Merge branch 'master' of https://github.com/Python-World/Python_and_the_Web into master

Author: rohitjoshi6

.all-contributorsrc

@@ -128,6 +128,33 @@
       "contributions": [
         "code"
       ]
+    },
+    {
+      "login": "Meghana-12",
+      "name": "Meghana Varanasi",
+      "avatar_url": "https://avatars0.githubusercontent.com/u/44519203?v=4",
+      "profile": "https://github.com/Meghana-12",
+      "contributions": [
+        "code"
+      ]
+    },
+    {
+      "login": "mendoza",
+      "name": "David Mendoza",
+      "avatar_url": "https://avatars1.githubusercontent.com/u/30415552?v=4",
+      "profile": "https://github.com/mendoza",
+      "contributions": [
+        "code"
+      ]
+    },
+    {
+      "login": "dkarmy12",
+      "name": "dkarmy12",
+      "avatar_url": "https://avatars1.githubusercontent.com/u/55491427?v=4",
+      "profile": "https://github.com/dkarmy12",
+      "contributions": [
+        "code"
+      ]
     }
   ],
   "contributorsPerLine": 7,

README.md

@@ -6,7 +6,7 @@
 [![forthebadge](https://forthebadge.com/images/badges/made-with-python.svg)](https://forthebadge.com)
 
 <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
-[![All Contributors](https://img.shields.io/badge/all_contributors-13-orange.svg?style=flat-square)](#contributors-)
+[![All Contributors](https://img.shields.io/badge/all_contributors-16-orange.svg?style=flat-square)](#contributors-)
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 
 ![Issues](https://img.shields.io/github/issues/Python-World/Python_and_the_Web)
@@ -108,6 +108,11 @@ We now have a section for miscellaneous scripts as well.
     <td align="center"><a href="https://github.com/tauseefmohammed2"><img src="https://avatars2.githubusercontent.com/u/35351464?v=4" width="100px;" alt=""/><br /><sub><b>tauseefmohammed2</b></sub></a><br /><a href="https://github.com/Python-World/Python_and_the_Web/commits?author=tauseefmohammed2" title="Code">💻</a></td>
     <td align="center"><a href="https://ameyanrd.github.io/"><img src="https://avatars1.githubusercontent.com/u/42608371?v=4" width="100px;" alt=""/><br /><sub><b>Ameya Deshpande</b></sub></a><br /><a href="https://github.com/Python-World/Python_and_the_Web/commits?author=ameyanrd" title="Code">💻</a></td>
     <td align="center"><a href="https://aravindha1234u.github.io"><img src="https://avatars0.githubusercontent.com/u/52521300?v=4" width="100px;" alt=""/><br /><sub><b>T3cH_W1z4rD</b></sub></a><br /><a href="https://github.com/Python-World/Python_and_the_Web/commits?author=Aravindha1234u" title="Code">💻</a></td>
+    <td align="center"><a href="https://github.com/Meghana-12"><img src="https://avatars0.githubusercontent.com/u/44519203?v=4" width="100px;" alt=""/><br /><sub><b>Meghana Varanasi</b></sub></a><br /><a href="https://github.com/Python-World/Python_and_the_Web/commits?author=Meghana-12" title="Code">💻</a></td>
+  </tr>
+  <tr>
+    <td align="center"><a href="https://github.com/mendoza"><img src="https://avatars1.githubusercontent.com/u/30415552?v=4" width="100px;" alt=""/><br /><sub><b>David Mendoza</b></sub></a><br /><a href="https://github.com/Python-World/Python_and_the_Web/commits?author=mendoza" title="Code">💻</a></td>
+    <td align="center"><a href="https://github.com/dkarmy12"><img src="https://avatars1.githubusercontent.com/u/55491427?v=4" width="100px;" alt=""/><br /><sub><b>dkarmy12</b></sub></a><br /><a href="https://github.com/Python-World/Python_and_the_Web/commits?author=dkarmy12" title="Code">💻</a></td>
   </tr>
 </table>
 

