                                validation_split=0.2)\n",
+        "train_generator = train_data.flow_from_directory('/content/drive/MyDrive/HA3_CV_dataset/Leaves/',\n",
+        "                                                  target_size = (img_h, img_w),\n",
+        "                                                  batch_size=16,\n",
+        "                                                  shuffle = True,\n",
+        "                                                  class_mode = 'categorical',\n",
+        "                                                  subset =\"training\")                                                 \n",
+        "validation_generator = train_data.flow_from_directory('/content/drive/MyDrive/HA3_CV_dataset/Leaves/',\n",
+        "                                                      target_size=(img_h, img_w),\n",
+        "                                                      batch_size=16,\n",
+        "                                                      shuffle = True,\n",
+        "                                                      class_mode='categorical',\n",
+        "                                                      subset =\"validation\")"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Found 490 images belonging to 5 classes.\n",
+            "Found 119 images belonging to 5 classes.\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7MYCkNV-1piu"
+      },
+      "source": [
+        "We have completed image preprocessing and now we are ready for Convolutional Architecture"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wF1yTGUpy67U"
+      },
+      "source": [
+        "import tensorflow as tf\n",
+        "from tensorflow import keras\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Activation, Dropout, Flatten"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_G8eGGwGGGkU"
+      },
+      "source": [
+        "### **Modelling part**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "P7gSxDDsc6_S"
+      },
+      "source": [
+        "**Experiment:** Model with 3 conv2D and Maxpooling2D layers"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ca7KEOI22Qwt"
+      },
+      "source": [
+        "model1 = Sequential()\n",
+        "model1.add(Conv2D(32, kernel_size=5, strides=1, padding=\"Same\", activation='relu', input_shape=(img_h,img_w,3)))\n",
+        "model1.add(MaxPooling2D(padding='Same'))\n",
+        "\n",
+        "model1.add(Conv2D(32, kernel_size=5, strides=1, padding=\"Same\", activation='relu'))\n",
+        "model1.add(MaxPooling2D(padding='Same'))\n",
+        "\n",
+        "model1.add(Conv2D(64, kernel_size=5, strides=1, padding=\"Same\", activation='relu'))\n",
+        "model1.add(MaxPooling2D(padding='Same'))\n",
+        "\n",
+        "model1.add(Flatten())\n",
+        "model1.add(Dense(1024,activation=\"relu\"))\n",
+        "model1.add(Dropout(0.3))\n",
+        "model1.add(Dense(5,activation=\"softmax\"))\n",
+        "\n",
+        "\n",
+        "model1.compile(optimizer=\"adam\",loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tRGdaRvV8ugN",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d74b7b0f-e612-4477-ac5a-7c3cf311020e"
+      },
+      "source": [
+        "model1.summary()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Model: \"sequential\"\n",
+            "_________________________________________________________________\n",
+            "Layer (type)                 Output Shape              Param #   \n",
+            "=================================================================\n",
+            "conv2d (Conv2D)              (None, 256, 256, 32)      2432      \n",
+            "_________________________________________________________________\n",
+            "max_pooling2d (MaxPooling2D) (None, 128, 128, 32)      0         \n",
+            "_________________________________________________________________\n",
+            "conv2d_1 (Conv2D)            (None, 128, 128, 32)      25632     \n",
+            "_________________________________________________________________\n",
+            "max_pooling2d_1 (MaxPooling2 (None, 64, 64, 32)        0         \n",
+            "_________________________________________________________________\n",
+            "conv2d_2 (Conv2D)            (None, 64, 64, 64)        51264     \n",
+            "_________________________________________________________________\n",
+            "max_pooling2d_2 (MaxPooling2 (None, 32, 32, 64)        0         \n",
+            "_________________________________________________________________\n",
+            "flatten (Flatten)            (None, 65536)             0         \n",
+            "_________________________________________________________________\n",
+            "dense (Dense)                (None, 1024)              67109888  \n",
+            "_________________________________________________________________\n",
+            "dropout (Dropout)            (None, 1024)              0         \n",
+            "_________________________________________________________________\n",
+            "dense_1 (Dense)              (None, 5)                 5125      \n",
+            "=================================================================\n",
+            "Total params: 67,194,341\n",
+            "Trainable params: 67,194,341\n",
+            "Non-trainable params: 0\n",
+            "_________________________________________________________________\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IHE9Lhg888M_",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d1608467-fd92-4040-f885-cd75aa0afe0f"
+      },
+      "source": [
+        "history=model1.fit_generator(\n",
+        "    train_generator,\n",
+        "    steps_per_epoch = 490//16, \n",
+        "    epochs=30,\n",
+        "    validation_data = validation_generator,\n",
+        "    validation_steps = 119//16\n",
+        ")"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
+            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/30\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 1.0525 - accuracy: 0.5063 - val_loss: 1.2161 - val_accuracy: 0.4464\n",
+            "Epoch 2/30\n",
+            "30/30 [==============================] - 10s 347ms/step - loss: 1.0442 - accuracy: 0.5359 - val_loss: 1.1302 - val_accuracy: 0.5089\n",
+            "Epoch 3/30\n",
+            "30/30 [==============================] - 10s 350ms/step - loss: 1.1486 - accuracy: 0.4557 - val_loss: 1.3049 - val_accuracy: 0.3750\n",
+            "Epoch 4/30\n",
+            "30/30 [==============================] - 10s 348ms/step - loss: 1.0585 - accuracy: 0.5316 - val_loss: 1.2427 - val_accuracy: 0.4554\n",
+            "Epoch 5/30\n",
+            "30/30 [==============================] - 10s 349ms/step - loss: 0.9756 - accuracy: 0.5570 - val_loss: 1.2612 - val_accuracy: 0.3393\n",
+            "Epoch 6/30\n",
+            "30/30 [==============================] - 10s 349ms/step - loss: 0.9490 - accuracy: 0.5823 - val_loss: 1.1442 - val_accuracy: 0.4107\n",
+            "Epoch 7/30\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 0.8177 - accuracy: 0.6350 - val_loss: 0.9511 - val_accuracy: 0.5357\n",
+            "Epoch 8/30\n",
+            "30/30 [==============================] - 10s 343ms/step - loss: 0.8584 - accuracy: 0.6329 - val_loss: 0.9878 - val_accuracy: 0.4911\n",
+            "Epoch 9/30\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 0.7659 - accuracy: 0.6477 - val_loss: 1.0183 - val_accuracy: 0.6161\n",
+            "Epoch 10/30\n",
+            "30/30 [==============================] - 10s 343ms/step - loss: 0.7852 - accuracy: 0.6624 - val_loss: 1.0588 - val_accuracy: 0.5446\n",
+            "Epoch 11/30\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 0.6746 - accuracy: 0.7215 - val_loss: 1.0865 - val_accuracy: 0.5089\n",
+            "Epoch 12/30\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 0.6392 - accuracy: 0.7489 - val_loss: 1.0526 - val_accuracy: 0.5357\n",
+            "Epoch 13/30\n",
+            "30/30 [==============================] - 10s 350ms/step - loss: 0.5984 - accuracy: 0.7532 - val_loss: 1.4194 - val_accuracy: 0.4196\n",
+            "Epoch 14/30\n",
+            "30/30 [==============================] - 10s 349ms/step - loss: 0.6227 - accuracy: 0.7321 - val_loss: 1.1302 - val_accuracy: 0.5625\n",
+            "Epoch 15/30\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 0.5660 - accuracy: 0.7468 - val_loss: 1.0489 - val_accuracy: 0.5446\n",
+            "Epoch 16/30\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 0.5386 - accuracy: 0.7996 - val_loss: 1.1707 - val_accuracy: 0.5893\n",
+            "Epoch 17/30\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 0.5933 - accuracy: 0.7363 - val_loss: 1.0592 - val_accuracy: 0.5089\n",
+            "Epoch 18/30\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 0.4860 - accuracy: 0.8017 - val_loss: 1.1676 - val_accuracy: 0.6250\n",
+            "Epoch 19/30\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 0.5312 - accuracy: 0.7679 - val_loss: 1.0386 - val_accuracy: 0.5000\n",
+            "Epoch 20/30\n",
+            "30/30 [==============================] - 10s 345ms/step - loss: 0.4260 - accuracy: 0.8333 - val_loss: 1.1630 - val_accuracy: 0.5179\n",
+            "Epoch 21/30\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 0.4594 - accuracy: 0.8080 - val_loss: 1.2922 - val_accuracy: 0.4286\n",
+            "Epoch 22/30\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 0.4556 - accuracy: 0.8122 - val_loss: 1.1118 - val_accuracy: 0.5357\n",
+            "Epoch 23/30\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 0.4611 - accuracy: 0.8207 - val_loss: 0.9501 - val_accuracy: 0.5625\n",
+            "Epoch 24/30\n",
+            "30/30 [==============================] - 10s 343ms/step - loss: 0.4057 - accuracy: 0.8397 - val_loss: 1.0264 - val_accuracy: 0.6161\n",
+            "Epoch 25/30\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 0.3487 - accuracy: 0.8544 - val_loss: 1.0910 - val_accuracy: 0.6607\n",
+            "Epoch 26/30\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 0.3157 - accuracy: 0.8819 - val_loss: 1.1480 - val_accuracy: 0.5446\n",
+            "Epoch 27/30\n",
+            "30/30 [==============================] - 10s 347ms/step - loss: 0.3200 - accuracy: 0.8734 - val_loss: 1.1493 - val_accuracy: 0.6429\n",
+            "Epoch 28/30\n",
+            "30/30 [==============================] - 10s 347ms/step - loss: 0.2899 - accuracy: 0.8882 - val_loss: 1.4611 - val_accuracy: 0.5714\n",
+            "Epoch 29/30\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 0.3287 - accuracy: 0.8650 - val_loss: 1.3737 - val_accuracy: 0.5982\n",
+            "Epoch 30/30\n",
+            "30/30 [==============================] - 10s 343ms/step - loss: 0.3498 - accuracy: 0.8565 - val_loss: 1.2025 - val_accuracy: 0.6339\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fZcMYHqC_Kz1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 312
+        },
+        "outputId": "b0c82dc7-49da-476e-8743-ed6c4923e157"
+      },
+      "source": [
+        "train_l = history.history['loss']\n",
+        "val_l = history.history['val_loss']\n",
+        "epochs = range(1,31)\n",
+        "plt.plot(epochs, train_l, 'b-', label='train loss')\n",
+        "plt.plot(epochs, val_l, 'g-', label='validation loss')\n",
+        "plt.title('Validation loss vs Train loss')\n",
+        "plt.xlabel('epochs')\n",
+        "plt.ylabel('loss')\n",
+        "plt.legend()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x7fcd000ad4d0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "DaK31Xn0CieJ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 312
+        },
+        "outputId": "4d059cef-bd33-4371-a558-590137d32db2"
+      },
+      "source": [
+        "train_l = history.history['accuracy']\n",
+        "val_l = history.history['val_accuracy']\n",
+        "epochs = range(1,31)\n",
+        "plt.plot(epochs, train_l, 'b-', label='accuracy')\n",
+        "plt.plot(epochs, val_l, 'g-', label='validation accuracy')\n",
+        "plt.title('Accuracy vs Validation Accuracy')\n",
+        "plt.xlabel('epochs')\n",
+        "plt.ylabel('accuracy')\n",
+        "plt.legend()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x7fcd00038b90>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 18
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h4vECMlbDbNC"
+      },
+      "source": [
+        "**Observstion:** As we are not getting smooth accuracy plots, let's try with signoid activation function in last layer"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "UHcr8C1mDZ5C"
+      },
+      "source": [
+        "model_sig = Sequential()\n",
+        "model_sig.add(Conv2D(32, kernel_size=5, strides=1, padding=\"Same\", activation='relu', input_shape=(img_h,img_w,3)))\n",
+        "model_sig.add(MaxPooling2D(padding='Same'))\n",
+        "\n",
+        "model_sig.add(Conv2D(32, kernel_size=5, strides=1, padding=\"Same\", activation='relu'))\n",
+        "model_sig.add(MaxPooling2D(padding='Same'))\n",
+        "\n",
+        "model_sig.add(Conv2D(64, kernel_size=5, strides=1, padding=\"Same\", activation='relu'))\n",
+        "model_sig.add(MaxPooling2D(padding='Same'))\n",
+        "\n",
+        "model_sig.add(Flatten())\n",
+        "model_sig.add(Dense(1024,activation=\"relu\"))\n",
+        "model_sig.add(Dropout(0.3))\n",
+        "model_sig.add(Dense(5,activation=\"sigmoid\"))\n",
+        "\n",
+        "\n",
+        "model_sig.compile(optimizer=\"adam\",loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "X1UImC4sDZ2g",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f2a7ca89-57c8-4079-8a8b-bce944bd01b2"
+      },
+      "source": [
+        "history_sig=model_sig.fit_generator(\n",
+        "    train_generator,\n",
+        "    steps_per_epoch = 490//16, \n",
+        "    epochs=15,\n",
+        "    validation_data = validation_generator,\n",
+        "    validation_steps = 119//16\n",
+        ")"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
+            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/15\n",
+            "30/30 [==============================] - 11s 351ms/step - loss: 2.6328 - accuracy: 0.3303 - val_loss: 1.2742 - val_accuracy: 0.3571\n",
+            "Epoch 2/15\n",
+            "30/30 [==============================] - 10s 342ms/step - loss: 1.1758 - accuracy: 0.4572 - val_loss: 1.1958 - val_accuracy: 0.3304\n",
+            "Epoch 3/15\n",
+            "30/30 [==============================] - 10s 337ms/step - loss: 1.0656 - accuracy: 0.5190 - val_loss: 1.2358 - val_accuracy: 0.4821\n",
+            "Epoch 4/15\n",
+            "30/30 [==============================] - 10s 336ms/step - loss: 1.0741 - accuracy: 0.4657 - val_loss: 1.3591 - val_accuracy: 0.3929\n",
+            "Epoch 5/15\n",
+            "30/30 [==============================] - 10s 337ms/step - loss: 1.1504 - accuracy: 0.4495 - val_loss: 1.2585 - val_accuracy: 0.3125\n",
+            "Epoch 6/15\n",
+            "30/30 [==============================] - 10s 337ms/step - loss: 1.4806 - accuracy: 0.4893 - val_loss: 1.3130 - val_accuracy: 0.4464\n",
+            "Epoch 7/15\n",
+            "30/30 [==============================] - 10s 336ms/step - loss: 1.0975 - accuracy: 0.4548 - val_loss: 1.2160 - val_accuracy: 0.4196\n",
+            "Epoch 8/15\n",
+            "30/30 [==============================] - 10s 335ms/step - loss: 1.0235 - accuracy: 0.5757 - val_loss: 1.2528 - val_accuracy: 0.3661\n",
+            "Epoch 9/15\n",
+            "30/30 [==============================] - 10s 337ms/step - loss: 0.9742 - accuracy: 0.5300 - val_loss: 1.1450 - val_accuracy: 0.4554\n",
+            "Epoch 10/15\n",
+            "30/30 [==============================] - 10s 340ms/step - loss: 0.9395 - accuracy: 0.5442 - val_loss: 1.1757 - val_accuracy: 0.4821\n",
+            "Epoch 11/15\n",
+            "30/30 [==============================] - 10s 341ms/step - loss: 0.9222 - accuracy: 0.5985 - val_loss: 1.2027 - val_accuracy: 0.4911\n",
+            "Epoch 12/15\n",
+            "30/30 [==============================] - 10s 339ms/step - loss: 0.8674 - accuracy: 0.6175 - val_loss: 1.1061 - val_accuracy: 0.4375\n",
+            "Epoch 13/15\n",
+            "30/30 [==============================] - 10s 339ms/step - loss: 0.8433 - accuracy: 0.6401 - val_loss: 1.1612 - val_accuracy: 0.4018\n",
+            "Epoch 14/15\n",
+            "30/30 [==============================] - 10s 336ms/step - loss: 0.8872 - accuracy: 0.5814 - val_loss: 1.1739 - val_accuracy: 0.3929\n",
+            "Epoch 15/15\n",
+            "30/30 [==============================] - 10s 336ms/step - loss: 0.8490 - accuracy: 0.6193 - val_loss: 0.9756 - val_accuracy: 0.5357\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "n7B3BBcyFJ2_"
+      },
+      "source": [
+        "**Observstion:** Here we are getting validation loss higher than training loss this might be showing the problem of over-fitting data. Now that can be due to many reasons but here it should be due to less number of samples in comaprision with features. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "S6Hk1kQoDZzg",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 312
+        },
+        "outputId": "5efda344-d324-4e36-8bdc-345a648ae1a8"
+      },
+      "source": [
+        "train_l = history_sig.history['loss']\n",
+        "val_l = history_sig.history['val_loss']\n",
+        "epochs = range(1,16)\n",
+        "plt.plot(epochs, train_l, 'b-', label='train loss')\n",
+        "plt.plot(epochs, val_l, 'g-', label='validation loss')\n",
+        "plt.title('Validation loss vs Train loss')\n",
+        "plt.xlabel('epochs')\n",
+        "plt.ylabel('loss')\n",
+        "plt.legend()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x7fccc4144090>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 21
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tESX5PTGDZxG",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 312
+        },
+        "outputId": "63d9e950-0c2e-4811-9c53-24332d1ca879"
+      },
+      "source": [
+        "train_l = history_sig.history['accuracy']\n",
+        "val_l = history_sig.history['val_accuracy']\n",
+        "epochs = range(1,16)\n",
+        "plt.plot(epochs, train_l, 'b-', label='accuracy')\n",
+        "plt.plot(epochs, val_l, 'g-', label='validation accuracy')\n",
+        "plt.title('Accuracy vs Validation Accuracy')\n",
+        "plt.xlabel('epochs')\n",
+        "plt.ylabel('accuracy')\n",
+        "plt.legend()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x7fccbc67cc90>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 22
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8lvuxdiUbzhp"
+      },
+      "source": [
+        "**Experiment:** Trying alpha values as learning rate for optimizer instead of adam optimizer. \n",
+        "\n",
+        "Alphas = [0.0001 , 0.001 , 0.01 , 0.1 , 1]\n",
+        "\n",
+        "Also we had tried model with 2 conv2D and maxpooling2D layers."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "08lSTvkfKsIL",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "09903502-e4ea-437d-d25e-ec76344071c8"
+      },
+      "source": [
+        "learning_rate = [0.0001,0.001,0.01,0.1,1]\n",
+        "for alpha in learning_rate:\n",
+        "  model2 = Sequential()\n",
+        "  model2.add(Conv2D(32, kernel_size=5, strides=1, padding=\"Same\", activation='relu', input_shape=(img_h,img_w,3)))\n",
+        "  model2.add(MaxPooling2D(padding='Same'))\n",
+        "\n",
+        "  model2.add(Conv2D(64, kernel_size=5, strides=1, padding=\"Same\", activation='relu'))\n",
+        "  model2.add(MaxPooling2D(padding='Same'))\n",
+        "\n",
+        "  model2.add(Flatten())\n",
+        "  model2.add(Dense(1024,activation=\"relu\"))\n",
+        "  model2.add(Dropout(0.3))\n",
+        "  model2.add(Dense(5,activation=\"softmax\"))\n",
+        "\n",
+        "  opti = tf.keras.optimizers.SGD(learning_rate=alpha)\n",
+        "  model2.compile(optimizer=opti,loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])\n",
+        "  print('For learning rate = ', alpha)\n",
+        "  model2.fit_generator(\n",
+        "                      train_generator,\n",
+        "                      steps_per_epoch = 490//16, \n",
+        "                      epochs=10,\n",
+        "                      validation_data = validation_generator,\n",
+        "                      validation_steps = 119//16)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "For learning rate =  0.0001\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
+            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/10\n",
+            "30/30 [==============================] - 11s 353ms/step - loss: 1.5222 - accuracy: 0.2822 - val_loss: 1.4276 - val_accuracy: 0.2679\n",
+            "Epoch 2/10\n",
+            "30/30 [==============================] - 10s 348ms/step - loss: 1.4204 - accuracy: 0.3089 - val_loss: 1.3749 - val_accuracy: 0.3125\n",
+            "Epoch 3/10\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 1.4063 - accuracy: 0.3119 - val_loss: 1.3530 - val_accuracy: 0.3214\n",
+            "Epoch 4/10\n",
+            "30/30 [==============================] - 10s 345ms/step - loss: 1.3817 - accuracy: 0.3407 - val_loss: 1.3651 - val_accuracy: 0.2321\n",
+            "Epoch 5/10\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 1.3954 - accuracy: 0.3282 - val_loss: 1.3435 - val_accuracy: 0.3036\n",
+            "Epoch 6/10\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 1.3874 - accuracy: 0.3423 - val_loss: 1.3305 - val_accuracy: 0.3214\n",
