Jiwon Kim kjw0612

## gist:dfec9e3c9ed684a5c283
convert -density 300 \[TensorFlow\]\ Sequence-to-Sequence\ Models.pdf TensorFlow/output.png

## compress_pdf.md

      
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                kjw0612
                / compress_pdf.md
            
            
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              November 12, 2015 14:07
            
              
                Compress PDF
              
          
      View compress_pdf.md
  
      
    gs -sDEVICE=pdfwrite -dCompatibilityLevel=1.4 -dPDFSETTINGS=/screen -dNOPAUSE -dQUIET -dBATCH -sOutputFile=output.pdf input.pdf

  
## invert_alexnet_conv5_deploy.prototxt
name: "CaffeNet"
layers {
  name: "data"
  type: DATA
  top: "data"
  data_param {
    source: "/misc/lmbraid10/dosovits/Datasets/ILSVRC2012/all/val_leveldb"
    backend: LEVELDB
    batch_size: 16
    crop_size: 227

## 128x128_train.prototxt
name: "CaffeNet"
layers {
  name: "data"
  type: DATA
  top: "data"
  top: "label"
  data_param {
    source: "@YOUR_PATH_TO_DATA@/chairs_128x128_reduced/data-lmdb"
    batch_size: 64
    scale: 0.00390625

## incremental_history_search.txt
An extremely handy tool :: Incremental history searching
In terminal enter:


gedit  ~/.inputrc
Then copy paste and save:


"\e[A": history-search-backward
"\e[B": history-search-forward

## cuda library path
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

## Pull from Recent Matconvnet
git remote add -t dag matconvnet https://github.com/vlfeat/matconvnet
git pull matconvnet dag

## git_submodules.md

      
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                kjw0612
                / git_submodules.md
            
            
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              September 11, 2015 17:32
            
          
      View git_submodules.md
  
      
    git push --recurse-submodules=on-demand

  
## matconvnet_compile.txt
addpath matlab
vl_compilenn('enableGPU', 1, 'cudaRoot', '/usr/local/cuda', 'cudaMethod', 'nvcc', 'enableCudnn', 1, 'cudnnRoot', 'local/');

## neural11lines.py
X = np.array([ [0,0,1],[0,1,1],[1,0,1],[1,1,1] ])
y = np.array([[0,1,1,0]]).T
syn0 = 2*np.random.random((3,4)) - 1
syn1 = 2*np.random.random((4,1)) - 1
for j in xrange(60000):
    l1 = 1/(1+np.exp(-(np.dot(X,syn0))))
    l2 = 1/(1+np.exp(-(np.dot(l1,syn1))))
    l2_delta = (y - l2)*(l2*(1-l2))
    l1_delta = l2_delta.dot(syn1.T) * (l1 * (1-l1))
    syn1 += l1.T.dot(l2_delta)
	name: "CaffeNet"
	layers {
	name: "data"
	type: DATA
	top: "data"
	data_param {
	source: "/misc/lmbraid10/dosovits/Datasets/ILSVRC2012/all/val_leveldb"
	backend: LEVELDB
	batch_size: 16
	crop_size: 227
	An extremely handy tool :: Incremental history searching
	In terminal enter:


	gedit ~/.inputrc
	Then copy paste and save:


	"\e[A": history-search-backward
	"\e[B": history-search-forward
	git remote add -t dag matconvnet https://github.com/vlfeat/matconvnet
	git pull matconvnet dag
	addpath matlab
	vl_compilenn('enableGPU', 1, 'cudaRoot', '/usr/local/cuda', 'cudaMethod', 'nvcc', 'enableCudnn', 1, 'cudnnRoot', 'local/');
	X = np.array([ [0,0,1],[0,1,1],[1,0,1],[1,1,1] ])
	y = np.array([[0,1,1,0]]).T
	syn0 = 2*np.random.random((3,4)) - 1
	syn1 = 2*np.random.random((4,1)) - 1
	for j in xrange(60000):
	l1 = 1/(1+np.exp(-(np.dot(X,syn0))))
	l2 = 1/(1+np.exp(-(np.dot(l1,syn1))))
	l2_delta = (y - l2)(l2(1-l2))
	l1_delta = l2_delta.dot(syn1.T) * (l1 * (1-l1))
	syn1 += l1.T.dot(l2_delta)