karpathy

--[[ 
   This layer expects an [n x d] Tensor and normalizes each
   row to have unit L2 norm.
]]--
local L2Normalize, parent = torch.class('nn.L2Normalize', 'nn.Module')
function L2Normalize:__init()
   parent.__init(self)
end
function L2Normalize:updateOutput(input)

karpathy

--[[ 
Efficient LSTM in Torch using nngraph library. This code was optimized 
by Justin Johnson (@jcjohnson) based on the trick of batching up the 
LSTM GEMMs, as also seen in my efficient Python LSTM gist.
--]]

function LSTM.fast_lstm(input_size, rnn_size)
  local x = nn.Identity()()
  local prev_c = nn.Identity()()
  local prev_h = nn.Identity()()

karpathy

"""
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):