+            "Epoch 7/10\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 1.3385 - accuracy: 0.4016 - val_loss: 1.3446 - val_accuracy: 0.3393\n",
+            "Epoch 8/10\n",
+            "30/30 [==============================] - 10s 345ms/step - loss: 1.3685 - accuracy: 0.3674 - val_loss: 1.3262 - val_accuracy: 0.3036\n",
+            "Epoch 9/10\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 1.3630 - accuracy: 0.4088 - val_loss: 1.3420 - val_accuracy: 0.3750\n",
+            "Epoch 10/10\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 1.3240 - accuracy: 0.4045 - val_loss: 1.3417 - val_accuracy: 0.3571\n",
+            "For learning rate =  0.001\n",
+            "Epoch 1/10\n",
+            "30/30 [==============================] - 11s 358ms/step - loss: 1.4453 - accuracy: 0.3384 - val_loss: 1.3568 - val_accuracy: 0.3036\n",
+            "Epoch 2/10\n",
+            "30/30 [==============================] - 10s 343ms/step - loss: 1.3536 - accuracy: 0.3335 - val_loss: 1.3355 - val_accuracy: 0.3393\n",
+            "Epoch 3/10\n",
+            "30/30 [==============================] - 10s 342ms/step - loss: 1.3485 - accuracy: 0.3730 - val_loss: 1.3075 - val_accuracy: 0.4821\n",
+            "Epoch 4/10\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 1.2970 - accuracy: 0.4173 - val_loss: 1.2721 - val_accuracy: 0.3750\n",
+            "Epoch 5/10\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 1.3062 - accuracy: 0.3817 - val_loss: 1.2739 - val_accuracy: 0.4464\n",
+            "Epoch 6/10\n",
+            "30/30 [==============================] - 10s 339ms/step - loss: 1.3026 - accuracy: 0.4162 - val_loss: 1.2847 - val_accuracy: 0.3125\n",
+            "Epoch 7/10\n",
+            "30/30 [==============================] - 10s 343ms/step - loss: 1.1955 - accuracy: 0.4739 - val_loss: 1.2509 - val_accuracy: 0.3214\n",
+            "Epoch 8/10\n",
+            "30/30 [==============================] - 10s 345ms/step - loss: 1.2250 - accuracy: 0.4720 - val_loss: 1.2628 - val_accuracy: 0.3750\n",
+            "Epoch 9/10\n",
+            "30/30 [==============================] - 10s 342ms/step - loss: 1.2400 - accuracy: 0.3972 - val_loss: 1.2039 - val_accuracy: 0.4196\n",
+            "Epoch 10/10\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 1.1632 - accuracy: 0.4532 - val_loss: 1.2186 - val_accuracy: 0.3929\n",
+            "For learning rate =  0.01\n",
+            "Epoch 1/10\n",
+            "30/30 [==============================] - 11s 356ms/step - loss: 1.6275 - accuracy: 0.2971 - val_loss: 1.3855 - val_accuracy: 0.2857\n",
+            "Epoch 2/10\n",
+            "30/30 [==============================] - 10s 341ms/step - loss: 1.4304 - accuracy: 0.3218 - val_loss: 1.3478 - val_accuracy: 0.2946\n",
+            "Epoch 3/10\n",
+            "30/30 [==============================] - 10s 349ms/step - loss: 1.3487 - accuracy: 0.3228 - val_loss: 1.2608 - val_accuracy: 0.3393\n",
+            "Epoch 4/10\n",
+            "30/30 [==============================] - 11s 352ms/step - loss: 1.2775 - accuracy: 0.3851 - val_loss: 1.3032 - val_accuracy: 0.3482\n",
+            "Epoch 5/10\n",
+            "30/30 [==============================] - 10s 339ms/step - loss: 1.2223 - accuracy: 0.4265 - val_loss: 1.6625 - val_accuracy: 0.3304\n",
+            "Epoch 6/10\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 1.1971 - accuracy: 0.4688 - val_loss: 1.1990 - val_accuracy: 0.4018\n",
+            "Epoch 7/10\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 1.1165 - accuracy: 0.4969 - val_loss: 1.2270 - val_accuracy: 0.4018\n",
+            "Epoch 8/10\n",
+            "30/30 [==============================] - 10s 340ms/step - loss: 1.1467 - accuracy: 0.4743 - val_loss: 1.1843 - val_accuracy: 0.3750\n",
+            "Epoch 9/10\n",
+            "30/30 [==============================] - 10s 341ms/step - loss: 1.0957 - accuracy: 0.4806 - val_loss: 1.1786 - val_accuracy: 0.4732\n",
+            "Epoch 10/10\n",
+            "30/30 [==============================] - 10s 341ms/step - loss: 0.9843 - accuracy: 0.5359 - val_loss: 1.2092 - val_accuracy: 0.3839\n",
+            "For learning rate =  0.1\n",
+            "Epoch 1/10\n",
+            "30/30 [==============================] - 11s 353ms/step - loss: 3342957572916081328128.0000 - accuracy: 0.2953 - val_loss: 1.5260 - val_accuracy: 0.3214\n",
+            "Epoch 2/10\n",
+            "30/30 [==============================] - 10s 345ms/step - loss: 1.5012 - accuracy: 0.3342 - val_loss: 1.4475 - val_accuracy: 0.3393\n",
+            "Epoch 3/10\n",
+            "30/30 [==============================] - 10s 341ms/step - loss: 1.4423 - accuracy: 0.3654 - val_loss: 1.4159 - val_accuracy: 0.3304\n",
+            "Epoch 4/10\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 1.4352 - accuracy: 0.3329 - val_loss: 1.4027 - val_accuracy: 0.3393\n",
+            "Epoch 5/10\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 1.4109 - accuracy: 0.3535 - val_loss: 1.4000 - val_accuracy: 0.3125\n",
+            "Epoch 6/10\n",
+            "30/30 [==============================] - 10s 342ms/step - loss: 1.3763 - accuracy: 0.3328 - val_loss: 1.3926 - val_accuracy: 0.3304\n",
+            "Epoch 7/10\n",
+            "30/30 [==============================] - 11s 354ms/step - loss: 1.4106 - accuracy: 0.3004 - val_loss: 1.3792 - val_accuracy: 0.3304\n",
+            "Epoch 8/10\n",
+            "30/30 [==============================] - 11s 354ms/step - loss: 1.4032 - accuracy: 0.3252 - val_loss: 1.3846 - val_accuracy: 0.3393\n",
+            "Epoch 9/10\n",
+            "30/30 [==============================] - 11s 359ms/step - loss: 1.3709 - accuracy: 0.3670 - val_loss: 1.3827 - val_accuracy: 0.3393\n",
+            "Epoch 10/10\n",
+            "30/30 [==============================] - 11s 353ms/step - loss: 1.3663 - accuracy: 0.3554 - val_loss: 1.3724 - val_accuracy: 0.3393\n",
+            "For learning rate =  1\n",
+            "Epoch 1/10\n",
+            "30/30 [==============================] - 11s 367ms/step - loss: nan - accuracy: 0.2652 - val_loss: nan - val_accuracy: 0.2857\n",
+            "Epoch 2/10\n",
+            "30/30 [==============================] - 10s 349ms/step - loss: nan - accuracy: 0.2642 - val_loss: nan - val_accuracy: 0.2768\n",
+            "Epoch 3/10\n",
+            "30/30 [==============================] - 11s 351ms/step - loss: nan - accuracy: 0.2751 - val_loss: nan - val_accuracy: 0.2857\n",
+            "Epoch 4/10\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: nan - accuracy: 0.2879 - val_loss: nan - val_accuracy: 0.2857\n",
+            "Epoch 5/10\n",
+            "30/30 [==============================] - 10s 345ms/step - loss: nan - accuracy: 0.2521 - val_loss: nan - val_accuracy: 0.2946\n",
+            "Epoch 6/10\n",
+            "30/30 [==============================] - 10s 343ms/step - loss: nan - accuracy: 0.2569 - val_loss: nan - val_accuracy: 0.2768\n",
+            "Epoch 7/10\n",
+            "30/30 [==============================] - 10s 343ms/step - loss: nan - accuracy: 0.2718 - val_loss: nan - val_accuracy: 0.2679\n",
+            "Epoch 8/10\n",
+            "30/30 [==============================] - 10s 345ms/step - loss: nan - accuracy: 0.2863 - val_loss: nan - val_accuracy: 0.2768\n",
+            "Epoch 9/10\n",
+            "30/30 [==============================] - 10s 348ms/step - loss: nan - accuracy: 0.2637 - val_loss: nan - val_accuracy: 0.2946\n",
+            "Epoch 10/10\n",
+            "30/30 [==============================] - 10s 342ms/step - loss: nan - accuracy: 0.2835 - val_loss: nan - val_accuracy: 0.2768\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DPf_XXarMwqE"
+      },
+      "source": [
+        "Plotting Accuracy (considering highest accuracy of 10 epochs) and validation accuracy (considering val_acc of respective accuracy)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5QBg7k8ZPgQa",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 312
+        },
+        "outputId": "ef00d681-a1b5-4386-f35f-8b1282a511c6"
+      },
+      "source": [
+        "Accuracy = [0.3860, 0.4372, 0.5840, 0.3728, 0.3428]\n",
+        "Val_acc = [0.3125, 0.4196, 0.3839, 0.3304, 0.3304]\n",
+        "alpha = range(0,5)\n",
+        "plt.bar(alpha, Accuracy)\n",
+        "plt.xlabel('Alpha')\n",
+        "plt.ylabel('Accuracy')\n",
+        "plt.title('Accuracy for different learning rates')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Accuracy for different learning rates')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 24
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "HmexAXQnN_US",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 312
+        },
+        "outputId": "9306e8a8-923b-474f-b9c3-158a3fd90c0e"
+      },
+      "source": [
+        "plt.bar(alpha, Val_acc)\n",
+        "plt.xlabel('Alpha')\n",
+        "plt.ylabel('Validation accuracy')\n",
+        "plt.title('Validation accuracy for different learning rates')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Validation accuracy for different learning rates')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 25
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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KOUGYmVmhSjupzax/G3vWr5sdQp9ZetHRPao/mNe9LO9BmJlZIScIMzMr5ARhZmaFnCDMzKyQE4SZmRVygjAzs0LdJghJn5I0vBHBmJlZ/1FmD+LlZM+Tvk7SZEmqOigzM2u+bi+Ui4gvSToHeCdwMvBtSdcB34+IB6oOsFEGykUzVV0wY2aDT6k+iIgI4J/p1QEMB34q6ZIKYzMzsybqdg9C0unAR4BHge8Bn4uI5yVtQ/YEuM9XG6KZmTVDmT2I3YD3RsSREXF9RDwPEBEbgXfXa5j6LBZLapd0VsH0UyX9TdICSbdJmpCbdnZqt1jSkT1cLzMz20JlEsRvgBce9ylpZ0kHAUTEfV01Ss+UngkcBUwAjs8ngOTaiNg/Ig4ALgEuTW0nkD3Dej9gMnBZ5zOqzcysMcokiO8AT+XGn0pl3ZkItEfEkohYD8wBpuQrRMQTudEdgUjDU4A5EbEuIh4E2tP8zMysQcrc7lupkxrIDi1JKtNuJLAsN74cOGizmUunAWcA2wOH5dreUdN2ZEHbacA0gDFjxpQIyczMyiqzB7FE0qclbZdepwNL+iqAiJgZEa8CvgB8qYdtZ0VES0S0jBgxoq9CMjMzyiWIU4G3Ait4cS9gWol2K4DRufFRqawrc4Bje9nWzMz6WJkL5R4h6zDuqfnAeEnjyDbuU4EP5itIGh8Rf0+jR5OdNgvQClwr6VJgD2A8cFcvYjAzs14qcx3EMOAUsjOKhnWWR8RH67WLiA5J04F5wBBgdkQslDQDaIuIVmC6pCOA54E1wImp7cJ0tfYisgvzTouIDb1ZQTMz650ync1XA/cDRwIzgA8BXZ7emhcRc4G5NWXn5oZPr9P2QuDCMssxM7O+V6YPYu+IOAd4OiKuJDsUtNnZSGZmNrCUSRDPp79rJb0W2AX4t+pCMjOz/qDMIaZZ6XkQXyLrPH4pcE6lUZmZWdPVTRDphnxPRMQa4FZgr4ZEZWZmTVf3EFO6IZ/v1mpmNgiV6YP4vaTPShotabfOV+WRmZlZU5Xpg/hA+ntarizw4SYzswGtzJXU4xoRiJmZ9S9lrqT+SFF5RFzV9+GYmVl/UeYQ05tyw8OAw4G/AE4QZmYDWJlDTJ/Kj0valezOq2ZmNoCVOYup1tOA+yXMzAa4Mn0Q/8WLjwLdhuz50tdVGZSZmTVfmT6Ir+eGO4CHImJ5RfGYmVk/USZBPAysjIjnACTtIGlsRCytNDIzM2uqMn0Q1wMbc+MbUlm3JE2WtFhSu6SzCqafIWmRpHsl/UHSnrlpGyQtSK/WMsszM7O+U2YPYtuIWN85EhHrJW3fXSNJQ4CZwCSyZ1nPl9QaEYty1e4BWiLiGUmfAC7hxSu3n42IA8quiJmZ9a0yexCrJB3TOSJpCvBoiXYTgfaIWJISzBxgSr5CRNwcEc+k0TuAUeXCNjOzqpVJEKcCX5T0sKSHgS8AHy/RbiSwLDe+PJV15RTgN7nxYZLaJN0h6diiBpKmpTptq1atKhGSmZmVVeZCuQeAN0t6aRp/qq+DkHQC0AIckiveMyJWSNoLuEnS31Is+dhmAbMAWlpaAjMz6zPd7kFI+qqkXSPiqYh4StJwSV8pMe8VwOjc+KhUVjv/I4D/AI6JiHWd5RGxIv1dAtwCHFhimWZm1kfKHGI6KiLWdo6kp8u9q0S7+cB4SeNSp/ZUskeWvkDSgcAVZMnhkVz5cElD0/DuwNuAfOe2mZlVrMxZTEMkDe38dS9pB2Bod40iokPSdGAeMASYHRELJc0A2iKiFfhPsmdcXy8J4OGIOAZ4DXCFpI1kSeyimrOfzMysYmUSxDXAHyT9II2fDFxZZuYRMReYW1N2bm74iC7a/RnYv8wyzMysGmU6qS+WdC/Zbb4BLoiIedWGZWZmzVZmD4KI+A2bnoJqZmYDXJmzmN4sab6kpyStT7fAeKIRwZmZWfOUOYvp28DxwN+BHYD/RXYLDTMzG8BKPTAoItqBIRGxISJ+AEyuNiwzM2u2Mn0Qz6TrGBZIugRYSe+eRGdmZluRMhv6D6d608keNzoaeF+VQZmZWfOVOc31oTT4HHB+teGYmVl/4UNFZmZWyAnCzMwKOUGYmVmhbvsgJO0DfA7YM18/Ig6rMC4zM2uyMqe5Xg9cDnwX2FBtOGZm1l+USRAdEfGdyiMxM7N+pUwfxH9J+qSkV0rarfNVeWRmZtZUZfYgTkx/P5crC2Cvvg/HzMz6i273ICJiXMGrVHKQNFnSYkntks4qmH6GpEWS7pX0B0l75qadKOnv6XVibVszM6tWmbOYtgM+ARycim4BroiI57tpN4Tsrq+TgOXAfEmtNY8OvQdoiYhnJH0CuAT4QDqEdR7QQra3cndqu6ZHa2dmZr1Wpg/iO8AbgcvS642prDsTgfaIWBIR64E5wJR8hYi4OSKeSaN3AKPS8JHAjRGxOiWFG/EdZM3MGqpMH8SbIuL1ufGbJP21RLuRwLLc+HLgoDr1T+HFp9YVtR1Z20DSNGAawJgxY0qEZGZmZZXZg9gg6VWdI5L2oo+vh5B0AtnhpP/sSbuImBURLRHRMmLEiL4Mycxs0CuzB/E54GZJSwCRXVF9col2K8huDd5pVCrbhKQjgP8ADomIdbm2h9a0vaXEMs3MrI+Uud33HySNB/ZNRYtzG/J65gPjJY0j2+BPBT6YryDpQOAKYHJEPJKbNA/4qqThafydwNkllmlmZn2kywQh6bCIuEnSe2sm7S2JiPhZvRlHRIek6WQb+yHA7IhYKGkG0BYRrWSHlF4KXC8J4OGIOCYiVku6gCzJAMyIiNW9W0UzM+uNensQhwA3Ae8pmBZA3QQBEBFzgbk1Zefmho+o03Y2MLu7ZZiZWTW6TBARcV4anBERD+anpcNGZmY2gJU5i+mGgrKf9nUgZmbWv9Trg3g1sB+wS00/xM7AsKoDMzOz5qrXB7Ev8G5gVzbth3gS+FiVQZmZWfPV64P4JfBLSW+JiNsbGJOZmfUDZS6Uu0fSaWSHm144tBQRH60sKjMza7oyndRXA68gu4HeH8muan6yyqDMzKz5yiSIvSPiHODpiLgSOJr6N90zM7MBoEyC6Hzuw1pJrwV2Af6tupDMzKw/KNMHMSvdE+kcoJXs1hjn1m9iZmZbuzI36/teGvwjfg61mdmgUe9CuTPqNYyIS/s+HDMz6y/q7UHslP7uC7yJ7PASZBfN3VVlUGZm1nz1LpQ7H0DSrcAbIuLJNP5l4NcNic7MzJqmzFlMLwfW58bXpzIzMxvAypzFdBVwl6Sfp/FjgR9WFpGZmfUL3e5BRMSFZM+gXpNeJ0fE18rMXNJkSYsltUs6q2D6wZL+IqlD0nE10zZIWpBerbVtzcysWvXOYto5Ip6QtBuwNL06p+3W3SNAJQ0BZgKTgOXAfEmtEbEoV+1h4CTgswWzeDYiDii5HmZm1sfqHWK6lux233eTPWK0k9J4d9dETATaI2IJgKQ5wBTghQQREUvTtI09DdzMzKpV7yymd6e/vX286EhgWW58OT27h9MwSW1AB3BRRPyitoKkacA0gDFjxvQyTDMzK1LvENMb6jWMiL/0fTib2DMiVkjaC7hJ0t8i4oGaGGYBswBaWlqiaCZmZtY79Q4xfaPOtAAO62beK4DRufFRqayUiFiR/i6RdAtwIPBA3UZmZtZn6h1i+h9bOO/5wHhJ48gSw1Tgg2UappsDPhMR6yTtDrwNuGQL4zEzsx4ocx0E6TbfE9j0iXJX1WsTER2SpgPzgCHA7IhYKGkG0BYRrZLeBPwcGA68R9L5EbEf8BrgitR5vQ1ZH8SiLhZlZmYV6DZBSDoPOJQsQcwFjgJuI7uArq6ImJva5MvOzQ3PJzv0VNvuz8D+3c3fzMyqU+ZWG8cBhwP/jIiTgdeTPTTIzMwGsDIJ4tmI2Ah0SNoZeIRNO5/NzGwAKtMH0SZpV+C7ZBfNPQXcXmlUZmbWdPWug5gJXBsRn0xFl0v6LbBzRNzbkOjMzKxp6u1B/DfwdUmvBK4DfhwR9zQmLDMza7Yu+yAi4v9ExFuAQ4DHgNmS7pd0nqR9GhahmZk1RZnbfT8UERdHxIHA8WTPg7iv8sjMzKypuk0QkraV9B5J1wC/ARYD7608MjMza6p6ndSTyPYY3gXcBcwBpkXE0w2KzczMmqheJ/XZZM+EODMi1jQoHjMz6yfq3ayvu7u1mpnZAFbmSmozMxuEnCDMzKyQE4SZmRVygjAzs0KVJghJkyUtltQu6ayC6QdL+oukDknH1Uw7UdLf0+vEKuM0M7PNVZYgJA0BZpI9YGgCcLykCTXVHgZOIjudNt92N+A84CBgInBeegypmZk1SJV7EBOB9ohYEhHryS60m5KvEBFL051hN9a0PRK4MSJWp2swbgQmVxirmZnVqDJBjASW5caXp7I+aytpmqQ2SW2rVq3qdaBmZra5rbqTOiJmRURLRLSMGDGi2eGYmQ0oVSaIFWz6aNJRqazqtmZm1geqTBDzgfGSxknaHpgKtJZsOw94p6ThqXP6nanMzMwapLIEEREdwHSyDft9wHURsVDSDEnHAEh6k6TlwPuBKyQtTG1XAxeQJZn5wIxUZmZmDVLvbq5bLCLmAnNrys7NDc8nO3xU1HY2MLvK+MzMrGtbdSe1mZlVxwnCzMwKOUGYmVkhJwgzMyvkBGFmZoWcIMzMrJAThJmZFXKCMDOzQk4QZmZWyAnCzMwKOUGYmVkhJwgzMyvkBGFmZoWcIMzMrJAThJmZFXKCMDOzQpUmCEmTJS2W1C7prILpQyX9JE2/U9LYVD5W0rOSFqTX5VXGaWZmm6vsiXKShgAzgUnAcmC+pNaIWJSrdgqwJiL2ljQVuBj4QJr2QEQcUFV8ZmZWX5V7EBOB9ohYEhHrgTnAlJo6U4Ar0/BPgcMlqcKYzMyspCoTxEhgWW58eSorrBMRHcDjwMvStHGS7pH0R0nvKFqApGmS2iS1rVq1qm+jNzMb5PprJ/VKYExEHAicAVwraefaShExKyJaIqJlxIgRDQ/SzGwgqzJBrABG58ZHpbLCOpK2BXYBHouIdRHxGEBE3A08AOxTYaxmZlajygQxHxgvaZyk7YGpQGtNnVbgxDR8HHBTRISkEamTG0l7AeOBJRXGamZmNSo7iykiOiRNB+YBQ4DZEbFQ0gygLSJage8DV0tqB1aTJRGAg4EZkp4HNgKnRsTqqmI1M7PNVZYgACJiLjC3puzc3PBzwPsL2t0A3FBlbGZmVl9/7aQ2M7Mmc4IwM7NCThBmZlbICcLMzAo5QZiZWSEnCDMzK+QEYWZmhZwgzMyskBOEmZkVcoIwM7NCThBmZlbICcLMzAo5QZiZWSEnCDMzK+QEYWZmhZwgzMysUKUJQtJkSYsltUs6q2D6UEk/SdPvlDQ2N+3sVL5Y0pFVxmlmZpurLEGkZ0rPBI4CJgDHS5pQU+0UYE1E7A18E7g4tZ1A9vjR/YDJwGWdz6g2M7PGqHIPYiLQHhFLImI9MAeYUlNnCnBlGv4pcLgkpfI5EbEuIh4E2tP8zMysQap8JvVIYFlufDlwUFd1IqJD0uPAy1L5HTVtR9YuQNI0YFoafUrS4r4JvTK7A49WuQBdXOXct0jl6w6De/0H87rD4F7/LVz3PbuaUGWCqFxEzAJmNTuOsiS1RURLs+NohsG87jC4138wrzts3etf5SGmFcDo3PioVFZYR9K2wC7AYyXbmplZhapMEPOB8ZLGSdqerNO5taZOK3BiGj4OuCkiIpVPTWc5jQPGA3dVGKuZmdWo7BBT6lOYDswDhgCzI2KhpBlAW0S0At8HrpbUDqwmSyKketcBi4AO4LSI2FBVrA201RwOq8BgXncY3Os/mNcdtuL1V/aD3czMbFO+ktrMzAo5QZiZWSEniAbp7rYjA5Wk2ZIekfT/mh1Lo0kaLelmSYskLZR0erNjaiRJwyTdJemvaf3Pb3ZMjSZpiKR7JP2q2bH0hhNEA5S87chA9UOy26UMRh3AmRExAXgzcNog+r8DrAMOi4jXAwcAkyW9uckxNdrpwH3NDqK3nCAao8xtRwakiLiV7Ay1QSciVkbEX9Lwk2Qbis3uCDBQReapNLpdeg2as2IkjQKOBr7X7Fh6ywmiMYpuO043CncAAAKxSURBVDJoNhQG6U7FBwJ3NjeSxkqHWBYAjwA3RsRgWv9vAZ8HNjY7kN5ygjCrmKSXAjcAn4mIJ5odTyNFxIaIOIDsbggTJb222TE1gqR3A49ExN3NjmVLOEE0hm8dMkhJ2o4sOVwTET9rdjzNEhFrgZsZPP1RbwOOkbSU7JDyYZJ+1NyQes4JojHK3HbEBph06/rvA/dFxKXNjqfRJI2QtGsa3gGYBNzf3KgaIyLOjohRETGW7Pt+U0Sc0OSweswJogEiogPovO3IfcB1EbGwuVE1hqQfA7cD+0paLumUZsfUQG8DPkz263FBer2r2UE10CuBmyXdS/Yj6caI2CpP9xysfKsNMzMr5D0IMzMr5ARhZmaFnCDMzKyQE4SZmRVygjAzs0JOEGa9IOlYSSHp1Wl8bHd3rC1Tx6w/cYIw653jgdvSX7MByQnCrIfSvZXeDpxCeo56zfSTJP1S0i2S/i7pvNzkIZK+m56P8Lt0hTGSPiZpfnp2wg2SXtKYtTHrmhOEWc9NAX4bEf8NPCbpjQV1JgLvA14HvF9SSyofD8yMiP2AtakOwM8i4k3p2Qn3kSUfs6ZygjDruePJbsBG+lt0mOnGiHgsIp4Ffka2xwHwYEQsSMN3A2PT8Gsl/V9JfwM+BOxXSeRmPbBtswMw25pI2g04DNhfUgBDyB6CM7Omau09bDrH1+XKNgA7pOEfAsdGxF8lnQQc2ndRm/WO9yDMeuY44OqI2DMixkbEaOBBNr2dO8AkSbulPoZjgT91M9+dgJXp9uAf6vOozXrBCcKsZ44Hfl5TdgNwdk3ZXan8XuCGiGjrZr7nkD1t7k8MkltiW//nu7ma9bF0iKglIqY3OxazLeE9CDMzK+Q9CDMzK+Q9CDMzK+QEYWZmhZwgzMyskBOEmZkVcoIwM7NC/x/wCDYfrAMdZQAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sGi0b_dvRU01"
+      },
+      "source": [
+        "**Experiment:** To avoid overfitting let's try to increase the Dropout percentage by 20%"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "k2wI6VW6QP_u"
+      },
+      "source": [
+        "model3 = Sequential()\n",
+        "model3.add(Conv2D(32, kernel_size=5, strides=1, padding=\"Same\", activation='relu', input_shape=(img_h,img_w,3)))\n",
+        "model3.add(MaxPooling2D(padding='Same'))\n",
+        "\n",
+        "model3.add(Conv2D(64, kernel_size=5, strides=1, padding=\"Same\", activation='relu'))\n",
+        "model3.add(MaxPooling2D(padding='Same'))\n",
+        "\n",
+        "model3.add(Flatten())\n",
+        "model3.add(Dense(1024,activation=\"relu\"))\n",
+        "model3.add(Dropout(0.5))\n",
+        "model3.add(Dense(5,activation=\"sigmoid\"))\n",
+        "\n",
+        "\n",
+        "model3.compile(optimizer=\"adam\",loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "bxvNhrfIRr7k",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a27ca111-6f17-4e32-8d37-02e347976410"
+      },
+      "source": [
+        "history_dropout = model3.fit_generator(\n",
+        "                                      train_generator,\n",
+        "                                      steps_per_epoch = 490//16, \n",
+        "                                      epochs=15,\n",
+        "                                      validation_data = validation_generator,\n",
+        "                                      validation_steps = 119//16)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
+            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/15\n",
+            "30/30 [==============================] - 11s 357ms/step - loss: 17.1107 - accuracy: 0.2773 - val_loss: 1.3198 - val_accuracy: 0.4107\n",
+            "Epoch 2/15\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 1.4086 - accuracy: 0.3483 - val_loss: 1.3501 - val_accuracy: 0.3571\n",
+            "Epoch 3/15\n",
+            "30/30 [==============================] - 10s 347ms/step - loss: 1.1883 - accuracy: 0.4610 - val_loss: 1.2203 - val_accuracy: 0.3750\n",
+            "Epoch 4/15\n",
+            "30/30 [==============================] - 10s 347ms/step - loss: 1.0996 - accuracy: 0.4769 - val_loss: 1.4612 - val_accuracy: 0.3393\n",
+            "Epoch 5/15\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 1.2838 - accuracy: 0.4381 - val_loss: 1.2427 - val_accuracy: 0.3929\n",
+            "Epoch 6/15\n",
+            "30/30 [==============================] - 10s 345ms/step - loss: 1.1260 - accuracy: 0.4792 - val_loss: 1.2267 - val_accuracy: 0.4196\n",
+            "Epoch 7/15\n",
+            "30/30 [==============================] - 10s 345ms/step - loss: 1.1132 - accuracy: 0.4719 - val_loss: 1.4523 - val_accuracy: 0.4107\n",
+            "Epoch 8/15\n",
+            "30/30 [==============================] - 10s 347ms/step - loss: 1.1301 - accuracy: 0.5173 - val_loss: 1.2245 - val_accuracy: 0.3839\n",
+            "Epoch 9/15\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 1.0280 - accuracy: 0.5309 - val_loss: 1.1859 - val_accuracy: 0.4732\n",
+            "Epoch 10/15\n",
+            "30/30 [==============================] - 10s 347ms/step - loss: 0.9376 - accuracy: 0.5699 - val_loss: 1.2315 - val_accuracy: 0.4286\n",
+            "Epoch 11/15\n",
+            "30/30 [==============================] - 10s 347ms/step - loss: 0.9314 - accuracy: 0.6008 - val_loss: 1.1156 - val_accuracy: 0.4554\n",
+            "Epoch 12/15\n",
+            "30/30 [==============================] - 10s 347ms/step - loss: 0.8987 - accuracy: 0.6011 - val_loss: 1.2151 - val_accuracy: 0.5089\n",
+            "Epoch 13/15\n",
+            "30/30 [==============================] - 10s 347ms/step - loss: 0.8625 - accuracy: 0.6031 - val_loss: 1.0832 - val_accuracy: 0.4286\n",
+            "Epoch 14/15\n",
+            "30/30 [==============================] - 10s 349ms/step - loss: 0.8143 - accuracy: 0.6649 - val_loss: 1.2405 - val_accuracy: 0.4286\n",
+            "Epoch 15/15\n",
+            "30/30 [==============================] - 10s 347ms/step - loss: 0.8107 - accuracy: 0.6257 - val_loss: 1.2507 - val_accuracy: 0.4464\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "vjWh4YoJSQt6",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 296
+        },
+        "outputId": "78dbfe27-49fb-406b-e202-befd44f4b98e"
+      },
+      "source": [
+        "acc = history_dropout.history['accuracy']\n",
+        "val_acc = history_dropout.history['val_accuracy']\n",
+        "epochs = range(0,15)\n",
+        "plt.plot(epochs, acc,'g-' ,label= 'Accuracy')\n",
+        "plt.plot(epochs, val_acc ,'b-', label='Validation Accuracy')\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('Accuracy')\n",
+        "plt.legend()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x7fccb23e4a50>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 28
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lavsY7pWEBUd"
+      },
+      "source": [
+        "**Observation:** Here we can see that accuracy curve turns out to be much better than previous results for both training as well as validation. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "7IQDD_mbT7hL"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file

