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+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"200782_Richa Sachan_Part_2.ipynb","provenance":[],"authorship_tag":"ABX9TyP5BpwQFVCbmOecvw4v2D95"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["## **Importing python libraries**"],"metadata":{"id":"ttKCaMvRJDt3"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"IijZndPUTS5i"},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","\n","\n","%matplotlib inline\n"]},{"cell_type":"code","source":["from google.colab import files\n","uploaded = files.upload()"],"metadata":{"colab":{"resources":{"http://localhost:8080/nbextensions/google.colab/files.js":{"data":"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Sachan","userId":"00233403609540832757"}},"outputId":"b4d69e68-f10f-492f-f11c-942489dba13c"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","     <input type=\"file\" id=\"files-d65fa4e9-62be-4795-8f23-ebbaf4d2678b\" name=\"files[]\" multiple disabled\n","        style=\"border:none\" />\n","     <output id=\"result-d65fa4e9-62be-4795-8f23-ebbaf4d2678b\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. Please rerun this cell to enable.\n","      </output>\n","      <script src=\"/nbextensions/google.colab/files.js\"></script> "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Saving House_prediction.csv to House_prediction.csv\n"]}]},{"cell_type":"code","source":["df= pd.read_csv(\"House_prediction.csv\")\n","df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":336},"id":"_Vc-gEM4W2zp","executionInfo":{"status":"ok","timestamp":1651727854044,"user_tz":-330,"elapsed":453,"user":{"displayName":"Richa Sachan","userId":"00233403609540832757"}},"outputId":"8aa06914-ad65-48d3-83cc-88839e43f072"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["           city  area  rooms  bathroom  parking spaces floor     animal  \\\n","0     São Paulo    70      2         1               1     7      acept   \n","1     São Paulo   320      4         4               0    20      acept   \n","2  Porto Alegre    80      1         1               1     6      acept   \n","3  Porto Alegre    51      2         1               0     2      acept   \n","4     São Paulo    25      1         1               0     1  not acept   \n","\n","       furniture  hoa (R$)  rent amount (R$)  property tax (R$)  \\\n","0      furnished      2065              3300                211   \n","1  not furnished      1200              4960               1750   \n","2  not furnished      1000              2800                  0   \n","3  not furnished       270              1112                 22   \n","4  not furnished         0               800                 25   \n","\n","   fire insurance (R$)  total (R$)  \n","0                   42        5618  \n","1                   63        7973  \n","2                   41        3841  \n","3                   17        1421  \n","4                   11         836  "],"text/html":["\n","  <div id=\"df-bcaf5a5e-18ad-4842-9e01-8d8c3cc78e0c\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>city</th>\n","      <th>area</th>\n","      <th>rooms</th>\n","      <th>bathroom</th>\n","      <th>parking spaces</th>\n","      <th>floor</th>\n","      <th>animal</th>\n","      <th>furniture</th>\n","      <th>hoa (R$)</th>\n","      <th>rent amount (R$)</th>\n","      <th>property tax (R$)</th>\n","      <th>fire insurance (R$)</th>\n","      <th>total (R$)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>São Paulo</td>\n","      <td>70</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>7</td>\n","      <td>acept</td>\n","      <td>furnished</td>\n","      <td>2065</td>\n","      <td>3300</td>\n","      <td>211</td>\n","      <td>42</td>\n","      <td>5618</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>São Paulo</td>\n","      <td>320</td>\n","      <td>4</td>\n","      <td>4</td>\n","      <td>0</td>\n","      <td>20</td>\n","      <td>acept</td>\n","      <td>not furnished</td>\n","      <td>1200</td>\n","      <td>4960</td>\n","      <td>1750</td>\n","      <td>63</td>\n","      <td>7973</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Porto Alegre</td>\n","      <td>80</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>6</td>\n","      <td>acept</td>\n","      <td>not furnished</td>\n","      <td>1000</td>\n","      <td>2800</td>\n","      <td>0</td>\n","      <td>41</td>\n","      <td>3841</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Porto Alegre</td>\n","      <td>51</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>acept</td>\n","      <td>not furnished</td>\n","      <td>270</td>\n","      <td>1112</td>\n","      <td>22</td>\n","      <td>17</td>\n","      <td>1421</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>São Paulo</td>\n","      <td>25</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>not acept</td>\n","      <td>not furnished</td>\n","      <td>0</td>\n","      <td>800</td>\n","      <td>25</td>\n","      <td>11</td>\n","      <td>836</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bcaf5a5e-18ad-4842-9e01-8d8c3cc78e0c')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg 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fire insurance (R$)    total (R$)  \n","count      10692.000000       10692.000000         10692.000000  1.069200e+04  \n","mean        3896.247194         366.704358            53.300879  5.490487e+03  \n","std         3408.545518        3107.832321            47.768031  1.648473e+04  \n","min          450.000000           0.000000             3.000000  4.990000e+02  \n","25%         1530.000000          38.000000            21.000000  2.061750e+03  \n","50%         2661.000000         125.000000            36.000000  3.581500e+03  \n","75%         5000.000000         375.000000            68.000000  6.768000e+03  \n","max        45000.000000      313700.000000           677.000000  1.120000e+06  "],"text/html":["\n","  <div id=\"df-260fc2dc-4a3b-44f7-b2e4-c0ccf547cc1a\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","     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<td>2.506079</td>\n","      <td>2.236813</td>\n","      <td>1.609147</td>\n","      <td>1.174022e+03</td>\n","      <td>3896.247194</td>\n","      <td>366.704358</td>\n","      <td>53.300879</td>\n","      <td>5.490487e+03</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>537.016942</td>\n","      <td>1.171266</td>\n","      <td>1.407198</td>\n","      <td>1.589521</td>\n","      <td>1.559231e+04</td>\n","      <td>3408.545518</td>\n","      <td>3107.832321</td>\n","      <td>47.768031</td>\n","      <td>1.648473e+04</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>11.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000e+00</td>\n","      <td>450.000000</td>\n","      <td>0.000000</td>\n","      <td>3.000000</td>\n","      <td>4.990000e+02</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>56.000000</td>\n","      <td>2.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>1.700000e+02</td>\n","      <td>1530.000000</td>\n","      <td>38.000000</td>\n","      <td>21.000000</td>\n","      <td>2.061750e+03</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>90.000000</td>\n","      <td>2.000000</td>\n","      <td>2.000000</td>\n","      <td>1.000000</td>\n","      <td>5.600000e+02</td>\n","      <td>2661.000000</td>\n","      <td>125.000000</td>\n","      <td>36.000000</td>\n","      <td>3.581500e+03</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>182.000000</td>\n","      <td>3.000000</td>\n","      <td>3.000000</td>\n","      <td>2.000000</td>\n","      <td>1.237500e+03</td>\n","      <td>5000.000000</td>\n","      <td>375.000000</td>\n","      <td>68.000000</td>\n","      <td>6.768000e+03</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>46335.000000</td>\n","      <td>13.000000</td>\n","      <td>10.000000</td>\n","      <td>12.000000</td>\n","      <td>1.117000e+06</td>\n","      <td>45000.000000</td>\n","      <td>313700.000000</td>\n","      <td>677.000000</td>\n","      <td>1.120000e+06</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-260fc2dc-4a3b-44f7-b2e4-c0ccf547cc1a')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":8}]},{"cell_type":"markdown","source":["### **Counting number of rooms as per the area**.\n","\n","---\n","\n"],"metadata":{"id":"tjbYdIDBbDk_"}},{"cell_type":"code","source":["\n","sns.countplot(x ='rooms' , data = df)\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"culR4UphYAYg","executionInfo":{"status":"ok","timestamp":1651727860750,"user_tz":-330,"elapsed":469,"user":{"displayName":"Richa Sachan","userId":"00233403609540832757"}},"outputId":"bd004709-fd22-46a2-e582-9d8842ab9939"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["df.hist(bins=50, figsize=(10, 8))\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":512},"id":"H_6RfkmweemM","executionInfo":{"status":"ok","timestamp":1651727865468,"user_tz":-330,"elapsed":2700,"user":{"displayName":"Richa Sachan","userId":"00233403609540832757"}},"outputId":"4c27a9a2-837d-4937-fecd-cbaf9efbc9ef"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 720x576 with 9 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["### **For finding the relationship between the features, we can find the correlation heatmap.**\n","\n","---\n","\n"],"metadata":{"id":"FpEGKegHioNP"}},{"cell_type":"code","source":["plt.figure(figsize=(8,8))\n","sns.heatmap(df.corr(),cbar=True, square =True , fmt='0.1f',annot_kws={\"size\":15},annot=True,)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":548},"id":"idxTp-V0mq3h","executionInfo":{"status":"ok","timestamp":1651727871454,"user_tz":-330,"elapsed":2611,"user":{"displayName":"Richa Sachan","userId":"00233403609540832757"}},"outputId":"ef2c8bcb-11c2-4836-fdf8-d15c28939bb3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f8515dfeb90>"]},"metadata":{},"execution_count":11},{"output_type":"display_data","data":{"text/plain":["<Figure size 576x576 with 2 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["**From above inferred heatmap we can select our features from which we can exploit more details from it as:**\n","\n","---\n","\n"],"metadata":{"id":"PCWXYAv2npdz"}},{"cell_type":"code","source":["from pandas.plotting import scatter_matrix\n","features=[\"rooms\", \"bathroom\", \"parking spaces\", \"rent amount (R$)\" ,\"fire insurance (R$)\"]\n","scatter_matrix(df[features],figsize=(10,10))\n","\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":621},"id":"s_ClC4c3n77r","executionInfo":{"status":"ok","timestamp":1651680195466,"user_tz":-330,"elapsed":3928,"user":{"displayName":"Richa Sachan","userId":"00233403609540832757"}},"outputId":"49d4b4c2-1517-4e43-b6ad-26b562b28ebb"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 720x720 with 25 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["From above pairplot we can see that (rent amount and fire insurance) and(fire insurance and parking space have positive correlation) have strong positive correlation )"],"metadata":{"id":"M5YXb1T9rxQi"}},{"cell_type":"markdown","source":["### **Now since we are able to see that, we can get some valid data's by plotting  total amount  with**\n","\n","### 1. Rent amount\n","### 2. Fire insurance\n","### 3. hoa "],"metadata":{"id":"sFnd4MGh7kMf"}},{"cell_type":"markdown","source":["## **1. Total vs rent amount**"],"metadata":{"id":"FZTz1NrE-K8I"}},{"cell_type":"code","source":["plt.figure(figsize=(8,8))\n","#plt.scatter(df[\"hoa (R$)\"],df['total (R$)'])\n","plt.scatter(df[\"rent amount (R$)\"],df['total (R$)'])\n","plt.xlim([0, 16000])\n","plt.ylim([0, 30000])\n","plt.xlabel('rent amount (R$)', fontsize=20)\n","plt.ylabel('total (R$)', fontsize=20)\n","plt.plot(np.unique(df[\"rent amount (R$)\"]), np.poly1d(np.polyfit(df[\"rent amount (R$)\"], df['total (R$)'], 1))\n","         (np.unique(df[\"rent amount (R$)\"])), color='red')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":510},"id":"hts9x-j5nVyg","executionInfo":{"status":"ok","timestamp":1651735628889,"user_tz":-330,"elapsed":936,"user":{"displayName":"Richa Sachan","userId":"00233403609540832757"}},"outputId":"b3d430a6-68cb-47f6-e4d4-2492326937ab"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 