jojonki

refs = []
while True:
    try:
        ref = input()
        refs.append(ref)
    except EOFError:
        break

jojonki

ああああああああああああああああ
ああああああああああああああああ
ああああああああああああああああ
ああああああああああああああああ
ああああああああああああああああ
ああああああああああああああああ
ああああああああああああああああ
ああああああああああああああああ
ああああああああああああああああ
ああああああああああああああああ

jojonki

find . -type f -print0 | xargs -0 nkf --overwrite -w -Lu

jojonki

import logging

logging.basicConfig(level=logging.DEBUG,
                    filename='output.log',
                    format='%(asctime)s [%(levelname)-7s] %(module)s | %(message)s',
                    datefmt='%Y%m%d-%H%M%S')

console = logging.StreamHandler()
console.setLevel(logging.DEBUG)
formatter = logging.Formatter('[%(levelname)-7s] %(message)s')

jojonki

# see original code
# https://discuss.pytorch.org/t/solved-titan-v-on-pytorch-0-3-0-cuda-9-0-cudnn-7-0-is-much-slower-than-1080-ti/11320/10?u=jef

import torch
from torchvision.models import vgg16,densenet121,resnet152
from time import time
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim
from torch.autograd import Variable

jojonki

def to_var(x):
    if torch.cuda.is_available():
        x = x.cuda()
    return torch.autograd.Variable(x)

def to_categorical(ys, n_classes):
    # 1-hot encodes tensors
    if len(ys) == 0:
        return ys
    d = [to_var(torch.from_numpy(np.eye(n_classes, dtype='uint8')[y])) for y in ys]

jojonki

from gensim.models.keyedvectors import KeyedVectors
model_path = './data/GoogleNews-vectors-negative300.bin'
model = KeyedVectors.load_word2vec_format('./data/GoogleNews-vectors-negative300.bin', binary=True)  
model.wv['computer'] # array([  1.07421875e-01,  -2.01171875e-01,   1.23046875e-01,
 
# you can use this weights like this.
def load_embd_weights(word2vec, vocab_size, embd_size, w2i):
    embedding_matrix = np.zeros((vocab_size, embd_size))
    print('embed_matrix.shape', embedding_matrix.shape)
    found_ct = 0

jojonki

# https://github.com/jcjohnson/pytorch-examples
for t in range(500):
  # Forward pass: compute predicted y by passing x to the model. Module objects
  # override the __call__ operator so you can call them like functions. When
  # doing so you pass a Variable of input data to the Module and it produces
  # a Variable of output data.
  y_pred = model(x)

  # Compute and print loss. We pass Variables containing the predicted and true
  # values of y, and the loss function returns a Variable containing the loss.

jojonki

# How to apply exponential moving average decay for variables? 
# https://discuss.pytorch.org/t/how-to-apply-exponential-moving-average-decay-for-variables/10856/2
class EMA(nn.Module):
    def __init__(self, mu):
        super(EMA, self).__init__()
        self.mu = mu
        
    def forward(self,x, last_average):
        new_average = self.mu*x + (1-self.mu)*last_average
        return new_average

jojonki

for name, param in model.named_parameters():
    if param.requires_grad:
        print(name, param.data)