Scripts/Miscellaneous/Activation_Functions_from_scratch_in_Keras/README.md

@@ -0,0 +1,43 @@
+# ActivationFunctions using Custom Layers in Keras
+Activation functions are an important are of deep learning research .Many new activation functions are being developed ,these include *bio-inspired* activtions, *purely mathematical activation functions* including others . Despite, such advancements we usually find ourselves using RELU and LeakyRELU commonly without using/thinking about others.
+In the following notebooks I showcase how easy/difficult it is to port an activation function using **Custom Layers in Keras and Tensorflow!**
+
+
+Link to main notebook --> [Activations.ipynb](https://github.com/Agrover112/ActivationFunctions/blob/master/src/Activation-Functions(GELU%2CSELU%2CELU%2CLeakyReLU%2CPRELU).ipynb)
+
+## Requirements
+Google Colab
+
+### Implemented activations:
+ 
+- LeakyReLu
+- ParametricReLu
+- Elu
+- SElu
+- GELU
+
+
+
+### Structure
+ ```  
+src
+|
+|-- Activations.ipynb
+|-- utils
+      |-- Utils.ipynb
+      |-- utils.py
+      
+references
+|
+|--Ref1
+|--Refn
+
+```
+
+###  Usage
+ ``` 
+ git clone  https://github.com/Agrover112/ActivationFunctions.git
+```
+
+### References
+- [References:D](https://github.com/Agrover112/ActivationFunctions/tree/master/references)

Scripts/Miscellaneous/Activation_Functions_from_scratch_in_Keras/src/Activation-Functions(GELU,SELU,ELU,LeakyReLU,PRELU).ipynb

@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Utils","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyMYHshcegvtTBQwBygO/eoj"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"_ImvYera1GfS","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595963420315,"user_tz":-330,"elapsed":2543,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}}},"source":["import tensorflow as tf\n","import numpy as np\n","import matplotlib.pyplot as plt\n","def load_data():\n","    (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n","    x_train = np.reshape(x_train, (x_train.shape[0], 784))/255.\n","    x_test = np.reshape(x_test, (x_test.shape[0], 784))/255.\n","    y_train = tf.keras.utils.to_categorical(y_train)\n","    y_test = tf.keras.utils.to_categorical(y_test)\n","    return (x_train, y_train), (x_test, y_test)\n","\n","def plot_random_examples(x, y, p=None):\n","    indices = np.random.choice(range(0, x.shape[0]), 10)\n","    y = np.argmax(y, axis=1)\n","    if p is None:\n","        p = y\n","    plt.figure(figsize=(10, 5))\n","    for i, index in enumerate(indices):\n","        plt.subplot(2, 5, i+1)\n","        plt.imshow(x[index].reshape((28, 28)), cmap='binary')\n","        plt.xticks([])\n","        plt.yticks([])\n","        if y[index] == p[index]:\n","            col = 'g'\n","        else:\n","            col = 'r'\n","        plt.xlabel(str(p[index]), color=col)\n","    return plt\n","\n","def plot_results(history):\n","    history = history.history\n","    plt.figure(figsize=(12, 4))\n","    epochs = len(history['val_loss'])\n","    plt.subplot(1, 2, 1)\n","    plt.plot(range(epochs), history['val_loss'], label='Val Loss')\n","    plt.plot(range(epochs), history['loss'], label='Train Loss')\n","    plt.xticks(list(range(epochs)))\n","    plt.xlabel('Epochs')\n","    plt.ylabel('Loss')\n","    plt.legend()\n","    plt.subplot(1, 2, 2)\n","    plt.plot(range(epochs), history['val_accuracy'], label='Val Acc')\n","    plt.plot(range(epochs), history['accuracy'], label='Acc')\n","    plt.xticks(list(range(epochs)))\n","    plt.xlabel('Epochs')\n","    plt.ylabel('Accuracy')\n","    plt.legend()\n","    return plt"],"execution_count":1,"outputs":[]},{"cell_type":"code","metadata":{"id":"iRwkOk0p1SPt","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]}]}