576x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["## **2. Total vs Fire insurance**"],"metadata":{"id":"nKoOHfcCBMQi"}},{"cell_type":"code","source":["plt.figure(figsize=(8,8))\n","plt.scatter(df[\"fire insurance (R$)\"],df['total (R$)'])\n","\n","plt.xlim([0, 400])\n","plt.ylim([0, 30000])\n","plt.xlabel('fire insurance (R$)', fontsize=20)\n","plt.ylabel('total (R$)', fontsize=20)\n","plt.plot(np.unique(df[\"fire insurance (R$)\"]), np.poly1d(np.polyfit(df[\"fire insurance (R$)\"], df['total (R$)'], 1))\n","         (np.unique(df[\"fire insurance (R$)\"])), color='red')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":510},"id":"pirD8lcrBHyu","executionInfo":{"status":"ok","timestamp":1651735457495,"user_tz":-330,"elapsed":1059,"user":{"displayName":"Richa Sachan","userId":"00233403609540832757"}},"outputId":"ed6a2090-ba02-4959-ba86-cbb59fe3dad7"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 576x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["## **3. Total vs Fire insurance**"],"metadata":{"id":"53poAR2ACgxn"}},{"cell_type":"code","source":["plt.figure(figsize=(8,8))\n","plt.scatter(df[\"hoa (R$)\"],df['total (R$)'])\n","\n","plt.xlim([0, 8000])\n","plt.ylim([0, 30000])\n","plt.xlabel('hoa (R$)', fontsize=20)\n","plt.ylabel('total (R$)', fontsize=20)\n","plt.plot(np.unique(df[\"hoa (R$)\"]), np.poly1d(np.polyfit(df[\"hoa (R$)\"], df['total (R$)'], 1))\n","         (np.unique(df[\"hoa (R$)\"])), color='red')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":510},"id":"btD7KqNrCnlG","executionInfo":{"status":"ok","timestamp":1651735365114,"user_tz":-330,"elapsed":853,"user":{"displayName":"Richa Sachan","userId":"00233403609540832757"}},"outputId":"3ffb8d28-8d61-4cd8-9c5a-8124e8525700"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 576x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]}]}
diff --git a/Assignment/Assignment_1/200782_Richa_Sachan_Part_1.ipynb b/Assignment/Assignment_1/200782_Richa_Sachan_Part_1.ipynb
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@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.8"},"colab":{"name":"200782_Richa_Sachan_Part_1.ipynb","provenance":[{"file_id":"1uhHm6ZG4hN8tfvCZQ_OjU172yQHiVziR","timestamp":1651608584086},{"file_id":"1786uw8K3wuT7l8ZxNHizgqRv8QNtFbow","timestamp":1650045374309},{"file_id":"https://github.com/StanfordVL/CS131_release/blob/master/fall_2020/hw0_release/hw0.ipynb","timestamp":1617561487811}],"collapsed_sections":[]}},"cells":[{"cell_type":"markdown","metadata":{"id":"rvFM645NE-D2"},"source":["# Assignment 1 - Part 1\n","In this assignment, we will go through basic linear algebra, NumPy, and image manipulation using Python to get everyone on the same page.\n","\n","One of the aims of this assignment is to get you to start getting comfortable searching for useful library functions online. So in many of the functions you will implement, you will have to look up helper functions.\n","\n","\\\n","\n","## Instructions\n","* This notebook contain blocks of code, you are required to complete those blocks(where required)\n","* You are required to copy this notebook (\"copy to drive\" above) and complete the code.(DO NOT CHANGE THE NAME OF THE FUNCTIONS)\n","\n","\\\n","\\\n","Also, I'd like to acknowledge the Stanford CS131. This assignment is highly based on the assignments from that course."]},{"cell_type":"markdown","metadata":{"id":"UhSVK4RoK9q5"},"source":["First Let's import some dependencies"]},{"cell_type":"code","metadata":{"id":"cCKqyfhIE-EQ","executionInfo":{"status":"ok","timestamp":1651739201882,"user_tz":-330,"elapsed":28,"user":{"displayName":"Richa Sachan","userId":"00233403609540832757"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"934c8fb9-bb80-4882-ca88-7a9d21f9baaf"},"source":["# Imports the print function from newer versions of python\n","from __future__ import print_function\n","\n","# Setup\n","\n","# The Random module implements pseudo-random number generators\n","import random \n","\n","# Numpy is the main package for scientific computing with Python. \n","# This will be one of our most used libraries in this project\n","import numpy as np\n","\n","# The Time library helps us time code runtimes\n","import time\n","\n","\n","# Some more magic so that the notebook will reload external python modules;\n","# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n","%load_ext autoreload\n","%autoreload 2\n","%reload_ext autoreload"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["The autoreload extension is already loaded. To reload it, use:\n","  %reload_ext autoreload\n"]}]},{"cell_type":"markdown","metadata":{"collapsed":true,"id":"QLtp15rqE-EU"},"source":["# Part 1: Linear Algebra and NumPy Review\n","In this section, we will review linear algebra and learn how to use vectors and matrices in python using numpy."]},{"cell_type":"markdown","metadata":{"id":"E8HDYpc0E-EV"},"source":["## Part 1.1 (5 points)\n","First, let's test whether you can define the following matrices and vectors using numpy. Look up `np.array()` for help. In the next code block, define $M$ as a $(4, 3)$ matrix, $a$ as a $(1, 3)$ row vector and $b$ as a $(3, 1)$ column vector:\n","\n","$$M = \\begin{bmatrix}\n","1 & 2 & 3 \\\\\n","4 & 5 & 6 \\\\\n","7 & 8 & 9 \\\\\n","10 & 11 & 12 \\end{bmatrix}\n","$$\n","\n","$$a = \\begin{bmatrix}\n","1 & 1 & 0\n","\\end{bmatrix}\n","$$\n","\n","$$b = \\begin{bmatrix}\n","-1 \\\\ 2 \\\\ 5\n","\\end{bmatrix}  \n","$$ "]},{"cell_type":"code","metadata":{"id":"mETk2NCME-EX","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1651739201883,"user_tz":-330,"elapsed":24,"user":{"displayName":"Richa Sachan","userId":"00233403609540832757"}},"outputId":"1fc766e3-71da-44bf-d7fc-41ef913563ef"},"source":["### YOUR CODE HERE\n","M = np.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])\n","a = np.array([1,1,0])\n","b = np.array([-1,2,5]).T\n","### END CODE HERE\n","print(\"M = \\n\", M)\n","print(\"The size of M is: \", M.size)\n","print()\n","print(\"a = \", a)\n","print(\"The size of a is: \", a.size)\n","print()\n","print(\"b = \", b)\n","print(\"The size of b is: \", b.size)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["M = \n"," [[ 1  2  3]\n"," [ 4  5  6]\n"," [ 7  8  9]\n"," [10 11 12]]\n","The size of M is:  12\n","\n","a =  [1 1 0]\n","The size of a is:  3\n","\n","b =  [-1  2  5]\n","The size of b is:  3\n"]}]},{"cell_type":"markdown","metadata":{"id":"rSta4NheE-EZ"},"source":["## Part 1.2 (5 points)\n","Implement the `dot_product()` method below and check that it returns the correct answer for $a^Tb$."]},{"cell_type":"code","metadata":{"id":"C5ZRjCE2MVOU"},"source":["def dot_product(a, b):\n","    \"\"\"Implement dot product between the two vectors: a and b.\n","    (optional): While you can solve this using for loops, we recommend\n","    that you look up `np.dot()` online and use that instead.\n","    Args:\n","        a: numpy array of shape (x, n)\n","        b: numpy array of shape (n, x)\n","    Returns:\n","        out: numpy array of shape (x, x) (scalar if x = 1)\n","    \"\"\"\n","    \n","    \n","    out= np.dot(a,b)\n","    if np.isscalar(out):\n","      out=np.dot(a,b).reshape(1,1)\n","    \n","    \n","    ### YOUR CODE HERE\n","    pass\n","    ### END YOUR CODE\n","    return out"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"pbLIS5vIE-Ea","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1651739201884,"user_tz":-330,"elapsed":19,"user":{"displayName":"Richa Sachan","userId":"00233403609540832757"}},"outputId":"a959cc64-8519-4f47-8242-cdd203ccdc91"},"source":["# Now, let's test out this dot product. Your answer should be [[1]].\n","aDotB = dot_product(a, b)\n","print(aDotB)\n","\n","print(\"The size is: \", aDotB.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[1]]\n","The size is:  (1, 1)\n"]}]},{"cell_type":"markdown","metadata":{"id":"0rGfcRU1E-Eb"},"source":["## Part 1.3 (5 points)\n","Implement the `complicated_matrix_function()` method and use it to compute $(ab)Ma^T$\n","\n","IMPORTANT NOTE: The `complicated_matrix_function()` method expects all inputs to be two dimensional numpy arrays, as opposed to 1-D arrays.  This is an important distinction, because 2-D arrays can be transposed, while 1-D arrays cannot.\n","\n","To transpose a 2-D array, you can use the syntax `array.T` "]},{"cell_type":"code","metadata":{"id":"dglQmbuLNOk6"},"source":["def complicated_matrix_function(M, a, b):\n","    \"\"\"Implement (a * b) * (M * a.T).\n","    (optional): Use the `dot_product(a, b)` function you wrote above\n","    as a helper function.\n","    Args:\n","        M: numpy matrix of shape (x, n).\n","        a: numpy array of shape (1, n).\n","        b: numpy array of shape (n, 1).\n","    Returns:\n","        out: numpy matrix of shape (x, 1).\n","    \"\"\"\n","    ab= np.dot(a,b)\n","    m = dot_product(M,a.T)\n","    rows= len(M)\n","    if isinstance(ab.item(),int):\n","     out = ab * (m.reshape(rows,1)) \n","    \n","    ### YOUR CODE HERE\n","    pass\n","    ### END YOUR CODE\n","    return out"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"da_uQQLhE-Ec","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1651739201885,"user_tz":-330,"elapsed":16,"user":{"displayName":"Richa Sachan","userId":"00233403609540832757"}},"outputId":"7424b73e-6700-48bf-9df4-48253ccbce6d"},"source":["# Your answer should be $[[3], [9], [15], [21]]$ of shape(4, 1).\n","ans = complicated_matrix_function(M, a, b)\n","print(ans)\n","print()\n","print(\"The size is: \", ans.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[ 3]\n"," [ 9]\n"," [15]\n"," [21]]\n","\n","The size is:  (4, 1)\n"]}]},{"cell_type":"code","metadata":{"id":"6CWXxSSOE-Ed","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1651739202445,"user_tz":-330,"elapsed":572,"user":{"displayName":"Richa Sachan","userId":"00233403609540832757"}},"outputId":"f1350e8d-a6c8-4aa7-d020-1d29ddeaac8a"},"source":["M_2 = np.array(range(4)).reshape((2,2))\n","a_2 = np.array([[1,1]])\n","b_2 = np.array([[10, 10]]).T\n","print(M_2.shape)\n","print(a_2.shape)\n","print(b_2.shape)\n","print()\n","\n","# Your answer should be $[[20], [100]]$ of shape(2, 1).\n","ans = complicated_matrix_function(M_2, a_2, b_2)\n","print(ans)\n","print()\n","print(\"The size is: \", ans.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(2, 2)\n","(1, 2)\n","(2, 1)\n","\n","[[ 20]\n"," [100]]\n","\n","The size is:  (2, 1)\n"]}]},{"cell_type":"markdown","metadata":{"id":"4fHLxLl4E-Ee"},"source":["## Part 1.4 (10 points) [Optional/Bonus]\n","Implement `eigen_decomp()` and `get_eigen_values_and_vectors()` methods. In this method, perform eigenvalue decomposition on the following matrix and return the largest k eigen values and corresponding eigen vectors (k is specified in the method calls below).\n","\n","$$M = \\begin{bmatrix}\n","1 & 2 & 3 \\\\\n","4 & 5 & 6 \\\\\n","7 & 8 & 9 \\end{bmatrix}\n","$$\n"]},{"cell_type":"code","metadata":{"id":"RfaCSoRMOIc8"},"source":["import numpy as np\n","#M = np.array([[1,2,3],[4,5,6],[7,8,9]])\n","def eigen_decomp(M):\n","    \"\"\"Implement eigenvalue decomposition.\n","    (optional): You might find the `np.linalg.eig` function useful.\n","    Args:\n","        matrix: numpy matrix of shape (m, n)\n","    Returns:\n","        w: numpy array of shape (m, m) such that the column v[:,i] is the eigenvector corresponding to the eigenvalue w[i].\n","        v: Matrix where every column is an eigenvector.\n","    \"\"\"\n","    w , v = np.linalg.eig(M)\n","   \n","    #print(w)\n","    #print(v)\n","    ### YOUR CODE HERE\n","    pass\n","    ### END YOUR CODE\n","    return w, v\n","\n","#eigen_decomp(M)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"YB120rb4ONBH"},"source":["def get_eigen_values_and_vectors(M, k):\n","    \"\"\"Return top k eigenvalues and eigenvectors of matrix M. By top k\n","    here we mean the eigenvalues with the top ABSOLUTE values (lookup\n","    np.argsort for a hint on how to do so.)\n","    (optional): Use the `eigen_decomp(M)` function you wrote above\n","    as a helper function\n","    Args:\n","        M: numpy matrix of shape (m, m).\n","        k: number of eigen values and respective vectors to return.