ahmadmustafaanis

# Socket Programming by Ahmad Mustafa Anis
import socket
class ServerSocket:

    def __init__(self, type=socket.AF_INET, family=socket.SOCK_STREAM, port = 8080):

        self.type = type
        self.family = family
        self.mySock = socket.socket(self.type, self.family) #Ipv4 and TCP Socket
        self.mySock.bind((socket.gethostname(), port))  #Binded to local computer ip and 8080 port

ahmadmustafaanis

import socket


class receveingSocket:
    def __init__(self, port=8080):

        self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)#ipv4 and tcp
        self.sock.connect((socket.gethostname(), port)) #connects to local computer and port 8080

    def send_lower_to_server(self):

ahmadmustafaanis

#include <iostream>
#include <string>
#include <stack>

using namespace std;

class InduvivualWordsReverser {
private:
	string h1;
	stack < char > s1;

ahmadmustafaanis

#include <iostream>
using namespace std;

struct node {
	int data;
	node* next;
	node* back;
};

class LinkedList {

ahmadmustafaanis

#include "BST.h"

node* BST::search(int x)
{
	if (x == root->data)
	{
		return root;
	}
	node* temp = root;
	while (temp)

ahmadmustafaanis

from sklearn.datasets import load_iris
from tensorflow.keras.models import Sequential
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.layers import Dense
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
iris = load_iris()
X = iris.data
y = iris.target
y = to_categorical(y) #converting output to one-hot vector

ahmadmustafaanis

model1 = Sequential([
    Dense(512, activation='tanh', input_shape = X_train[0].shape),
    Dense(512//2, activation='tanh'),
    Dense(512//4, activation='tanh'),
    Dense(512//8, activation='tanh'),
    Dense(32, activation='relu'),
    Dense(3, activation='softmax')
])
print(model1.summary())
model1.compile(optimizer='sgd',loss='categorical_crossentropy', metrics=['acc', 'mse'])

ahmadmustafaanis

model2 = Sequential([
    Dense(512, activation='tanh', input_shape = X_train[0].shape, kernel_regularizer='l1'), #Only change is here where we add kernel_regularizer
    Dense(512//2, activation='tanh'),
    Dense(512//4, activation='tanh'),
    Dense(512//8, activation='tanh'),
    Dense(32, activation='relu'),
    Dense(3, activation='softmax')
])

model2.compile(optimizer='sgd',loss='categorical_crossentropy', metrics=['acc', 'mse'])

ahmadmustafaanis

import numpy as np
from fastapi import FastAPI, Form
import pandas as pd
from starlette.responses import HTMLResponse 
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
import tensorflow as tf
import re

def preProcess_data(text): #cleaning the data

ahmadmustafaanis

import numpy as np
import matplotlib.pyplot as plt
import cv2

def template_detection(image, template):

    methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',
                'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']