From 08a4d0a794b398375700fa59f8c1c32e175f2641 Mon Sep 17 00:00:00 2001
From: Yash Patel <57082017+yashpatel301@users.noreply.github.com>
Date: Sat, 17 Apr 2021 22:27:41 +0530
Subject: [PATCH 07/17] Update Readme.md

---
 Assignment 4 & 5/Readme.md | 24 +++++++++++++++++++++++-
 1 file changed, 23 insertions(+), 1 deletion(-)

diff --git a/Assignment 4 & 5/Readme.md b/Assignment 4 & 5/Readme.md
index c02d793..9db391a 100644
--- a/Assignment 4 & 5/Readme.md	
+++ b/Assignment 4 & 5/Readme.md	
@@ -1 +1,23 @@
-Please add the assignment-4 & 5 details here
+## **Introduction:** 
+</br> Convolutional Neural Network has been developed so well when it comes to image classification. There has been some amazing applications of Image classification in computer vision and classifying the disease infected leaves and normal leaves is one of the important application of Image classification. By automatic and an early diagnosis of a disease and its severity, effective, and timely treatment can be taken in advance [4]. The proposed work presents the comparison of two different Convolutional Neural Network (CNN) architectures to classify diseases of the citrus leaf. We have created our own CNN with 3 Conv - 3 Max pooling and 2 Dense layers [3] and also have added Dropout layer to overcome overfitting faced. This work also comprises of expermentation with hyper-parameters such as learning rate, dropout percentage and activation function at output layer and has compared the performance.
+</br>
+
+## **Approach:**
+Approach will consists of the architecture of CNN which is used and the experiments we have carried out based on values of hyper-parameters.
+#### ***Architecture***
+There has been 3 Convolutional and 3 max pooling layer used by stacking one behind other. After getting higher level features, flattening of the feature matrix has been done and passed through Dense layers. There has been one dropout layer present in between two Dense layers.
+#### ***Expermients with hyper-parameters***
+There has been ReLU activation used for all the layers except output layer, and for the output layer two different activation functions have been experimented i.e., sigmoid and softmax. Sigmoid activation function maps any values from −∞ to ∞, to 0-1 and gives the predictive analysis of classification. Softmax activation is also famous for its mapping of large values to range of 0 − 1 as it is a probabilistic function which gives proportion of similarity in probabilistic manner to different class and hence it is used at the outputlayer. </br>
+A list of learning rates, starting with 10^-3 and increment by multiple of 10 till 1. We have got five different values. Mapping of alpha values with index: [1 : 0.0001, 2 : 0.001, 3 : 0.01, 4 : 0.1, 5 : 1]. </br>
+When we talk about accuracy plots between training and validation accuracy, there can be overfitting and underfitting where we don’t want to stuck. In first scenerio where we can see that training accuracy is quite higher than validation and also the rate of increment in validation accuracy is not so encouraging and thus we can say that there might be problem of overfitting.</br>
+
+## **Results:**
+Results of experimenting with Sigmoid and Softmax activation function. 
+
+## **References:** 
+1. https://keras.io/api/preprocessing/image/
+2. https://vijayabhaskar96.medium.com/tutorial-image-classification-with-keras-flow-from-directory-and-generators-95f75ebe5720
+3. https://www.geeksforgeeks.org/python-image-classification-using-keras/
+4. Singh, U., Chouhan, S., Jain, S. and Jain, S., 2021. Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease. [online] Ieeexplore.ieee.org. Available at: <https://ieeexplore.ieee.org/document/8675730?denied=> [Accessed 17 April 2021].
+5. Afifi,  A.;   Alhumam,  A.;Abdelwahab, A. Convolutional NeuralNetwork for Automatic Identificationof Plant Diseases with Limited Data.Plants2021,10, 28.https://dx.doi.org/10.3390/plants10010028
+6. https://www.sciencedirect.com/science/article/abs/pii/S0168169920302258

From 88a94320741ec50896f0653a6290690b1a1c850d Mon Sep 17 00:00:00 2001
From: Yash Patel <57082017+yashpatel301@users.noreply.github.com>
Date: Sat, 17 Apr 2021 22:36:48 +0530
Subject: [PATCH 08/17] Update Readme.md

---
 Assignment 4 & 5/Readme.md | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)

diff --git a/Assignment 4 & 5/Readme.md b/Assignment 4 & 5/Readme.md
index 9db391a..97de688 100644
--- a/Assignment 4 & 5/Readme.md	
+++ b/Assignment 4 & 5/Readme.md	
@@ -13,6 +13,28 @@ When we talk about accuracy plots between training and validation accuracy, ther
 
 ## **Results:**
 Results of experimenting with Sigmoid and Softmax activation function. 
+</br> Sigmoid function </br>
+![Sigmoid function](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Citrus-leaves-Classification/Results/LOSS_SIGMOID.png)
+
+</br> Softmax function </br>
+![Softmax function](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Citrus-leaves-Classification/Results/LOSS_SOFTMAX.png)
+
+</br>Results of experimenting with different learning rates.
+</br> Accuracy </br>
+![Accuracy](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Citrus-leaves-Classification/Results/LR_accuracy.png)
+
+</br> Validation Accuracy </br>
+![Validation Accuracy](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Citrus-leaves-Classification/Results/LR_val_acc.png)
+
+</br>Results of experimenting with Dropout percentage.
+</br> Dropout 30% </br>
+![Accuracy](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Citrus-leaves-Classification/Results/Dropout_30.png)
+
+</br> Dropout 50% </br>
+![Validation Accuracy](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Citrus-leaves-Classification/Results/Dropout_50.png)
+
+## **Installation guidelines and platform details:**
+
 
 ## **References:** 
 1. https://keras.io/api/preprocessing/image/

From 8702fb734dc02e573c1b721cca215f84da1f328a Mon Sep 17 00:00:00 2001
From: Yash Patel <57082017+yashpatel301@users.noreply.github.com>
Date: Sat, 17 Apr 2021 22:40:07 +0530
Subject: [PATCH 09/17] Update Readme.md

---
 Assignment 4 & 5/Readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Assignment 4 & 5/Readme.md b/Assignment 4 & 5/Readme.md
index 97de688..5f05cc5 100644
--- a/Assignment 4 & 5/Readme.md	
+++ b/Assignment 4 & 5/Readme.md	
@@ -34,7 +34,7 @@ Results of experimenting with Sigmoid and Softmax activation function.
 ![Validation Accuracy](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Citrus-leaves-Classification/Results/Dropout_50.png)
 
 ## **Installation guidelines and platform details:**
-
+This assigment is performed on Google colab only. All the libraries are mentioned at the beginning of the code and are imported without any installation needed and all the required libraries are mentioned in the requirements.txt. 
 
 ## **References:** 
 1. https://keras.io/api/preprocessing/image/

From fd7551f8202883212bb3a3931dbe5d34b3d9fc0c Mon Sep 17 00:00:00 2001
From: Yash Patel <57082017+yashpatel301@users.noreply.github.com>
Date: Sat, 17 Apr 2021 22:40:36 +0530
Subject: [PATCH 10/17] Update Readme.md

---
 Assignment 2 & 3/Readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Assignment 2 & 3/Readme.md b/Assignment 2 & 3/Readme.md
index e0e481b..a95425c 100644
--- a/Assignment 2 & 3/Readme.md	
+++ b/Assignment 2 & 3/Readme.md	
@@ -27,7 +27,7 @@ Image 1 </br>
 ![Accuracy of Classifiers](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Face%20Recognition/Results/Accuracy_SVM_vs_KNN.png)
 
 ## **Installation guidelines and platform details:**
-</br> This assigment is performed on Google colab only. All the libraries are mentioned at the beginning of the code and are imported with any installment. All the libraries can be used by only importing in colab. For Anaconda environment requirement.txt can be used which is available in this folder only. 
+</br> This assigment is performed on Google colab only. All the libraries are mentioned at the beginning of the code and are imported without any installation needed and all the required libraries are mentioned in the requirements.txt
 
 ## **References:**
 1. https://stackoverflow.com/questions/11256433/how-to-show-math-equations-in-general-githubs-markdownnot-githubs-blog

From ee27cf0c99f89ea7c396ca4accf411ffe005c074 Mon Sep 17 00:00:00 2001
From: Yash Patel <57082017+yashpatel301@users.noreply.github.com>
Date: Sat, 17 Apr 2021 22:42:57 +0530
Subject: [PATCH 11/17] Update Readme.md

---
 Assignment 2 & 3/Readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Assignment 2 & 3/Readme.md b/Assignment 2 & 3/Readme.md
index a95425c..9661e01 100644
--- a/Assignment 2 & 3/Readme.md	
+++ b/Assignment 2 & 3/Readme.md	
@@ -27,7 +27,7 @@ Image 1 </br>
 ![Accuracy of Classifiers](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Face%20Recognition/Results/Accuracy_SVM_vs_KNN.png)
 
 ## **Installation guidelines and platform details:**
-</br> This assigment is performed on Google colab only. All the libraries are mentioned at the beginning of the code and are imported without any installation needed and all the required libraries are mentioned in the requirements.txt
+</br> This assigment is performed on Google colab only. All the libraries are mentioned at the beginning of the code and are imported without any installation needed in colab and for anaconda environment all the required libraries are mentioned in the requirements.txt
 
 ## **References:**
 1. https://stackoverflow.com/questions/11256433/how-to-show-math-equations-in-general-githubs-markdownnot-githubs-blog

From 3b083f2ba10fac14f31d22558b5402aa94ab198f Mon Sep 17 00:00:00 2001
From: Yash Patel <57082017+yashpatel301@users.noreply.github.com>
Date: Sat, 17 Apr 2021 22:43:30 +0530
Subject: [PATCH 12/17] Update Readme.md

---
 Assignment 4 & 5/Readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Assignment 4 & 5/Readme.md b/Assignment 4 & 5/Readme.md
index 5f05cc5..29a355e 100644
--- a/Assignment 4 & 5/Readme.md	
+++ b/Assignment 4 & 5/Readme.md	
@@ -34,7 +34,7 @@ Results of experimenting with Sigmoid and Softmax activation function.
 ![Validation Accuracy](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Citrus-leaves-Classification/Results/Dropout_50.png)
 
 ## **Installation guidelines and platform details:**
-This assigment is performed on Google colab only. All the libraries are mentioned at the beginning of the code and are imported without any installation needed and all the required libraries are mentioned in the requirements.txt. 
+This assigment is performed on Google colab only. All the libraries are mentioned at the beginning of the code and are imported without any installation needed colab and for anaconda environment and all the required libraries are mentioned in the requirements.txt. 
 
 ## **References:** 
 1. https://keras.io/api/preprocessing/image/

From 2df27d94b48bfca0d1cb71bce8b38e2283888213 Mon Sep 17 00:00:00 2001
From: Yash Patel <57082017+yashpatel301@users.noreply.github.com>
Date: Sun, 18 Apr 2021 15:02:28 +0530
Subject: [PATCH 13/17] Delete classification_of_citrus_leaves.ipynb

---
 .../classification_of_citrus_leaves.ipynb     | 1192 -----------------
 1 file changed, 1192 deletions(-)
 delete mode 100644 Assignment 4 & 5/classification_of_citrus_leaves.ipynb

