How to get a current time and date using pyhton ?

Here are some examples using datatime module to get current time and date

# %% Import necessary library

from datetime import date , time , timezone , datetime

# %%  Run using function .now to get right present time

datetime.now() 

... datetime.datetime(2020, 8, 31, 11, 23, 42, 962037)

# %%

dt = datetime.now()

dt.strftime('%c')

... 'Mon Aug 31 10:45:55 2020'                             

# %%  To get only time

t = dt.time()
t.strftime('%r')

... '10:45:55 AM'

# %%  To get only date

d = date.today()
d.isoformat()

... '2020-08-31'

# %%  You can also use this for various style 

a.strftime('%m-%d')       # month and date 

... '08-31'   

a.strftime('%m-%a')       # month and day 

'08-Mon'

Comments

Multiple classification from many of directories

# %% Import nessacary libraries import numpy as np import pandas as pd import cv2 import matplotlib.pyplot as plt import os import glob # %% Keras Tensorflow libraries from keras import layers from keras.models import Model from keras.optimizers import RMSprop , Adam , Nadam from keras.preprocessing.image import ImageDataGenerator from keras.layers import Input, BatchNormalization, Dense, Dropout, Conv2D, Flatten, GlobalAveragePooling2D, LeakyReLU from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img # %% Path path = r 'G:/Machine Learning/Project/Lego Mnifigures Classification/dataset' open_dir = os....

Classification & Confusion Matrix & Accuracy Paradox

Classification work on voting the object belongs from which classes has more probability There are two types of classification : Binary classification : There are two classes we have ex: male-female , cat-dog , yes-not Multiple classification : There are classes more than two we have ex: traffic signs , face recognition , flower race , Digit Recognition Confusion matrix : Confusion matrix is one type of technique to evaluate the model accuracy for classification problem. In this technique we consider how many of positive and negative data points we predict correctly. The main consideration terms are accuracy, precision and recall The accuracy was an appealing matric, because it was a single number. Here precision and recall(sensitivity) are two numbers. So to get the final score (accuracy) of our model we use F1 score, so that we have a single number. Here is the F1 score's mathematical formula: F1 = 2x precision x recall / (precision ...

Digit Recognition

Here you can import digit dataset from scikit learn library which is in-built, So you don't need to download from other else Note: If you use visual code, I recommend you to turn your color theme to Monokai because it has a few extra and important keyword and attractive colors than other theme. # %% Import libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt import random # %% Load dataset from sklearn.datasets import load_digits dataset = load_digits() dataset.keys() output: d ict_keys(['data', 'target', 'target_names', 'images', 'DESCR']) You have to check all to direct print them Here DESCR is a description of dataset # %% divide the dataset into input and target inputs = dataset.data target = dataset.target # %% ...

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