thuwarakeshm

import dash
import dash_core_components as dcc
import dash_html_components as html
from dash_html_components.Label import Label
from pandas.io.formats import style
import plotly.express as px
import pandas as pd
from dash.dependencies import Input, Output

app = dash.Dash(

thuwarakeshm

thuwarakeshm

repos:
-   repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v4.0.1
    hooks:
    -   id: requirements-txt-fixer

thuwarakeshm

# trainable property is available on model instances
model.trainable = False

# Also on individual layers
layer.trainable = False

thuwarakeshm

import tensorflow as tf

# create and train a keras neural network
classifier = tf.keras.models.Sequential([
    tf.keras.layers.Dense(units=1, input_shape=[1]),
    tf.keras.layers.Dense(units=28, activation='relu'),
    tf.keras.layers.Dense(units=1)
])
classifier.compile(optimizer='sgd', loss='mean_squared_error')
classifier.fit(x=[-1, 0, 1], y=[-3, -1, 1], epochs=5)

thuwarakeshm

from aim.sdk.adapters.keras import AimCallback

model.fit(x_train, y_train, epochs=epochs, callbacks=[
    AimCallback(repo='/path/to/logs/dir', experiment='experiment_name')
])

thuwarakeshm

import sys
from PyQt5.QtWidgets import QApplication, QWidget, QLabel
from PyQt5.QtGui import QIcon
from PyQt5.QtCore import pyqtSlot

def window():
   app = QApplication(sys.argv)
   widget = QWidget()

   textLabel = QLabel(widget)

thuwarakeshm

from django.urls import path

from views import sum_odd_numbers

urlpatterns = [
    path("sum_odd_numbers/<int:n>", sum_odd_numbers),
]

thuwarakeshm

INSTALLED_APPS = [
    "django.contrib.admin",
    "django.contrib.auth",
    "django.contrib.contenttypes",
    "django.contrib.sessions",
    "django.contrib.messages",
    "django.contrib.staticfiles",
    # Your app
    "myawesomeapp",
]

thuwarakeshm

from pipe import chain

nested_list = [[1, 2, 3], [4, 5, 6], [7, [8, 9]]]

unfolded_list = list(nested_list
                     | chain
                     )

print(unfolded_list)