diff --git a/Assignment 4 & 5/classification_of_citrus_leaves.ipynb b/Assignment 4 & 5/classification_of_citrus_leaves.ipynb
deleted file mode 100644
index 85442fa..0000000
--- a/Assignment 4 & 5/classification_of_citrus_leaves.ipynb	
+++ /dev/null
@@ -1,1192 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "HA3_CV.ipynb",
-      "provenance": [],
-      "collapsed_sections": [],
-      "toc_visible": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "accelerator": "GPU"
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "hdWq98vVGLW4"
-      },
-      "source": [
-        "### **Necessary Imports**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "G0vEOKHsqE4j"
-      },
-      "source": [
-        "import pandas as pd\n",
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "%matplotlib inline\n",
-        "import PIL"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "9S-eBH0P9q2y",
-        "outputId": "ac8ff836-036a-43e2-8cb3-c68d6982c668"
-      },
-      "source": [
-        "from google.colab import drive\n",
-        "drive.mount('/content/drive')"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Mounted at /content/drive\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "JPFvt09kqE12",
-        "outputId": "81a905be-8a5a-4c34-cc6e-49872dba84bb"
-      },
-      "source": [
-        "from PIL import Image\n",
-        "image1 = Image.open('/content/drive/MyDrive/HA3_CV_dataset/Leaves/Black spot/b (14).png')\n",
-        "image2 = Image.open('/content/drive/MyDrive/HA3_CV_dataset/Leaves/greening/g (102).png')\n",
-        "print('Image-1 shape: ',image1.size)\n",
-        "print('Image-2 shape: ',image2.size)\n",
-        "print('Image-1 mode: ', image1.mode)\n",
-        "print('Image-2 mode: ', image2.mode)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Image-1 shape:  (256, 256)\n",
-            "Image-2 shape:  (256, 256)\n",
-            "Image-1 mode:  RGB\n",
-            "Image-2 mode:  RGB\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 286
-        },
-        "id": "3cPSwcGf96eD",
-        "outputId": "45701be7-179b-4e23-e15a-4eef9129b690"
-      },
-      "source": [
-        "plt.imshow(image1)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<matplotlib.image.AxesImage at 0x7fcd4ec98750>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 8
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_v6b3X_asY8V"
-      },
-      "source": [
-        "Here both the images are of same size but what if any of the image is not of same size, hence we need to resize all images to 256 x 256"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "l_4SvTGYqEup"
-      },
-      "source": [
-        "img_w, img_h = 256, 256"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "33K2cTrGstq7"
-      },
-      "source": [
-        "### **For data-generation part**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "a026yVIPthQb"
-      },
-      "source": [
-        "from keras.preprocessing.image import ImageDataGenerator\n",
-        "from keras.preprocessing.image import img_to_array, array_to_img, load_img"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "g62gQmUofA24",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "1bec9877-7625-425e-a565-f3c1ebe657eb"
-      },
-      "source": [
-        "train_data = ImageDataGenerator(rescale = 1./255,\n",
-        "                                shear_range = 0.2,\n",
-        "                                zoom_range = 0.2,\n",
-        "                                horizontal_flip=True,\n",
-        "                                validation_split=0.2)\n",
-        "train_generator = train_data.flow_from_directory('/content/drive/MyDrive/HA3_CV_dataset/Leaves/',\n",
-        "                                                  target_size = (img_h, img_w),\n",
-        "                                                  batch_size=16,\n",
-        "                                                  shuffle = True,\n",
-        "                                                  class_mode = 'categorical',\n",
-        "                                                  subset =\"training\")                                                 \n",
-        "validation_generator = train_data.flow_from_directory('/content/drive/MyDrive/HA3_CV_dataset/Leaves/',\n",
-        "                                                      target_size=(img_h, img_w),\n",
-        "                                                      batch_size=16,\n",
-        "                                                      shuffle = True,\n",
-        "                                                      class_mode='categorical',\n",
-        "                                                      subset =\"validation\")"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Found 490 images belonging to 5 classes.\n",
-            "Found 119 images belonging to 5 classes.\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "7MYCkNV-1piu"
-      },
-      "source": [
-        "We have completed image preprocessing and now we are ready for Convolutional Architecture"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "wF1yTGUpy67U"
-      },
-      "source": [
-        "import tensorflow as tf\n",
-        "from tensorflow import keras\n",
-        "from tensorflow.keras.models import Sequential\n",
-        "from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Activation, Dropout, Flatten"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "_G8eGGwGGGkU"
-      },
-      "source": [
-        "### **Modelling part**"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "P7gSxDDsc6_S"
-      },
-      "source": [
-        "**Experiment:** Model with 3 conv2D and Maxpooling2D layers"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "ca7KEOI22Qwt"
-      },
-      "source": [
-        "model1 = Sequential()\n",
-        "model1.add(Conv2D(32, kernel_size=5, strides=1, padding=\"Same\", activation='relu', input_shape=(img_h,img_w,3)))\n",
-        "model1.add(MaxPooling2D(padding='Same'))\n",
-        "\n",
-        "model1.add(Conv2D(32, kernel_size=5, strides=1, padding=\"Same\", activation='relu'))\n",
-        "model1.add(MaxPooling2D(padding='Same'))\n",
-        "\n",
-        "model1.add(Conv2D(64, kernel_size=5, strides=1, padding=\"Same\", activation='relu'))\n",
-        "model1.add(MaxPooling2D(padding='Same'))\n",
-        "\n",
-        "model1.add(Flatten())\n",
-        "model1.add(Dense(1024,activation=\"relu\"))\n",
-        "model1.add(Dropout(0.3))\n",
-        "model1.add(Dense(5,activation=\"softmax\"))\n",
-        "\n",
-        "\n",
-        "model1.compile(optimizer=\"adam\",loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "tRGdaRvV8ugN",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "d74b7b0f-e612-4477-ac5a-7c3cf311020e"
-      },
-      "source": [
-        "model1.summary()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Model: \"sequential\"\n",
-            "_________________________________________________________________\n",
-            "Layer (type)                 Output Shape              Param #   \n",
-            "=================================================================\n",
-            "conv2d (Conv2D)              (None, 256, 256, 32)      2432      \n",
-            "_________________________________________________________________\n",
-            "max_pooling2d (MaxPooling2D) (None, 128, 128, 32)      0         \n",
-            "_________________________________________________________________\n",
-            "conv2d_1 (Conv2D)            (None, 128, 128, 32)      25632     \n",
-            "_________________________________________________________________\n",
-            "max_pooling2d_1 (MaxPooling2 (None, 64, 64, 32)        0         \n",
-            "_________________________________________________________________\n",
-            "conv2d_2 (Conv2D)            (None, 64, 64, 64)        51264     \n",
-            "_________________________________________________________________\n",
-            "max_pooling2d_2 (MaxPooling2 (None, 32, 32, 64)        0         \n",
-            "_________________________________________________________________\n",
-            "flatten (Flatten)            (None, 65536)             0         \n",
-            "_________________________________________________________________\n",
-            "dense (Dense)                (None, 1024)              67109888  \n",
-            "_________________________________________________________________\n",
-            "dropout (Dropout)            (None, 1024)              0         \n",
-            "_________________________________________________________________\n",
-            "dense_1 (Dense)              (None, 5)                 5125      \n",
-            "=================================================================\n",
-            "Total params: 67,194,341\n",
-            "Trainable params: 67,194,341\n",
-            "Non-trainable params: 0\n",
-            "_________________________________________________________________\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "IHE9Lhg888M_",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "d1608467-fd92-4040-f885-cd75aa0afe0f"
-      },
-      "source": [
-        "history=model1.fit_generator(\n",
-        "    train_generator,\n",
-        "    steps_per_epoch = 490//16, \n",
-        "    epochs=30,\n",
-        "    validation_data = validation_generator,\n",
-        "    validation_steps = 119//16\n",
-        ")"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
-            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "Epoch 1/30\n",
-            "30/30 [==============================] - 10s 346ms/step - loss: 1.0525 - accuracy: 0.5063 - val_loss: 1.2161 - val_accuracy: 0.4464\n",
-            "Epoch 2/30\n",
-            "30/30 [==============================] - 10s 347ms/step - loss: 1.0442 - accuracy: 0.5359 - val_loss: 1.1302 - val_accuracy: 0.5089\n",
-            "Epoch 3/30\n",
-            "30/30 [==============================] - 10s 350ms/step - loss: 1.1486 - accuracy: 0.4557 - val_loss: 1.3049 - val_accuracy: 0.3750\n",
-            "Epoch 4/30\n",
-            "30/30 [==============================] - 10s 348ms/step - loss: 1.0585 - accuracy: 0.5316 - val_loss: 1.2427 - val_accuracy: 0.4554\n",
-            "Epoch 5/30\n",
-            "30/30 [==============================] - 10s 349ms/step - loss: 0.9756 - accuracy: 0.5570 - val_loss: 1.2612 - val_accuracy: 0.3393\n",
-            "Epoch 6/30\n",
-            "30/30 [==============================] - 10s 349ms/step - loss: 0.9490 - accuracy: 0.5823 - val_loss: 1.1442 - val_accuracy: 0.4107\n",
-            "Epoch 7/30\n",
-            "30/30 [==============================] - 10s 346ms/step - loss: 0.8177 - accuracy: 0.6350 - val_loss: 0.9511 - val_accuracy: 0.5357\n",
-            "Epoch 8/30\n",
-            "30/30 [==============================] - 10s 343ms/step - loss: 0.8584 - accuracy: 0.6329 - val_loss: 0.9878 - val_accuracy: 0.4911\n",
-            "Epoch 9/30\n",
-            "30/30 [==============================] - 10s 346ms/step - loss: 0.7659 - accuracy: 0.6477 - val_loss: 1.0183 - val_accuracy: 0.6161\n",
-            "Epoch 10/30\n",
-            "30/30 [==============================] - 10s 343ms/step - loss: 0.7852 - accuracy: 0.6624 - val_loss: 1.0588 - val_accuracy: 0.5446\n",
-            "Epoch 11/30\n",
-            "30/30 [==============================] - 10s 346ms/step - loss: 0.6746 - accuracy: 0.7215 - val_loss: 1.0865 - val_accuracy: 0.5089\n",
-            "Epoch 12/30\n",
-            "30/30 [==============================] - 10s 346ms/step - loss: 0.6392 - accuracy: 0.7489 - val_loss: 1.0526 - val_accuracy: 0.5357\n",
-            "Epoch 13/30\n",
-            "30/30 [==============================] - 10s 350ms/step - loss: 0.5984 - accuracy: 0.7532 - val_loss: 1.4194 - val_accuracy: 0.4196\n",
-            "Epoch 14/30\n",
-            "30/30 [==============================] - 10s 349ms/step - loss: 0.6227 - accuracy: 0.7321 - val_loss: 1.1302 - val_accuracy: 0.5625\n",
-            "Epoch 15/30\n",
-            "30/30 [==============================] - 10s 344ms/step - loss: 0.5660 - accuracy: 0.7468 - val_loss: 1.0489 - val_accuracy: 0.5446\n",
-            "Epoch 16/30\n",
-            "30/30 [==============================] - 10s 344ms/step - loss: 0.5386 - accuracy: 0.7996 - val_loss: 1.1707 - val_accuracy: 0.5893\n",
-            "Epoch 17/30\n",
-            "30/30 [==============================] - 10s 344ms/step - loss: 0.5933 - accuracy: 0.7363 - val_loss: 1.0592 - val_accuracy: 0.5089\n",
-            "Epoch 18/30\n",
-            "30/30 [==============================] - 10s 346ms/step - loss: 0.4860 - accuracy: 0.8017 - val_loss: 1.1676 - val_accuracy: 0.6250\n",
-            "Epoch 19/30\n",
-            "30/30 [==============================] - 10s 344ms/step - loss: 0.5312 - accuracy: 0.7679 - val_loss: 1.0386 - val_accuracy: 0.5000\n",
-            "Epoch 20/30\n",
-            "30/30 [==============================] - 10s 345ms/step - loss: 0.4260 - accuracy: 0.8333 - val_loss: 1.1630 - val_accuracy: 0.5179\n",
-            "Epoch 21/30\n",
-            "30/30 [==============================] - 10s 346ms/step - loss: 0.4594 - accuracy: 0.8080 - val_loss: 1.2922 - val_accuracy: 0.4286\n",
-            "Epoch 22/30\n",
-            "30/30 [==============================] - 10s 346ms/step - loss: 0.4556 - accuracy: 0.8122 - val_loss: 1.1118 - val_accuracy: 0.5357\n",
-            "Epoch 23/30\n",
-            "30/30 [==============================] - 10s 344ms/step - loss: 0.4611 - accuracy: 0.8207 - val_loss: 0.9501 - val_accuracy: 0.5625\n",
-            "Epoch 24/30\n",
-            "30/30 [==============================] - 10s 343ms/step - loss: 0.4057 - accuracy: 0.8397 - val_loss: 1.0264 - val_accuracy: 0.6161\n",
-            "Epoch 25/30\n",
-            "30/30 [==============================] - 10s 344ms/step - loss: 0.3487 - accuracy: 0.8544 - val_loss: 1.0910 - val_accuracy: 0.6607\n",
-            "Epoch 26/30\n",
-            "30/30 [==============================] - 10s 346ms/step - loss: 0.3157 - accuracy: 0.8819 - val_loss: 1.1480 - val_accuracy: 0.5446\n",
-            "Epoch 27/30\n",
-            "30/30 [==============================] - 10s 347ms/step - loss: 0.3200 - accuracy: 0.8734 - val_loss: 1.1493 - val_accuracy: 0.6429\n",
-            "Epoch 28/30\n",
-            "30/30 [==============================] - 10s 347ms/step - loss: 0.2899 - accuracy: 0.8882 - val_loss: 1.4611 - val_accuracy: 0.5714\n",
-            "Epoch 29/30\n",
-            "30/30 [==============================] - 10s 346ms/step - loss: 0.3287 - accuracy: 0.8650 - val_loss: 1.3737 - val_accuracy: 0.5982\n",
-            "Epoch 30/30\n",
-            "30/30 [==============================] - 10s 343ms/step - loss: 0.3498 - accuracy: 0.8565 - val_loss: 1.2025 - val_accuracy: 0.6339\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "fZcMYHqC_Kz1",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 312
-        },
-        "outputId": "b0c82dc7-49da-476e-8743-ed6c4923e157"
-      },
-      "source": [
-        "train_l = history.history['loss']\n",
-        "val_l = history.history['val_loss']\n",
-        "epochs = range(1,31)\n",
-        "plt.plot(epochs, train_l, 'b-', label='train loss')\n",
-        "plt.plot(epochs, val_l, 'g-', label='validation loss')\n",
-        "plt.title('Validation loss vs Train loss')\n",
-        "plt.xlabel('epochs')\n",
-        "plt.ylabel('loss')\n",
-        "plt.legend()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<matplotlib.legend.Legend at 0x7fcd000ad4d0>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 17
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "DaK31Xn0CieJ",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 312
-        },
-        "outputId": "4d059cef-bd33-4371-a558-590137d32db2"
-      },
-      "source": [
-        "train_l = history.history['accuracy']\n",
-        "val_l = history.history['val_accuracy']\n",
-        "epochs = range(1,31)\n",
-        "plt.plot(epochs, train_l, 'b-', label='accuracy')\n",
-        "plt.plot(epochs, val_l, 'g-', label='validation accuracy')\n",
-        "plt.title('Accuracy vs Validation Accuracy')\n",
-        "plt.xlabel('epochs')\n",
-        "plt.ylabel('accuracy')\n",
-        "plt.legend()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<matplotlib.legend.Legend at 0x7fcd00038b90>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 18
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "h4vECMlbDbNC"
-      },
-      "source": [
-        "**Observstion:** As we are not getting smooth accuracy plots, let's try with signoid activation function in last layer"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "UHcr8C1mDZ5C"
-      },
-      "source": [
-        "model_sig = Sequential()\n",
-        "model_sig.add(Conv2D(32, kernel_size=5, strides=1, padding=\"Same\", activation='relu', input_shape=(img_h,img_w,3)))\n",
-        "model_sig.add(MaxPooling2D(padding='Same'))\n",
-        "\n",
-        "model_sig.add(Conv2D(32, kernel_size=5, strides=1, padding=\"Same\", activation='relu'))\n",
-        "model_sig.add(MaxPooling2D(padding='Same'))\n",
-        "\n",
-        "model_sig.add(Conv2D(64, kernel_size=5, strides=1, padding=\"Same\", activation='relu'))\n",
-        "model_sig.add(MaxPooling2D(padding='Same'))\n",
-        "\n",
-        "model_sig.add(Flatten())\n",
-        "model_sig.add(Dense(1024,activation=\"relu\"))\n",
-        "model_sig.add(Dropout(0.3))\n",
-        "model_sig.add(Dense(5,activation=\"sigmoid\"))\n",
-        "\n",
-        "\n",
-        "model_sig.compile(optimizer=\"adam\",loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "X1UImC4sDZ2g",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "f2a7ca89-57c8-4079-8a8b-bce944bd01b2"
-      },
-      "source": [
-        "history_sig=model_sig.fit_generator(\n",
-        "    train_generator,\n",
-        "    steps_per_epoch = 490//16, \n",
-        "    epochs=15,\n",
-        "    validation_data = validation_generator,\n",
-        "    validation_steps = 119//16\n",
-        ")"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
-            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "Epoch 1/15\n",
-            "30/30 [==============================] - 11s 351ms/step - loss: 2.6328 - accuracy: 0.3303 - val_loss: 1.2742 - val_accuracy: 0.3571\n",
-            "Epoch 2/15\n",
-            "30/30 [==============================] - 10s 342ms/step - loss: 1.1758 - accuracy: 0.4572 - val_loss: 1.1958 - val_accuracy: 0.3304\n",
-            "Epoch 3/15\n",
-            "30/30 [==============================] - 10s 337ms/step - loss: 1.0656 - accuracy: 0.5190 - val_loss: 1.2358 - val_accuracy: 0.4821\n",
-            "Epoch 4/15\n",
-            "30/30 [==============================] - 10s 336ms/step - loss: 1.0741 - accuracy: 0.4657 - val_loss: 1.3591 - val_accuracy: 0.3929\n",
-            "Epoch 5/15\n",
-            "30/30 [==============================] - 10s 337ms/step - loss: 1.1504 - accuracy: 0.4495 - val_loss: 1.2585 - val_accuracy: 0.3125\n",
-            "Epoch 6/15\n",
-            "30/30 [==============================] - 10s 337ms/step - loss: 1.4806 - accuracy: 0.4893 - val_loss: 1.3130 - val_accuracy: 0.4464\n",
-            "Epoch 7/15\n",
-            "30/30 [==============================] - 10s 336ms/step - loss: 1.0975 - accuracy: 0.4548 - val_loss: 1.2160 - val_accuracy: 0.4196\n",
-            "Epoch 8/15\n",
-            "30/30 [==============================] - 10s 335ms/step - loss: 1.0235 - accuracy: 0.5757 - val_loss: 1.2528 - val_accuracy: 0.3661\n",
-            "Epoch 9/15\n",
-            "30/30 [==============================] - 10s 337ms/step - loss: 0.9742 - accuracy: 0.5300 - val_loss: 1.1450 - val_accuracy: 0.4554\n",
-            "Epoch 10/15\n",
-            "30/30 [==============================] - 10s 340ms/step - loss: 0.9395 - accuracy: 0.5442 - val_loss: 1.1757 - val_accuracy: 0.4821\n",
-            "Epoch 11/15\n",
-            "30/30 [==============================] - 10s 341ms/step - loss: 0.9222 - accuracy: 0.5985 - val_loss: 1.2027 - val_accuracy: 0.4911\n",
-            "Epoch 12/15\n",
-            "30/30 [==============================] - 10s 339ms/step - loss: 0.8674 - accuracy: 0.6175 - val_loss: 1.1061 - val_accuracy: 0.4375\n",
-            "Epoch 13/15\n",
-            "30/30 [==============================] - 10s 339ms/step - loss: 0.8433 - accuracy: 0.6401 - val_loss: 1.1612 - val_accuracy: 0.4018\n",
-            "Epoch 14/15\n",
-            "30/30 [==============================] - 10s 336ms/step - loss: 0.8872 - accuracy: 0.5814 - val_loss: 1.1739 - val_accuracy: 0.3929\n",
-            "Epoch 15/15\n",
-            "30/30 [==============================] - 10s 336ms/step - loss: 0.8490 - accuracy: 0.6193 - val_loss: 0.9756 - val_accuracy: 0.5357\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "n7B3BBcyFJ2_"
-      },
-      "source": [
-        "**Observstion:** Here we are getting validation loss higher than training loss this might be showing the problem of over-fitting data. Now that can be due to many reasons but here it should be due to less number of samples in comaprision with features. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "S6Hk1kQoDZzg",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 312
-        },
-        "outputId": "5efda344-d324-4e36-8bdc-345a648ae1a8"
-      },
-      "source": [
-        "train_l = history_sig.history['loss']\n",
-        "val_l = history_sig.history['val_loss']\n",
-        "epochs = range(1,16)\n",
-        "plt.plot(epochs, train_l, 'b-', label='train loss')\n",
-        "plt.plot(epochs, val_l, 'g-', label='validation loss')\n",
-        "plt.title('Validation loss vs Train loss')\n",
-        "plt.xlabel('epochs')\n",
-        "plt.ylabel('loss')\n",
-        "plt.legend()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<matplotlib.legend.Legend at 0x7fccc4144090>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 21
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "tESX5PTGDZxG",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 312
-        },
-        "outputId": "63d9e950-0c2e-4811-9c53-24332d1ca879"
-      },
-      "source": [
-        "train_l = history_sig.history['accuracy']\n",
-        "val_l = history_sig.history['val_accuracy']\n",
-        "epochs = range(1,16)\n",
-        "plt.plot(epochs, train_l, 'b-', label='accuracy')\n",
-        "plt.plot(epochs, val_l, 'g-', label='validation accuracy')\n",
-        "plt.title('Accuracy vs Validation Accuracy')\n",
-        "plt.xlabel('epochs')\n",
-        "plt.ylabel('accuracy')\n",
-        "plt.legend()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<matplotlib.legend.Legend at 0x7fccbc67cc90>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 22
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "8lvuxdiUbzhp"
-      },
-      "source": [
-        "**Experiment:** Trying alpha values as learning rate for optimizer instead of adam optimizer. \n",
-        "\n",
-        "Alphas = [0.0001 , 0.001 , 0.01 , 0.1 , 1]\n",
-        "\n",
-        "Also we had tried model with 2 conv2D and maxpooling2D layers."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "08lSTvkfKsIL",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "09903502-e4ea-437d-d25e-ec76344071c8"
-      },
-      "source": [
-        "learning_rate = [0.0001,0.001,0.01,0.1,1]\n",
-        "for alpha in learning_rate:\n",
-        "  model2 = Sequential()\n",
-        "  model2.add(Conv2D(32, kernel_size=5, strides=1, padding=\"Same\", activation='relu', input_shape=(img_h,img_w,3)))\n",
-        "  model2.add(MaxPooling2D(padding='Same'))\n",
-        "\n",
-        "  model2.add(Conv2D(64, kernel_size=5, strides=1, padding=\"Same\", activation='relu'))\n",
-        "  model2.add(MaxPooling2D(padding='Same'))\n",
-        "\n",
-        "  model2.add(Flatten())\n",
-        "  model2.add(Dense(1024,activation=\"relu\"))\n",
