Standard way of creating a neural network with Keras

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Thu Aug 18 2022 17:37:26 GMT+0000 (Coordinated Universal Time)

Saved by @epanikas ##python ##keras ##neuralnetwork

model = Sequential()
model.add(Dense(300, 
                activation = 'relu', 
                input_shape = x_train.shape[1:]))
model.add(Dense(100, 
                activation = 'relu'))
model.add(Dense(1))
model.compile(optimizer = 'adam', 
              loss = 'mse', 
              metrics = ['mae'])
model.fit(x_train, y_train, 
          epochs = 30, 
          batch_size = 32, 
          validation_split = 0.1)

scores = model.evaluate(x_test, y_test, verbose = 0)
predict = model.predict(x_test)

scores = model.evaluate(x_test, y_test, verbose = 0)
predict = model.predict(x_test)

scores = model.evaluate(x_test, y_test, verbose = 0)
predict = model.predict(x_test)

scores = model.evaluate(x_test, y_test, verbose = 0)
predict = model.predict(x_test)

scores = model.evaluate(x_test, y_test, verbose = 0)
predict = model.predict(x_test)

scores = model.evaluate(x_test, y_test, verbose = 0)
predict = model.predict(x_test)

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a standard way of creating a **neural network** with *Keras* library would look like this

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Standard way of creating a neural network with Keras

#python ##python #strings #comments

Create simple string along with variables

#assign a value to a variable:
types_of_people = 10 
# make a string using variable name:
X = f “there are {types_of_people} types of people.”

Output:
There are 10 types of people

##python ##keras ##neuralnetwork

Standard way of creating a neural network with Keras

model = Sequential()
model.add(Dense(300, 
                activation = 'relu', 
                input_shape = x_train.shape[1:]))
model.add(Dense(100, 
                activation = 'relu'))
model.add(Dense(1))
model.compile(optimizer = 'adam', 
              loss = 'mse', 
              metrics = ['mae'])
model.fit(x_train, y_train, 
          epochs = 30, 
          batch_size = 32, 
          validation_split = 0.1)

scores = model.evaluate(x_test, y_test, verbose = 0)
predict = model.predict(x_test)

scores = model.evaluate(x_test, y_test, verbose = 0)
predict = model.predict(x_test)

scores = model.evaluate(x_test, y_test, verbose = 0)
predict = model.predict(x_test)

scores = model.evaluate(x_test, y_test, verbose = 0)
predict = model.predict(x_test)

scores = model.evaluate(x_test, y_test, verbose = 0)
predict = model.predict(x_test)

scores = model.evaluate(x_test, y_test, verbose = 0)
predict = model.predict(x_test)

#python ##python ##pythonlists

How to use for loop with range function.

  #use for loop and set the range
for index in range(10):
  Print (index)
           
           #when you run this program, the output will be:
               0 
               1 
               2 
               3 
               4 
               5
               6
               7
               8 
               9

##python ##python2.7 ##codecademy

Calculator For Best Shipping Option with Grounded Values

def ground_cost(weight):
  if weight <= 2:
    return weight * 1.50 + 20
  elif weight > 2 and weight <= 6:
    return weight * 3.00 + 20
  elif weight > 6 and weight <= 10:
    return weight * 4.00 + 20
  elif weight > 10:
    return weight * 4.75 + 20

premground_cost = 125.0

def drone_cost(weight):
  if weight <= 2:
    return weight * 4.50
  elif weight > 2 and weight <= 6:
    return weight * 9
  elif weight > 6 and weight <= 10:
    return weight * 12
  elif weight > 10:
    return weight * 14.25


def pricecheck(weight):
  if ground_cost(weight) < premground_cost and ground_cost(weight) < drone_cost(weight):
    return "Ground shipping is the cheapest option for the selected weight. Your total cost will be $" + str(ground_cost(weight)) + "."
  elif premground_cost < ground_cost(weight) and premground_cost < drone_cost(weight):
    return "Our premium option is for you. The total price is set at 125 dollars."
  elif drone_cost(weight) < ground_cost(weight) and drone_cost(weight) < premground_cost:
    return "Drone shipping seems to be the cheapest for you! Your cost will be $" + str(drone_cost(weight)) + "."

##python ##virtualenv ##env

Python virtualenv

# instala la libreria
pip install virtualenv

# genera el virtualenv env
virtualenv env

#activo el virtual env
source env/bin/activate

#guardar todo las librerias en un archivo de texto
pip freeze > requirements.txt

#instalar las librerias de un env a partir de requirements.txt
source env/bin/activate
pip install -r requirements.txt

##python ##outlook

Send an outlook email using python with out SMTP

import win32com.client
from win32com.client import Dispatch, constants

const=win32com.client.constants
olMailItem = 0x0
obj = win32com.client.Dispatch("Outlook.Application")
newMail = obj.CreateItem(olMailItem)
newMail.Subject = "I AM SUBJECT!!"
# newMail.Body = "I AM\nTHE BODY MESSAGE!"
newMail.BodyFormat = 2 # olFormatHTML https://msdn.microsoft.com/en-us/library/office/aa219371(v=office.11).aspx
newMail.HTMLBody = "<HTML><BODY>Enter the <span style='color:red'>message</span> text here.</BODY></HTML>"
newMail.To = "testemail@company.com"
attachment1 = r"C:\Temp\example.pdf"
newMail.Attachments.Add(Source=attachment1)
newMail.display(True)
newMail.Send()

