carlosgmartin

from itertools import cycle, count
import string

alphabet = string.ascii_lowercase + string.ascii_uppercase + ' ' + string.digits + string.punctuation

encode = dict(zip(alphabet, count())).get
decode = dict(zip(count(), alphabet)).get

add = lambda a, b: decode((encode(b) + encode(a)) % len(alphabet))
sub = lambda a, b: decode((encode(b) - encode(a)) % len(alphabet))

carlosgmartin

import torch
import torchvision
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.backend_bases import MouseEvent
import torch

def onclick(event):
    x = torch.tensor([event.ydata, event.xdata]).float()
    d = (y - x).float().norm(dim=-1)

carlosgmartin

import numpy as np
from time import sleep
from itertools import count
from pdb import set_trace
import matplotlib.pyplot as plt

def softmax(x):
    y = np.exp(x - x.max(-1, keepdims=True))
    return y / y.sum(-1, keepdims=True)

carlosgmartin

import numpy
import torch
import torchvision
import matplotlib.pyplot as plt

def relu(x):
    return numpy.maximum(0, x)

def relu_derivative(x):
    return (x > 0).astype(float)