-        "  model2.add(Dropout(0.3))\n",
-        "  model2.add(Dense(5,activation=\"softmax\"))\n",
-        "\n",
-        "  opti = tf.keras.optimizers.SGD(learning_rate=alpha)\n",
-        "  model2.compile(optimizer=opti,loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])\n",
-        "  print('For learning rate = ', alpha)\n",
-        "  model2.fit_generator(\n",
-        "                      train_generator,\n",
-        "                      steps_per_epoch = 490//16, \n",
-        "                      epochs=10,\n",
-        "                      validation_data = validation_generator,\n",
-        "                      validation_steps = 119//16)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "For learning rate =  0.0001\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
-            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "Epoch 1/10\n",
-            "30/30 [==============================] - 11s 353ms/step - loss: 1.5222 - accuracy: 0.2822 - val_loss: 1.4276 - val_accuracy: 0.2679\n",
-            "Epoch 2/10\n",
-            "30/30 [==============================] - 10s 348ms/step - loss: 1.4204 - accuracy: 0.3089 - val_loss: 1.3749 - val_accuracy: 0.3125\n",
-            "Epoch 3/10\n",
-            "30/30 [==============================] - 10s 346ms/step - loss: 1.4063 - accuracy: 0.3119 - val_loss: 1.3530 - val_accuracy: 0.3214\n",
-            "Epoch 4/10\n",
-            "30/30 [==============================] - 10s 345ms/step - loss: 1.3817 - accuracy: 0.3407 - val_loss: 1.3651 - val_accuracy: 0.2321\n",
-            "Epoch 5/10\n",
-            "30/30 [==============================] - 10s 346ms/step - loss: 1.3954 - accuracy: 0.3282 - val_loss: 1.3435 - val_accuracy: 0.3036\n",
-            "Epoch 6/10\n",
-            "30/30 [==============================] - 10s 346ms/step - loss: 1.3874 - accuracy: 0.3423 - val_loss: 1.3305 - val_accuracy: 0.3214\n",
-            "Epoch 7/10\n",
-            "30/30 [==============================] - 10s 344ms/step - loss: 1.3385 - accuracy: 0.4016 - val_loss: 1.3446 - val_accuracy: 0.3393\n",
-            "Epoch 8/10\n",
-            "30/30 [==============================] - 10s 345ms/step - loss: 1.3685 - accuracy: 0.3674 - val_loss: 1.3262 - val_accuracy: 0.3036\n",
-            "Epoch 9/10\n",
-            "30/30 [==============================] - 10s 344ms/step - loss: 1.3630 - accuracy: 0.4088 - val_loss: 1.3420 - val_accuracy: 0.3750\n",
-            "Epoch 10/10\n",
-            "30/30 [==============================] - 10s 346ms/step - loss: 1.3240 - accuracy: 0.4045 - val_loss: 1.3417 - val_accuracy: 0.3571\n",
-            "For learning rate =  0.001\n",
-            "Epoch 1/10\n",
-            "30/30 [==============================] - 11s 358ms/step - loss: 1.4453 - accuracy: 0.3384 - val_loss: 1.3568 - val_accuracy: 0.3036\n",
-            "Epoch 2/10\n",
-            "30/30 [==============================] - 10s 343ms/step - loss: 1.3536 - accuracy: 0.3335 - val_loss: 1.3355 - val_accuracy: 0.3393\n",
-            "Epoch 3/10\n",
-            "30/30 [==============================] - 10s 342ms/step - loss: 1.3485 - accuracy: 0.3730 - val_loss: 1.3075 - val_accuracy: 0.4821\n",
-            "Epoch 4/10\n",
-            "30/30 [==============================] - 10s 346ms/step - loss: 1.2970 - accuracy: 0.4173 - val_loss: 1.2721 - val_accuracy: 0.3750\n",
-            "Epoch 5/10\n",
-            "30/30 [==============================] - 10s 344ms/step - loss: 1.3062 - accuracy: 0.3817 - val_loss: 1.2739 - val_accuracy: 0.4464\n",
-            "Epoch 6/10\n",
-            "30/30 [==============================] - 10s 339ms/step - loss: 1.3026 - accuracy: 0.4162 - val_loss: 1.2847 - val_accuracy: 0.3125\n",
-            "Epoch 7/10\n",
-            "30/30 [==============================] - 10s 343ms/step - loss: 1.1955 - accuracy: 0.4739 - val_loss: 1.2509 - val_accuracy: 0.3214\n",
-            "Epoch 8/10\n",
-            "30/30 [==============================] - 10s 345ms/step - loss: 1.2250 - accuracy: 0.4720 - val_loss: 1.2628 - val_accuracy: 0.3750\n",
-            "Epoch 9/10\n",
-            "30/30 [==============================] - 10s 342ms/step - loss: 1.2400 - accuracy: 0.3972 - val_loss: 1.2039 - val_accuracy: 0.4196\n",
-            "Epoch 10/10\n",
-            "30/30 [==============================] - 10s 344ms/step - loss: 1.1632 - accuracy: 0.4532 - val_loss: 1.2186 - val_accuracy: 0.3929\n",
-            "For learning rate =  0.01\n",
-            "Epoch 1/10\n",
-            "30/30 [==============================] - 11s 356ms/step - loss: 1.6275 - accuracy: 0.2971 - val_loss: 1.3855 - val_accuracy: 0.2857\n",
-            "Epoch 2/10\n",
-            "30/30 [==============================] - 10s 341ms/step - loss: 1.4304 - accuracy: 0.3218 - val_loss: 1.3478 - val_accuracy: 0.2946\n",
-            "Epoch 3/10\n",
-            "30/30 [==============================] - 10s 349ms/step - loss: 1.3487 - accuracy: 0.3228 - val_loss: 1.2608 - val_accuracy: 0.3393\n",
-            "Epoch 4/10\n",
-            "30/30 [==============================] - 11s 352ms/step - loss: 1.2775 - accuracy: 0.3851 - val_loss: 1.3032 - val_accuracy: 0.3482\n",
-            "Epoch 5/10\n",
-            "30/30 [==============================] - 10s 339ms/step - loss: 1.2223 - accuracy: 0.4265 - val_loss: 1.6625 - val_accuracy: 0.3304\n",
-            "Epoch 6/10\n",
-            "30/30 [==============================] - 10s 344ms/step - loss: 1.1971 - accuracy: 0.4688 - val_loss: 1.1990 - val_accuracy: 0.4018\n",
-            "Epoch 7/10\n",
-            "30/30 [==============================] - 10s 344ms/step - loss: 1.1165 - accuracy: 0.4969 - val_loss: 1.2270 - val_accuracy: 0.4018\n",
-            "Epoch 8/10\n",
-            "30/30 [==============================] - 10s 340ms/step - loss: 1.1467 - accuracy: 0.4743 - val_loss: 1.1843 - val_accuracy: 0.3750\n",
-            "Epoch 9/10\n",
-            "30/30 [==============================] - 10s 341ms/step - loss: 1.0957 - accuracy: 0.4806 - val_loss: 1.1786 - val_accuracy: 0.4732\n",
-            "Epoch 10/10\n",
-            "30/30 [==============================] - 10s 341ms/step - loss: 0.9843 - accuracy: 0.5359 - val_loss: 1.2092 - val_accuracy: 0.3839\n",
-            "For learning rate =  0.1\n",
-            "Epoch 1/10\n",
-            "30/30 [==============================] - 11s 353ms/step - loss: 3342957572916081328128.0000 - accuracy: 0.2953 - val_loss: 1.5260 - val_accuracy: 0.3214\n",
-            "Epoch 2/10\n",
-            "30/30 [==============================] - 10s 345ms/step - loss: 1.5012 - accuracy: 0.3342 - val_loss: 1.4475 - val_accuracy: 0.3393\n",
-            "Epoch 3/10\n",
-            "30/30 [==============================] - 10s 341ms/step - loss: 1.4423 - accuracy: 0.3654 - val_loss: 1.4159 - val_accuracy: 0.3304\n",
-            "Epoch 4/10\n",
-            "30/30 [==============================] - 10s 344ms/step - loss: 1.4352 - accuracy: 0.3329 - val_loss: 1.4027 - val_accuracy: 0.3393\n",
-            "Epoch 5/10\n",
-            "30/30 [==============================] - 10s 346ms/step - loss: 1.4109 - accuracy: 0.3535 - val_loss: 1.4000 - val_accuracy: 0.3125\n",
-            "Epoch 6/10\n",
-            "30/30 [==============================] - 10s 342ms/step - loss: 1.3763 - accuracy: 0.3328 - val_loss: 1.3926 - val_accuracy: 0.3304\n",
-            "Epoch 7/10\n",
-            "30/30 [==============================] - 11s 354ms/step - loss: 1.4106 - accuracy: 0.3004 - val_loss: 1.3792 - val_accuracy: 0.3304\n",
-            "Epoch 8/10\n",
-            "30/30 [==============================] - 11s 354ms/step - loss: 1.4032 - accuracy: 0.3252 - val_loss: 1.3846 - val_accuracy: 0.3393\n",
-            "Epoch 9/10\n",
-            "30/30 [==============================] - 11s 359ms/step - loss: 1.3709 - accuracy: 0.3670 - val_loss: 1.3827 - val_accuracy: 0.3393\n",
-            "Epoch 10/10\n",
-            "30/30 [==============================] - 11s 353ms/step - loss: 1.3663 - accuracy: 0.3554 - val_loss: 1.3724 - val_accuracy: 0.3393\n",
-            "For learning rate =  1\n",
-            "Epoch 1/10\n",
-            "30/30 [==============================] - 11s 367ms/step - loss: nan - accuracy: 0.2652 - val_loss: nan - val_accuracy: 0.2857\n",
-            "Epoch 2/10\n",
-            "30/30 [==============================] - 10s 349ms/step - loss: nan - accuracy: 0.2642 - val_loss: nan - val_accuracy: 0.2768\n",
-            "Epoch 3/10\n",
-            "30/30 [==============================] - 11s 351ms/step - loss: nan - accuracy: 0.2751 - val_loss: nan - val_accuracy: 0.2857\n",
-            "Epoch 4/10\n",
-            "30/30 [==============================] - 10s 346ms/step - loss: nan - accuracy: 0.2879 - val_loss: nan - val_accuracy: 0.2857\n",
-            "Epoch 5/10\n",
-            "30/30 [==============================] - 10s 345ms/step - loss: nan - accuracy: 0.2521 - val_loss: nan - val_accuracy: 0.2946\n",
-            "Epoch 6/10\n",
-            "30/30 [==============================] - 10s 343ms/step - loss: nan - accuracy: 0.2569 - val_loss: nan - val_accuracy: 0.2768\n",
-            "Epoch 7/10\n",
-            "30/30 [==============================] - 10s 343ms/step - loss: nan - accuracy: 0.2718 - val_loss: nan - val_accuracy: 0.2679\n",
-            "Epoch 8/10\n",
-            "30/30 [==============================] - 10s 345ms/step - loss: nan - accuracy: 0.2863 - val_loss: nan - val_accuracy: 0.2768\n",
-            "Epoch 9/10\n",
-            "30/30 [==============================] - 10s 348ms/step - loss: nan - accuracy: 0.2637 - val_loss: nan - val_accuracy: 0.2946\n",
-            "Epoch 10/10\n",
-            "30/30 [==============================] - 10s 342ms/step - loss: nan - accuracy: 0.2835 - val_loss: nan - val_accuracy: 0.2768\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "DPf_XXarMwqE"
-      },
-      "source": [
-        "Plotting Accuracy (considering highest accuracy of 10 epochs) and validation accuracy (considering val_acc of respective accuracy)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "5QBg7k8ZPgQa",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 312
-        },
-        "outputId": "ef00d681-a1b5-4386-f35f-8b1282a511c6"
-      },
-      "source": [
-        "Accuracy = [0.3860, 0.4372, 0.5840, 0.3728, 0.3428]\n",
-        "Val_acc = [0.3125, 0.4196, 0.3839, 0.3304, 0.3304]\n",
-        "alpha = range(0,5)\n",
-        "plt.bar(alpha, Accuracy)\n",
-        "plt.xlabel('Alpha')\n",
-        "plt.ylabel('Accuracy')\n",
-        "plt.title('Accuracy for different learning rates')"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "Text(0.5, 1.0, 'Accuracy for different learning rates')"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 24
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "HmexAXQnN_US",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 312
-        },
-        "outputId": "9306e8a8-923b-474f-b9c3-158a3fd90c0e"
-      },
-      "source": [
-        "plt.bar(alpha, Val_acc)\n",
-        "plt.xlabel('Alpha')\n",
-        "plt.ylabel('Validation accuracy')\n",
-        "plt.title('Validation accuracy for different learning rates')"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "Text(0.5, 1.0, 'Validation accuracy for different learning rates')"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 25
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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KOUGYmVmhSjupzax/G3vWr5sdQp9ZetHRPao/mNe9LO9BmJlZIScIMzMr5ARhZmaFnCDMzKyQE4SZmRVygjAzs0LdJghJn5I0vBHBmJlZ/1FmD+LlZM+Tvk7SZEmqOigzM2u+bi+Ui4gvSToHeCdwMvBtSdcB34+IB6oOsFEGykUzVV0wY2aDT6k+iIgI4J/p1QEMB34q6ZIKYzMzsybqdg9C0unAR4BHge8Bn4uI5yVtQ/YEuM9XG6KZmTVDmT2I3YD3RsSREXF9RDwPEBEbgXfXa5j6LBZLapd0VsH0UyX9TdICSbdJmpCbdnZqt1jSkT1cLzMz20JlEsRvgBce9ylpZ0kHAUTEfV01Ss+UngkcBUwAjs8ngOTaiNg/Ig4ALgEuTW0nkD3Dej9gMnBZ5zOqzcysMcokiO8AT+XGn0pl3ZkItEfEkohYD8wBpuQrRMQTudEdgUjDU4A5EbEuIh4E2tP8zMysQcrc7lupkxrIDi1JKtNuJLAsN74cOGizmUunAWcA2wOH5dreUdN2ZEHbacA0gDFjxpQIyczMyiqzB7FE0qclbZdepwNL+iqAiJgZEa8CvgB8qYdtZ0VES0S0jBgxoq9CMjMzyiWIU4G3Ait4cS9gWol2K4DRufFRqawrc4Bje9nWzMz6WJkL5R4h6zDuqfnAeEnjyDbuU4EP5itIGh8Rf0+jR5OdNgvQClwr6VJgD2A8cFcvYjAzs14qcx3EMOAUsjOKhnWWR8RH67WLiA5J04F5wBBgdkQslDQDaIuIVmC6pCOA54E1wImp7cJ0tfYisgvzTouIDb1ZQTMz650ync1XA/cDRwIzgA8BXZ7emhcRc4G5NWXn5oZPr9P2QuDCMssxM7O+V6YPYu+IOAd4OiKuJDsUtNnZSGZmNrCUSRDPp79rJb0W2AX4t+pCMjOz/qDMIaZZ6XkQXyLrPH4pcE6lUZmZWdPVTRDphnxPRMQa4FZgr4ZEZWZmTVf3EFO6IZ/v1mpmNgiV6YP4vaTPShotabfOV+WRmZlZU5Xpg/hA+ntarizw4SYzswGtzJXU4xoRiJmZ9S9lrqT+SFF5RFzV9+GYmVl/UeYQ05tyw8OAw4G/AE4QZmYDWJlDTJ/Kj0valezOq2ZmNoCVOYup1tOA+yXMzAa4Mn0Q/8WLjwLdhuz50tdVGZSZmTVfmT6Ir+eGO4CHImJ5RfGYmVk/USZBPAysjIjnACTtIGlsRCytNDIzM2uqMn0Q1wMbc+MbUlm3JE2WtFhSu6SzCqafIWmRpHsl/UHSnrlpGyQtSK/WMsszM7O+U2YPYtuIWN85EhHrJW3fXSNJQ4CZwCSyZ1nPl9QaEYty1e4BWiLiGUmfAC7hxSu3n42IA8quiJmZ9a0yexCrJB3TOSJpCvBoiXYTgfaIWJISzBxgSr5CRNwcEc+k0TuAUeXCNjOzqpVJEKcCX5T0sKSHgS8AHy/RbiSwLDe+PJV15RTgN7nxYZLaJN0h6diiBpKmpTptq1atKhGSmZmVVeZCuQeAN0t6aRp/qq+DkHQC0AIckiveMyJWSNoLuEnS31Is+dhmAbMAWlpaAjMz6zPd7kFI+qqkXSPiqYh4StJwSV8pMe8VwOjc+KhUVjv/I4D/AI6JiHWd5RGxIv1dAtwCHFhimWZm1kfKHGI6KiLWdo6kp8u9q0S7+cB4SeNSp/ZUskeWvkDSgcAVZMnhkVz5cElD0/DuwNuAfOe2mZlVrMxZTEMkDe38dS9pB2Bod40iokPSdGAeMASYHRELJc0A2iKiFfhPsmdcXy8J4OGIOAZ4DXCFpI1kSeyimrOfzMysYmUSxDXAHyT9II2fDFxZZuYRMReYW1N2bm74iC7a/RnYv8wyzMysGmU6qS+WdC/Zbb4BLoiIedWGZWZmzVZmD4KI+A2bnoJqZmYDXJmzmN4sab6kpyStT7fAeKIRwZmZWfOUOYvp28DxwN+BHYD/RXYLDTMzG8BKPTAoItqBIRGxISJ+AEyuNiwzM2u2Mn0Qz6TrGBZIugRYSe+eRGdmZluRMhv6D6d608keNzoaeF+VQZmZWfOVOc31oTT4HHB+teGYmVl/4UNFZmZWyAnCzMwKOUGYmVmhbvsgJO0DfA7YM18/Ig6rMC4zM2uyMqe5Xg9cDnwX2FBtOGZm1l+USRAdEfGdyiMxM7N+pUwfxH9J+qSkV0rarfNVeWRmZtZUZfYgTkx/P5crC2Cvvg/HzMz6i273ICJiXMGrVHKQNFnSYkntks4qmH6GpEWS7pX0B0l75qadKOnv6XVibVszM6tWmbOYtgM+ARycim4BroiI57tpN4Tsrq+TgOXAfEmtNY8OvQdoiYhnJH0CuAT4QDqEdR7QQra3cndqu6ZHa2dmZr1Wpg/iO8AbgcvS642prDsTgfaIWBIR64E5wJR8hYi4OSKeSaN3AKPS8JHAjRGxOiWFG/EdZM3MGqpMH8SbIuL1ufGbJP21RLuRwLLc+HLgoDr1T+HFp9YVtR1Z20DSNGAawJgxY0qEZGZmZZXZg9gg6VWdI5L2oo+vh5B0AtnhpP/sSbuImBURLRHRMmLEiL4Mycxs0CuzB/E54GZJSwCRXVF9col2K8huDd5pVCrbhKQjgP8ADomIdbm2h9a0vaXEMs3MrI+Uud33HySNB/ZNRYtzG/J65gPjJY0j2+BPBT6YryDpQOAKYHJEPJKbNA/4qqThafydwNkllmlmZn2kywQh6bCIuEnSe2sm7S2JiPhZvRlHRIek6WQb+yHA7IhYKGkG0BYRrWSHlF4KXC8J4OGIOCYiVku6gCzJAMyIiNW9W0UzM+uNensQhwA3Ae8pmBZA3QQBEBFzgbk1Zefmho+o03Y2MLu7ZZiZWTW6TBARcV4anBERD+anpcNGZmY2gJU5i+mGgrKf9nUgZmbWv9Trg3g1sB+wS00/xM7AsKoDMzOz5qrXB7Ev8G5gVzbth3gS+FiVQZmZWfPV64P4JfBLSW+JiNsbGJOZmfUDZS6Uu0fSaWSHm144tBQRH60sKjMza7oyndRXA68gu4HeH8muan6yyqDMzKz5yiSIvSPiHODpiLgSOJr6N90zM7MBoEyC6Hzuw1pJrwV2Af6tupDMzKw/KNMHMSvdE+kcoJXs1hjn1m9iZmZbuzI36/teGvwjfg61mdmgUe9CuTPqNYyIS/s+HDMz6y/q7UHslP7uC7yJ7PASZBfN3VVlUGZm1nz1LpQ7H0DSrcAbIuLJNP5l4NcNic7MzJqmzFlMLwfW58bXpzIzMxvAypzFdBVwl6Sfp/FjgR9WFpGZmfUL3e5BRMSFZM+gXpNeJ0fE18rMXNJkSYsltUs6q2D6wZL+IqlD0nE10zZIWpBerbVtzcysWvXOYto5Ip6QtBuwNL06p+3W3SNAJQ0BZgKTgOXAfEmtEbEoV+1h4CTgswWzeDYiDii5HmZm1sfqHWK6lux233eTPWK0k9J4d9dETATaI2IJgKQ5wBTghQQREUvTtI09DdzMzKpV7yymd6e/vX286EhgWW58OT27h9MwSW1AB3BRRPyitoKkacA0gDFjxvQyTDMzK1LvENMb6jWMiL/0fTib2DMiVkjaC7hJ0t8i4oGaGGYBswBaWlqiaCZmZtY79Q4xfaPOtAAO62beK4DRufFRqayUiFiR/i6RdAtwIPBA3UZmZtZn6h1i+h9bOO/5wHhJ48gSw1Tgg2UappsDPhMR6yTtDrwNuGQL4zEzsx4ocx0E6TbfE9j0iXJX1WsTER2SpgPzgCHA7IhYKGkG0BYRrZLeBPwcGA68R9L5EbEf8BrgitR5vQ1ZH8SiLhZlZmYV6DZBSDoPOJQsQcwFjgJuI7uArq6ImJva5MvOzQ3PJzv0VNvuz8D+3c3fzMyqU+ZWG8cBhwP/jIiTgdeTPTTIzMwGsDIJ4tmI2Ah0SNoZeIRNO5/NzGwAKtMH0SZpV+C7ZBfNPQXcXmlUZmbWdPWug5gJXBsRn0xFl0v6LbBzRNzbkOjMzKxp6u1B/DfwdUmvBK4DfhwR9zQmLDMza7Yu+yAi4v9ExFuAQ4DHgNmS7pd0nqR9GhahmZk1RZnbfT8UERdHxIHA8WTPg7iv8sjMzKypuk0QkraV9B5J1wC/ARYD7608MjMza6p6ndSTyPYY3gXcBcwBpkXE0w2KzczMmqheJ/XZZM+EODMi1jQoHjMz6yfq3ayvu7u1mpnZAFbmSmozMxuEnCDMzKyQE4SZmRVygjAzs0KVJghJkyUtltQu6ayC6QdL+oukDknH1Uw7UdLf0+vEKuM0M7PNVZYgJA0BZpI9YGgCcLykCTXVHgZOIjudNt92N+A84CBgInBeegypmZk1SJV7EBOB9ohYEhHryS60m5KvEBFL051hN9a0PRK4MSJWp2swbgQmVxirmZnVqDJBjASW5caXp7I+aytpmqQ2SW2rVq3qdaBmZra5rbqTOiJmRURLRLSMGDGi2eGYmQ0oVSaIFWz6aNJRqazqtmZm1geqTBDzgfGSxknaHpgKtJZsOw94p6ThqXP6nanMzMwapLIEEREdwHSyDft9wHURsVDSDEnHAEh6k6TlwPuBKyQtTG1XAxeQJZn5wIxUZmZmDVLvbq5bLCLmAnNrys7NDc8nO3xU1HY2MLvK+MzMrGtbdSe1mZlVxwnCzMwKOUGYmVkhJwgzMyvkBGFmZoWcIMzMrJAThJmZFXKCMDOzQk4QZmZWyAnCzMwKOUGYmVkhJwgzMyvkBGFmZoWcIMzMrJAThJmZFXKCMDOzQpUmCEmTJS2W1C7prILpQyX9JE2/U9LYVD5W0rOSFqTX5VXGaWZmm6vsiXKShgAzgUnAcmC+pNaIWJSrdgqwJiL2ljQVuBj4QJr2QEQcUFV8ZmZWX5V7EBOB9ohYEhHrgTnAlJo6U4Ar0/BPgcMlqcKYzMyspCoTxEhgWW58eSorrBMRHcDjwMvStHGS7pH0R0nvKFqApGmS2iS1rVq1qm+jNzMb5PprJ/VKYExEHAicAVwraefaShExKyJaIqJlxIgRDQ/SzGwgqzJBrABG58ZHpbLCOpK2BXYBHouIdRHxGEBE3A08AOxTYaxmZlajygQxHxgvaZyk7YGpQGtNnVbgxDR8HHBTRISkEamTG0l7AeOBJRXGamZmNSo7iykiOiRNB+YBQ4DZEbFQ0gygLSJage8DV0tqB1aTJRGAg4EZkp4HNgKnRsTqqmI1M7PNVZYgACJiLjC3puzc3PBzwPsL2t0A3FBlbGZmVl9/7aQ2M7Mmc4IwM7NCThBmZlbICcLMzAo5QZiZWSEnCDMzK+QEYWZmhZwgzMyskBOEmZkVcoIwM7NCThBmZlbICcLMzAo5QZiZWSEnCDMzK+QEYWZmhZwgzMysUKUJQtJkSYsltUs6q2D6UEk/SdPvlDQ2N+3sVL5Y0pFVxmlmZpurLEGkZ0rPBI4CJgDHS5pQU+0UYE1E7A18E7g4tZ1A9vjR/YDJwGWdz6g2M7PGqHIPYiLQHhFLImI9MAeYUlNnCnBlGv4pcLgkpfI5EbEuIh4E2tP8zMysQap8JvVIYFlufDlwUFd1IqJD0uPAy1L5HTVtR9YuQNI0YFoafUrS4r4JvTK7A49WuQBdXOXct0jl6w6De/0H87rD4F7/LVz3PbuaUGWCqFxEzAJmNTuOsiS1RURLs+NohsG87jC4138wrzts3etf5SGmFcDo3PioVFZYR9K2wC7AYyXbmplZhapMEPOB8ZLGSdqerNO5taZOK3BiGj4OuCkiIpVPTWc5jQPGA3dVGKuZmdWo7BBT6lOYDswDhgCzI2KhpBlAW0S0At8HrpbUDqwmSyKketcBi4AO4LSI2FBVrA201RwOq8BgXncY3Os/mNcdtuL1V/aD3czMbFO+ktrMzAo5QZiZWSEniAbp7rYjA5Wk2ZIekfT/mh1Lo0kaLelmSYskLZR0erNjaiRJwyTdJemvaf3Pb3ZMjSZpiKR7JP2q2bH0hhNEA5S87chA9UOy26UMRh3AmRExAXgzcNog+r8DrAMOi4jXAwcAkyW9uckxNdrpwH3NDqK3nCAao8xtRwakiLiV7Ay1QSciVkbEX9Lwk2Qbis3uCDBQReapNLpdeg2as2IkjQKOBr7X7Fh6ywmiMYpuO043CncAAAKxSURBVDJoNhQG6U7FBwJ3NjeSxkqHWBYAjwA3RsRgWv9vAZ8HNjY7kN5ygjCrmKSXAjcAn4mIJ5odTyNFxIaIOIDsbggTJb222TE1gqR3A49ExN3NjmVLOEE0hm8dMkhJ2o4sOVwTET9rdjzNEhFrgZsZPP1RbwOOkbSU7JDyYZJ+1NyQes4JojHK3HbEBph06/rvA/dFxKXNjqfRJI2QtGsa3gGYBNzf3KgaIyLOjohRETGW7Pt+U0Sc0OSweswJogEiogPovO3IfcB1EbGwuVE1hqQfA7cD+0paLumUZsfUQG8DPkz263FBer2r2UE10CuBmyXdS/Yj6caI2CpP9xysfKsNMzMr5D0IMzMr5ARhZmaFnCDMzKyQE4SZmRVygjAzs0JOEGa9IOlYSSHp1Wl8bHd3rC1Tx6w/cYIw653jgdvSX7MByQnCrIfSvZXeDpxCeo56zfSTJP1S0i2S/i7pvNzkIZK+m56P8Lt0hTGSPiZpfnp2wg2SXtKYtTHrmhOEWc9NAX4bEf8NPCbpjQV1JgLvA14HvF9SSyofD8yMiP2AtakOwM8i4k3p2Qn3kSUfs6ZygjDruePJbsBG+lt0mOnGiHgsIp4Ffka2xwHwYEQsSMN3A2PT8Gsl/V9JfwM+BOxXSeRmPbBtswMw25pI2g04DNhfUgBDyB6CM7Omau09bDrH1+XKNgA7pOEfAsdGxF8lnQQc2ndRm/WO9yDMeuY44OqI2DMixkbEaOBBNr2dO8AkSbulPoZjgT91M9+dgJXp9uAf6vOozXrBCcKsZ44Hfl5TdgNwdk3ZXan8XuCGiGjrZr7nkD1t7k8MkltiW//nu7ma9bF0iKglIqY3OxazLeE9CDMzK+Q9CDMzK+Q9CDMzK+QEYWZmhZwgzMyskBOEmZkVcoIwM7NC/x/wCDYfrAMdZQAAAABJRU5ErkJggg==\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sGi0b_dvRU01"
-      },
-      "source": [
-        "**Experiment:** To avoid overfitting let's try to increase the Dropout percentage by 20%"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "k2wI6VW6QP_u"
-      },
-      "source": [
-        "model3 = Sequential()\n",
-        "model3.add(Conv2D(32, kernel_size=5, strides=1, padding=\"Same\", activation='relu', input_shape=(img_h,img_w,3)))\n",
-        "model3.add(MaxPooling2D(padding='Same'))\n",
-        "\n",
-        "model3.add(Conv2D(64, kernel_size=5, strides=1, padding=\"Same\", activation='relu'))\n",
-        "model3.add(MaxPooling2D(padding='Same'))\n",
-        "\n",
-        "model3.add(Flatten())\n",
-        "model3.add(Dense(1024,activation=\"relu\"))\n",
-        "model3.add(Dropout(0.5))\n",
-        "model3.add(Dense(5,activation=\"sigmoid\"))\n",
-        "\n",
-        "\n",
-        "model3.compile(optimizer=\"adam\",loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "bxvNhrfIRr7k",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "a27ca111-6f17-4e32-8d37-02e347976410"
-      },
-      "source": [
-        "history_dropout = model3.fit_generator(\n",
-        "                                      train_generator,\n",
-        "                                      steps_per_epoch = 490//16, \n",
-        "                                      epochs=15,\n",
-        "                                      validation_data = validation_generator,\n",
-        "                                      validation_steps = 119//16)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
-            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "Epoch 1/15\n",
-            "30/30 [==============================] - 11s 357ms/step - loss: 17.1107 - accuracy: 0.2773 - val_loss: 1.3198 - val_accuracy: 0.4107\n",
-            "Epoch 2/15\n",
-            "30/30 [==============================] - 10s 344ms/step - loss: 1.4086 - accuracy: 0.3483 - val_loss: 1.3501 - val_accuracy: 0.3571\n",
-            "Epoch 3/15\n",
-            "30/30 [==============================] - 10s 347ms/step - loss: 1.1883 - accuracy: 0.4610 - val_loss: 1.2203 - val_accuracy: 0.3750\n",
-            "Epoch 4/15\n",
-            "30/30 [==============================] - 10s 347ms/step - loss: 1.0996 - accuracy: 0.4769 - val_loss: 1.4612 - val_accuracy: 0.3393\n",
-            "Epoch 5/15\n",
-            "30/30 [==============================] - 10s 346ms/step - loss: 1.2838 - accuracy: 0.4381 - val_loss: 1.2427 - val_accuracy: 0.3929\n",
-            "Epoch 6/15\n",
-            "30/30 [==============================] - 10s 345ms/step - loss: 1.1260 - accuracy: 0.4792 - val_loss: 1.2267 - val_accuracy: 0.4196\n",
-            "Epoch 7/15\n",
-            "30/30 [==============================] - 10s 345ms/step - loss: 1.1132 - accuracy: 0.4719 - val_loss: 1.4523 - val_accuracy: 0.4107\n",
-            "Epoch 8/15\n",
-            "30/30 [==============================] - 10s 347ms/step - loss: 1.1301 - accuracy: 0.5173 - val_loss: 1.2245 - val_accuracy: 0.3839\n",
-            "Epoch 9/15\n",
-            "30/30 [==============================] - 10s 346ms/step - loss: 1.0280 - accuracy: 0.5309 - val_loss: 1.1859 - val_accuracy: 0.4732\n",
-            "Epoch 10/15\n",
-            "30/30 [==============================] - 10s 347ms/step - loss: 0.9376 - accuracy: 0.5699 - val_loss: 1.2315 - val_accuracy: 0.4286\n",
-            "Epoch 11/15\n",
-            "30/30 [==============================] - 10s 347ms/step - loss: 0.9314 - accuracy: 0.6008 - val_loss: 1.1156 - val_accuracy: 0.4554\n",
-            "Epoch 12/15\n",
-            "30/30 [==============================] - 10s 347ms/step - loss: 0.8987 - accuracy: 0.6011 - val_loss: 1.2151 - val_accuracy: 0.5089\n",
-            "Epoch 13/15\n",
-            "30/30 [==============================] - 10s 347ms/step - loss: 0.8625 - accuracy: 0.6031 - val_loss: 1.0832 - val_accuracy: 0.4286\n",
-            "Epoch 14/15\n",
-            "30/30 [==============================] - 10s 349ms/step - loss: 0.8143 - accuracy: 0.6649 - val_loss: 1.2405 - val_accuracy: 0.4286\n",
-            "Epoch 15/15\n",
-            "30/30 [==============================] - 10s 347ms/step - loss: 0.8107 - accuracy: 0.6257 - val_loss: 1.2507 - val_accuracy: 0.4464\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "vjWh4YoJSQt6",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 296
-        },
-        "outputId": "78dbfe27-49fb-406b-e202-befd44f4b98e"
-      },
-      "source": [
-        "acc = history_dropout.history['accuracy']\n",
-        "val_acc = history_dropout.history['val_accuracy']\n",
-        "epochs = range(0,15)\n",
-        "plt.plot(epochs, acc,'g-' ,label= 'Accuracy')\n",
-        "plt.plot(epochs, val_acc ,'b-', label='Validation Accuracy')\n",
-        "plt.xlabel('Epochs')\n",
-        "plt.ylabel('Accuracy')\n",
-        "plt.legend()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<matplotlib.legend.Legend at 0x7fccb23e4a50>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 28
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "lavsY7pWEBUd"
-      },
-      "source": [
-        "**Observation:** Here we can see that accuracy curve turns out to be much better than previous results for both training as well as validation. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "7IQDD_mbT7hL"
-      },
-      "source": [
-        ""
-      ],
-      "execution_count": null,
-      "outputs": []
-    }
-  ]
-}
\ No newline at end of file

