Andreas Stergioulas andrster1

andrster1 / gist:91177df0146681eb1bb4cc9cd0976c61

Created March 1, 2019 12:27 — forked from karpathy/gist:7bae8033dcf5ca2630ba

Efficient LSTM cell in Torch

	--[[
	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()()

andrster1 / gist:f688f90cdd4e9e25301475c63cc12b7b

Created March 1, 2019 12:27 — forked from karpathy/gist:f3ee599538ff78e1bbe9

Batched L2 Normalization Layer for Torch nn package


	--[[
	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)

andrster1 / min-char-rnn.py

Created March 1, 2019 12:26 — forked from karpathy/min-char-rnn.py

Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy

	"""
	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)