xfLee

embeddings_mu = nn.Embedding(n_words, n_dim)
embeddings_lv = nn.Embedding(n_words, n_dim)
...
vector_mu = embeddings_mu(c_index)
vector_lv = embeddings_lv(c_index)

def normal(mu, lv):
    random = torch.FloatTensor(std.size()).normal_()
    return mu + random * torch.exp(0.5 * lv) 

xfLee

from torch_trainer.trainer import Trainer
from torch_trainer.callbacks import rms_callback
from torch import nn
from torch.optim import Adam
import torch.nn.functional as F

import numpy as np
import pandas as pd