subhadarship

## collate_fn_example.py
import torch
from torch.utils.data import Dataset, DataLoader
import numpy as np


class MyDataset(Dataset):
    def __init__(self):
        x = np.random.rand(1000, 3)  # 1000 3-dim samples
        self.x = [x[i].tolist() for i in range(1000)]
        y = np.random.randint(low=0, high=2, size=(1000,))

## byte_pair_encoding.ipynb

      
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                subhadarship
                / byte_pair_encoding.ipynb
            
            
              Created
              January 31, 2020 01:10
            
          
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## net_using_pytorch.py
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader

from tqdm import tqdm
import matplotlib.pyplot as plt


class MyDataset(Dataset):
    def __init__(self):
	import torch
	from torch.utils.data import Dataset, DataLoader
	import numpy as np


	class MyDataset(Dataset):
	def __init__(self):
	x = np.random.rand(1000, 3) # 1000 3-dim samples
	self.x = [x[i].tolist() for i in range(1000)]
	y = np.random.randint(low=0, high=2, size=(1000,))
	import torch
	from torch import nn
	from torch.utils.data import Dataset, DataLoader

	from tqdm import tqdm
	import matplotlib.pyplot as plt


	class MyDataset(Dataset):
	def __init__(self):