Guna  Rakulan

Guna Rakulan


Python tips and tricks : Performing Additional Tasks During Data Augmentation in Keras

In this video we will perform additional tasks during data augmentation in keras

For example scaling inputs, performing preprocessing operations, converting masks ​to categorical​, etc.

Code snippet from the video…

import segmentation_models as sm
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
from keras.utils import to_categorical

#Some scaling operation to be applied to images
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
from keras.utils import to_categorical

#Some preprocessing operation on images
BACKBONE = ‘resnet34’
preprocess_input = sm.get_preprocessing(BACKBONE)

#Define a function to perform additional preprocessing after datagen.
#For example, scale images, convert masks to categorical, etc.

def preprocess_data(img, mask, num_class):
#Scale images
img = scaler.fit_transform(img.reshape(-1, img.shape[-1])).reshape(img.shape)
img = preprocess_input(img) #Preprocess based on the pretrained backbone…
#Convert mask to one-hot
mask = to_categorical(mask, num_class)

return (img,mask)

#Define the generator.
#We are not doing any rotation or zoom to make sure mask values are not interpolated.
#It is important to keep pixel values in mask as 0, 1, 2, 3, …
from tensorflow.keras.preprocessing.image import ImageDataGenerator
def trainGenerator(train_img_path, train_mask_path, num_class):

img_data_gen_args = dict(horizontal_flip=True,

image_datagen = ImageDataGenerator(**img_data_gen_args)
mask_datagen = ImageDataGenerator(**img_data_gen_args)

image_generator = image_datagen.flow_from_directory(
    class_mode = None,
    batch_size = batch_size,
    seed = seed)

mask_generator = mask_datagen.flow_from_directory(
    class_mode = None,
    color_mode = 'grayscale',
    batch_size = batch_size,
    seed = seed)

train_generator = zip(image_generator, mask_generator)

for (img, mask) in train_generator:
    img, mask = preprocess_data(img, mask, num_class)
    yield (img, mask)

train_img_path = “data/data_for_keras_aug/train_images/”
train_mask_path = “data/data_for_keras_aug/train_masks/”
train_img_gen = trainGenerator(train_img_path, train_mask_path, num_class=4)

#Make sure the generator is working and that images and masks are indeed lined up.
#Verify generator… In python 3 next() is renamed as next()
x, y =

for i in range(0,3):
image = x[i]
mask = np.argmax(y[i], axis=2)
plt.imshow(mask, cmap=‘gray’)

#keras #python

What is GEEK

Buddha Community

Python tips and tricks : Performing Additional Tasks During Data Augmentation in Keras
 iOS App Dev

iOS App Dev


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Arvel  Parker

Arvel Parker


Basic Data Types in Python | Python Web Development For Beginners

At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Table of Contents  hide

I Mutable objects

II Immutable objects

III Built-in data types in Python

Mutable objects

The Size and declared value and its sequence of the object can able to be modified called mutable objects.

Mutable Data Types are list, dict, set, byte array

Immutable objects

The Size and declared value and its sequence of the object can able to be modified.

Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.

id() and type() is used to know the Identity and data type of the object







Built-in data types in Python

a**=str(“Hello python world”)****#str**














Numbers (int,Float,Complex)

Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.

#signed interger




Python supports 3 types of numeric data.

int (signed integers like 20, 2, 225, etc.)

float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)

complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)

A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).


The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.

# String Handling

‘Hello Python’

#single (') Quoted String

“Hello Python”

# Double (") Quoted String

“”“Hello Python”“”

‘’‘Hello Python’‘’

# triple (‘’') (“”") Quoted String

In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.

The operator “+” is used to concatenate strings and “*” is used to repeat the string.


output**:****‘Hello python’**

"python "*****2

'Output : Python python ’

#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type

Ray  Patel

Ray Patel


Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Art  Lind

Art Lind


Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development

Paula  Hall

Paula Hall


3 Python Pandas Tricks for Efficient Data Analysis

Explained with examples.

Pandas is one of the predominant data analysis tools which is highly appreciated among data scientists. It provides numerous flexible and versatile functions to perform efficient data analysis.

In this article, we will go over 3 pandas tricks that I think will make you a more happy pandas user. It is better to explain these tricks with some examples. Thus, we start by creating a data frame to wok on.

The data frame contains daily sales quantities of 3 different stores. We first create a period of 10 days using the date_range function of pandas.

import numpy as np
import pandas as pd

days = pd.date_range("2020-01-01", periods=10, freq="D")

The days variable will be used as a column. We also need a sales quantity column which can be generated by the randint function of numpy. Then, we create a data frame with 3 columns for each store.

#machine-learning #data-science #python #python pandas tricks #efficient data analysis #python pandas tricks for efficient data analysis