Haque Ishfaq hmishfaq

## gist:587454dc0146a6ae21fc
"""
This is a batched LSTM forward and backward pass
"""
import numpy as np
import code

class LSTM:

  @staticmethod
  def init(input_size, hidden_size, fancy_forget_bias_init = 3):

## min-char-rnn.py
"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

## rf.ipynb

      
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              12 stars
            
          
                arunmallya
                / rf.ipynb
            
            
              Last active
              March 30, 2023 09:29
            
              
                A Jupyter notebook to get the receptive field and effective stride of layers in a CNN. Supports dilated convolutions as well.
              
          
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## pytorch_finetune.py
import argparse
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.utils.data import DataLoader

import torchvision
import torchvision.transforms as T
from torchvision.datasets import ImageFolder
	"""
	This is a batched LSTM forward and backward pass
	"""
	import numpy as np
	import code

	class LSTM:

	@staticmethod
	def init(input_size, hidden_size, fancy_forget_bias_init = 3):
	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)
	import argparse
	import torch
	import torch.nn as nn
	from torch.autograd import Variable
	from torch.utils.data import DataLoader

	import torchvision
	import torchvision.transforms as T
	from torchvision.datasets import ImageFolder