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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 |
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import matplotlib.pyplot as plt | |
plt.rcParams.update({ "axes.facecolor":"383838", "axes.edgecolor":"white", "axes.labelcolor":"white", "xtick.color":"white", "ytick.color":"white"}) |
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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)) |
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import torch | |
X = torch.rand((8,8)) | |
p = 0.3 | |
X = (X > p)*X/(1-p) # dropout itself |
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print(sum(p.numel() for p in model.parameters() if p.requires_grad)*NUM_MODELS) |
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from IPython.display import FileLink | |
FileLink(r'file_name.csv') |
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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 |
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nan_idx = [] | |
for i in range(len(df)): | |
if True in list(df.iloc[i].isnull()): | |
nan_idx.append(i) |
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АБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЭЮЯабвгдежзийклмнопрстуфхцчшщъыьэюяё |
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!pip install kaggle | |
!mkdir ~/.kaggle | |
!cp kaggle.json ~/.kaggle/ | |
!chmod 600 ~/.kaggle/kaggle.json | |
!kaggle datasets download <dataset-name> | |
!unzip /content/<dataset-name>.zip |
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