From 5679c06b16cdfa443a9d5cdf21f54d2c81b123b0 Mon Sep 17 00:00:00 2001
From: Yash Patel <57082017+yashpatel301@users.noreply.github.com>
Date: Sun, 18 Apr 2021 15:02:37 +0530
Subject: [PATCH 14/17] Delete requirements.txt

---
 Assignment 4 & 5/requirements.txt | 35 -------------------------------
 1 file changed, 35 deletions(-)
 delete mode 100644 Assignment 4 & 5/requirements.txt

diff --git a/Assignment 4 & 5/requirements.txt b/Assignment 4 & 5/requirements.txt
deleted file mode 100644
index 074f76e..0000000
--- a/Assignment 4 & 5/requirements.txt	
+++ /dev/null
@@ -1,35 +0,0 @@
-imageio==2.4.1
-Keras==2.4.3
-Keras-Preprocessing==1.1.2
-keras-vis==0.4.1
-matplotlib==3.2.2
-matplotlib-venn==0.11.6
-networkx==2.5.1
-nltk==3.2.5
-numpy==1.19.5
-opencv-contrib-python==4.1.2.30
-opencv-python==4.1.2.30
-pandas==1.1.5
-pandas-datareader==0.9.0
-pandas-gbq==0.13.3
-pandas-profiling==1.4.1
-pickleshare==0.7.5
-Pillow==7.1.2
-pip-tools==4.5.1
-pymc3==3.7
-regex==2019.12.20
-scikit-image==0.16.2
-scikit-learn==0.22.2.post1
-scipy==1.4.1
-seaborn==0.11.1
-sklearn==0.0
-sklearn-pandas==1.8.0
-tensorboard==2.4.1
-tensorboard-plugin-wit==1.8.0
-tensorflow==2.4.1
-tensorflow-datasets==4.0.1
-tensorflow-estimator==2.4.0
-tensorflow-gcs-config==2.4.0
-tensorflow-hub==0.12.0
-tensorflow-metadata==0.29.0
-tensorflow-probability==0.12.1

From fc48a59e87886ab27d7ba4c09abc9f4eaa83b96e Mon Sep 17 00:00:00 2001
From: Yash Patel <57082017+yashpatel301@users.noreply.github.com>
Date: Sun, 18 Apr 2021 15:02:49 +0530
Subject: [PATCH 15/17] Update Readme.md

---
 Assignment 4 & 5/Readme.md | 46 +-------------------------------------
 1 file changed, 1 insertion(+), 45 deletions(-)

diff --git a/Assignment 4 & 5/Readme.md b/Assignment 4 & 5/Readme.md
index 29a355e..c02d793 100644
--- a/Assignment 4 & 5/Readme.md	
+++ b/Assignment 4 & 5/Readme.md	
@@ -1,45 +1 @@
-## **Introduction:** 
-</br> Convolutional Neural Network has been developed so well when it comes to image classification. There has been some amazing applications of Image classification in computer vision and classifying the disease infected leaves and normal leaves is one of the important application of Image classification. By automatic and an early diagnosis of a disease and its severity, effective, and timely treatment can be taken in advance [4]. The proposed work presents the comparison of two different Convolutional Neural Network (CNN) architectures to classify diseases of the citrus leaf. We have created our own CNN with 3 Conv - 3 Max pooling and 2 Dense layers [3] and also have added Dropout layer to overcome overfitting faced. This work also comprises of expermentation with hyper-parameters such as learning rate, dropout percentage and activation function at output layer and has compared the performance.
-</br>
-
-## **Approach:**
-Approach will consists of the architecture of CNN which is used and the experiments we have carried out based on values of hyper-parameters.
-#### ***Architecture***
-There has been 3 Convolutional and 3 max pooling layer used by stacking one behind other. After getting higher level features, flattening of the feature matrix has been done and passed through Dense layers. There has been one dropout layer present in between two Dense layers.
-#### ***Expermients with hyper-parameters***
-There has been ReLU activation used for all the layers except output layer, and for the output layer two different activation functions have been experimented i.e., sigmoid and softmax. Sigmoid activation function maps any values from −∞ to ∞, to 0-1 and gives the predictive analysis of classification. Softmax activation is also famous for its mapping of large values to range of 0 − 1 as it is a probabilistic function which gives proportion of similarity in probabilistic manner to different class and hence it is used at the outputlayer. </br>
-A list of learning rates, starting with 10^-3 and increment by multiple of 10 till 1. We have got five different values. Mapping of alpha values with index: [1 : 0.0001, 2 : 0.001, 3 : 0.01, 4 : 0.1, 5 : 1]. </br>
-When we talk about accuracy plots between training and validation accuracy, there can be overfitting and underfitting where we don’t want to stuck. In first scenerio where we can see that training accuracy is quite higher than validation and also the rate of increment in validation accuracy is not so encouraging and thus we can say that there might be problem of overfitting.</br>
-
-## **Results:**
-Results of experimenting with Sigmoid and Softmax activation function. 
-</br> Sigmoid function </br>
-![Sigmoid function](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Citrus-leaves-Classification/Results/LOSS_SIGMOID.png)
-
-</br> Softmax function </br>
-![Softmax function](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Citrus-leaves-Classification/Results/LOSS_SOFTMAX.png)
-
-</br>Results of experimenting with different learning rates.
-</br> Accuracy </br>
-![Accuracy](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Citrus-leaves-Classification/Results/LR_accuracy.png)
-
-</br> Validation Accuracy </br>
-![Validation Accuracy](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Citrus-leaves-Classification/Results/LR_val_acc.png)
-
-</br>Results of experimenting with Dropout percentage.
-</br> Dropout 30% </br>
-![Accuracy](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Citrus-leaves-Classification/Results/Dropout_30.png)
-
-</br> Dropout 50% </br>
-![Validation Accuracy](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Citrus-leaves-Classification/Results/Dropout_50.png)
-
-## **Installation guidelines and platform details:**
-This assigment is performed on Google colab only. All the libraries are mentioned at the beginning of the code and are imported without any installation needed colab and for anaconda environment and all the required libraries are mentioned in the requirements.txt. 
-
-## **References:** 
-1. https://keras.io/api/preprocessing/image/
-2. https://vijayabhaskar96.medium.com/tutorial-image-classification-with-keras-flow-from-directory-and-generators-95f75ebe5720
-3. https://www.geeksforgeeks.org/python-image-classification-using-keras/
-4. Singh, U., Chouhan, S., Jain, S. and Jain, S., 2021. Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease. [online] Ieeexplore.ieee.org. Available at: <https://ieeexplore.ieee.org/document/8675730?denied=> [Accessed 17 April 2021].
-5. Afifi,  A.;   Alhumam,  A.;Abdelwahab, A. Convolutional NeuralNetwork for Automatic Identificationof Plant Diseases with Limited Data.Plants2021,10, 28.https://dx.doi.org/10.3390/plants10010028
-6. https://www.sciencedirect.com/science/article/abs/pii/S0168169920302258
+Please add the assignment-4 & 5 details here

From 5ef1791cc2ba5f6ed6ac69d1d21993fb53c0a4d0 Mon Sep 17 00:00:00 2001
From: Yash Patel <57082017+yashpatel301@users.noreply.github.com>
Date: Sun, 18 Apr 2021 15:07:05 +0530
Subject: [PATCH 16/17] Update Readme.md

---
 Assignment 4 & 5/Readme.md | 46 +++++++++++++++++++++++++++++++++++++-
 1 file changed, 45 insertions(+), 1 deletion(-)

diff --git a/Assignment 4 & 5/Readme.md b/Assignment 4 & 5/Readme.md
index c02d793..29a355e 100644
--- a/Assignment 4 & 5/Readme.md	
+++ b/Assignment 4 & 5/Readme.md	
@@ -1 +1,45 @@
-Please add the assignment-4 & 5 details here
+## **Introduction:** 
+</br> Convolutional Neural Network has been developed so well when it comes to image classification. There has been some amazing applications of Image classification in computer vision and classifying the disease infected leaves and normal leaves is one of the important application of Image classification. By automatic and an early diagnosis of a disease and its severity, effective, and timely treatment can be taken in advance [4]. The proposed work presents the comparison of two different Convolutional Neural Network (CNN) architectures to classify diseases of the citrus leaf. We have created our own CNN with 3 Conv - 3 Max pooling and 2 Dense layers [3] and also have added Dropout layer to overcome overfitting faced. This work also comprises of expermentation with hyper-parameters such as learning rate, dropout percentage and activation function at output layer and has compared the performance.
+</br>
+
+## **Approach:**
+Approach will consists of the architecture of CNN which is used and the experiments we have carried out based on values of hyper-parameters.
+#### ***Architecture***
+There has been 3 Convolutional and 3 max pooling layer used by stacking one behind other. After getting higher level features, flattening of the feature matrix has been done and passed through Dense layers. There has been one dropout layer present in between two Dense layers.
+#### ***Expermients with hyper-parameters***
+There has been ReLU activation used for all the layers except output layer, and for the output layer two different activation functions have been experimented i.e., sigmoid and softmax. Sigmoid activation function maps any values from −∞ to ∞, to 0-1 and gives the predictive analysis of classification. Softmax activation is also famous for its mapping of large values to range of 0 − 1 as it is a probabilistic function which gives proportion of similarity in probabilistic manner to different class and hence it is used at the outputlayer. </br>
+A list of learning rates, starting with 10^-3 and increment by multiple of 10 till 1. We have got five different values. Mapping of alpha values with index: [1 : 0.0001, 2 : 0.001, 3 : 0.01, 4 : 0.1, 5 : 1]. </br>
+When we talk about accuracy plots between training and validation accuracy, there can be overfitting and underfitting where we don’t want to stuck. In first scenerio where we can see that training accuracy is quite higher than validation and also the rate of increment in validation accuracy is not so encouraging and thus we can say that there might be problem of overfitting.</br>
+
+## **Results:**
+Results of experimenting with Sigmoid and Softmax activation function. 
+</br> Sigmoid function </br>
+![Sigmoid function](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Citrus-leaves-Classification/Results/LOSS_SIGMOID.png)
+
+</br> Softmax function </br>
+![Softmax function](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Citrus-leaves-Classification/Results/LOSS_SOFTMAX.png)
+
+</br>Results of experimenting with different learning rates.
+</br> Accuracy </br>
+![Accuracy](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Citrus-leaves-Classification/Results/LR_accuracy.png)
+
+</br> Validation Accuracy </br>
+![Validation Accuracy](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Citrus-leaves-Classification/Results/LR_val_acc.png)
+
+</br>Results of experimenting with Dropout percentage.
+</br> Dropout 30% </br>
+![Accuracy](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Citrus-leaves-Classification/Results/Dropout_30.png)
+
+</br> Dropout 50% </br>
+![Validation Accuracy](https://github.com/yashpatel301/Computer-Vision-Basics/blob/main/Citrus-leaves-Classification/Results/Dropout_50.png)
+
+## **Installation guidelines and platform details:**
+This assigment is performed on Google colab only. All the libraries are mentioned at the beginning of the code and are imported without any installation needed colab and for anaconda environment and all the required libraries are mentioned in the requirements.txt. 
+
+## **References:** 
+1. https://keras.io/api/preprocessing/image/
+2. https://vijayabhaskar96.medium.com/tutorial-image-classification-with-keras-flow-from-directory-and-generators-95f75ebe5720
+3. https://www.geeksforgeeks.org/python-image-classification-using-keras/
+4. Singh, U., Chouhan, S., Jain, S. and Jain, S., 2021. Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease. [online] Ieeexplore.ieee.org. Available at: <https://ieeexplore.ieee.org/document/8675730?denied=> [Accessed 17 April 2021].
+5. Afifi,  A.;   Alhumam,  A.;Abdelwahab, A. Convolutional NeuralNetwork for Automatic Identificationof Plant Diseases with Limited Data.Plants2021,10, 28.https://dx.doi.org/10.3390/plants10010028
+6. https://www.sciencedirect.com/science/article/abs/pii/S0168169920302258

From e76477854f1b421f11490b18a5ae164df45f73eb Mon Sep 17 00:00:00 2001
From: Yash Patel <57082017+yashpatel301@users.noreply.github.com>
Date: Sun, 18 Apr 2021 15:07:38 +0530
Subject: [PATCH 17/17] Add files via upload

---
 .../classification_of_citrus_leaves.ipynb     | 1192 +++++++++++++++++
 Assignment 4 & 5/requirements.txt             |   35 +
 2 files changed, 1227 insertions(+)
 create mode 100644 Assignment 4 & 5/classification_of_citrus_leaves.ipynb
 create mode 100644 Assignment 4 & 5/requirements.txt