Scripts/Miscellaneous/Activation_Functions_from_scratch_in_Keras/src/utils/Utils

@@ -0,0 +1,49 @@
+import tensorflow as tf
+import numpy as np
+import matplotlib.pyplot as plt  # loading dependencies
+
+def load_data():  # method for loading mnist dataset
+    (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
+    x_train = np.reshape(x_train, (x_train.shape[0], 784))/255.
+    x_test = np.reshape(x_test, (x_test.shape[0], 784))/255.  # normalization of images
+    y_train = tf.keras.utils.to_categorical(y_train)  # converting to categorical fearures
+    y_test = tf.keras.utils.to_categorical(y_test)
+    return (x_train, y_train), (x_test, y_test)
+
+def plot_random_examples(x, y, p=None):  # function that samples randomly and plots images
+    indices = np.random.choice(range(0, x.shape[0]), 10)
+    y = np.argmax(y, axis=1)
+    if p is None:
+        p = y
+    plt.figure(figsize=(10, 5))
+    for i, index in enumerate(indices):                                   
+        plt.subplot(2, 5, i+1)
+        plt.imshow(x[index].reshape((28, 28)), cmap='binary')
+        plt.xticks([])
+        plt.yticks([])
+        if y[index] == p[index]:
+            col = 'g'
+        else:
+            col = 'r'
+        plt.xlabel(str(p[index]), color=col)
+    return plt
+
+def plot_results(history):  # function that accepts history object from keras and plots the Loss,Accuracy,Validation Accuracy
+    history = history.history
+    plt.figure(figsize=(12, 4))
+    epochs = len(history['val_loss'])
+    plt.subplot(1, 2, 1)
+    plt.plot(range(epochs), history['val_loss'], label='Val Loss')
+    plt.plot(range(epochs), history['loss'], label='Train Loss')
+    plt.xticks(list(range(epochs)))
+    plt.xlabel('Epochs')
+    plt.ylabel('Loss')
+    plt.legend()
+    plt.subplot(1, 2, 2)
+    plt.plot(range(epochs), history['val_accuracy'], label='Val Acc')
+    plt.plot(range(epochs), history['accuracy'], label='Acc')
+    plt.xticks(list(range(epochs)))
+    plt.xlabel('Epochs')
+    plt.ylabel('Accuracy')
+    plt.legend()
+    return plt

Scripts/Miscellaneous/Activation_Functions_from_scratch_in_Keras/src/utils/utils.py

@@ -0,0 +1,20 @@
+#Script to convert JSON file to .xlsx file(Excel).
+#Requirements : 
+# Pandas (Data Manipulation Library. Install : pip install pandas)
+
+#  Importing Pandas and json
+import json
+from pandas import DataFrame
+import os
+# Importing the data from a file using the load method
+def convert(inp_json_file:str) -> None:
+    """Simple function to convert any .json file to a xlsx file of any name"""
+    with open(inp_json_file) as json_file:
+        data = json.load(json_file)
+    #  Creating a dataframe
+        df = DataFrame(data)
+        out_xlsx_file = inp_json_file[:-4] + 'xlsx'
+        df.to_excel(out_xlsx_file)
+
+if __name__ == "__main__":
+    convert(os.path.join((os.path.abspath(".")),"Python_and_the_web\Scripts\Miscellaneous\JSONtoExcel\sampleData.json"))

Scripts/Miscellaneous/JSONtoExcel/JSONtoEXCEL.py

@@ -0,0 +1,22 @@
+# Python script that converts JSON file to Excel (xlsx) file.
+A Python script that converts JSON file to Excel (xlsx) file.
+
+### Prerequisites
+You will have to install pandas 
+The installation commands are provided in requirements.txt
+
+### How to run the script
+First you need to go to the JSONtoExcel directory.
+
+```cd Scripts/Miscellaneous/JSONtoExcel```
+
+ Do the changes required (like changing the file names or content). And then run the following command once you are in project directory
+
+```python3 JSONtoEXCEL.py```
+
+
+### Sample use of the script
+Sample file sampleData.json gives the file exported_json_data.xlsx . 
+
+## *Author Name*
+Meghana Varanasi

Scripts/Miscellaneous/JSONtoExcel/README.md

@@ -0,0 +1 @@
+pandas==1.1.2