GitHubEmploy

const week = (date) => date.getDay()% 6 !== 0;
console.log(week(new Date(2021, 0, 11))); // Result: true (Monday)
console.log(week(new Date(2021, 0, 15))); // Result: false (Sunday)

GitHubEmploy

const randomBoolean = () => Math.random() >= 0.5;
console.log(randomBoolean());

GitHubEmploy

import time

start_time = time.time()

for loop in range(1, 100000000): 
    pass

print('FINISHED EXECUTION')
print("--- %s seconds ---" % round(time.time() - start_time, 4))

GitHubEmploy

// ==UserScript==
// @name		Geoguessr Cheat
// @namespace		https://www.leonbrandt.com
// @version		2.0.0
// @description		Press SHIFT + ALT + G and the location of your current geoguessr game will open in a new tab
// @author		Leon Brandt
// @homepage		https://www.leonbrandt.com
// @updateURL		https://gist.githubusercontent.com/leonbrandt/16b3a70ef70939359357c908e6b0f06d/raw/geoguessr-cheat.user.js
// @match		http*://*/*
// @grant		none

GitHubEmploy

torch.save(model.state_dict(), 'model.ckpt')

GitHubEmploy

with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        images = images.to(device)
        labels = labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

GitHubEmploy

# Train the model
total_step = len(train_loader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        # Move tensors to the configured device
        images = images.to(device)
        labels = labels.to(device)

        # Forward pass
        outputs = model(images)

GitHubEmploy

model.to(device)

GitHubEmploy

optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
loss_function = nn.CrossEntropyLoss()

GitHubEmploy

# MNIST dataset
train_dataset = torchvision.datasets.MNIST(root='data',
                                           train=True, 
                                           transform=transforms.ToTensor(),
                                           download=True)

test_dataset = torchvision.datasets.MNIST(root='data',
                                          train=False,
                                          transform=transforms.ToTensor())