\n","    Returns:\n","        eigenvalues: list of length k containing the top k eigenvalues\n","        eigenvectors: list of length k containing the top k eigenvectors\n","            of shape (m,)\n","    \"\"\"\n","    eigenvalues = []\n","    eigenvectors = []\n","\n","    w,v=eigen_decomp(M)\n","    w_indices = np.argsort(w, axis=0) \n","    v_indices = np.argsort(v, axis=1)\n","    #print(v_i)\n","    eigenvalues.append(np.take_along_axis(w, w_indices, axis=0)[:k])\n","    \n","    for i in range(k):\n","      eigenvectors.append(np.take_along_axis(v, v_indices, axis=1)[:,i])\n","\n","    ### YOUR CODE HERE\n","    pass\n","    ### END YOUR CODE\n","    return eigenvalues, eigenvectors"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"t0_GkrJwE-Ee","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1651908890609,"user_tz":-330,"elapsed":445,"user":{"displayName":"Richa Sachan","userId":"00233403609540832757"}},"outputId":"299a0857-73df-4035-bd78-a919db1206af"},"source":["# Let's define M.\n","M = np.array([[1,2,3],[4,5,6],[7,8,9]])\n","\n","# Now let's grab the first eigenvalue and first eigenvector.\n","# You should get back a single eigenvalue and a single eigenvector.\n","val, vec = get_eigen_values_and_vectors(M[:,:3], 1)\n","print(\"First eigenvalue =\", val[0])\n","print()\n","print(\"First eigenvector =\", vec[0])\n","print()\n","assert len(vec) == 1\n","\n","# Now, let's get the first two eigenvalues and eigenvectors.\n","# You should get back a list of two eigenvalues and a list of two eigenvector arrays.\n","val, vec = get_eigen_values_and_vectors(M[:,:3], 2)\n","print(\"Eigenvalues =\", val)\n","print()\n","print(\"Eigenvectors =\", vec)\n","assert len(vec) == 2"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["First eigenvalue = [-1.11684397]\n","\n","First eigenvector = [-0.78583024 -0.81649658 -0.8186735 ]\n","\n","Eigenvalues = [array([-1.11684397e+00, -1.30367773e-15])]\n","\n","Eigenvectors = [array([-0.78583024, -0.81649658, -0.8186735 ]), array([-0.23197069, -0.52532209,  0.40824829])]\n"]}]},{"cell_type":"markdown","metadata":{"id":"Yeh-V5x1PYz5"},"source":["## Part 1.5 (10 points)\n","In this section, you'll implement a gaussian elimination.\n","\n","The algorithm to to reduce a matrix to rref using gaussian elimination contains 2 parts, First reducing the matrix to partial reduced form, then back substituting to calculate the rref. First algorithm can be summed up as:\n","1. Partial pivoting: Find the kth pivot by swapping rows, to move the entry with the largest absolute value to the pivot position. This imparts computational stability to the algorithm.\n","2. For each row below the pivot, calculate the factor f which makes the kth entry zero, and for every element in the row subtract the fth multiple of the corresponding element in the kth row.\n","3. Repeat above steps for each unknown. We will be left with a partial r.e.f. matrix.\n","\n","$$\\begin{bmatrix}\n","1 & 2 & 3 \\\\\n","4 & 5 & 6 \\\\\n","7 & 8 & 9 \\end{bmatrix}\n","=>\n","\\begin{bmatrix}\n","7 & 8 & 9 \\\\\n","4 & 5 & 6 \\\\\n","1 & 2 & 3 \\end{bmatrix}\n","=>\n","\\begin{bmatrix}\n","7 & 8 & 9 \\\\\n","0 & 0.42 & 0.85 \\\\\n","0 & 0.85 & 1.71 \\end{bmatrix}\n","=>\n","\\begin{bmatrix}\n","7 & 8 & 9 \\\\\n","0 & 0.85 & 1.71 \\\\\n","0 & 0.45 & 0.85 \\end{bmatrix}\n","=>\n","\\begin{bmatrix}\n","7 & 8 & 9 \\\\\n","0 & 0.42 & 0.85 \\\\\n","0 & 0 & -0.05 \\end{bmatrix}\n","$$\n","Second algorithm:\n","1. Take a pivot from the last row.\n","2. For each row above the pivot, calculate the factor f which makes the kth entry zero, and for every element in the row subtract the fth multiple of the corresponding element in the kth row\n","3. Repeat the above step untill the matrix is in rref\n","$$\\begin{bmatrix}\n","7 & 8 & 0 \\\\\n","0 & 0.42 & 0 \\\\\n","0 & 0 & -0.05 \\end{bmatrix}\n","=>\n","\\begin{bmatrix}\n","7 & 0 & 0 \\\\\n","0 & 0.42 & 0 \\\\\n","0 & 0 & -0.05 \\end{bmatrix}\n","$$\n","\n","Steps for implementation:\n","1. Complete the function `swap_rows()`\n","2. Complete the function `apply_row()`\n","3. Complete `forward()` and `backward()`\n","4. Finally implement `rref()` using the `forward()` and `backward()`\n","\n","Note: You can skip this part if you want."]},{"cell_type":"code","metadata":{"id":"qUFujiFAPYz6"},"source":["def swap_rows(M):\n","    \"\"\"Implement row swapping to make the largest element in the pivotial column to be the first row.\n","    Args:\n","        matrix: numpy matrix of shape (m, n)\n","    Returns:\n","        Ms: matrix with swapped row\n","    \"\"\"\n","    iii=0\n","    maximum_i=[]\n","    row_1=[] \n","    max_elemt=[]\n","    for j in range(3):\n","      maxm= M[0][j]\n","      row_1.append(M[0][j]) \n","\n","      for i in range(3):\n","        if M[i][j]>maxm:\n","         maxm=M[i][j]\n","         iii=i\n","        else :\n","          continue\n","      maximum_i.append(iii)\n","      \n","      max_elemt.append(maxm)\n","    \n","    for k in range(len(row_1)):\n","\n","      M[0][k]=max_elemt[k]\n","      M[maximum_i[k]][k]=row_1[k]\n","      \n","\n","    \n","    out=M\n","    ### YOUR CODE HERE\n","    pass\n","    ### END YOUR CODE\n","    return out"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"S8lbAUSWWpyO"},"source":["def apply_rows(M):\n","    \"\"\"For each row below the pivot, calculate the factor f which makes the kth\n","    entry zero, and for every element in the row subtract the fth multiple of the\n","    corresponding element in the kth row.\n","    Args:\n","        matrix: numpy matrix of shape (m, n)\n","    Returns:\n","        Ms: matrix with all other entries of the pivotal col zero\n","    \"\"\"\n","    \n","\n","    out = None\n","    ### YOUR CODE HERE\n","    pass\n","    ### END YOUR CODE\n","    return out"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"GnE_-JLxPYz7"},"source":["def forward(M):\n","    \"\"\"Return a partial ref using the algo described above\n","    Args:\n","        M: numpy matrix of shape (m, n).\n","    Returns:\n","        Ms: ref of M\n","    \"\"\"\n","    out = None\n","    ### YOUR CODE HERE\n","    pass\n","    ### END YOUR CODE\n","    return out"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Wb7pPGP4XmJu"},"source":["def backward(M):\n","    \"\"\"Return a rref using the algo described above\n","    Args:\n","        M: numpy matrix of shape (m, n).\n","    Returns:\n","        Ms: rref of M\n","    \"\"\"\n","    out = None\n","    ### YOUR CODE HERE\n","    pass\n","    ### END YOUR CODE\n","    return out"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"XLq81xzXYR85"},"source":["def rref(M):\n","    \"\"\"Return a rref using the algo descrbed above\n","    Args:\n","        M: numpy matrix of shape (m, n).\n","    Returns:\n","        Ms: ref of M\n","    \"\"\"\n","    out = None\n","    ### YOUR CODE HERE\n","    pass\n","    ### END YOUR CODE\n","    return out"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Eiz6EbsWPYz8","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1651742125245,"user_tz":-330,"elapsed":743,"user":{"displayName":"Richa Sachan","userId":"00233403609540832757"}},"outputId":"fc295da0-0cc5-467e-d897-b84f0eb962b6"},"source":["# Let's define M.\n","M = np.array([[1,2,3],[4,5,6],[7,8,9]])\n","\n","# Now let's calculate it's rref.\n","# Note that your code may be evaluated on other test cases as well\n","#Mrref = rref(M)\n","print(swap_rows(M))\n","\n"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[7 8 9]\n"," [4 5 6]\n"," [1 2 3]]\n"]}]},{"cell_type":"markdown","metadata":{"id":"G46pyDzAE-Ef"},"source":["## Part 1.6 (10 points)\n","\n","To wrap up our overview of NumPy, let's implement something fun &mdash; a helper function for computing the Euclidean distance between two $n$-dimensional points!\n","\n","In the 2-dimensional case, computing the Euclidean distance reduces to solving the Pythagorean theorem $c = \\sqrt{a^2 + b^2}$. where, given two points $(x_1, y_1)$ and $(x_2, y_2)$, $a = x_1 - x_2$ and $b = y_1 - y_2$.\n","\n","\n","More generally, given two $n$-dimensional vectors, the Euclidean distance can be computed by:\n","\n","1. Performing an elementwise subtraction between the two vectors, to get $n$ difference values.\n","2. Squaring each of the $n$ difference values, and summing the squares.\n","4. Taking the square root of our sum.\n","\n","Alternatively, the Euclidean distance between length-$n$ vectors $u$ and $v$ can be written as:\n","\n","$\n","\\quad\\textbf{distance}(u, v) = \\sqrt{\\sum_{i=1}^n (u_i - v_i)^2}\n","$\n","\n","\n","Try implementing this function: first using native Python with a `for` loop in the `euclidean_distance_native()` function, then in NumPy **without any loops** in the `euclidean_distance_numpy()` function.\n","We've added some `assert`  statements here to help you check functionality (if it prints nothing, then your implementation is correct)!"]},{"cell_type":"code","metadata":{"id":"5xvHopPqO29C"},"source":["def euclidean_distance_native(u, v):\n","    \"\"\"Computes the Euclidean distance between two vectors, represented as Python\n","    lists.\n","    Args:\n","        u (List[float]): A vector, represented as a list of floats.\n","        v (List[float]): A vector, represented as a list of floats.\n","    Returns:\n","        float: Euclidean distance between `u` and `v`.\n","    \"\"\"\n","    # First, run some checks:\n","    assert isinstance(u, list)\n","    assert isinstance(v, list)\n","    assert len(u) == len(v)\n","\n","    # Compute the distance!\n","    # Notes:\n","    #  1) Try breaking this problem down: first, we want to get\n","    #     the difference between corresponding elements in our\n","    #     input arrays. Then, we want to square these differences.\n","    #     Finally, we want to sum the squares and square root the\n","    #     sum.\n","    diff =[]\n","    sum=0\n","    for i in range(len(u)):\n","      diff.append((u[i]-v[i])*(u[i]-v[i]))\n","\n","    for j in range(len(v)):\n","      sum +=diff[j]\n","\n","\n","    out = np.sqrt(sum)\n","    ### YOUR CODE HERE\n","    pass\n","    ### END YOUR CODE\n","    return out"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"wvLuK8MuO3LH"},"source":["def euclidean_distance_numpy(u, v):\n","    \"\"\"Computes the Euclidean distance between two vectors, represented as NumPy\n","    arrays.\n","    Args:\n","        u (np.ndarray): A vector, represented as a NumPy array.\n","        v (np.ndarray): A vector, represented as a NumPy array.\n","    Returns:\n","        float: Euclidean distance between `u` and `v`.\n","    \"\"\"\n","    # First, run some checks:\n","    assert isinstance(u, np.ndarray)\n","    assert isinstance(v, np.ndarray)\n","    assert u.shape == v.shape\n","\n","\n","    # Compute the distance!\n","    # Note:\n","    #  1) You shouldn't need any loops\n","    #  2) Some functions you can Google that might be useful:\n","    #         np.sqrt(), np.sum()\n","    #  3) Try breaking this problem down: first, we want to get\n","    #     the difference between corresponding elements in our\n","    #     input arrays. Then, we want to square these differences.\n","    #     Finally, we want to sum the squares and square root the\n","    #     sum.\n","    Difference= (u - v)**2\n","    sum = np.sum(Difference)\n","    out = np.sqrt(sum)\n","\n","    ### YOUR CODE HERE\n","    pass\n","    ### END YOUR CODE\n","    return out"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"wu9MimVJE-Eg"},"source":["## Testing native Python function\n","assert euclidean_distance_native([7.0], [6.0]) == 1.0\n","assert euclidean_distance_native([7.0, 0.0], [3.0, 3.0]) == 5.0\n","assert euclidean_distance_native([7.0, 0.0, 0.0], [3.0, 0.0, 3.0]) == 5.0"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"kJDk88g1E-Ej"},"source":["## Testing NumPy function\n","assert euclidean_distance_numpy(\n","    np.array([7.0]),\n","    np.array([6.0])\n",") == 1.0\n","assert euclidean_distance_numpy(\n","    np.array([7.0, 0.0]),\n","    np.array([3.0, 3.0])\n",") == 5.0\n","assert euclidean_distance_numpy(\n","    np.array([7.0, 0.0, 0.0]),\n","    np.array([3.0, 0.0, 3.0])\n",") == 5.0"],"execution_count":null,"outputs":[]},{"cell_type":"code","source":["n = 1000\n","\n","# Create some length-n lists and/or n-dimensional arrays\n","a = [0.0] * n\n","b = [10.0] * n\n","a_array = np.array(a)\n","b_array = np.array(b)\n","\n","# Compute runtime for native implementation\n","start_time = time.time()\n","for i in range(10000):\n","    euclidean_distance_native(a, b)\n","print(\"Native:\", (time.time() - start_time), \"seconds\")\n","\n","# Compute runtime for numpy implementation\n","# Start by grabbing the current time in seconds\n","start_time = time.time()\n","for i in range(10000):\n","    euclidean_distance_numpy(a_array, b_array)\n","print(\"NumPy:\", (time.time() - start_time), \"seconds\")"],"metadata":{"id":"E7Z38WwHhoNl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1651739282730,"user_tz":-330,"elapsed":3609,"user":{"displayName":"Richa Sachan","userId":"00233403609540832757"}},"outputId":"d689051f-0748-4e05-d015-caac44f5a42d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Native: 2.93746280670166 seconds\n","NumPy: 0.15026068687438965 seconds\n"]}]},{"cell_type":"markdown","metadata":{"id":"Mjik4mQXE-Ek"},"source":["Next, let's take a look at how these two implementations compare in terms of runtime:"]},{"cell_type":"markdown","metadata":{"id":"t4e6MfhHE-Em"},"source":["As you can see, doing vectorized calculations (i.e. no for loops) with NumPy results in significantly faster computations! "]},{"cell_type":"markdown","source":["Congrats You've come to the end of this notebook. If you solved everything above, impressive. If not, you might need to read/think a bit more. You can always ask doubts. Also, Note that you should submit it even if you cannot solve everything. We might evaluate these using a script later."],"metadata":{"id":"XvFE0Q5bhx6-"}}]}
