Mirodil

(function(){
	
	angular.module('app', [])
	.config(['$httpProvider', function($httpProvider) {
	  $httpProvider.interceptors.push(['$q', '$window', '$timeout', '$injector', function ($q, $window, $timeout, $injector) {
	    var $login, $http, $auth;
      $timeout(function () {
        $login = $injector.get('$login');
        $http = $injector.get('$http');
        $auth = $injector.get('$auth');

Mirodil

import numpy as np
from keras import backend as K
from keras.preprocessing.image import Iterator, load_img, img_to_array

class FileListIterator(Iterator):
    """Iterator capable of reading images from an array of the filenames.
    # Arguments
        filenames: Path to the directory to read images from.
            Each subdirectory in this directory will be
            considered to contain images from one class,

Mirodil

class LearningRateFinder(Callback):
    '''
    This callback implements a learning rate finder(LRF)
    The learning rate is constantly increased during training.
    On training end, the training loss is plotted against the learning rate.
    One may choose a learning rate for a model based on the given graph,
    selecting a value slightly before the minimal training loss.
    # Example
        lrf = LearningRateFinder([0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05])
        model.fit(x_train, y_train, epochs=1, batch_size=128, callbacks=[lrf])

Mirodil

def find_learning_rate(model, data_loader, criterion, lr:tuple=(1e-7, 1), epochs:int=1):

    history = []
    min_lr, max_lr = lr
    num_batches = epochs * len(data_loader)
    last_avg_loss, i, beta = 0, 0, 0.98

    # preserve initial state
    initial_weights = './temp.model'
    torch.save(model.state_dict(), initial_weights)

Mirodil

Scenario

Program turnstile that controls the operation of a subway turnstile ( gate that you have to walk through before you get to the subway train)

Mirodil

// Much of the Node.js core API is built around an idiomatic asynchronous event-driven architecture 
// in which certain kinds of objects (called "emitters") emit named events that cause 
// Function objects ("listeners") to be called.
Class: EventEmitter
    // Synchronously calls each of the listeners registered for the event named eventName, 
    // in the order they were registered, passing the supplied arguments to each.
    emit(eventName[, ...args])

    // Removes the specified listener from the listener array for the event named eventName.