##python ##pandas ##format

f string format

number = 1_000_000.50
f"${number:,.0f}"
'$1,000,000'

f"${number:,.2f}"
'$1,000,000.50'

##python ##for-loop ##if-statements

A prime Checker Code

## Your code should check if each number in the list is a prime number
check_prime = [26, 39, 51, 53, 57, 79, 85]

## write your code here
## HINT: You can use the modulo operator to find a facto
prime_numbers = []
counter_prime = 0
for prime in check_prime:
    if (prime % 2 == 0):
       print("{} is not a prime number".format(prime))
    elif (prime % 3 == 0):
        print("{} is not a prime number".format(prime))
    elif(prime % 5 == 0):
        print("{} is not a prime number".format(prime))
    elif(prime % 7 == 0):
        print("{} is not a prime number".format(prime))
    else:
        prime_numbers.append(prime)
        print("{} is a prime number".format(prime))
    counter_prime += 1

print("These are the prime numbers : {}".format(prime_numbers))



## Altenatively,

check_prime = [26, 39, 51, 53, 57, 79, 85]

# iterate through the check_prime list
for num in check_prime:

# search for factors, iterating through numbers ranging from 2 to the number itself
    for i in range(2, num):

# number is not prime if modulo is 0
        if (num % i) == 0:
            print("{} is NOT a prime number, because {} is a factor of {}".format(num, i, num))
            break

# otherwise keep checking until we've searched all possible factors, and then declare it prime
        if i == num -1:    
            print("{} IS a prime number".format(num))

##python ##imageprocessing

Box Blur | MLPro

import numpy as np

# Please do not change the below function name and parameters
def box_blur(image):
    # print(sum(image))
    image = np.asarray(image)
    image =image.astype(np.float64)
    # print(type(image))
    # print("1")
    h,w = len(image), len(image[0])
    # print(h,w)
    new_arr = np.zeros((h+2,w+2))
    # print(new_arr)
    # print("1")
    for i in range(1,h+1):
        new_arr[i][1:w+1] = image[i-1]
    # print(new_arr)
    # new_arr[1:h+1][1:w+1] = image
    # print(new_arr)

    for i in range(1,h+1):
        for j in range(1,w+1):
            image[i-1][j-1] = (sum(new_arr[i-1][j-1:j+2])+ sum(new_arr[i][j-1:j+2])+sum(new_arr[i+1][j-1:j+2]))/9
            # print(image[i-1][j-1])
    print(image)
    return image

##python ##for-loop ##if-statements

Installing Virtualenv on Windows, MacOX, Linux

STEP 1: UNIVERSAL INSTALL THE VIRTUAL ENV PACKAGE ON YOUR TERMINA
$ pip install virtualenv // this installs the virtualenv

STEP 2: MOVE TO THE DIRECTORY OF YOUR CHOICE 
$ cd Documents/chisomloius/Data-labelling //for example


STEP 3: CREATE THE VIRTUAL ENV NAME OF YOUR CHOICE //
$ virtualenv [name of virtual environment] or python -m virtualenv [name of virtual environment] //this create a directory with the name e.g virtual_env
$ virtualenv env or python -m virtualenv virtual_env //this create a directory with the name 'virtual_env'


STEP 4: ACTIVATE/DEACTIVATE VIRTUAL ENV
$ .\/virtual_env/scripts/activate // use this to activate for command prompt
$ .\/virtual_env/Scripts/activate.ps1 // use this to activate for Powershell
$ source bin/activate // use this to MacOSX and Linux
---------------------------------
$ deactivate // deactiate virtaul env

STEP 5: ADDING OTHER PACKAGES ON THE VIRTUAL ENVIRONMENT OR USING THE REQUIREMENT FILE
pip install -r requirements.txt //import more than one package
pip install pageName

##python ##machinelearning

Box-Muller Transformation

#!/usr/bin/python3

import numpy as np

# Please do not change the below function name and parameters
def transform(u1,u2):
    z1 = np.sqrt(-2* np.log(u1)) * np.cos(2*np.pi*u2)
    z2 = np.sqrt(-2* np.log(u1)) * np.sin(2*np.pi*u2)
    return (z1,z2)

##python ##machinelearning

Bubble Sort | MLPro

def bubbleSort(values):
    for i in range(len(values)):
        for j in range(len(values)-1):
            if values[j] > values[j+1]:
                values[j] ,values[j+1] = values[j+1], values[j]
    return values

##pandas ##python

find the percentage of missing values in pandas

percent_missing = df.isnull().sum() * 100 / len(df)
missing_value_df = pd.DataFrame({'column_name': df.columns,
                                 'percent_missing': percent_missing})

##flask ##python

Flask Basic Configuration

from flask import Flask, redirect, url_for, request, render_template, flash

app = Flask(__name__)
  
@app.route('/', methods = ['GET'])
def index():
    return render_template('index.html')
  
if __name__ == '__main__':
    app.run()

##python ##array ##input

Get Multiple inputs - Python

numArray1 = list(map(int, input().split()))

Standard way of creating a neural network with Keras

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Comments

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Standard way of creating a neural network with Keras

Save snippets that work from anywhere online with our extensions

Comments

More like this

medium-blog Fluid NN Builder - a fun way of working with neural networks

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