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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rvFM645NE-D2"
+      },
+      "source": [
+        "# Assignment 2\n",
+        "In this assignment, we will go through Perceptron, Linear Classifiers, Loss Functions, Gradient Descent and Back Propagation.\n",
+        "\n",
+        "\n",
+        "PS. this one is not from Stanford's course.\n",
+        "\n",
+        "\n",
+        "\n",
+        "\\\n",
+        "\n",
+        "## Instructions\n",
+        "* This notebook contain blocks of code, you are required to complete those blocks(where required)\n",
+        "* You are required to copy this notebook (\"copy to drive\" above) and complete the code.(DO NOT CHANGE THE NAME OF THE FUNCTIONS)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "collapsed": true,
+        "id": "QLtp15rqE-EU"
+      },
+      "source": [
+        "# Part 1: Perceptron\n",
+        "In this section, we will see how to implement a perceptron. Goal would be for you to delve into the mathematics.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Zao4e-DphaGA"
+      },
+      "source": [
+        "## Intro\n",
+        "What's a perceptron? It's an algorithm modelled on biological computational model to classify things into binary classes. It's a supervides learning algorithm, meaning that you need to provide labelled data containing features and the actual classifications. A perceptron would take these features as input and spit out a binary value (0 or 1). While training the model with training data, we try to minimise the error and learn the parameters involved."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wDTUoAd6ixm-"
+      },
+      "source": [
+        "**How does it work?**\\\n",
+        "A perceptron is modelled on a biological neuron. A neuron has input dendrites and the output is carried by axons. Similarly, a perceptron takes inputs called \"features\". After processing, a perceptron gives output. For computation, it has a \"weight\" vector which is multipled with feature vector. An activation function is added to introduce some non linearities and the output is given out.\\\n",
+        "It can be represented as: $$  f=\\sum_{i=1}^{m} w_ix_i +b$$\n",
+        "\n",
+        "Let's implement this simple function to give an output.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "id": "iXezofBIgzId"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "class perceptron():\n",
+        "    def __init__(self,num_input_features=8):\n",
+        "        self.weights = np.random.randn(num_input_features)\n",
+        "        self.bias = np.random.random()\n",
+        "\n",
+        "    def activation(self,x):\n",
+        "      \n",
+        "        '''\n",
+        "            Implement heavside step activation function here (google ;))\n",
+        "        '''\n",
+        "        if x>0:\n",
+        "          return 1\n",
+        "        else:\n",
+        "          return 0\n",
+        "        pass\n",
+        "\n",
+        "    def forward(self,x: np.ndarray):\n",
+        "        '''\n",
+        "            you have random initialized weights and bias\n",
+        "            you can access then using `self.weights` and `self.bias`\n",
+        "            you should use activation function before returning\n",
+        "        \n",
+        "            x : input features\n",
+        "            return : a binary value as the output of the perceptron \n",
+        "        '''\n",
+        "        \n",
+        "\n",
+        "        # YOUR CODE HERE\n",
+        "        z=np.dot(self.weights,x)+self.bias\n",
+        "        output= self.activation(z)\n",
+        "        return output\n",
+        "        pass\n",
+        "        # YOUR CODE HERE"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "id": "oSKwDFAyocVo"
+      },
+      "outputs": [],
+      "source": [
+        "np.random.seed(0)\n",
+        "perc = perceptron(8)\n",
+        "assert perc.forward(np.arange(8))==1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "collapsed": true,
+        "id": "NWTTg1e9r7uM"
+      },
+      "source": [
+        "# Part 2: Linear Classifier\n",
+        "In this section, we will see how to implement a linear Classifier.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DYDO4GcHr7uM"
+      },
+      "source": [
+        "## Intro\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-HFvjH06r7uN"
+      },
+      "source": [
+        "**How does it work?**\n",
+        "\n",
+        "Linear Classifier uses the following function: $$Y = WX+b$$ Where, $W$ is a 2d array of weights with shape (#features, #classes).\n",
+        "\n",
+        "\n",
+        "Let's implement this classifier.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "id": "9A13CEkGr7uN"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "class LinearClassifier():\n",
+        "    def __init__(self,num_input_features=32,num_classes=5):\n",
+        "        self.weights = np.random.randn(num_classes, num_input_features)\n",
+        "        self.bias = np.random.rand(num_classes,1)\n",
+        "\n",
+        "    def forward(self,x: np.ndarray):\n",
+        "        '''\n",
+        "            x: input features\n",
+        "            you have random initialized weights and bias\n",
+        "            you can access then using `self.weights` and `self.bias`\n",
+        "            return an output vector of num_classes size\n",
+        "        '''\n",
+        "        # YOUR CODE HERE\n",
+        "        y=np.dot(self.weights,x)+self.bias\n",
+        "        print(y)\n",
+        "        pass\n",
+        "        # YOUR CODE HERE"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zgzPxyTsr7uN",
+        "outputId": "45db95b4-9960-4fc4-82ec-5fad1e1e9358"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[  7.07730669]\n",
+            " [-10.24067722]\n",
+            " [  0.75398702]\n",
+            " [  9.8019519 ]\n",
+            " [  2.36684038]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "np.random.seed(0)\n",
+        "lc = LinearClassifier()\n",
+        "lc.forward(np.random.rand(32,1))\n",
+        "# Should be close to:\n",
+        "# array([[  7.07730669],\n",
+        "    #    [-10.24067722],\n",
+        "    #    [  0.75398702],\n",
+        "    #    [  9.8019519 ],\n",
+        "    #    [  2.36684038]])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "collapsed": true,
+        "id": "ZVgOVzJetuqo"
+      },
+      "source": [
+        "# Part 3: Loss Functions, Gradient descent and Backpropagation\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4pXryjpctuqy"
+      },
+      "source": [
+        "## Intro\n",
+        "\n",
+        "Loss Functions tells how \"off\" the output od our model is. Based upon the application, you can use several different loss functions. Formally, A loss function is a function $L:(z,y)\\in\\mathbb{R}\\times Y\\longmapsto L(z,y)\\in\\mathbb{R}$ that takes as inputs the predicted value $z$ corresponding to the real data value yy and outputs how different they are We'll implement L1 loss, L2 loss, Logistic loss, hinge loss and cross entropy loss functions."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QGRb8BHotuqy"
+      },
+      "source": [
+        "### **L1 loss**\n",
+        "L1 loss is the linear loss function  $L = \\dfrac{1}{2}(y−z) $\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "id": "YxVh6IL2tuqz"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "def L1Loss(z,y):\n",
+        "    '''\n",
+        "        y : True output.\n",
+        "        z : Predicted output.\n",
+        "        return : L\n",
+        "    '''\n",
+        "    L=(1/2)*(y-z)\n",
+        "    return L\n",
+        "    pass"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2xy8ZS84cKtQ"
+      },
+      "source": [
+        "### **L2 loss**\n",
+        "L2 loss is the quadratic loss function or the least square error function  $L = \\dfrac{1}{2}(y−z)^2 $\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "id": "JThp5P-KcKtS"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "def L2Loss(z,y):\n",
+        "    '''\n",
+        "        y : True output. \n",
+        "        z : Predicted output. \n",
+        "        return : L\n",
+        "    '''\n",
+        "    L=(1/2)((y-z)*2)\n",
+        "    return L\n",
+        "    pass"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Z2JNLnWYcLSC"
+      },
+      "source": [
+        "### **Hinge Loss**\n",
+        "Hinge loss is: $ L = max( 0, 1 - yz ) $"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "id": "gQ1YM4J-cLSC"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "def hingeLoss(z,y):\n",
+        "    '''\n",
+        "        y : True output. \n",
+        "        z : Predicted output. \n",
+        "        return : L\n",
+        "    '''\n",
+        "   \n",
+        "    L=[max(0, 1-x*y) for x, y in zip(y, z)]\n",
+        "    return L\n",
+        "    pass"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "m15_MjradMNY"
+      },
+      "source": [
+        "### **Cross Entropy Loss**\n",
+        "Another very famous loss function is Cross Entropy loss: $ L = −[ylog(z)+(1−y)log(1−z)] $."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "id": "snJLqhszdMNY"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "def CELoss(z,y):\n",
+        "    '''\n",
+        "        y : True output. \n",
+        "        z : Predicted output. \n",
+        "        return : L\n",
+        "    '''\n",
+        "    L=-(y*np.log(z)+(1-y)*np.log(1-z))\n",
+        "    \n",
+        "    return L\n",
+        "\n",
+        "    pass\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OsRPsfzxyEVL"
+      },
+      "source": [
+        "### **0-1 Loss**\n",
+        "Loss Function used by perceptron is: $ \\begin{cases} \n",
+        "      0=z-y & z=y \\\\\n",
+        "      1=\\dfrac{z-y}{z-y} & z\\neq y\n",
+        "   \\end{cases} $."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "id": "5sA7GxLHyEVM"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "def zeroOneLoss(z,y):\n",
+        "    '''\n",
+        "        y : True output. \n",
+        "        z : Predicted output. \n",
+        "        return : L\n",
+        "    '''\n",
+        "    if np.array_equal(z, y):\n",
+        "      L=1\n",
+        "      return L\n",
+        "    else:\n",
+        "      L=0\n",
