dylandjian

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.multiprocessing as multiprocessing
import time

DEVICE = torch.device("cuda")

class Model1(nn.Module):
    def __init__(self):

dylandjian

def gaussian_distribution(y, mu, sigma):
    ## Prepare the input to be transformed into a gaussian distribution
    y = y.unsqueeze(1)
    y = y.expand(-1, GAUSSIANS, LATENT_VEC)
    res = torch.exp((-0.5 * ((y - mu) / sigma) ** 2) / TEMPERATURE)

    result = MDN_CONST * res / sigma
    return result

dylandjian

import numpy as np
from torch.autograd import Variable
import torch
import torch.nn as nn
import torch.nn.functional as F



# length of board
L = 10

dylandjian

import multiprocessing
import multiprocessing.pool
import torch


class NoDaemonProcess(multiprocessing.Process):
    # make 'daemon' attribute always return False
    def _get_daemon(self):
        return False
    def _set_daemon(self, value):