diff --git a/Assignment 4 & 5/classification_of_citrus_leaves.ipynb b/Assignment 4 & 5/classification_of_citrus_leaves.ipynb
new file mode 100644
index 0000000..85442fa
--- /dev/null
+++ b/Assignment 4 & 5/classification_of_citrus_leaves.ipynb	
@@ -0,0 +1,1192 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "HA3_CV.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hdWq98vVGLW4"
+      },
+      "source": [
+        "### **Necessary Imports**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "G0vEOKHsqE4j"
+      },
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "%matplotlib inline\n",
+        "import PIL"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9S-eBH0P9q2y",
+        "outputId": "ac8ff836-036a-43e2-8cb3-c68d6982c668"
+      },
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Mounted at /content/drive\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JPFvt09kqE12",
+        "outputId": "81a905be-8a5a-4c34-cc6e-49872dba84bb"
+      },
+      "source": [
+        "from PIL import Image\n",
+        "image1 = Image.open('/content/drive/MyDrive/HA3_CV_dataset/Leaves/Black spot/b (14).png')\n",
+        "image2 = Image.open('/content/drive/MyDrive/HA3_CV_dataset/Leaves/greening/g (102).png')\n",
+        "print('Image-1 shape: ',image1.size)\n",
+        "print('Image-2 shape: ',image2.size)\n",
+        "print('Image-1 mode: ', image1.mode)\n",
+        "print('Image-2 mode: ', image2.mode)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Image-1 shape:  (256, 256)\n",
+            "Image-2 shape:  (256, 256)\n",
+            "Image-1 mode:  RGB\n",
+            "Image-2 mode:  RGB\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 286
+        },
+        "id": "3cPSwcGf96eD",
+        "outputId": "45701be7-179b-4e23-e15a-4eef9129b690"
+      },
+      "source": [
+        "plt.imshow(image1)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.image.AxesImage at 0x7fcd4ec98750>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_v6b3X_asY8V"
+      },
+      "source": [
+        "Here both the images are of same size but what if any of the image is not of same size, hence we need to resize all images to 256 x 256"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "l_4SvTGYqEup"
+      },
+      "source": [
+        "img_w, img_h = 256, 256"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "33K2cTrGstq7"
+      },
+      "source": [
+        "### **For data-generation part**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "a026yVIPthQb"
+      },
+      "source": [
+        "from keras.preprocessing.image import ImageDataGenerator\n",
+        "from keras.preprocessing.image import img_to_array, array_to_img, load_img"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "g62gQmUofA24",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1bec9877-7625-425e-a565-f3c1ebe657eb"
+      },
+      "source": [
+        "train_data = ImageDataGenerator(rescale = 1./255,\n",
+        "                                shear_range = 0.2,\n",
+        "                                zoom_range = 0.2,\n",
+        "                                horizontal_flip=True,\n",
+        "                                validation_split=0.2)\n",
+        "train_generator = train_data.flow_from_directory('/content/drive/MyDrive/HA3_CV_dataset/Leaves/',\n",
+        "                                                  target_size = (img_h, img_w),\n",
+        "                                                  batch_size=16,\n",
+        "                                                  shuffle = True,\n",
+        "                                                  class_mode = 'categorical',\n",
+        "                                                  subset =\"training\")                                                 \n",
+        "validation_generator = train_data.flow_from_directory('/content/drive/MyDrive/HA3_CV_dataset/Leaves/',\n",
+        "                                                      target_size=(img_h, img_w),\n",
+        "                                                      batch_size=16,\n",
+        "                                                      shuffle = True,\n",
+        "                                                      class_mode='categorical',\n",
+        "                                                      subset =\"validation\")"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Found 490 images belonging to 5 classes.\n",
+            "Found 119 images belonging to 5 classes.\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7MYCkNV-1piu"
+      },
+      "source": [
+        "We have completed image preprocessing and now we are ready for Convolutional Architecture"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wF1yTGUpy67U"
+      },
+      "source": [
+        "import tensorflow as tf\n",
+        "from tensorflow import keras\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Activation, Dropout, Flatten"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_G8eGGwGGGkU"
+      },
+      "source": [
+        "### **Modelling part**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "P7gSxDDsc6_S"
+      },
+      "source": [
+        "**Experiment:** Model with 3 conv2D and Maxpooling2D layers"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ca7KEOI22Qwt"
+      },
+      "source": [
+        "model1 = Sequential()\n",
+        "model1.add(Conv2D(32, kernel_size=5, strides=1, padding=\"Same\", activation='relu', input_shape=(img_h,img_w,3)))\n",
+        "model1.add(MaxPooling2D(padding='Same'))\n",
+        "\n",
+        "model1.add(Conv2D(32, kernel_size=5, strides=1, padding=\"Same\", activation='relu'))\n",
+        "model1.add(MaxPooling2D(padding='Same'))\n",
+        "\n",
+        "model1.add(Conv2D(64, kernel_size=5, strides=1, padding=\"Same\", activation='relu'))\n",
+        "model1.add(MaxPooling2D(padding='Same'))\n",
+        "\n",
+        "model1.add(Flatten())\n",
+        "model1.add(Dense(1024,activation=\"relu\"))\n",
+        "model1.add(Dropout(0.3))\n",
+        "model1.add(Dense(5,activation=\"softmax\"))\n",
+        "\n",
+        "\n",
+        "model1.compile(optimizer=\"adam\",loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tRGdaRvV8ugN",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d74b7b0f-e612-4477-ac5a-7c3cf311020e"
+      },
+      "source": [
+        "model1.summary()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Model: \"sequential\"\n",
+            "_________________________________________________________________\n",
+            "Layer (type)                 Output Shape              Param #   \n",
+            "=================================================================\n",
+            "conv2d (Conv2D)              (None, 256, 256, 32)      2432      \n",
+            "_________________________________________________________________\n",
+            "max_pooling2d (MaxPooling2D) (None, 128, 128, 32)      0         \n",
+            "_________________________________________________________________\n",
+            "conv2d_1 (Conv2D)            (None, 128, 128, 32)      25632     \n",
+            "_________________________________________________________________\n",
+            "max_pooling2d_1 (MaxPooling2 (None, 64, 64, 32)        0         \n",
+            "_________________________________________________________________\n",
+            "conv2d_2 (Conv2D)            (None, 64, 64, 64)        51264     \n",
+            "_________________________________________________________________\n",
+            "max_pooling2d_2 (MaxPooling2 (None, 32, 32, 64)        0         \n",
+            "_________________________________________________________________\n",
+            "flatten (Flatten)            (None, 65536)             0         \n",
+            "_________________________________________________________________\n",
+            "dense (Dense)                (None, 1024)              67109888  \n",
+            "_________________________________________________________________\n",
+            "dropout (Dropout)            (None, 1024)              0         \n",
+            "_________________________________________________________________\n",
+            "dense_1 (Dense)              (None, 5)                 5125      \n",
+            "=================================================================\n",
+            "Total params: 67,194,341\n",
+            "Trainable params: 67,194,341\n",
+            "Non-trainable params: 0\n",
+            "_________________________________________________________________\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IHE9Lhg888M_",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d1608467-fd92-4040-f885-cd75aa0afe0f"
+      },
+      "source": [
+        "history=model1.fit_generator(\n",
+        "    train_generator,\n",
+        "    steps_per_epoch = 490//16, \n",
+        "    epochs=30,\n",
+        "    validation_data = validation_generator,\n",
+        "    validation_steps = 119//16\n",
+        ")"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
+            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/30\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 1.0525 - accuracy: 0.5063 - val_loss: 1.2161 - val_accuracy: 0.4464\n",
+            "Epoch 2/30\n",
+            "30/30 [==============================] - 10s 347ms/step - loss: 1.0442 - accuracy: 0.5359 - val_loss: 1.1302 - val_accuracy: 0.5089\n",
+            "Epoch 3/30\n",
+            "30/30 [==============================] - 10s 350ms/step - loss: 1.1486 - accuracy: 0.4557 - val_loss: 1.3049 - val_accuracy: 0.3750\n",
+            "Epoch 4/30\n",
+            "30/30 [==============================] - 10s 348ms/step - loss: 1.0585 - accuracy: 0.5316 - val_loss: 1.2427 - val_accuracy: 0.4554\n",
+            "Epoch 5/30\n",
+            "30/30 [==============================] - 10s 349ms/step - loss: 0.9756 - accuracy: 0.5570 - val_loss: 1.2612 - val_accuracy: 0.3393\n",
+            "Epoch 6/30\n",
+            "30/30 [==============================] - 10s 349ms/step - loss: 0.9490 - accuracy: 0.5823 - val_loss: 1.1442 - val_accuracy: 0.4107\n",
+            "Epoch 7/30\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 0.8177 - accuracy: 0.6350 - val_loss: 0.9511 - val_accuracy: 0.5357\n",
+            "Epoch 8/30\n",
+            "30/30 [==============================] - 10s 343ms/step - loss: 0.8584 - accuracy: 0.6329 - val_loss: 0.9878 - val_accuracy: 0.4911\n",
+            "Epoch 9/30\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 0.7659 - accuracy: 0.6477 - val_loss: 1.0183 - val_accuracy: 0.6161\n",
+            "Epoch 10/30\n",
+            "30/30 [==============================] - 10s 343ms/step - loss: 0.7852 - accuracy: 0.6624 - val_loss: 1.0588 - val_accuracy: 0.5446\n",
+            "Epoch 11/30\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 0.6746 - accuracy: 0.7215 - val_loss: 1.0865 - val_accuracy: 0.5089\n",
+            "Epoch 12/30\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 0.6392 - accuracy: 0.7489 - val_loss: 1.0526 - val_accuracy: 0.5357\n",
+            "Epoch 13/30\n",
+            "30/30 [==============================] - 10s 350ms/step - loss: 0.5984 - accuracy: 0.7532 - val_loss: 1.4194 - val_accuracy: 0.4196\n",
+            "Epoch 14/30\n",
+            "30/30 [==============================] - 10s 349ms/step - loss: 0.6227 - accuracy: 0.7321 - val_loss: 1.1302 - val_accuracy: 0.5625\n",
+            "Epoch 15/30\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 0.5660 - accuracy: 0.7468 - val_loss: 1.0489 - val_accuracy: 0.5446\n",
+            "Epoch 16/30\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 0.5386 - accuracy: 0.7996 - val_loss: 1.1707 - val_accuracy: 0.5893\n",
+            "Epoch 17/30\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 0.5933 - accuracy: 0.7363 - val_loss: 1.0592 - val_accuracy: 0.5089\n",
+            "Epoch 18/30\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 0.4860 - accuracy: 0.8017 - val_loss: 1.1676 - val_accuracy: 0.6250\n",
+            "Epoch 19/30\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 0.5312 - accuracy: 0.7679 - val_loss: 1.0386 - val_accuracy: 0.5000\n",
+            "Epoch 20/30\n",
+            "30/30 [==============================] - 10s 345ms/step - loss: 0.4260 - accuracy: 0.8333 - val_loss: 1.1630 - val_accuracy: 0.5179\n",
+            "Epoch 21/30\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 0.4594 - accuracy: 0.8080 - val_loss: 1.2922 - val_accuracy: 0.4286\n",
+            "Epoch 22/30\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 0.4556 - accuracy: 0.8122 - val_loss: 1.1118 - val_accuracy: 0.5357\n",
+            "Epoch 23/30\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 0.4611 - accuracy: 0.8207 - val_loss: 0.9501 - val_accuracy: 0.5625\n",
+            "Epoch 24/30\n",
+            "30/30 [==============================] - 10s 343ms/step - loss: 0.4057 - accuracy: 0.8397 - val_loss: 1.0264 - val_accuracy: 0.6161\n",
+            "Epoch 25/30\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 0.3487 - accuracy: 0.8544 - val_loss: 1.0910 - val_accuracy: 0.6607\n",
+            "Epoch 26/30\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 0.3157 - accuracy: 0.8819 - val_loss: 1.1480 - val_accuracy: 0.5446\n",
+            "Epoch 27/30\n",
+            "30/30 [==============================] - 10s 347ms/step - loss: 0.3200 - accuracy: 0.8734 - val_loss: 1.1493 - val_accuracy: 0.6429\n",
+            "Epoch 28/30\n",
+            "30/30 [==============================] - 10s 347ms/step - loss: 0.2899 - accuracy: 0.8882 - val_loss: 1.4611 - val_accuracy: 0.5714\n",
+            "Epoch 29/30\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 0.3287 - accuracy: 0.8650 - val_loss: 1.3737 - val_accuracy: 0.5982\n",
+            "Epoch 30/30\n",
+            "30/30 [==============================] - 10s 343ms/step - loss: 0.3498 - accuracy: 0.8565 - val_loss: 1.2025 - val_accuracy: 0.6339\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fZcMYHqC_Kz1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 312
+        },
+        "outputId": "b0c82dc7-49da-476e-8743-ed6c4923e157"
+      },
+      "source": [
+        "train_l = history.history['loss']\n",
+        "val_l = history.history['val_loss']\n",
+        "epochs = range(1,31)\n",
+        "plt.plot(epochs, train_l, 'b-', label='train loss')\n",
+        "plt.plot(epochs, val_l, 'g-', label='validation loss')\n",
+        "plt.title('Validation loss vs Train loss')\n",
+        "plt.xlabel('epochs')\n",
+        "plt.ylabel('loss')\n",
+        "plt.legend()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x7fcd000ad4d0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "DaK31Xn0CieJ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 312
+        },
+        "outputId": "4d059cef-bd33-4371-a558-590137d32db2"
+      },
+      "source": [
+        "train_l = history.history['accuracy']\n",
+        "val_l = history.history['val_accuracy']\n",
+        "epochs = range(1,31)\n",
+        "plt.plot(epochs, train_l, 'b-', label='accuracy')\n",
+        "plt.plot(epochs, val_l, 'g-', label='validation accuracy')\n",
+        "plt.title('Accuracy vs Validation Accuracy')\n",
+        "plt.xlabel('epochs')\n",
+        "plt.ylabel('accuracy')\n",
+        "plt.legend()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x7fcd00038b90>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 18
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h4vECMlbDbNC"
+      },
+      "source": [
+        "**Observstion:** As we are not getting smooth accuracy plots, let's try with signoid activation function in last layer"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "UHcr8C1mDZ5C"
+      },
+      "source": [
+        "model_sig = Sequential()\n",
+        "model_sig.add(Conv2D(32, kernel_size=5, strides=1, padding=\"Same\", activation='relu', input_shape=(img_h,img_w,3)))\n",
+        "model_sig.add(MaxPooling2D(padding='Same'))\n",
+        "\n",
+        "model_sig.add(Conv2D(32, kernel_size=5, strides=1, padding=\"Same\", activation='relu'))\n",
+        "model_sig.add(MaxPooling2D(padding='Same'))\n",
+        "\n",
+        "model_sig.add(Conv2D(64, kernel_size=5, strides=1, padding=\"Same\", activation='relu'))\n",
+        "model_sig.add(MaxPooling2D(padding='Same'))\n",
+        "\n",
+        "model_sig.add(Flatten())\n",
+        "model_sig.add(Dense(1024,activation=\"relu\"))\n",
+        "model_sig.add(Dropout(0.3))\n",
+        "model_sig.add(Dense(5,activation=\"sigmoid\"))\n",
+        "\n",
+        "\n",
+        "model_sig.compile(optimizer=\"adam\",loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "X1UImC4sDZ2g",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f2a7ca89-57c8-4079-8a8b-bce944bd01b2"
+      },
+      "source": [
+        "history_sig=model_sig.fit_generator(\n",
+        "    train_generator,\n",
+        "    steps_per_epoch = 490//16, \n",
+        "    epochs=15,\n",
+        "    validation_data = validation_generator,\n",
+        "    validation_steps = 119//16\n",
+        ")"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
+            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/15\n",
+            "30/30 [==============================] - 11s 351ms/step - loss: 2.6328 - accuracy: 0.3303 - val_loss: 1.2742 - val_accuracy: 0.3571\n",
+            "Epoch 2/15\n",
+            "30/30 [==============================] - 10s 342ms/step - loss: 1.1758 - accuracy: 0.4572 - val_loss: 1.1958 - val_accuracy: 0.3304\n",
+            "Epoch 3/15\n",
+            "30/30 [==============================] - 10s 337ms/step - loss: 1.0656 - accuracy: 0.5190 - val_loss: 1.2358 - val_accuracy: 0.4821\n",
+            "Epoch 4/15\n",
+            "30/30 [==============================] - 10s 336ms/step - loss: 1.0741 - accuracy: 0.4657 - val_loss: 1.3591 - val_accuracy: 0.3929\n",
+            "Epoch 5/15\n",
+            "30/30 [==============================] - 10s 337ms/step - loss: 1.1504 - accuracy: 0.4495 - val_loss: 1.2585 - val_accuracy: 0.3125\n",
+            "Epoch 6/15\n",
+            "30/30 [==============================] - 10s 337ms/step - loss: 1.4806 - accuracy: 0.4893 - val_loss: 1.3130 - val_accuracy: 0.4464\n",
+            "Epoch 7/15\n",
+            "30/30 [==============================] - 10s 336ms/step - loss: 1.0975 - accuracy: 0.4548 - val_loss: 1.2160 - val_accuracy: 0.4196\n",
+            "Epoch 8/15\n",
+            "30/30 [==============================] - 10s 335ms/step - loss: 1.0235 - accuracy: 0.5757 - val_loss: 1.2528 - val_accuracy: 0.3661\n",
+            "Epoch 9/15\n",
+            "30/30 [==============================] - 10s 337ms/step - loss: 0.9742 - accuracy: 0.5300 - val_loss: 1.1450 - val_accuracy: 0.4554\n",
+            "Epoch 10/15\n",
+            "30/30 [==============================] - 10s 340ms/step - loss: 0.9395 - accuracy: 0.5442 - val_loss: 1.1757 - val_accuracy: 0.4821\n",
+            "Epoch 11/15\n",
+            "30/30 [==============================] - 10s 341ms/step - loss: 0.9222 - accuracy: 0.5985 - val_loss: 1.2027 - val_accuracy: 0.4911\n",
+            "Epoch 12/15\n",
+            "30/30 [==============================] - 10s 339ms/step - loss: 0.8674 - accuracy: 0.6175 - val_loss: 1.1061 - val_accuracy: 0.4375\n",
+            "Epoch 13/15\n",
+            "30/30 [==============================] - 10s 339ms/step - loss: 0.8433 - accuracy: 0.6401 - val_loss: 1.1612 - val_accuracy: 0.4018\n",
+            "Epoch 14/15\n",
+            "30/30 [==============================] - 10s 336ms/step - loss: 0.8872 - accuracy: 0.5814 - val_loss: 1.1739 - val_accuracy: 0.3929\n",
+            "Epoch 15/15\n",
+            "30/30 [==============================] - 10s 336ms/step - loss: 0.8490 - accuracy: 0.6193 - val_loss: 0.9756 - val_accuracy: 0.5357\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "n7B3BBcyFJ2_"
+      },
+      "source": [
+        "**Observstion:** Here we are getting validation loss higher than training loss this might be showing the problem of over-fitting data. Now that can be due to many reasons but here it should be due to less number of samples in comaprision with features. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "S6Hk1kQoDZzg",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 312
+        },
+        "outputId": "5efda344-d324-4e36-8bdc-345a648ae1a8"
+      },
+      "source": [
+        "train_l = history_sig.history['loss']\n",
+        "val_l = history_sig.history['val_loss']\n",
+        "epochs = range(1,16)\n",
+        "plt.plot(epochs, train_l, 'b-', label='train loss')\n",
+        "plt.plot(epochs, val_l, 'g-', label='validation loss')\n",
+        "plt.title('Validation loss vs Train loss')\n",
+        "plt.xlabel('epochs')\n",
+        "plt.ylabel('loss')\n",
+        "plt.legend()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x7fccc4144090>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 21
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tESX5PTGDZxG",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 312
+        },
+        "outputId": "63d9e950-0c2e-4811-9c53-24332d1ca879"
+      },
+      "source": [
+        "train_l = history_sig.history['accuracy']\n",
+        "val_l = history_sig.history['val_accuracy']\n",
+        "epochs = range(1,16)\n",
+        "plt.plot(epochs, train_l, 'b-', label='accuracy')\n",
+        "plt.plot(epochs, val_l, 'g-', label='validation accuracy')\n",
+        "plt.title('Accuracy vs Validation Accuracy')\n",
+        "plt.xlabel('epochs')\n",
+        "plt.ylabel('accuracy')\n",
+        "plt.legend()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x7fccbc67cc90>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 22
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8lvuxdiUbzhp"
+      },
+      "source": [
+        "**Experiment:** Trying alpha values as learning rate for optimizer instead of adam optimizer. \n",
+        "\n",
+        "Alphas = [0.0001 , 0.001 , 0.01 , 0.1 , 1]\n",
+        "\n",
+        "Also we had tried model with 2 conv2D and maxpooling2D layers."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "08lSTvkfKsIL",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "09903502-e4ea-437d-d25e-ec76344071c8"
+      },
+      "source": [
+        "learning_rate = [0.0001,0.001,0.01,0.1,1]\n",
+        "for alpha in learning_rate:\n",
+        "  model2 = Sequential()\n",
+        "  model2.add(Conv2D(32, kernel_size=5, strides=1, padding=\"Same\", activation='relu', input_shape=(img_h,img_w,3)))\n",
+        "  model2.add(MaxPooling2D(padding='Same'))\n",
+        "\n",
+        "  model2.add(Conv2D(64, kernel_size=5, strides=1, padding=\"Same\", activation='relu'))\n",
+        "  model2.add(MaxPooling2D(padding='Same'))\n",
+        "\n",
+        "  model2.add(Flatten())\n",
+        "  model2.add(Dense(1024,activation=\"relu\"))\n",
+        "  model2.add(Dropout(0.3))\n",
+        "  model2.add(Dense(5,activation=\"softmax\"))\n",
+        "\n",
+        "  opti = tf.keras.optimizers.SGD(learning_rate=alpha)\n",
+        "  model2.compile(optimizer=opti,loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])\n",
+        "  print('For learning rate = ', alpha)\n",
+        "  model2.fit_generator(\n",
+        "                      train_generator,\n",
+        "                      steps_per_epoch = 490//16, \n",
+        "                      epochs=10,\n",
+        "                      validation_data = validation_generator,\n",
+        "                      validation_steps = 119//16)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "For learning rate =  0.0001\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