+        "      return L\n",
+        "    pass"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CWhbibHcgRR8"
+      },
+      "source": [
+        "## Cost Function\n",
+        "The cost function $J$ is commonly used to assess the performance of a model, and is defined with the loss function $L$ as follows:\n",
+        "$$\\boxed{J(\\theta)=\\sum_{i=1}^mL(h_\\theta(x^{(i)}), y^{(i)})}$$\n",
+        "where $h_\\theta$ is the hypothesis function i.e. the function used to predict the output."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "id": "SSbmhW4og97t"
+      },
+      "outputs": [],
+      "source": [
+        "lossFunctions = {\n",
+        "    \"l1\" : L1Loss,\n",
+        "    \"l2\" : L2Loss,\n",
+        "    \"hinge\" : hingeLoss,\n",
+        "    \"cross-entropy\" : CELoss,\n",
+        "    \"0-1\" : zeroOneLoss\n",
+        "}\n",
+        "\n",
+        "def cost(Z : np.ndarray, Y : np.ndarray, loss : str):\n",
+        "    '''\n",
+        "        Z : a numpy array of predictions.\n",
+        "        Y : a numpy array of true values.\n",
+        "        return : A numpy array of costs calculated for each example.\n",
+        "    '''\n",
+        "    loss_func = lossFunctions[loss]\n",
+        "    # YOUR CODE HERE\n",
+        "    J = loss_func(Z , Y)\n",
+        "    return J\n",
+        "    # YOUR CODE HERE\n",
+        "    pass\n",
+        "\n",
+        "#A=  np.array([1,2,3])\n",
+        "#B = np.array([4,1,6])\n",
+        "#cost(A,B,\"hinge\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "upsN7A0zjGqx"
+      },
+      "source": [
+        "## Gradient Descent and Back Propagation\n",
+        "Gradient Descent is an algorithm that minimizes the loss function by calculating it's gradient. By noting $\\alpha\\in\\mathbb{R}$ the learning rate, the update rule for gradient descent is expressed with the learning rate $\\alpha$ and the cost function $J$ as follows:\n",
+        "\n",
+        "$$\\boxed{ W \\longleftarrow W -\\alpha\\nabla J( W )}$$\n",
+        "​\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AFCN-fYCqidi"
+      },
+      "source": [
+        "But we need to find the partial derivative of Loss function wrt every parameter to know what is the slight change that we need to apply to our parameters. This becomes particularly hard if we have more than 1 layer in our algorithm. Here's where **Back Propagation** comes in. It's a way to find gradients wrt every parameter using the chain rule. Backpropagation is a method to update the weights in the neural network by taking into account the actual output and the desired output. The derivative with respect to weight ww is computed using chain rule and is of the following form:\n",
+        "\n",
+        "$$\\boxed{\\frac{\\partial L(z,y)}{\\partial w}=\\frac{\\partial L(z,y)}{\\partial a}\\times\\frac{\\partial a}{\\partial z}\\times\\frac{\\partial z}{\\partial w}}$$\n",
+        "​\n",
+        " \n",
+        "As a result, the weight is updated as follows:\n",
+        "\n",
+        "$$\\boxed{w\\longleftarrow w-\\alpha\\frac{\\partial L(z,y)}{\\partial w}}$$\n",
+        "\n",
+        "So, In a neural network, weights are updated as follows:\n",
+        "\n",
+        "* Step 1: Take a batch of training data.\n",
+        "* Step 2: Perform forward propagation to obtain the corresponding loss.\n",
+        "* Step 3: Backpropagate the loss to get the gradients.\n",
+        "* Step 4: Use the gradients to update the weights of the network.\n",
+        "​\n",
+        "\n",
+        "Bonus Problem\n",
+        " \n",
+        "Now, Assuming that you know Back Propagation (read a bit about it, if you don't), we'll now implement an image classification model on CIFAR-10."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sJoG5kkYopRN"
+      },
+      "source": [
+        "# **Bonus Problem**\n",
+        "\n",
+        "Now, Assuming that you know Back Propagation (read a bit about it, if you don't), we'll now implement an image classification model on CIFAR-10."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_4-4RceVsor_",
+        "outputId": "a0bb6159-1938-412d-8cb6-817ac6e8c76c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "2.8.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "import tensorflow as tf  \n",
+        " \n",
+        "# Display the version\n",
+        "print(tf.__version__)    \n",
+        " \n",
+        "# other imports\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from tensorflow.keras.layers import Input, Conv2D, Dense, Flatten, Dropout\n",
+        "from tensorflow.keras.layers import GlobalMaxPooling2D, MaxPooling2D\n",
+        "from tensorflow.keras.layers import BatchNormalization\n",
+        "from tensorflow.keras.models import Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yyplk5PLEUsJ",
+        "outputId": "8b4bad03-b2f0-4ae9-b5cf-5146efa31fa6"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
+            "170500096/170498071 [==============================] - 6s 0us/step\n",
+            "170508288/170498071 [==============================] - 6s 0us/step\n",
+            "(50000, 32, 32, 3) (50000, 1) (10000, 32, 32, 3) (10000, 1)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Load in the data\n",
+        "cifar10 = tf.keras.datasets.cifar10\n",
+        " \n",
+        "# Distribute it to train and test set\n",
+        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
+        "print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)\n",
+        "\n",
+        "# Reduce pixel values\n",
+        "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
+        " \n",
+        "# flatten the label values\n",
+        "y_train, y_test = y_train.flatten(), y_test.flatten()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 269
+        },
+        "id": "qQhkATYhEkkC",
+        "outputId": "c2b0c62a-bd09-414e-e5fb-ec2639ea1451"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 100 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ],
+      "source": [
+        "from os import ctermid\n",
+        "'''visualize data by plotting images'''\n",
+        "# YOUR CODE HERE\n",
+        "fig, ax = plt.subplots(10, 10)\n",
+        "ctr = 0\n",
+        " \n",
+        "for i in range(10):\n",
+        "    for j in range(10):\n",
+        "        ax[i][j].imshow(x_train[ctr], aspect='auto')\n",
+        "        ctr += 1\n",
+        " \n",
+        "plt.show()\n",
+        "pass\n",
+        "# YOUR CODE HERE"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yJgho2AEBFbx",
+        "outputId": "b003e50c-58af-4c05-8ead-52c3d8501531"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "number of classes: 10\n",
+            "Model: \"model\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " input_1 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
+            "                                                                 \n",
+            " conv2d (Conv2D)             (None, 32, 32, 32)        896       \n",
+            "                                                                 \n",
+            " batch_normalization (BatchN  (None, 32, 32, 32)       128       \n",
+            " ormalization)                                                   \n",
+            "                                                                 \n",
+            " conv2d_1 (Conv2D)           (None, 32, 32, 32)        9248      \n",
+            "                                                                 \n",
+            " batch_normalization_1 (Batc  (None, 32, 32, 32)       128       \n",
+            " hNormalization)                                                 \n",
+            "                                                                 \n",
+            " max_pooling2d (MaxPooling2D  (None, 16, 16, 32)       0         \n",
+            " )                                                               \n",
+            "                                                                 \n",
+            " conv2d_2 (Conv2D)           (None, 16, 16, 64)        18496     \n",
+            "                                                                 \n",
+            " batch_normalization_2 (Batc  (None, 16, 16, 64)       256       \n",
+            " hNormalization)                                                 \n",
+            "                                                                 \n",
+            " conv2d_3 (Conv2D)           (None, 16, 16, 64)        36928     \n",
+            "                                                                 \n",
+            " batch_normalization_3 (Batc  (None, 16, 16, 64)       256       \n",
+            " hNormalization)                                                 \n",
+            "                                                                 \n",
+            " max_pooling2d_1 (MaxPooling  (None, 8, 8, 64)         0         \n",
+            " 2D)                                                             \n",
+            "                                                                 \n",
+            " conv2d_4 (Conv2D)           (None, 8, 8, 128)         73856     \n",
+            "                                                                 \n",
+            " batch_normalization_4 (Batc  (None, 8, 8, 128)        512       \n",
+            " hNormalization)                                                 \n",
+            "                                                                 \n",
+            " conv2d_5 (Conv2D)           (None, 8, 8, 128)         147584    \n",
+            "                                                                 \n",
+            " batch_normalization_5 (Batc  (None, 8, 8, 128)        512       \n",
+            " hNormalization)                                                 \n",
+            "                                                                 \n",
+            " max_pooling2d_2 (MaxPooling  (None, 4, 4, 128)        0         \n",
+            " 2D)                                                             \n",
+            "                                                                 \n",
+            " flatten (Flatten)           (None, 2048)              0         \n",
+            "                                                                 \n",
+            " dropout (Dropout)           (None, 2048)              0         \n",
+            "                                                                 \n",
+            " dense (Dense)               (None, 1024)              2098176   \n",
+            "                                                                 \n",
+            " dropout_1 (Dropout)         (None, 1024)              0         \n",
+            "                                                                 \n",
+            " dense_1 (Dense)             (None, 10)                10250     \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 2,397,226\n",
+            "Trainable params: 2,396,330\n",
+            "Non-trainable params: 896\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ],
+      "source": [
+        "\n",
+        "# number of classes\n",
+        "K = len(set(y_train))\n",
+        "'''\n",
+        " calculate total number of classes\n",
+        " for output layer\n",
+        "'''\n",
+        "\n",
+        "print(\"number of classes:\", K)\n",
+        "''' \n",
+        " Build the model using the functional API\n",
+        " input layer\n",
+        "'''\n",
+        "\n",
+        "\n",
+        "i = Input(shape=x_train[0].shape)\n",
+        "x = Conv2D(32, (3, 3), activation='relu', padding='same')(i)\n",
+        "x = BatchNormalization()(x)\n",
+        "x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\n",
+        "x = BatchNormalization()(x)\n",
+        "x = MaxPooling2D((2, 2))(x)\n",
+        " \n",
+        "x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
+        "x = BatchNormalization()(x)\n",
+        "x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
+        "x = BatchNormalization()(x)\n",
+        "x = MaxPooling2D((2, 2))(x)\n",
+        "\n",
+        " \n",
