jeasinema

#!/usr/bin/env python
# -*- coding:UTF-8 -*-

import torch
import torch.nn as nn
import torch.nn.init as init


def weight_init(m):
    '''

yzh119

from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

def sample_gumbel(shape, eps=1e-20):
    U = torch.rand(shape).cuda()
    return -Variable(torch.log(-torch.log(U + eps) + eps))

kaniblu

def init_gru(cell, gain=1):
    cell.reset_parameters()

    # orthogonal initialization of recurrent weights
    for _, hh, _, _ in cell.all_weights:
        for i in range(0, hh.size(0), cell.hidden_size):
            I.orthogonal(hh[i:i + cell.hidden_size], gain=gain)


def init_lstm(cell, gain=1):