Konstantin Verner konverner

## rename_files.py
import os

# here a path to a folder with files
PATH = r'C:\folder_to_rename'

# Filename extension
extension = '.png'

# in this case it numerates files from 0 to N, where N is a number of files.
i = 0

## gist:693906c001938f6863ae4cf2d45fd78b
import matplotlib.pyplot as plt
plt.rcParams.update({ "axes.facecolor":"383838", "axes.edgecolor":"white", "axes.labelcolor":"white", "xtick.color":"white", "ytick.color":"white"})

## gist:b63802b9a51076f03a7b8f1d02632261
def print_matrix(M,precision=3):
  M_copy = M.copy()
  M_copy = np.round(M_copy,precision)
  for row in M_copy:
    print(str("{:>10} "*len(M)).format(*row))

## gist:9f1b62ae75b240586ba8e648c4a37b62
import torch
X = torch.rand((8,8))
p = 0.3
X = (X > p)*X/(1-p) # dropout itself

## count_parameters_in_pytorch_model.py
print(sum(p.numel() for p in model.parameters() if p.requires_grad)*NUM_MODELS)

## donwnload.py
from IPython.display import FileLink
FileLink(r'file_name.csv')

## word2id.py
def get_vocab(texts):
  id2word = {0 : "<pad>", 1 : "<unk>"}
  word2id = {"<pad>" : 0, 1 : "<unk>"}
  i = 1
  for text in texts:
    for word in text:
      word = word.lower()
      if word not in word2id.keys():
        word2id[word] = i
        id2word[i] = word

## wherenan.py
nan_idx = []
for i in range(len(df)):
  if True in list(df.iloc[i].isnull()):
    nan_idx.append(i)

## cyrillic letters
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## from_kaggle.txt
!pip install kaggle
!mkdir ~/.kaggle
!cp kaggle.json ~/.kaggle/
!chmod 600 ~/.kaggle/kaggle.json
!kaggle datasets download <dataset-name>
!unzip /content/<dataset-name>.zip
	import os

	# here a path to a folder with files
	PATH = r'C:\folder_to_rename'

	# Filename extension
	extension = '.png'

	# in this case it numerates files from 0 to N, where N is a number of files.
	i = 0
	import matplotlib.pyplot as plt
	plt.rcParams.update({ "axes.facecolor":"383838", "axes.edgecolor":"white", "axes.labelcolor":"white", "xtick.color":"white", "ytick.color":"white"})
	def print_matrix(M,precision=3):
	M_copy = M.copy()
	M_copy = np.round(M_copy,precision)
	for row in M_copy:
	print(str("{:>10} "len(M)).format(row))
	import torch
	X = torch.rand((8,8))
	p = 0.3
	X = (X > p)*X/(1-p) # dropout itself
	from IPython.display import FileLink
	FileLink(r'file_name.csv')
	def get_vocab(texts):
	id2word = {0 : "<pad>", 1 : "<unk>"}
	word2id = {"<pad>" : 0, 1 : "<unk>"}
	i = 1
	for text in texts:
	for word in text:
	word = word.lower()
	if word not in word2id.keys():
	word2id[word] = i
	id2word[i] = word
	nan_idx = []
	for i in range(len(df)):
	if True in list(df.iloc[i].isnull()):
	nan_idx.append(i)
	!pip install kaggle
	!mkdir ~/.kaggle
	!cp kaggle.json ~/.kaggle/
	!chmod 600 ~/.kaggle/kaggle.json
	!kaggle datasets download <dataset-name>
	!unzip /content/<dataset-name>.zip