+        "x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)\n",
+        "x = BatchNormalization()(x)\n",
+        "x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)\n",
+        "x = BatchNormalization()(x)\n",
+        "x = MaxPooling2D((2, 2))(x)\n",
+        " \n",
+        "x = Flatten()(x)\n",
+        "x = Dropout(0.2)(x)\n",
+        " \n",
+        "'''Hidden layer'''\n",
+        "# YOUR CODE HERE\n",
+        "x = Dense(1024, activation='relu')(x)\n",
+        "x = Dropout(0.2)(x)\n",
+        "pass\n",
+        "# YOUR CODE HERE\n",
+        " \n",
+        "\"\"\"last hidden layer i.e.. output layer\"\"\"\n",
+        "# YOUR CODE HERE\n",
+        "x = Dense(K, activation='softmax')(x)\n",
+        " \n",
+        "model = Model(i, x)\n",
+        "pass\n",
+        "# YOUR CODE HERE\n",
+        " \n",
+        "\"\"\"model description\"\"\"\n",
+        "model.summary()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {
+        "id": "PLc4Bay65TyA"
+      },
+      "outputs": [],
+      "source": [
+        "# Compile\n",
+        "model.compile(optimizer='adam',\n",
+        "              loss='sparse_categorical_crossentropy',\n",
+        "              metrics=['accuracy'])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "U0fGsDCRsQrn",
+        "outputId": "f53f93e3-522d-4cb4-c4e4-3617ef5c2146"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1563/1563 [==============================] - 482s 308ms/step - loss: 0.3144 - accuracy: 0.8924 - val_loss: 0.6058 - val_accuracy: 0.8085\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Fit\n",
+        "r = model.fit(\n",
+        "  x_train, y_train, validation_data=(x_test, y_test), epochs=1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 52,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 283
+        },
+        "id": "RDq_RE6osSh8",
+        "outputId": "c2510b35-b9fd-4974-91f6-99b170dc940b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Original label is cat and predicted label is cat\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ],
+      "source": [
+        "# label mapping\n",
+        " \n",
+        "labels = '''airplane automobile bird cat deerdog frog horseship truck'''.split()\n",
+        " \n",
+        "# select the image from our test dataset\n",
+        "image_number = 0\n",
+        " \n",
+        "# display the image\n",
+        "plt.imshow(x_test[image_number])\n",
+        " \n",
+        "# load the image in an array\n",
+        "n = np.array(x_test[image_number])\n",
+        " \n",
+        "# reshape it\n",
+        "p = n.reshape(1, 32, 32, 3)\n",
+        " \n",
+        "# pass in the network for prediction and\n",
+        "# save the predicted label\n",
+        "predicted_label = labels[model.predict(p).argmax()]\n",
+        " \n",
+        "# load the original label\n",
+        "original_label = labels[y_test[image_number]]\n",
+        " \n",
+        "# display the result\n",
+        "print(\"Original label is {} and predicted label is {}\".format(\n",
+        "    original_label, predicted_label))"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "name": "200782_Richa_Sachan_Assignment_2.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/Assignment/Assignment_3/200782_Richa_Sachan_Assignment_3.ipynb b/Assignment/Assignment_3/200782_Richa_Sachan_Assignment_3.ipynb
new file mode 100644
index 0000000..263c1d3
--- /dev/null
+++ b/Assignment/Assignment_3/200782_Richa_Sachan_Assignment_3.ipynb
@@ -0,0 +1,608 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "200782_Richa_Sachan_Assignment_3.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Task : Build a recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow"
+      ],
+      "metadata": {
+        "id": "IKyVtWdFwn2u"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##**Importing Pre-requisite python models**"
+      ],
+      "metadata": {
+        "id": "0jrR6T7VvCWD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Va6XNK6A9ccp"
+      },
+      "outputs": [],
+      "source": [
+        "from keras.models import Sequential\n",
+        "from keras.layers import LSTM, Dense\n",
+        "from keras.datasets import mnist\n",
+        "from keras.utils import np_utils\n",
+        "from keras import initializers\n",
+        "\n",
+        "def init_weights(shape, name=None):\n",
+        "    return initializers.normal(shape, scale=0.01, name=name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##**Declaring hyper parameters , and Parameters for LSTM network**"
+      ],
+      "metadata": {
+        "id": "cHgf7DLovO6G"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Hyper parameters\n",
+        "batch_size = 128    # represent how many number of images you want to feed into LSTM cells \n",
+        "nb_epoch = 20\n",
+        "\n",
+        "# Parameters for MNIST dataset\n",
+        "img_rows, img_cols = 28, 28     #1 image is divided into 28 * 28 pixel values where each pixel has a range of 255 \n",
+        "nb_classes = 10\n",
+        "\n",
+        "# Parameters for LSTM network\n",
+        "nb_lstm_outputs = 30\n",
+        "nb_time_steps = img_rows      # time- steps are taken as 28 that means the LSTM network will run 28 times per image where in each step it will take input of 1st row of 1st image \n",
+        "dim_input_vector = img_cols"
+      ],
+      "metadata": {
+        "id": "_6FTQWxX-RD0"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### **The MNIST data is split into training sets , testing sets, validation sets**\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "3O7P1pNLOZfz"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# here I have used test_train_split to further split the (X_train, y_train) to  (X_train, X_val, y_train, y_val)\n",
+        "\n",
+        "from sklearn.model_selection import train_test_split \n",
+        "\n",
+        "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
+        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2)\n",
+        "\n",
+        "\n",
+        "input_shape = (nb_time_steps, dim_input_vector)\n",
+        "\n",
+        "#preprocessed the X_train , X_test and changed the shape of y_train, y_test, y_evaluate\n",
+        "\n",
+        "# we can normalize data before hands such that large terms of the calculations can be reduced to smaller terms. \n",
+        "# Like, we can normalize the x_train and x_test data by dividing it by 255.\n",
+        "\n",
+        "X_train = X_train.astype('float32') / 255.\n",
+        "X_test = X_test.astype('float32') / 255.\n",
+        "X_val = X_val.astype('float32') / 255.\n",
+        "\n",
+        "# Since the output of the model can comprise any of the digits between 0 to 9.\n",
+        "# so, we need 10 classes in output. To make output for 10 classes, use keras.utils.to_categorical function,\n",
+        "# which will provide the 10 columns. Out of these 10 columns,\n",
+        "# only one value will be one and the rest 9 will be zero and this one value of the output will denote the class of the digit.\n",
+        "\n",
+        "Y_train = np_utils.to_categorical(y_train, nb_classes)\n",
+        "Y_test = np_utils.to_categorical(y_test, nb_classes)\n",
+        "Y_val = np_utils.to_categorical(y_val, nb_classes)\n",
+        "\n",
+        "#printing X_train,Y_train, X_test, Y_test, X_val, Y_val shapes.\n",
+        "# note here that the y_test shape is scalar (10000,) while Y_test shape is 2D (10000, 10)-----> due to above code.\n",
+        "\n",
+        "print('X_train shape:', X_train.shape)\n",
+        "print('Y_train shape:', Y_train.shape)\n",
+        "print('X_test shape:', X_test.shape)\n",
+        "print('Y_test shape:', Y_test.shape)\n",
+        "print('X_val shape:', X_val.shape)\n",
+        "print('Y_val shape:', Y_val.shape)\n",
+        "\n",
+        "# summarizing train, test , validation samples.\n",
+        "print(X_train.shape[0], 'train samples')\n",
+        "print(X_test.shape[0], 'test samples')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LCHFYwrU-Rwv",
+        "outputId": "b549031e-ec8c-4893-cb03-abbb898ec847"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
+            "11493376/11490434 [==============================] - 0s 0us/step\n",
+            "11501568/11490434 [==============================] - 0s 0us/step\n",
+            "X_train shape: (48000, 28, 28)\n",
+            "Y_train shape: (48000, 10)\n",
+            "X_test shape: (10000, 28, 28)\n",
+            "Y_test shape: (10000, 10)\n",
+            "X_val shape: (12000, 28, 28)\n",
+            "Y_val shape: (12000, 10)\n",
+            "48000 train samples\n",
+            "10000 test samples\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##**Visualizing the MNIST data**"
+      ],
+      "metadata": {
+        "id": "yxVPRQ_LVzVw"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "####**Plotting  the images along with the label as numerical values if we use y_train.** "
+      ],
+      "metadata": {
+        "id": "ivokUMWTZMFV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(15,4.5))\n",
+        "for i in range(9):  \n",
+        "    plt.subplot(3, 3, i+1)\n",
+        "    plt.title('Labeled in numerical_val as {label}'.format(label=y_train[i]))\n",
+        "    plt.imshow(X_train[i].reshape((28,28)),cmap=plt.cm.binary)\n",
+        "    plt.axis('off')\n",
+        "plt.subplots_adjust(wspace=-0.1, hspace=1)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 292
+        },
+        "id": "29RwYGjcV7b1",
+        "outputId": "ba2217a2-a6bc-4773-b6ff-a75c9d63cd0d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1080x324 with 9 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "####**Plotting  the images along with the label as its one hot encoded form which was stored in Y_train as shown below** \n"
+      ],
+      "metadata": {
+        "id": "gp2_EwnCZxa2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(15,4.5))\n",
+        "for i in range(9):  \n",
+        "    plt.subplot(3, 3, i+1)\n",
+        "    plt.title('one hot encoded {label}'.format(label=Y_train[i])) # here is the change :)\n",
+        "    plt.imshow(X_train[i].reshape((28,28)),cmap=plt.cm.binary)\n",
+        "    plt.axis('off')\n",
+        "plt.subplots_adjust(wspace=0, hspace=1)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 292
+        },
+        "id": "tBs4CmkwaFue",
+        "outputId": "6a149846-c52d-4e22-8cfd-4a4f88a36f10"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1080x324 with 9 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##**Build LSTM network through sequential model**"
+      ],
+      "metadata": {
+        "id": "Zpgzd8knFVZp"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Build LSTM network\n",
+        "model = Sequential()\n",
+        "model.add(LSTM(nb_lstm_outputs, input_shape=input_shape))\n",
+        "model.add(Dense(nb_classes, activation='softmax'))\n",
+        "model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])\n",
+        "model.summary()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UihM2o8S-SiN",
+        "outputId": "4234e652-45bb-4c31-cefc-81e043315b6b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"sequential_3\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " lstm_3 (LSTM)               (None, 30)                7080      \n",
+            "                                                                 \n",
+            " dense_3 (Dense)             (None, 10)                310       \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 7,390\n",
+            "Trainable params: 7,390\n",
+            "Non-trainable params: 0\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##**Acquiring model history**\n",
+        "\n",
+        "The history object is returned from calls to the fit() function used to train the model. Metrics are stored in a dictionary in the history member of the object returned.\n",
+        "\n",
+        "Here this includes the loss and the accuracy (for classification problems)"
+      ],
+      "metadata": {
