ayooshkathuria

import matplotlib.pyplot as plt 
import pandas as pd
import plotly.express as px
pd.options.plotting.backend = "plotly"


UTILIZATION = 0.1

OP_UPFRONT_COST = 3300
ELECTRICTY_COST_KWH = 0.13 

ayooshkathuria

import numpy as np 
import matplotlib.pyplot as plt 
import random
import time 

# Size of dataset to be generated. The final size is 4 * data_size
data_size = 1000
num_iters = 50
num_clusters = 4

ayooshkathuria

import numpy as np 
import matplotlib.pyplot as plt 
import random
import time 

# Size of dataset to be generated. The final size is 4 * data_size
data_size = 1000
num_iters = 50
num_clusters = 4

ayooshkathuria

import numpy as np 
import matplotlib.pyplot as plt 
import random
import time

# Size of dataset to be generated. The final size is 4 * data_size
data_size = 1000
num_iters = 50
num_clusters = 4

ayooshkathuria

import os 
from shutil import copy
import random 
from tqdm import tqdm

partition_percentage = 90 
annotations_dir = 'GauGAN_Annotations'
annotations_files = os.listdir(annotations_dir)
annotations_files = [os.path.join(os.path.realpath("."), annotations_dir, x) for x in annotations_files]

ayooshkathuria

import numpy as np
import os 
import cv2
from copy import deepcopy
from matplotlib import pyplot as plt
from tqdm import tqdm
from shutil import copy

with open("label_colors.txt", "r") as file:
    label_colors = file.read().split("\n")[:-1]

ayooshkathuria

import numpy as np
import os 
import cv2
from copy import deepcopy
from matplotlib import pyplot as plt
from tqdm import tqdm
from shutil import copy

with open("label_colors.txt", "r") as file:
    label_colors = file.read().split("\n")[:-1]

ayooshkathuria

Gradient of L w.r.t to w1: -36.0 

Gradient of L w.r.t to w2: -28.0 

Gradient of L w.r.t to w3: -8.0 

Gradient of L w.r.t to w4: -20.0 

ayooshkathuria

from torch import FloatTensor
from torch.autograd import Variable


# Define the leaf nodes
a = Variable(FloatTensor([4]))

weights = [Variable(FloatTensor([i]), requires_grad=True) for i in (2, 5, 9, 7)]

# unpack the weights for nicer assignment

ayooshkathuria

import torch 

x = torch.Tensor(5, 4)
print(x)