+            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/10\n",
+            "30/30 [==============================] - 11s 353ms/step - loss: 1.5222 - accuracy: 0.2822 - val_loss: 1.4276 - val_accuracy: 0.2679\n",
+            "Epoch 2/10\n",
+            "30/30 [==============================] - 10s 348ms/step - loss: 1.4204 - accuracy: 0.3089 - val_loss: 1.3749 - val_accuracy: 0.3125\n",
+            "Epoch 3/10\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 1.4063 - accuracy: 0.3119 - val_loss: 1.3530 - val_accuracy: 0.3214\n",
+            "Epoch 4/10\n",
+            "30/30 [==============================] - 10s 345ms/step - loss: 1.3817 - accuracy: 0.3407 - val_loss: 1.3651 - val_accuracy: 0.2321\n",
+            "Epoch 5/10\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 1.3954 - accuracy: 0.3282 - val_loss: 1.3435 - val_accuracy: 0.3036\n",
+            "Epoch 6/10\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 1.3874 - accuracy: 0.3423 - val_loss: 1.3305 - val_accuracy: 0.3214\n",
+            "Epoch 7/10\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 1.3385 - accuracy: 0.4016 - val_loss: 1.3446 - val_accuracy: 0.3393\n",
+            "Epoch 8/10\n",
+            "30/30 [==============================] - 10s 345ms/step - loss: 1.3685 - accuracy: 0.3674 - val_loss: 1.3262 - val_accuracy: 0.3036\n",
+            "Epoch 9/10\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 1.3630 - accuracy: 0.4088 - val_loss: 1.3420 - val_accuracy: 0.3750\n",
+            "Epoch 10/10\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 1.3240 - accuracy: 0.4045 - val_loss: 1.3417 - val_accuracy: 0.3571\n",
+            "For learning rate =  0.001\n",
+            "Epoch 1/10\n",
+            "30/30 [==============================] - 11s 358ms/step - loss: 1.4453 - accuracy: 0.3384 - val_loss: 1.3568 - val_accuracy: 0.3036\n",
+            "Epoch 2/10\n",
+            "30/30 [==============================] - 10s 343ms/step - loss: 1.3536 - accuracy: 0.3335 - val_loss: 1.3355 - val_accuracy: 0.3393\n",
+            "Epoch 3/10\n",
+            "30/30 [==============================] - 10s 342ms/step - loss: 1.3485 - accuracy: 0.3730 - val_loss: 1.3075 - val_accuracy: 0.4821\n",
+            "Epoch 4/10\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 1.2970 - accuracy: 0.4173 - val_loss: 1.2721 - val_accuracy: 0.3750\n",
+            "Epoch 5/10\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 1.3062 - accuracy: 0.3817 - val_loss: 1.2739 - val_accuracy: 0.4464\n",
+            "Epoch 6/10\n",
+            "30/30 [==============================] - 10s 339ms/step - loss: 1.3026 - accuracy: 0.4162 - val_loss: 1.2847 - val_accuracy: 0.3125\n",
+            "Epoch 7/10\n",
+            "30/30 [==============================] - 10s 343ms/step - loss: 1.1955 - accuracy: 0.4739 - val_loss: 1.2509 - val_accuracy: 0.3214\n",
+            "Epoch 8/10\n",
+            "30/30 [==============================] - 10s 345ms/step - loss: 1.2250 - accuracy: 0.4720 - val_loss: 1.2628 - val_accuracy: 0.3750\n",
+            "Epoch 9/10\n",
+            "30/30 [==============================] - 10s 342ms/step - loss: 1.2400 - accuracy: 0.3972 - val_loss: 1.2039 - val_accuracy: 0.4196\n",
+            "Epoch 10/10\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 1.1632 - accuracy: 0.4532 - val_loss: 1.2186 - val_accuracy: 0.3929\n",
+            "For learning rate =  0.01\n",
+            "Epoch 1/10\n",
+            "30/30 [==============================] - 11s 356ms/step - loss: 1.6275 - accuracy: 0.2971 - val_loss: 1.3855 - val_accuracy: 0.2857\n",
+            "Epoch 2/10\n",
+            "30/30 [==============================] - 10s 341ms/step - loss: 1.4304 - accuracy: 0.3218 - val_loss: 1.3478 - val_accuracy: 0.2946\n",
+            "Epoch 3/10\n",
+            "30/30 [==============================] - 10s 349ms/step - loss: 1.3487 - accuracy: 0.3228 - val_loss: 1.2608 - val_accuracy: 0.3393\n",
+            "Epoch 4/10\n",
+            "30/30 [==============================] - 11s 352ms/step - loss: 1.2775 - accuracy: 0.3851 - val_loss: 1.3032 - val_accuracy: 0.3482\n",
+            "Epoch 5/10\n",
+            "30/30 [==============================] - 10s 339ms/step - loss: 1.2223 - accuracy: 0.4265 - val_loss: 1.6625 - val_accuracy: 0.3304\n",
+            "Epoch 6/10\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 1.1971 - accuracy: 0.4688 - val_loss: 1.1990 - val_accuracy: 0.4018\n",
+            "Epoch 7/10\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 1.1165 - accuracy: 0.4969 - val_loss: 1.2270 - val_accuracy: 0.4018\n",
+            "Epoch 8/10\n",
+            "30/30 [==============================] - 10s 340ms/step - loss: 1.1467 - accuracy: 0.4743 - val_loss: 1.1843 - val_accuracy: 0.3750\n",
+            "Epoch 9/10\n",
+            "30/30 [==============================] - 10s 341ms/step - loss: 1.0957 - accuracy: 0.4806 - val_loss: 1.1786 - val_accuracy: 0.4732\n",
+            "Epoch 10/10\n",
+            "30/30 [==============================] - 10s 341ms/step - loss: 0.9843 - accuracy: 0.5359 - val_loss: 1.2092 - val_accuracy: 0.3839\n",
+            "For learning rate =  0.1\n",
+            "Epoch 1/10\n",
+            "30/30 [==============================] - 11s 353ms/step - loss: 3342957572916081328128.0000 - accuracy: 0.2953 - val_loss: 1.5260 - val_accuracy: 0.3214\n",
+            "Epoch 2/10\n",
+            "30/30 [==============================] - 10s 345ms/step - loss: 1.5012 - accuracy: 0.3342 - val_loss: 1.4475 - val_accuracy: 0.3393\n",
+            "Epoch 3/10\n",
+            "30/30 [==============================] - 10s 341ms/step - loss: 1.4423 - accuracy: 0.3654 - val_loss: 1.4159 - val_accuracy: 0.3304\n",
+            "Epoch 4/10\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 1.4352 - accuracy: 0.3329 - val_loss: 1.4027 - val_accuracy: 0.3393\n",
+            "Epoch 5/10\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 1.4109 - accuracy: 0.3535 - val_loss: 1.4000 - val_accuracy: 0.3125\n",
+            "Epoch 6/10\n",
+            "30/30 [==============================] - 10s 342ms/step - loss: 1.3763 - accuracy: 0.3328 - val_loss: 1.3926 - val_accuracy: 0.3304\n",
+            "Epoch 7/10\n",
+            "30/30 [==============================] - 11s 354ms/step - loss: 1.4106 - accuracy: 0.3004 - val_loss: 1.3792 - val_accuracy: 0.3304\n",
+            "Epoch 8/10\n",
+            "30/30 [==============================] - 11s 354ms/step - loss: 1.4032 - accuracy: 0.3252 - val_loss: 1.3846 - val_accuracy: 0.3393\n",
+            "Epoch 9/10\n",
+            "30/30 [==============================] - 11s 359ms/step - loss: 1.3709 - accuracy: 0.3670 - val_loss: 1.3827 - val_accuracy: 0.3393\n",
+            "Epoch 10/10\n",
+            "30/30 [==============================] - 11s 353ms/step - loss: 1.3663 - accuracy: 0.3554 - val_loss: 1.3724 - val_accuracy: 0.3393\n",
+            "For learning rate =  1\n",
+            "Epoch 1/10\n",
+            "30/30 [==============================] - 11s 367ms/step - loss: nan - accuracy: 0.2652 - val_loss: nan - val_accuracy: 0.2857\n",
+            "Epoch 2/10\n",
+            "30/30 [==============================] - 10s 349ms/step - loss: nan - accuracy: 0.2642 - val_loss: nan - val_accuracy: 0.2768\n",
+            "Epoch 3/10\n",
+            "30/30 [==============================] - 11s 351ms/step - loss: nan - accuracy: 0.2751 - val_loss: nan - val_accuracy: 0.2857\n",
+            "Epoch 4/10\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: nan - accuracy: 0.2879 - val_loss: nan - val_accuracy: 0.2857\n",
+            "Epoch 5/10\n",
+            "30/30 [==============================] - 10s 345ms/step - loss: nan - accuracy: 0.2521 - val_loss: nan - val_accuracy: 0.2946\n",
+            "Epoch 6/10\n",
+            "30/30 [==============================] - 10s 343ms/step - loss: nan - accuracy: 0.2569 - val_loss: nan - val_accuracy: 0.2768\n",
+            "Epoch 7/10\n",
+            "30/30 [==============================] - 10s 343ms/step - loss: nan - accuracy: 0.2718 - val_loss: nan - val_accuracy: 0.2679\n",
+            "Epoch 8/10\n",
+            "30/30 [==============================] - 10s 345ms/step - loss: nan - accuracy: 0.2863 - val_loss: nan - val_accuracy: 0.2768\n",
+            "Epoch 9/10\n",
+            "30/30 [==============================] - 10s 348ms/step - loss: nan - accuracy: 0.2637 - val_loss: nan - val_accuracy: 0.2946\n",
+            "Epoch 10/10\n",
+            "30/30 [==============================] - 10s 342ms/step - loss: nan - accuracy: 0.2835 - val_loss: nan - val_accuracy: 0.2768\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DPf_XXarMwqE"
+      },
+      "source": [
+        "Plotting Accuracy (considering highest accuracy of 10 epochs) and validation accuracy (considering val_acc of respective accuracy)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5QBg7k8ZPgQa",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 312
+        },
+        "outputId": "ef00d681-a1b5-4386-f35f-8b1282a511c6"
+      },
+      "source": [
+        "Accuracy = [0.3860, 0.4372, 0.5840, 0.3728, 0.3428]\n",
+        "Val_acc = [0.3125, 0.4196, 0.3839, 0.3304, 0.3304]\n",
+        "alpha = range(0,5)\n",
+        "plt.bar(alpha, Accuracy)\n",
+        "plt.xlabel('Alpha')\n",
+        "plt.ylabel('Accuracy')\n",
+        "plt.title('Accuracy for different learning rates')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Accuracy for different learning rates')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 24
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "HmexAXQnN_US",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 312
+        },
+        "outputId": "9306e8a8-923b-474f-b9c3-158a3fd90c0e"
+      },
+      "source": [
+        "plt.bar(alpha, Val_acc)\n",
+        "plt.xlabel('Alpha')\n",
+        "plt.ylabel('Validation accuracy')\n",
+        "plt.title('Validation accuracy for different learning rates')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Validation accuracy for different learning rates')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 25
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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KOUGYmVmhSjupzax/G3vWr5sdQp9ZetHRPao/mNe9LO9BmJlZIScIMzMr5ARhZmaFnCDMzKyQE4SZmRVygjAzs0LdJghJn5I0vBHBmJlZ/1FmD+LlZM+Tvk7SZEmqOigzM2u+bi+Ui4gvSToHeCdwMvBtSdcB34+IB6oOsFEGykUzVV0wY2aDT6k+iIgI4J/p1QEMB34q6ZIKYzMzsybqdg9C0unAR4BHge8Bn4uI5yVtQ/YEuM9XG6KZmTVDmT2I3YD3RsSREXF9RDwPEBEbgXfXa5j6LBZLapd0VsH0UyX9TdICSbdJmpCbdnZqt1jSkT1cLzMz20JlEsRvgBce9ylpZ0kHAUTEfV01Ss+UngkcBUwAjs8ngOTaiNg/Ig4ALgEuTW0nkD3Dej9gMnBZ5zOqzcysMcokiO8AT+XGn0pl3ZkItEfEkohYD8wBpuQrRMQTudEdgUjDU4A5EbEuIh4E2tP8zMysQcrc7lupkxrIDi1JKtNuJLAsN74cOGizmUunAWcA2wOH5dreUdN2ZEHbacA0gDFjxpQIyczMyiqzB7FE0qclbZdepwNL+iqAiJgZEa8CvgB8qYdtZ0VES0S0jBgxoq9CMjMzyiWIU4G3Ait4cS9gWol2K4DRufFRqawrc4Bje9nWzMz6WJkL5R4h6zDuqfnAeEnjyDbuU4EP5itIGh8Rf0+jR5OdNgvQClwr6VJgD2A8cFcvYjAzs14qcx3EMOAUsjOKhnWWR8RH67WLiA5J04F5wBBgdkQslDQDaIuIVmC6pCOA54E1wImp7cJ0tfYisgvzTouIDb1ZQTMz650ync1XA/cDRwIzgA8BXZ7emhcRc4G5NWXn5oZPr9P2QuDCMssxM7O+V6YPYu+IOAd4OiKuJDsUtNnZSGZmNrCUSRDPp79rJb0W2AX4t+pCMjOz/qDMIaZZ6XkQXyLrPH4pcE6lUZmZWdPVTRDphnxPRMQa4FZgr4ZEZWZmTVf3EFO6IZ/v1mpmNgiV6YP4vaTPShotabfOV+WRmZlZU5Xpg/hA+ntarizw4SYzswGtzJXU4xoRiJmZ9S9lrqT+SFF5RFzV9+GYmVl/UeYQ05tyw8OAw4G/AE4QZmYDWJlDTJ/Kj0valezOq2ZmNoCVOYup1tOA+yXMzAa4Mn0Q/8WLjwLdhuz50tdVGZSZmTVfmT6Ir+eGO4CHImJ5RfGYmVk/USZBPAysjIjnACTtIGlsRCytNDIzM2uqMn0Q1wMbc+MbUlm3JE2WtFhSu6SzCqafIWmRpHsl/UHSnrlpGyQtSK/WMsszM7O+U2YPYtuIWN85EhHrJW3fXSNJQ4CZwCSyZ1nPl9QaEYty1e4BWiLiGUmfAC7hxSu3n42IA8quiJmZ9a0yexCrJB3TOSJpCvBoiXYTgfaIWJISzBxgSr5CRNwcEc+k0TuAUeXCNjOzqpVJEKcCX5T0sKSHgS8AHy/RbiSwLDe+PJV15RTgN7nxYZLaJN0h6diiBpKmpTptq1atKhGSmZmVVeZCuQeAN0t6aRp/qq+DkHQC0AIckiveMyJWSNoLuEnS31Is+dhmAbMAWlpaAjMz6zPd7kFI+qqkXSPiqYh4StJwSV8pMe8VwOjc+KhUVjv/I4D/AI6JiHWd5RGxIv1dAtwCHFhimWZm1kfKHGI6KiLWdo6kp8u9q0S7+cB4SeNSp/ZUskeWvkDSgcAVZMnhkVz5cElD0/DuwNuAfOe2mZlVrMxZTEMkDe38dS9pB2Bod40iokPSdGAeMASYHRELJc0A2iKiFfhPsmdcXy8J4OGIOAZ4DXCFpI1kSeyimrOfzMysYmUSxDXAHyT9II2fDFxZZuYRMReYW1N2bm74iC7a/RnYv8wyzMysGmU6qS+WdC/Zbb4BLoiIedWGZWZmzVZmD4KI+A2bnoJqZmYDXJmzmN4sab6kpyStT7fAeKIRwZmZWfOUOYvp28DxwN+BHYD/RXYLDTMzG8BKPTAoItqBIRGxISJ+AEyuNiwzM2u2Mn0Qz6TrGBZIugRYSe+eRGdmZluRMhv6D6d608keNzoaeF+VQZmZWfOVOc31oTT4HHB+teGYmVl/4UNFZmZWyAnCzMwKOUGYmVmhbvsgJO0DfA7YM18/Ig6rMC4zM2uyMqe5Xg9cDnwX2FBtOGZm1l+USRAdEfGdyiMxM7N+pUwfxH9J+qSkV0rarfNVeWRmZtZUZfYgTkx/P5crC2Cvvg/HzMz6i273ICJiXMGrVHKQNFnSYkntks4qmH6GpEWS7pX0B0l75qadKOnv6XVibVszM6tWmbOYtgM+ARycim4BroiI57tpN4Tsrq+TgOXAfEmtNY8OvQdoiYhnJH0CuAT4QDqEdR7QQra3cndqu6ZHa2dmZr1Wpg/iO8AbgcvS642prDsTgfaIWBIR64E5wJR8hYi4OSKeSaN3AKPS8JHAjRGxOiWFG/EdZM3MGqpMH8SbIuL1ufGbJP21RLuRwLLc+HLgoDr1T+HFp9YVtR1Z20DSNGAawJgxY0qEZGZmZZXZg9gg6VWdI5L2oo+vh5B0AtnhpP/sSbuImBURLRHRMmLEiL4Mycxs0CuzB/E54GZJSwCRXVF9col2K8huDd5pVCrbhKQjgP8ADomIdbm2h9a0vaXEMs3MrI+Uud33HySNB/ZNRYtzG/J65gPjJY0j2+BPBT6YryDpQOAKYHJEPJKbNA/4qqThafydwNkllmlmZn2kywQh6bCIuEnSe2sm7S2JiPhZvRlHRIek6WQb+yHA7IhYKGkG0BYRrWSHlF4KXC8J4OGIOCYiVku6gCzJAMyIiNW9W0UzM+uNensQhwA3Ae8pmBZA3QQBEBFzgbk1Zefmho+o03Y2MLu7ZZiZWTW6TBARcV4anBERD+anpcNGZmY2gJU5i+mGgrKf9nUgZmbWv9Trg3g1sB+wS00/xM7AsKoDMzOz5qrXB7Ev8G5gVzbth3gS+FiVQZmZWfPV64P4JfBLSW+JiNsbGJOZmfUDZS6Uu0fSaWSHm144tBQRH60sKjMza7oyndRXA68gu4HeH8muan6yyqDMzKz5yiSIvSPiHODpiLgSOJr6N90zM7MBoEyC6Hzuw1pJrwV2Af6tupDMzKw/KNMHMSvdE+kcoJXs1hjn1m9iZmZbuzI36/teGvwjfg61mdmgUe9CuTPqNYyIS/s+HDMz6y/q7UHslP7uC7yJ7PASZBfN3VVlUGZm1nz1LpQ7H0DSrcAbIuLJNP5l4NcNic7MzJqmzFlMLwfW58bXpzIzMxvAypzFdBVwl6Sfp/FjgR9WFpGZmfUL3e5BRMSFZM+gXpNeJ0fE18rMXNJkSYsltUs6q2D6wZL+IqlD0nE10zZIWpBerbVtzcysWvXOYto5Ip6QtBuwNL06p+3W3SNAJQ0BZgKTgOXAfEmtEbEoV+1h4CTgswWzeDYiDii5HmZm1sfqHWK6lux233eTPWK0k9J4d9dETATaI2IJgKQ5wBTghQQREUvTtI09DdzMzKpV7yymd6e/vX286EhgWW58OT27h9MwSW1AB3BRRPyitoKkacA0gDFjxvQyTDMzK1LvENMb6jWMiL/0fTib2DMiVkjaC7hJ0t8i4oGaGGYBswBaWlqiaCZmZtY79Q4xfaPOtAAO62beK4DRufFRqayUiFiR/i6RdAtwIPBA3UZmZtZn6h1i+h9bOO/5wHhJ48gSw1Tgg2UappsDPhMR6yTtDrwNuGQL4zEzsx4ocx0E6TbfE9j0iXJX1WsTER2SpgPzgCHA7IhYKGkG0BYRrZLeBPwcGA68R9L5EbEf8BrgitR5vQ1ZH8SiLhZlZmYV6DZBSDoPOJQsQcwFjgJuI7uArq6ImJva5MvOzQ3PJzv0VNvuz8D+3c3fzMyqU+ZWG8cBhwP/jIiTgdeTPTTIzMwGsDIJ4tmI2Ah0SNoZeIRNO5/NzGwAKtMH0SZpV+C7ZBfNPQXcXmlUZmbWdPWug5gJXBsRn0xFl0v6LbBzRNzbkOjMzKxp6u1B/DfwdUmvBK4DfhwR9zQmLDMza7Yu+yAi4v9ExFuAQ4DHgNmS7pd0nqR9GhahmZk1RZnbfT8UERdHxIHA8WTPg7iv8sjMzKypuk0QkraV9B5J1wC/ARYD7608MjMza6p6ndSTyPYY3gXcBcwBpkXE0w2KzczMmqheJ/XZZM+EODMi1jQoHjMz6yfq3ayvu7u1mpnZAFbmSmozMxuEnCDMzKyQE4SZmRVygjAzs0KVJghJkyUtltQu6ayC6QdL+oukDknH1Uw7UdLf0+vEKuM0M7PNVZYgJA0BZpI9YGgCcLykCTXVHgZOIjudNt92N+A84CBgInBeegypmZk1SJV7EBOB9ohYEhHryS60m5KvEBFL051hN9a0PRK4MSJWp2swbgQmVxirmZnVqDJBjASW5caXp7I+aytpmqQ2SW2rVq3qdaBmZra5rbqTOiJmRURLRLSMGDGi2eGYmQ0oVSaIFWz6aNJRqazqtmZm1geqTBDzgfGSxknaHpgKtJZsOw94p6ThqXP6nanMzMwapLIEEREdwHSyDft9wHURsVDSDEnHAEh6k6TlwPuBKyQtTG1XAxeQJZn5wIxUZmZmDVLvbq5bLCLmAnNrys7NDc8nO3xU1HY2MLvK+MzMrGtbdSe1mZlVxwnCzMwKOUGYmVkhJwgzMyvkBGFmZoWcIMzMrJAThJmZFXKCMDOzQk4QZmZWyAnCzMwKOUGYmVkhJwgzMyvkBGFmZoWcIMzMrJAThJmZFXKCMDOzQpUmCEmTJS2W1C7prILpQyX9JE2/U9LYVD5W0rOSFqTX5VXGaWZmm6vsiXKShgAzgUnAcmC+pNaIWJSrdgqwJiL2ljQVuBj4QJr2QEQcUFV8ZmZWX5V7EBOB9ohYEhHrgTnAlJo6U4Ar0/BPgcMlqcKYzMyspCoTxEhgWW58eSorrBMRHcDjwMvStHGS7pH0R0nvKFqApGmS2iS1rVq1qm+jNzMb5PprJ/VKYExEHAicAVwraefaShExKyJaIqJlxIgRDQ/SzGwgqzJBrABG58ZHpbLCOpK2BXYBHouIdRHxGEBE3A08AOxTYaxmZlajygQxHxgvaZyk7YGpQGtNnVbgxDR8HHBTRISkEamTG0l7AeOBJRXGamZmNSo7iykiOiRNB+YBQ4DZEbFQ0gygLSJage8DV0tqB1aTJRGAg4EZkp4HNgKnRsTqqmI1M7PNVZYgACJiLjC3puzc3PBzwPsL2t0A3FBlbGZmVl9/7aQ2M7Mmc4IwM7NCThBmZlbICcLMzAo5QZiZWSEnCDMzK+QEYWZmhZwgzMyskBOEmZkVcoIwM7NCThBmZlbICcLMzAo5QZiZWSEnCDMzK+QEYWZmhZwgzMysUKUJQtJkSYsltUs6q2D6UEk/SdPvlDQ2N+3sVL5Y0pFVxmlmZpurLEGkZ0rPBI4CJgDHS5pQU+0UYE1E7A18E7g4tZ1A9vjR/YDJwGWdz6g2M7PGqHIPYiLQHhFLImI9MAeYUlNnCnBlGv4pcLgkpfI5EbEuIh4E2tP8zMysQap8JvVIYFlufDlwUFd1IqJD0uPAy1L5HTVtR9YuQNI0YFoafUrS4r4JvTK7A49WuQBdXOXct0jl6w6De/0H87rD4F7/LVz3PbuaUGWCqFxEzAJmNTuOsiS1RURLs+NohsG87jC4138wrzts3etf5SGmFcDo3PioVFZYR9K2wC7AYyXbmplZhapMEPOB8ZLGSdqerNO5taZOK3BiGj4OuCkiIpVPTWc5jQPGA3dVGKuZmdWo7BBT6lOYDswDhgCzI2KhpBlAW0S0At8HrpbUDqwmSyKketcBi4AO4LSI2FBVrA201RwOq8BgXncY3Os/mNcdtuL1V/aD3czMbFO+ktrMzAo5QZiZWSEniAbp7rYjA5Wk2ZIekfT/mh1Lo0kaLelmSYskLZR0erNjaiRJwyTdJemvaf3Pb3ZMjSZpiKR7JP2q2bH0hhNEA5S87chA9UOy26UMRh3AmRExAXgzcNog+r8DrAMOi4jXAwcAkyW9uckxNdrpwH3NDqK3nCAao8xtRwakiLiV7Ay1QSciVkbEX9Lwk2Qbis3uCDBQReapNLpdeg2as2IkjQKOBr7X7Fh6ywmiMYpuO043CncAAAKxSURBVDJoNhQG6U7FBwJ3NjeSxkqHWBYAjwA3RsRgWv9vAZ8HNjY7kN5ygjCrmKSXAjcAn4mIJ5odTyNFxIaIOIDsbggTJb222TE1gqR3A49ExN3NjmVLOEE0hm8dMkhJ2o4sOVwTET9rdjzNEhFrgZsZPP1RbwOOkbSU7JDyYZJ+1NyQes4JojHK3HbEBph06/rvA/dFxKXNjqfRJI2QtGsa3gGYBNzf3KgaIyLOjohRETGW7Pt+U0Sc0OSweswJogEiogPovO3IfcB1EbGwuVE1hqQfA7cD+0paLumUZsfUQG8DPkz263FBer2r2UE10CuBmyXdS/Yj6caI2CpP9xysfKsNMzMr5D0IMzMr5ARhZmaFnCDMzKyQE4SZmRVygjAzs0JOEGa9IOlYSSHp1Wl8bHd3rC1Tx6w/cYIw653jgdvSX7MByQnCrIfSvZXeDpxCeo56zfSTJP1S0i2S/i7pvNzkIZK+m56P8Lt0hTGSPiZpfnp2wg2SXtKYtTHrmhOEWc9NAX4bEf8NPCbpjQV1JgLvA14HvF9SSyofD8yMiP2AtakOwM8i4k3p2Qn3kSUfs6ZygjDruePJbsBG+lt0mOnGiHgsIp4Ffka2xwHwYEQsSMN3A2PT8Gsl/V9JfwM+BOxXSeRmPbBtswMw25pI2g04DNhfUgBDyB6CM7Omau09bDrH1+XKNgA7pOEfAsdGxF8lnQQc2ndRm/WO9yDMeuY44OqI2DMixkbEaOBBNr2dO8AkSbulPoZjgT91M9+dgJXp9uAf6vOozXrBCcKsZ44Hfl5TdgNwdk3ZXan8XuCGiGjrZr7nkD1t7k8MkltiW//nu7ma9bF0iKglIqY3OxazLeE9CDMzK+Q9CDMzK+Q9CDMzK+QEYWZmhZwgzMyskBOEmZkVcoIwM7NC/x/wCDYfrAMdZQAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sGi0b_dvRU01"
+      },
+      "source": [
+        "**Experiment:** To avoid overfitting let's try to increase the Dropout percentage by 20%"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "k2wI6VW6QP_u"
+      },
+      "source": [
+        "model3 = Sequential()\n",
+        "model3.add(Conv2D(32, kernel_size=5, strides=1, padding=\"Same\", activation='relu', input_shape=(img_h,img_w,3)))\n",
+        "model3.add(MaxPooling2D(padding='Same'))\n",
+        "\n",
+        "model3.add(Conv2D(64, kernel_size=5, strides=1, padding=\"Same\", activation='relu'))\n",
+        "model3.add(MaxPooling2D(padding='Same'))\n",
+        "\n",
+        "model3.add(Flatten())\n",
+        "model3.add(Dense(1024,activation=\"relu\"))\n",
+        "model3.add(Dropout(0.5))\n",
+        "model3.add(Dense(5,activation=\"sigmoid\"))\n",
+        "\n",
+        "\n",
+        "model3.compile(optimizer=\"adam\",loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "bxvNhrfIRr7k",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a27ca111-6f17-4e32-8d37-02e347976410"
+      },
+      "source": [
+        "history_dropout = model3.fit_generator(\n",
+        "                                      train_generator,\n",
+        "                                      steps_per_epoch = 490//16, \n",
+        "                                      epochs=15,\n",
+        "                                      validation_data = validation_generator,\n",
+        "                                      validation_steps = 119//16)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1844: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
+            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/15\n",
+            "30/30 [==============================] - 11s 357ms/step - loss: 17.1107 - accuracy: 0.2773 - val_loss: 1.3198 - val_accuracy: 0.4107\n",
+            "Epoch 2/15\n",
+            "30/30 [==============================] - 10s 344ms/step - loss: 1.4086 - accuracy: 0.3483 - val_loss: 1.3501 - val_accuracy: 0.3571\n",
+            "Epoch 3/15\n",
+            "30/30 [==============================] - 10s 347ms/step - loss: 1.1883 - accuracy: 0.4610 - val_loss: 1.2203 - val_accuracy: 0.3750\n",
+            "Epoch 4/15\n",
+            "30/30 [==============================] - 10s 347ms/step - loss: 1.0996 - accuracy: 0.4769 - val_loss: 1.4612 - val_accuracy: 0.3393\n",
+            "Epoch 5/15\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 1.2838 - accuracy: 0.4381 - val_loss: 1.2427 - val_accuracy: 0.3929\n",
+            "Epoch 6/15\n",
+            "30/30 [==============================] - 10s 345ms/step - loss: 1.1260 - accuracy: 0.4792 - val_loss: 1.2267 - val_accuracy: 0.4196\n",
+            "Epoch 7/15\n",
+            "30/30 [==============================] - 10s 345ms/step - loss: 1.1132 - accuracy: 0.4719 - val_loss: 1.4523 - val_accuracy: 0.4107\n",
+            "Epoch 8/15\n",
+            "30/30 [==============================] - 10s 347ms/step - loss: 1.1301 - accuracy: 0.5173 - val_loss: 1.2245 - val_accuracy: 0.3839\n",
+            "Epoch 9/15\n",
+            "30/30 [==============================] - 10s 346ms/step - loss: 1.0280 - accuracy: 0.5309 - val_loss: 1.1859 - val_accuracy: 0.4732\n",
+            "Epoch 10/15\n",
+            "30/30 [==============================] - 10s 347ms/step - loss: 0.9376 - accuracy: 0.5699 - val_loss: 1.2315 - val_accuracy: 0.4286\n",
+            "Epoch 11/15\n",
+            "30/30 [==============================] - 10s 347ms/step - loss: 0.9314 - accuracy: 0.6008 - val_loss: 1.1156 - val_accuracy: 0.4554\n",
+            "Epoch 12/15\n",
+            "30/30 [==============================] - 10s 347ms/step - loss: 0.8987 - accuracy: 0.6011 - val_loss: 1.2151 - val_accuracy: 0.5089\n",
+            "Epoch 13/15\n",
+            "30/30 [==============================] - 10s 347ms/step - loss: 0.8625 - accuracy: 0.6031 - val_loss: 1.0832 - val_accuracy: 0.4286\n",
+            "Epoch 14/15\n",
+            "30/30 [==============================] - 10s 349ms/step - loss: 0.8143 - accuracy: 0.6649 - val_loss: 1.2405 - val_accuracy: 0.4286\n",
+            "Epoch 15/15\n",
+            "30/30 [==============================] - 10s 347ms/step - loss: 0.8107 - accuracy: 0.6257 - val_loss: 1.2507 - val_accuracy: 0.4464\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "vjWh4YoJSQt6",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 296
+        },
+        "outputId": "78dbfe27-49fb-406b-e202-befd44f4b98e"
+      },
+      "source": [
+        "acc = history_dropout.history['accuracy']\n",
+        "val_acc = history_dropout.history['val_accuracy']\n",
+        "epochs = range(0,15)\n",
+        "plt.plot(epochs, acc,'g-' ,label= 'Accuracy')\n",
+        "plt.plot(epochs, val_acc ,'b-', label='Validation Accuracy')\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('Accuracy')\n",
+        "plt.legend()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x7fccb23e4a50>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 28
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lavsY7pWEBUd"
+      },
+      "source": [
+        "**Observation:** Here we can see that accuracy curve turns out to be much better than previous results for both training as well as validation. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "7IQDD_mbT7hL"
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Assignment 4 & 5/requirements.txt b/Assignment 4 & 5/requirements.txt
new file mode 100644
index 0000000..074f76e
--- /dev/null
+++ b/Assignment 4 & 5/requirements.txt	
@@ -0,0 +1,35 @@
+imageio==2.4.1
+Keras==2.4.3
+Keras-Preprocessing==1.1.2
+keras-vis==0.4.1
+matplotlib==3.2.2
+matplotlib-venn==0.11.6
+networkx==2.5.1
+nltk==3.2.5
+numpy==1.19.5
+opencv-contrib-python==4.1.2.30
+opencv-python==4.1.2.30
+pandas==1.1.5
+pandas-datareader==0.9.0
+pandas-gbq==0.13.3
+pandas-profiling==1.4.1
+pickleshare==0.7.5
+Pillow==7.1.2
+pip-tools==4.5.1
+pymc3==3.7
+regex==2019.12.20
+scikit-image==0.16.2
+scikit-learn==0.22.2.post1
+scipy==1.4.1
+seaborn==0.11.1
+sklearn==0.0
+sklearn-pandas==1.8.0
+tensorboard==2.4.1
+tensorboard-plugin-wit==1.8.0
+tensorflow==2.4.1
+tensorflow-datasets==4.0.1
+tensorflow-estimator==2.4.0
+tensorflow-gcs-config==2.4.0
+tensorflow-hub==0.12.0
+tensorflow-metadata==0.29.0
+tensorflow-probability==0.12.1