+        "id": "SDFJeti_ASRr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "history = model.fit(X_train, Y_train, epochs=nb_epoch, validation_data = (X_val, Y_val))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "v_AvBz8B-Tmh",
+        "outputId": "229a8444-c84a-4e3a-cd80-aa6d72ba8c9a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/20\n",
+            "1500/1500 [==============================] - 20s 12ms/step - loss: 0.7763 - accuracy: 0.7602 - val_loss: 0.3418 - val_accuracy: 0.8986\n",
+            "Epoch 2/20\n",
+            "1500/1500 [==============================] - 17s 11ms/step - loss: 0.2776 - accuracy: 0.9171 - val_loss: 0.2068 - val_accuracy: 0.9417\n",
+            "Epoch 3/20\n",
+            "1500/1500 [==============================] - 17s 11ms/step - loss: 0.1836 - accuracy: 0.9459 - val_loss: 0.1831 - val_accuracy: 0.9462\n",
+            "Epoch 4/20\n",
+            "1500/1500 [==============================] - 17s 11ms/step - loss: 0.1444 - accuracy: 0.9579 - val_loss: 0.1398 - val_accuracy: 0.9588\n",
+            "Epoch 5/20\n",
+            "1500/1500 [==============================] - 17s 11ms/step - loss: 0.1225 - accuracy: 0.9643 - val_loss: 0.1196 - val_accuracy: 0.9655\n",
+            "Epoch 6/20\n",
+            "1500/1500 [==============================] - 17s 11ms/step - loss: 0.1061 - accuracy: 0.9684 - val_loss: 0.1083 - val_accuracy: 0.9693\n",
+            "Epoch 7/20\n",
+            "1500/1500 [==============================] - 17s 11ms/step - loss: 0.0949 - accuracy: 0.9720 - val_loss: 0.1119 - val_accuracy: 0.9684\n",
+            "Epoch 8/20\n",
+            "1500/1500 [==============================] - 17s 11ms/step - loss: 0.0863 - accuracy: 0.9741 - val_loss: 0.1040 - val_accuracy: 0.9697\n",
+            "Epoch 9/20\n",
+            "1500/1500 [==============================] - 17s 11ms/step - loss: 0.0773 - accuracy: 0.9781 - val_loss: 0.0938 - val_accuracy: 0.9731\n",
+            "Epoch 10/20\n",
+            "1500/1500 [==============================] - 17s 11ms/step - loss: 0.0724 - accuracy: 0.9791 - val_loss: 0.0962 - val_accuracy: 0.9705\n",
+            "Epoch 11/20\n",
+            "1500/1500 [==============================] - 17s 11ms/step - loss: 0.0657 - accuracy: 0.9808 - val_loss: 0.0910 - val_accuracy: 0.9747\n",
+            "Epoch 12/20\n",
+            "1500/1500 [==============================] - 17s 11ms/step - loss: 0.0628 - accuracy: 0.9816 - val_loss: 0.0825 - val_accuracy: 0.9772\n",
+            "Epoch 13/20\n",
+            "1500/1500 [==============================] - 17s 11ms/step - loss: 0.0587 - accuracy: 0.9827 - val_loss: 0.0891 - val_accuracy: 0.9733\n",
+            "Epoch 14/20\n",
+            "1500/1500 [==============================] - 18s 12ms/step - loss: 0.0552 - accuracy: 0.9838 - val_loss: 0.0836 - val_accuracy: 0.9753\n",
+            "Epoch 15/20\n",
+            "1500/1500 [==============================] - 17s 11ms/step - loss: 0.0522 - accuracy: 0.9842 - val_loss: 0.0845 - val_accuracy: 0.9774\n",
+            "Epoch 16/20\n",
+            "1500/1500 [==============================] - 17s 11ms/step - loss: 0.0498 - accuracy: 0.9856 - val_loss: 0.0847 - val_accuracy: 0.9762\n",
+            "Epoch 17/20\n",
+            "1500/1500 [==============================] - 17s 11ms/step - loss: 0.0480 - accuracy: 0.9857 - val_loss: 0.0841 - val_accuracy: 0.9765\n",
+            "Epoch 18/20\n",
+            "1500/1500 [==============================] - 17s 11ms/step - loss: 0.0455 - accuracy: 0.9867 - val_loss: 0.0799 - val_accuracy: 0.9771\n",
+            "Epoch 19/20\n",
+            "1500/1500 [==============================] - 17s 12ms/step - loss: 0.0433 - accuracy: 0.9874 - val_loss: 0.0759 - val_accuracy: 0.9801\n",
+            "Epoch 20/20\n",
+            "1500/1500 [==============================] - 19s 12ms/step - loss: 0.0423 - accuracy: 0.9872 - val_loss: 0.0795 - val_accuracy: 0.9773\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##**Evaluating Loss , Accuracy**"
+      ],
+      "metadata": {
+        "id": "W1KbFuZ6AIgb"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "evaluation = model.evaluate(X_test, Y_test, batch_size=batch_size, verbose=1)\n",
+        "print('Summary: Loss over the test dataset: %.2f, Accuracy: %.2f' % (evaluation[0], evaluation[1]))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qZXlvrl1-T96",
+        "outputId": "ed66997c-d84c-4ceb-a8c9-49d9f511ca3f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "79/79 [==============================] - 1s 9ms/step - loss: 0.0753 - accuracy: 0.9794\n",
+            "Summary: Loss over the test dataset: 0.08, Accuracy: 0.98\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##**Visualizing and Plotting Loss**"
+      ],
+      "metadata": {
+        "id": "mKfk0EWS__eT"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "print(history.history.keys())\n",
+        "# summarize history for accuracy\n",
+        "plt.figure(figsize=(6, 4))                   # making the graph scale big \n",
+        "plt.plot(history.history['loss'])             # plotting loss key of history\n",
+        "plt.plot(history.history['val_loss'])         # ploting validation loss \n",
+        "plt.title('Training loss VS validation loss')\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('Loss')\n",
+        "plt.legend(['train_loss', 'val_loss'], loc='upper right')\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 312
+        },
+        "id": "NJs7Opk3DLxP",
+        "outputId": "98f37f3c-9ab6-4627-8379-409786245950"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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NTdx9993MmjWrV7ttaGjg+uuvZ8mSJQQCAe655x7OOOMMVqxYwdVXX01jYyORSIQ//elPFBUVcdFFF1FeXk5LSwv//d//zcUXX9ynt91TiexHMBVYq6rrAUTkMWAWsLKT8pcC305gPIDTNATWqcwY0+biiy/mtttua00E8+fP5/nnn+eWW24hJyeHXbt2MW3aNGbOnImI9Hi78+bNQ0RYtmwZq1at4qyzzmL16tXcf//93HrrrVx++eU0NjbS0tLCwoULKSoq4tlnnwWcm90lSyITQTGwOWa6HDgpXkERORwoBV7uZPm1wLUAo0aN6lNQWekBcsNBu4TUmIGqi1/uiXLcccexc+dOtm7dSkVFBfn5+YwYMYIvfelLvP766/h8PrZs2cKOHTsYMWJEj7f7xhtvcPPNNwMwYcIEDj/8cFavXs3JJ5/M9773PcrLyzn//PMZO3YskydP5vbbb+drX/sa55xzDh/72McS9XYPMFBOFl8CPKmqLfEWquoDqlqmqmWFhYV93lmxXUJqjOngwgsv5Mknn+Txxx/n4osv5tFHH6WiooJ3332XpUuXMnz48LjPITgYl112GQsWLCAcDjNjxgxefvllxo0bx7/+9S8mT57Mt771LebOndsv++qJRNYItgAjY6ZL3HnxXALcmMBY2inKC1O+x04WG2PaXHzxxXzhC19g165dvPbaa8yfP59hw4YRDAZ55ZVX2LhxY6+3+bGPfYxHH32UT3ziE6xevZpNmzYxfvx41q9fz5gxY7jlllvYtGkT77//PhMmTGDIkCFcccUV5OXl8etf/zoB7zK+RCaCxcBYESnFSQCXAJd1LCQiE4B84K0ExtJOSX6YRev758k+xpjB4eijj2bfvn0UFxdz2GGHcfnll3PuuecyefJkysrKmDBhQq+3ecMNN3D99dczefJkAoEADz30EOnp6cyfP5+HH36YYDDIiBEj+MY3vsHixYu544478Pl8BINB7rvvvgS8y/gS+jwCEZkB/AzwAw+q6vdEZC6wRFUXuGXuAkKqesDlpfH05XkEUQ+8vo7/WbiK9+86i5xQsE/bMsb0nT2PoH/19nkECb37qKouBBZ2mHdnh+m7EhlDPMV5GYBz5VDOYZYIjDHe5qnbUEcV5YUA53bURx2Wk+JojDGHomXLlnHllVe2m5eens6iRYtSFNHB82QiaOtdbFcOGTNQqGqvrtFPtcmTJ7N06dJUh3GAg2nuHyiXjybV0Mx00vw+SwTGDBChUIjKysqD+hIzbVSVyspKQqFQr9bzZI3A5xOK8kLWu9iYAaKkpITy8nIqKipSHcohLxQKUVJS0qt1PJkIwOlLYL2LjRkYgsEgpaWlqQ7DszzZNATWu9gYY6K8mwjyw+zct5/G5kiqQzHGmJTybCIoygujCtur+ufeIcYYc6jybCIocW9HXW4PqDHGeJxnE0H0uQRb91qNwBjjbZ5NBIe5vYvtElJjjNd5NhGkB/wUZqfbJaTGGM/zbCIAu4TUGGPAEoHVCIwxnuftRJDv1Ajs/ibGGC/zdCIoyg2xvzlCZW1jqkMxxpiU8XQiKM5ve0CNMcZ4lacTQewDaowxxqsSmghEZLqIfCgia0Uk7jOJReQiEVkpIitE5A+JjKejkugjKy0RGGM8LGG3oRYRPzAP+BRQDiwWkQWqujKmzFjg68ApqrpHRIYlKp54csIBMtP8lgiMMZ6WyBrBVGCtqq5X1UbgMWBWhzJfAOap6h4AVd2ZwHgOICLOlUN2jsAY42GJTATFwOaY6XJ3XqxxwDgReVNE3haR6fE2JCLXisgSEVnS308wKsoLs7XKEoExxrtSfbI4AIwFTgcuBX4lInkdC6nqA6papqplhYWF/RpAcZ7VCIwx3pbIRLAFGBkzXeLOi1UOLFDVJlX9CFiNkxiSpjg/zJ66Juoam5O5W2OMGTASmQgWA2NFpFRE0oBLgAUdyvwZpzaAiAzFaSpan8CYDlDcejtqqxUYY7wpYYlAVZuBm4DngQ+A+aq6QkTmishMt9jzQKWIrAReAe5Q1cpExRRPNBGUW/OQMcajEnb5KICqLgQWdph3Z8y4Al92h5SwB9QYY7wu1SeLU254Tgi/T9hij6w0xniU5xOB3yeMyAlZjcAY41meTwSAdSozxniaJQLsSWXGGG+zRICTCLZXN9DcEkl1KMYYk3SWCHCuHGqJKDv37U91KMYYk3SWCHDOEYDdjtoY402WCIBie0CNMcbDLBHQ1qnMehcbY7zIEgGQkRYgPyNoNQJjjCdZInAV59slpMYYb7JE4CrKDVuNwBjjSZYIXNHexc598IwxxjssEbiK88LUNrZQXW8PqDHGeIslAlfrcwnsLqTGGI+xROCKdiqzu5AaY7zGEoEr2pdgyx6rERhjvMUSgasgM430gM8uITXGeE5CE4GITBeRD0VkrYjMibN8tohUiMhSd/h8IuPpiohQnBe2piFjjOck7JnFIuIH5gGfAsqBxSKyQFVXdij6uKrelKg4eqM4P0y51QiMMR6TyBrBVGCtqq5X1UbgMWBWAvfXZ9apzBjjRYlMBMXA5pjpcndeRxeIyPsi8qSIjIy3IRG5VkSWiMiSioqKRMQKODWCin37aWhqSdg+jDFmoEn1yeK/AKNV9Rjg78Dv4hVS1QdUtUxVywoLCxMWTPTKoe1Vdp7AGOMdiUwEW4DYX/gl7rxWqlqpqtHHgv0aOCGB8XQr2qnMrhwyxnhJIhPBYmCsiJSKSBpwCbAgtoCIHBYzORP4IIHxdMsSgTHGixJ21ZCqNovITcDzgB94UFVXiMhcYImqLgBuEZGZQDOwG5idqHh6YkRuCBHYYg+oMcZ4SMISAYCqLgQWdph3Z8z414GvJzKG3kgL+BiWnW5XDhljPCXVJ4sHnOI8e0CNMcZbLBF0UJRnfQmMMd5iiaCD4nznNhORiD2gxhjjDZYIOijOC9PYEmFX7f7uCxtjzCBgiaCD1ktI7cohY4xHWCLowB5QY4zxGksEHbQ+oMYeWWmM8QhLBB3khIJkhwLWNGSM8QxLBHE4fQmsacgY4w2WCOKwTmXGGC/pUSIQkUwR8bnj40RkpogEExta6linMmOMl/S0RvA6EBKRYuAF4ErgoUQFlWrF+WGq6puo2d+c6lCMMSbhepoIRFXrgPOBX6rqhcDRiQsrtaJXDlmtwBjjBT1OBCJyMnA58Kw7z5+YkFLPOpUZY7ykp4ngNpzbRT/tPlNgDPBK4sJKLXtAjTHGS3r0PAJVfQ14DcA9abxLVW9JZGCpNCw7naBfLBEYYzyhp1cN/UFEckQkE1gOrBSROxIbWur4fMKI3JCdIzDGeEJPm4Ymqmo1cB7wHFCKc+VQl0Rkuoh8KCJrRWROF+UuEBEVkbIexpNwxXlhO0dgjPGEniaCoNtv4Dxggao2AV3esF9E/MA84GxgInCpiEyMUy4buBVY1JvAE836EhhjvKKnieD/gA1AJvC6iBwOVHezzlRgraquV9VG4DFgVpxy3wX+FxhQ93QoyQuzvbqBppZIqkMxxpiE6lEiUNV7VbVYVWeoYyNwRjerFQObY6bL3XmtROR4YKSqPssAU5QXJqKwo3pA5SdjjOl3PT1ZnCsi94jIEnf4CU7t4KC5Vx/dA9zeg7LXRvddUVHRl932WPS5BHaewBgz2PW0aehBYB9wkTtUA7/tZp0twMiY6RJ3XlQ2MAl4VUQ2ANOABfFOGKvqA6papqplhYWFPQy5b1p7F1dZIjDGDG496kcAHKGqF8RMf0dElnazzmJgrIiU4iSAS4DLogtVtQoYGp0WkVeBr6jqkh7GlFDWu9gY4xU9rRHUi8ip0QkROQXo8htSVZuBm4DngQ+A+W6v5LkiMvNgA06WUNDP0Kw061RmjBn0elojuA74vYjkutN7gKu6W0lVFwILO8y7s5Oyp/cwlqQpsgfUGGM8oKdXDb2nqscCxwDHqOpxwCcSGtkA4HQqs2cXG2MGt149oUxVq90exgBfTkA8A4rTqawB1S77zhljzCGtL4+qlH6LYoAqzgtT39TCnrqmVIdijDEJ05dEMOh/JtsDaowxXtDlyWIR2Uf8L3wBwgmJaAApcTuVle+pZ1JxbjeljTHm0NRlIlDV7GQFMhBZjcAY4wV9aRoa9PIzgoSDfutLYIwZ1CwRdEFEKMqzB9QYYwY3SwTdKM7PsBqBMWZQ804i2PYevHx3r1crthqBMWaQ804i2LQIXv8RbF7cq9WK88LsqmmkoaklQYEZY0xqeScRTLkM0nPh7V/2ajW7csgYM9h5JxGkZ8EJn4WVz8Dezd2Xd7XejtoSgTFmkPJOIgCY+kXn9Z0HeryK1QiMMYOdtxJB3kiYOBPe/R3sr+nRKiNyQ/jEHlBjjBm8vJUIAKbdAPurYOkfelQ86PcxPCdEudUIjDGDlPcSwcipUFwGi+6DSKRHqxTnha1pyBgzaHkvEQCcfAPsXg9rnu9R8eL8sJ0sNsYMWglNBCIyXUQ+FJG1IjInzvLrRGSZiCwVkTdEZGIi42l11CzIKYG35vWoeFFemO1VDbREBv2dt40xHpSwRCAifmAecDYwEbg0zhf9H1R1sqpOAX4I3JOoeNrxB2DqF2DDP2Db+90WL84L09SiVOzbn4TgjDEmuRJZI5gKrFXV9araCDwGzIotEPPYS4BMkvmwmxOugmAGvH1ft0WtL4ExZjBLZCIoBmJ7bpW789oRkRtFZB1OjeCWBMbTXjgfplwOy5+EfTu6LFqcb4nAGDN4pfxksarOU9UjgK8B34pXRkSuFZElIrKkoqKi/3Y+7XpoaYIlv+mymHUqM8YMZolMBFuAkTHTJe68zjwGnBdvgao+oKplqlpWWFjYfxEWHAHjpsPi30BTQ6fFstID5IaD1qnMGDMoJTIRLAbGikipiKQBlwALYguIyNiYyU8DaxIYT3zTroe6XbBsfpfFiqwvgTFmkEpYIlDVZuAm4HngA2C+qq4QkbkiMtMtdpOIrBCRpcCXgasSFU+nSk+D4ZOck8ba+bnq4jzrS2CMGZy6fHh9X6nqQmBhh3l3xozfmsj994iIc9uJZ26A9a/CEWfELVacF2LRR5XJjc0YY5Ig5SeLB4TJn4HMwi6fVTCqIJN9Dc2s2FqVxMCMMSbxLBEABNLhxM/DmhegYnXcIhccX0xBZhrfeHq59TA2xgwqlgiiyq4Bf7pzM7o48jLSuPPciby3eS8Pv7UhqaEZY0wiWSKIyiqEYy6EpX+Eut1xi8w8toiPjyvkR89/aFcQGWMGDUsEsabdAM318O5DcReLCHefN4mIwp3PLEe7uMrIGGMOFZYIYg0/Gko/Du/8yulxHMfIIRncftY4XvxgJ39bvj3JARpjTP+zRNDRyTfCvq2w4s+dFpn9H6OZVJzDtxesoKo+fsIwxphDhSWCjo78FBSMhbfnddrBLOD38YPzj2FXzX5++LdVSQ7QGGP6lyWCjnw+mHYdbP03bF7UabFJxblcc2opjy7axOIN8U8uG2PMocASQTzHXgqhvG6fYPalT42jOC/M159axv7mliQFZ4wx/csSQTxpmXDCbFj1V9izodNiGWkB7v6vSazdWcP/vbY+aeEZY0x/skTQmanXgvhg0QNdFjtj/DBmHlvEL15ey7qKmiQFZ4wx/ccSQWdyi2HiefCv30NDdZdF//uciYTT/Hz9qWVE7PYTxphDjCWCrky7ARr3wdJHuyxWmJ3ON2ccxTsf7eaJdzd3WdYYYwYaSwRdKTkBRp7kPKsg0vXJ4AvLSpg2Zgjfe/YDKvbtT1KAxhjTd5YIujPtBti7ET5c2GUxEeF//msyDc0R5v51ZZKCM8aYvrNE0J0J50DuKHir82cVRI0pzOLmM47kL+9t5ZVVO5MQnDHG9J0lgu74A3DSF2HTP51OZt344sePYOywLL715+XU7m9OQoDGGNM3CU0EIjJdRD4UkbUiMifO8i+LyEoReV9EXhKRwxMZz0E7/kpIy3LOFXQjLeDjBxdMZsveen769/gPuTHGmIEkYYlARPzAPOBsYCJwqYhM7FDs30CZqh4DPAn8MFHx9EkoF467Et6fD49cAB/8pdO7kwKccPgQrpg2igff/Ij3y/cmMVBjjOm9RNYIpoD7QqkAABYaSURBVAJrVXW9qjYCjwGzYguo6iuqWudOvg2UJDCevjnzv+G0O2DHSnj8Cvjp0fDS3E57Hn91+gSGZqUz50/LaG6JJDdWY4zphUQmgmIg9qL6cndeZ64Bnou3QESuFZElIrKkoqKiH0PshbRM+MQ34bZlcOljUHQcvPFT+H/Hwu/Pc25b3dzYWjwnFGTurKNZua2aB9/8KDUxG2NMDwyIk8UicgVQBvwo3nJVfUBVy1S1rLCwMLnBdeQPwPiz4bLH4bblcPo3YNcaeOIq+OlE+Pu3oXIdAP959Ag+NXE49/x9NZt313WzYWOMSY1EJoItwMiY6RJ3Xjsi8kngm8BMVT20emLlFsPpX4Pb3ofLnoCSqfDPn8PPj4ffnYuseIq5nz4Svwjf/LM92tIYMzAFErjtxcBYESnFSQCXAJfFFhCR44D/A6ar6qF74b3PD+POcobqbbD0EXj39/Dk5zgso4DHS8/h5tXHsOC9YmZN6ap1zBhjkk8S+StVRGYAPwP8wIOq+j0RmQssUdUFIvIiMBnY5q6ySVVndrXNsrIyXbJkScJi7jeRFlj/Crz7EPrhc0ikmUVMxj/zZ5QdX5bq6IwxHiMi76pq3C+fhCaCRDhkEkGsfTuofPNB0t7+OaItPDvqDs6+/FZyQsFUR2aM8YiuEsGAOFk86GUPp2D61wne9BZ7ssdz8ebv8sYPP8M/ltvDbIwxqWeJIIlCQw9n5JdeZtuUW/jPyOsUzz+bn/5+PlV1nXdOM8aYRLNEkGz+AIed911arnyGwlALN667jgd//BVeXLE91ZEZYzzKEkGKpB1xGtm3vUP96DP5UuQhfI9dzDcfeZk9tY3dr2yMMf3IEkEqZQwhd/Z8mqf/kNMCK7l1zdV84yc/57ll27pf1xhj+oklglQTITDtiwS++Aq5+YXMi3yXjx7/Kjc/8g67ag6t/nXGmEOTJYKBYsQk0m94DY77LDcEFnDN6uuZfc8T/OW9rdYj2RiTUJYIBpK0THyz7oXP/JbJoZ08rnfw/OO/5LpH3mXnvoZUR2eMGaQsEQxEk87Hf/0bZBQdzS/Sfs4n13yXmT95gSeWbKax2W5pbYzpX9azeCBraYJXv4/+4x62+ov5Qt0NbM8Yx8xjizj/+GImF+ciIqmO0hhzCLBbTBzq1r+GPnUt1O5kdehYHqk5gb82lVEwzEkI500ppigvnOoojTEDmCWCwaB2Fyy6H5Y/BbvXERE/7wen8GjtCTwfOZHJR4zi/ONKmD5pBJnpibyprDHmUGSJYDBRhe3vOwlhxVOwdxMtEuBtmcITDVN5w38ip00aw/nHl3DyEQX4fdZ0ZIyxRDB4qcKWf8GKp9AVTyPVW2iSNF7TKfy58SSWZ57M9OOP4ILjixk7PDvV0RpjUsgSgRdEIlD+Dix/Cl3xZ6R2B/slxN+bj+MvLdOoGHEanz6+lE9MGMboggw7yWyMx1gi8JpIC2z8J6x4isiKZ/DVV1JHmBdbprA0ciQVWeMYNraMEyeM4T+OLLDnIhjjAZYIvKylGTa8Dsufonn1CwRqd7Qu2hQp5ANGsydnApmjjueIY05m/Njx+P3WvcSYwcYSgWlTsxO2v0/zlveoWv8usmMZeQ2b8eF8DvaQzY6McTDiGEaMn0remBOg4EjnucxdaWqA/dXQUAUN1dCw1xmPndfcALkjIX+0M+SNgrSMhL9lY0wKE4GITAf+H84zi3+tqj/osPw0nGcaHwNcoqpPdrdNSwQJsH8fVRv+zcYVb1O3aSm5ez9gjG4iXZoBaPSFaMifQGbRBPwtDR2+4N0v+ZZubpAnPvAFDyyXNbwtMeSPhrzD28azDwOf1U6M6Q8pSQQi4gdWA58CyoHFwKWqujKmzGggB/gKsMASwcCgqqzaUsny9xZTuXYJoV0rmCAbOFx20BzIIJKWQyAjj1D2ELLzhpCWmQ/pORDKbRtap93XtCxn43WVsGeDO3zkvm50Xqu3gMbcQsOf5iaGaHIohWETYNhEJ0nYCW9jeqyrRJDInkdTgbWqut4N4jFgFtCaCFR1g7vMbqAzgIgIR5UM5aiSs4GzqW9s4e2PKnlofSUrt1azcms1lXvaHqBzeEEGEw/LcYbsHI4ensvwnPT4VyZlDnWGkjifx+ZGqNockyjcYe9G2LwY9le1lQ3lOglh2FHtXzOG9O/BMMYDEpkIioHNMdPlwEkHsyERuRa4FmDUqFF9j8z0SjjNzxnjh3HG+GGAU2PYuW+/kxS2VbNiaxUrt1bz3PK2x20OyUxzEkNRDkcXOUmidGgmga5ORAfSoOAIZ4inthIqPoCdH8DOlc7r8j9Bw4NtZbKGxyQHdygcD+lZ3b/RliZoqoPGOue1qd4dap3XlkYI50NmoTOEh1jTlRkUDol7EajqA8AD4DQNpTgczxMRhueEGJ4T4owJw1rn1+xvZtW2ala4tYaV26p56M0NNLY4Fb60gI/Dh2Qwemgmowuc19KCTEYPzWRETghfd72gMwsg81QYfWrbPFXYt60tMUSTxJLfQnN9W7m8w50EE2l2vtTjfdlHmnt5IHyQMdRJClmFbQkicyhkDms/nTUMgnY/KDMwJTIRbAFGxkyXuPPMIJWVHqBs9BDKRrc1zzS1RFhXUcPKrdWs2r6Pj3bVsrGyltdWV7S7pXZ6wMfhBRmMLsikdGimmywyGT00g+HZXSQJEcgpcoYjP9k2P9LiNCnF1h52f+Scd0jLcmoOwbA7ZLqvGc5VTNHx1iHszPcFoH4P1FY4936q2dk2XlsBe5Y4r4018WNNy3bOdRSMgSFjYIhb+xkyxomnv895RCJOPNVboHqrMx7Oc5JU1nAnOaVn27kWk9BEsBgYKyKlOAngEuCyBO7PDEBBv48JI3KYMCKn3fxIRNlW3cCGXbWtyeGjXXWs31XLqx9WtNYiAEJBn5MUCjIpLcxkzNBMxhRmcURhJnkZafF37PO7X7ZjYMKnE/kWD9RYB3W72ieJmp1Qs8NJRjtWwKpn29dAgplOrD1NEpEWZ3vVW9u+6Ftft0LVFqemFGnqOtZAyEkIsckhOrTOK3Re0zI7xBABbXHeR+vQzXT0YgBVQKG1fq9t89ot17blAIjztxVf+6HdPL9zvDrO8/mdixiCoV78Mb0h0ZePzsC5PNQPPKiq3xORucASVV0gIicCTwP5QAOwXVWP7mqbdtXQ4NcSUbburWdjZR0fVdayYZczfFRZy6bKOpojbZ/Z/IwgYwqzGDM0miScBDGqIIP0QDd9H1Kppdk5Mb57vTNUrnPH1zknyOMlidxiqNvtfNHv2+Z8CccKhNzaUXFbLSl2PLPQudy3Zqdbm3GTU3Q6Oq92FzHf0DHbDztfsB2/1A81wQznXE94iFNDCuc7FxmE82Pmd5yXD4H0VEfeJ9ahzAwaTS0RNu+u46NdtayvqGX9rhr3tZaKfW19FHwCJfkZjCl0mprGFGZRWpBJYXY6QzLTyM8Idn3iOpU6JolooqjeAhkF7b/cc0vavvDD+f3TzNPS7FzmG00S0YRRu8v9pR2IGfwdpuPNc6cl+gs9GqO44+50dLz1LXRYDji1hIhbG4kOLe4891W1bV7s/Eizkwjr97Qf6na747u7Pk8UzHQSRyivF6/5zrg/5jYuqk7zYWvny479cqoO7IwZnT59Dky64KD+rKm6fNSYfhf0+5waQGEWZx7Vfll1QxMbogmiooZ1u2r5qKKWRet3U9/U/tezCOSGgwzJTKMgM42CzHSGZDnjQ9xhaFZ66/L8zDSCyUoc/gAMKXUGzkzOPjvuP3u4M3hJ9Au6XXJwE0T9Hqjb4/SYr9/rvO7ZANvc6abarrcdzHT61DTVO1/o3dWmAqED++LkjXRqKwlgicAMGjmhIMeU5HFMSV67+ZGIsr26gY2VdVTW7md3bSOVNY3txtdV1LB4QyN76hqJdFJJzg0HOSw35Ax5YYpyQxyWG+awPPc1N0QoOICbo0zXRJyT5+nZzu1PeqO50f31HpMoOr427HWapeJ1uEyP6YwZykl6M5QlAjPo+XxCUV64R4/zbIkoVfVNVNbsp7K20UkUtY3srmlkV81+tlU1sK2qnvfKq9hd23jA+kMy09xkEaYor/3riJwQ2aEAmekB0gIDtFnKHJxAmntSvTDVkRwUSwTGxPD7pLVpaGw3ZRuaWpzEsLeerbGvVfVs3l3HOx9VUt0Qv8056Bcy0gJkpQfISPOTmR4gM91PZlqg3XhGmjue7szPDQfJDQfJc19zwkF7Cp3pM0sExhykUNBP6VDnZHRnavY3s72qnq17G9he3UDt/mZnaGxxx93XxmbqGluorKmjtrFt/v7m7q/MyQ4FyMuIJog0J1lktE8YeRlO0sgJBckOBch2X5N23sMMaJYIjEmgrPQARw7L5shhB/eo0KaWCHVu0qjZ30x1fRNV9U3srXNf65ti5jVSVd/E1qp6qt0yzZ2d8HCFgr7WpJAdCpITCjjj6e0TRnQ8y62tZKUHWmspmWn+gXsFlukRSwTGDGBBv4/csI/ccO+fIqeq1DW2tEsc+xqa2NfQzL6GJqrdV2e6mWp3fOve+tZ5Ha+26kx6wNcuOWTFNGdlpbXNC6c5TWHhoJ9wmr/DeKDd/FDQb81eSWKJwJhBSkRav4x7cqI8nqaWCDUxiSLajBVtuqqJNm81Rsfblu2ubWTT7rq2eY3N9LbbUnrA1y5ZpAf8pAd9pPl9pAf97quPdPc1Oj894GtdFjsvNulkpLUlnYxggHCa37Mn8S0RGGM6FfT7yHf7UfSVqrK/2Wnqqmtspr6xhfqmFuoaWzqMO+dL6puc+XXuUN/UTGNzhP3uUFXf5E63tM6PTu9vjvQ66QAEfNK+tuImjNhkFAr4CQV9hIL+mMHX+hoO+kkPxivnI+j3EfAJfp8Q9Pvw+4SAT+Lfsj2JLBEYY5JCRFq/FIf0Q2LpiqrSHNH2yaEp0i7x1DU2t05HE1D75W0JqGZ/MxX79tPQ5CSohqYIDU0tPTqZ3xM+gUBMkgj45IBpv0+47ZPjOPfYon7ZZyxLBMaYQUdECPqdX90ksG9WJKI0tkSob2yhobktQTjJwkk+scmjJRKhqUVpiTiJKt50c0RpbjlwuiWi5GX0/lxRT1giMMaYg+TzCSGf/5DvUe7NMyPGGGNaWSIwxhiPs0RgjDEeZ4nAGGM8zhKBMcZ4nCUCY4zxOEsExhjjcZYIjDHG4w65h9eLSAWw8SBXHwrs6sdw+pvF1zcWX98N9BgtvoN3uKrGfYTaIZcI+kJElqhqWarj6IzF1zcWX98N9BgtvsSwpiFjjPE4SwTGGONxXksED6Q6gG5YfH1j8fXdQI/R4ksAT50jMMYYcyCv1QiMMcZ0YInAGGM8blAmAhGZLiIfishaEZkTZ3m6iDzuLl8kIqOTGNtIEXlFRFaKyAoRuTVOmdNFpEpElrrDncmKz93/BhFZ5u57SZzlIiL3usfvfRE5PomxjY85LktFpFpEbutQJunHT0QeFJGdIrI8Zt4QEfm7iKxxX/M7Wfcqt8waEbkqSbH9SERWuX+/p0Ukr5N1u/wsJDjGu0RkS8zfcUYn63b5/57A+B6PiW2DiCztZN2kHMM+UdVBNQB+YB0wBkgD3gMmdihzA3C/O34J8HgS4zsMON4dzwZWx4nvdOCvKTyGG4ChXSyfATwHCDANWJTCv/V2nI4yKT1+wGnA8cDymHk/BOa443OA/42z3hBgvfua747nJyG2s4CAO/6/8WLryWchwTHeBXylB5+BLv/fExVfh+U/Ae5M5THsyzAYawRTgbWqul5VG4HHgFkdyswCfueOPwmcKSKSjOBUdZuq/ssd3wd8ABQnY9/9aBbwe3W8DeSJyGEpiONMYJ2qHmxP836jqq8DuzvMjv2c/Q44L86q/wn8XVV3q+oe4O/A9ETHpqovqGqzO/k2UNKf++ytTo5fT/Tk/73PuorP/e64CPhjf+83WQZjIigGNsdMl3PgF21rGfefoQooSEp0MdwmqeOARXEWnywi74nIcyJydFIDAwVeEJF3ReTaOMt7coyT4RI6/+dL5fGLGq6q29zx7cDwOGUGwrH8HE4NL57uPguJdpPbfPVgJ01rA+H4fQzYoaprOlme6mPYrcGYCA4JIpIF/Am4TVWrOyz+F05zx7HAz4E/Jzm8U1X1eOBs4EYROS3J+++WiKQBM4En4ixO9fE7gDptBAPuWm0R+SbQDDzaSZFUfhbuA44ApgDbcJpfBqJL6bo2MOD/nwZjItgCjIyZLnHnxS0jIgEgF6hMSnTOPoM4SeBRVX2q43JVrVbVGnd8IRAUkaHJik9Vt7ivO4GncarfsXpyjBPtbOBfqrqj44JUH78YO6JNZu7rzjhlUnYsRWQ2cA5wuZuoDtCDz0LCqOoOVW1R1Qjwq072ndLPovv9cT7weGdlUnkMe2owJoLFwFgRKXV/NV4CLOhQZgEQvTrjM8DLnf0j9De3PfE3wAeqek8nZUZEz1mIyFScv1NSEpWIZIpIdnQc56Ti8g7FFgCfda8emgZUxTSBJEunv8JSefw6iP2cXQU8E6fM88BZIpLvNn2c5c5LKBGZDnwVmKmqdZ2U6clnIZExxp53+q9O9t2T//dE+iSwSlXL4y1M9THssVSfrU7EgHNVy2qcqwm+6c6bi/OhBwjhNCmsBd4BxiQxtlNxmgjeB5a6wwzgOuA6t8xNwAqcKyDeBv4jifGNcff7nhtD9PjFxifAPPf4LgPKkvz3zcT5Ys+NmZfS44eTlLYBTTjt1NfgnHd6CVgDvAgMccuWAb+OWfdz7mdxLXB1kmJbi9O2Hv0MRq+iKwIWdvVZSOLxe9j9fL2P8+V+WMcY3ekD/t+TEZ87/6Ho5y6mbEqOYV8Gu8WEMcZ43GBsGjLGGNMLlgiMMcbjLBEYY4zHWSIwxhiPs0RgjDEeZ4nAGJeItHS4s2m/3clSREbH3rnSmIEkkOoAjBlA6lV1SqqDMCbZrEZgTDfc+8n/0L2n/DsicqQ7f7SIvOzeFO0lERnlzh/u3uP/PXf4D3dTfhH5lTjPoXhBRMJu+VvEeT7F+yLyWIrepvEwSwTGtAl3aBq6OGZZlapOBn4B/Myd93Pgd6p6DM5N2+51598LvKbOTe+Ox+lRCjAWmKeqRwN7gQvc+XOA49ztXJeoN2dMZ6xnsTEuEalR1aw48zcAn1DV9e4NA7eraoGI7MK57UGTO3+bqg4VkQqgRFX3x2xjNM5zB8a6018Dgqp6t4j8DajBuUvqn9W9YZ4xyWI1AmN6RjsZ7439MeMttJ2j+zTOvZuOBxa7d7Q0JmksERjTMxfHvL7ljv8T526XAJcD/3DHXwKuBxARv4jkdrZREfEBI1X1FeBrOLdEP6BWYkwi2S8PY9qEOzyA/G+qGr2ENF9E3sf5VX+pO+9m4LcicgdQAVztzr8VeEBErsH55X89zp0r4/EDj7jJQoB7VXVvv70jY3rAzhEY0w33HEGZqu5KdSzGJII1DRljjMdZjcAYYzzOagTGGONxlgiMMcbjLBEYY4zHWSIwxhiPs0RgjDEe9/8BlfKUXAItNlgAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##**Visualizing and Plotting Accuracy**"
+      ],
+      "metadata": {
+        "id": "nZBdHMmZ_4cq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "plt.figure(figsize=(6, 4))                   # making the graph scale big \n",
+        "plt.plot(history.history['accuracy'])         # ploting validation loss \n",
+        "plt.plot(history.history['val_accuracy'])         # ploting validation loss \n",
+        "\n",
+        "plt.title('Training accuracy Vs Validation Accuracy')\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('accuracy')\n",
+        "plt.legend(['train_accuracy', 'val_accuracy'], loc='upper right')\n",
+        "\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        },
+        "id": "J0IFtL_G9mrO",
+        "outputId": "3c5758ec-d585-4f36-d1c5-20fb7bc20238"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##**Finally showing model predicted value**"
+      ],
+      "metadata": {
+        "id": "PK2V7M0qcxpu"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "predictions = model.predict(X_test)\n",
+        "\n",
+        "plt.figure(figsize=(15,4.5))\n",
+        "# predictions = model.predict(y_test)\n",
+        "for i in range(1):  \n",
+        "    print('Model Predicted one hot encoded form: \\n{label}\\n'.format(label=(predictions[i])))\n",
+        "    print('Model rounded prediction: {label}\\n'.format(label=np.round(predictions[i])))\n",
+        "    print('Model predicted label: {label}\\n'.format(label = np.argmax(np.round(predictions[i]))))\n",
+        "    print('Actual label: {label}\\n'.format(label = y_test[i]))\n",
+        "    # print(Model predicted number from 0 to 1)\n",
+        "    plt.subplot(1, 1, i+1)\n",
+        "    # plt.title('Model Predicted Label: {label}'.format(label=(predictions[i])))\n",
+        "    \n",
+        "    plt.imshow(X_test[i].reshape((28,28)),cmap=plt.cm.binary)\n",
+        "    \n",
+        "\n",
+        "    plt.axis('off')\n",
+        "plt.subplots_adjust(wspace=1, hspace=0)\n",
+        "plt.show()\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 450
+        },
+        "id": "yOXZvk_Lc2bB",
+        "outputId": "406293b8-36d7-4c55-b1a5-d9b9e0e13fdc"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model Predicted one hot encoded form: \n",
+            "[1.9996548e-07 1.0462644e-06 1.4459002e-05 6.3955587e-05 2.0830514e-07\n",
+            " 5.5986939e-06 3.9419082e-10 9.9990106e-01 1.0360257e-06 1.2263427e-05]\n",
+            "\n",
+            "Model rounded prediction: [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
+            "\n",
+            "Model predicted label: 7\n",
+            "\n",
+            "Actual label: 7\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1080x324 with 1 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQMAAAEDCAYAAAAx0WHLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAGLUlEQVR4nO3dMYte1RaA4XcuhgiKSLAQIYI4EBEECwu10EIRFC1ErPwHFpb21lpqIfoTYiOiaUSDKYQIwcbCSm0EQS0sBFHG4q7mFnP2d8kkmUmep12L8x1SvGzI5szewcFBAP+50S8AHA9iAFRiAAwxACoxAIYYAFXdtpj7f0e4uewdNnAyACoxAIYYAJUYAEMMgEoMgCEGQCUGwBADoBIDYIgBUIkBMMQAqMQAGGIAVGIADDEAKjEAhhgAlRgAQwyASgyAIQZAJQbAEAOgEgNgiAFQiQEwxACoxAAYYgBUYgAMMQAqMQCGGACVGABDDIBKDIAhBkAlBsAQA6ASA2CIAVCJATDEAKjEABhiAFRiAAwxACoxAIYYAJUYAEMMgEoMgCEGQCUGwBADoBIDYIgBUIkBMG670S9wPZ0/f36588EHHyx37rvvvuXO7bffvjl/7bXXls+49957lzv7+/vLHdiFkwFQiQEwxACoxAAYYgBUYgAMMQAqMQDG3sHBwdZ8c3jSPPDAA8udH3744dq/yI7uuuuu5c7DDz98Hd7k5Dl79uxy580339ycP/bYY0f1OsfJ3mEDJwOgEgNgiAFQiQEwxACoxAAYYgBUYgCMW+pLRx9++OFy59tvv13u7HLR57vvvtucX7lyZfmML7/8crnz9ddfL3fuv//+zflPP/20fMZROXXq1Ob8nnvuWT7j559/Xu7s8u+yuph0k146OpSTAVCJATDEAKjEABhiAFRiAAwxAKpb7OMmJ83vv/++3NnlvsLq/8svX7688ztdrdOnT2/Oz507t3zGQw89tNz57bffljvvvffe5vz1119fPuME8nETYJsYAJUYAEMMgEoMgCEGQCUGwBADoHLpiGPmo48+Wu68+uqry51HHnlkufPFF19szs+cObN8xgnk0hGwTQyASgyAIQZAJQbAEAOgEgNgiAFQuXTEdfbLL79szne5LLR6RtX58+eXO6+88spy5ybk0hGwTQyASgyAIQZAJQbAEAOgEgNgiAFQ1W03+gW4taz+pNkuF4ruvvvu5c4uf6aN/+VkAFRiAAwxACoxAIYYAJUYAEMMgMrHTThCly5dWu4888wzm/O//vpr+YyLFy8ud5566qnlzi3Kx02AbWIAVGIADDEAKjEAhhgAlRgAQwyAysdNOEKffvrpcmd1qejZZ59dPuOJJ57Y+Z3YnZMBUIkBMMQAqMQAGGIAVGIADDEAKjEAhktH7OTPP/9c7ly4cGG5c/r06c35W2+9tXzGqVOnljv8/5wMgEoMgCEGQCUGwBADoBIDYIgBUIkBMFw6Yidvv/32cufKlSvLneeff35z/uSTT+78ThwtJwOgEgNgiAFQiQEwxACoxAAYYgBUtXdwcLA13xxyc/jkk0+WOy+//PJy54477ljufPbZZ5tzfy3pmts7bOBkAFRiAAwxACoxAIYYAJUYAEMMgEoMgOHjJreAX3/9dXP+xhtvLJ/x999/L3deeOGF5Y5LRceXkwFQiQEwxACoxAAYYgBUYgAMMQAqMQCGLx2dcP/8889y5/HHH9+cf/PNN8tn7O/vL3cuXLiw3HnwwQeXO1xTvnQEbBMDoBIDYIgBUIkBMMQAqMQAGGIAVC4dnXjff//9cufcuXNX/Tsff/zxcuell1666t/hmnPpCNgmBkAlBsAQA6ASA2CIAVCJATD8RaVj7Mcff1zuPPfcc1f9O++8885y58UXX7zq3+F4czIAKjEAhhgAlRgAQwyASgyAIQZAJQbAcOnoGHv//feXO7tcTFp5+umnlzt7e4d+E4ObhJMBUIkBMMQAqMQAGGIAVGIADDEAKjEAhktHN8hXX3213Hn33Xevw5vAfzkZAJUYAEMMgEoMgCEGQCUGwBADoBIDYLh0dINcunRpufPHH38cyW/t7+9vzu+8884j+R1ONicDoBIDYIgBUIkBMMQAqMQAGGIAVO4ZnHiPPvrocufzzz/fnJ85c+aoXocTzMkAqMQAGGIAVGIADDEAKjEAhhgAlRgAY+/g4GBrvjkETpy9wwZOBkAlBsAQA6ASA2CIAVCJATDEAKjEABirLx0dekEBuLk4GQCVGABDDIBKDIAhBkAlBsD4F8dcwfeW4AY6AAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Assignment/Assignment_3/test.txt b/Assignment/Assignment_3/test.txt
deleted file mode 100644
index e69de29..0000000