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# Typical setup to include TensorFlow.
import tensorflow as tf

# Make a queue of file names including all the JPEG images files in the relative
# image directory.
filename_queue = tf.train.string_input_producer(
    tf.train.match_filenames_once("./images/*.jpg"))

# Read an entire image file which is required since they're JPEGs, if the images
# are too large they could be split in advance to smaller files or use the Fixed

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// From http://stackoverflow.com/questions/9128519/reading-many-values-from-numpy-c-api
/*
Make

g++ -o matrix_multiply.o -c matrix_multiply.cpp  -I{python include file  for numpy/noprefix.h} -I{python include}  -fPIC -O2 -Wno-deprecated -Wno-write-strings
g++ -o matrix_multiply.so -shared matrix_multiply.o -L{boost lib path} -lz -lm -ldl -lpthread -boost_python

Python code:
import numpy
import matrix_multiply

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""" Deep Auto-Encoder implementation
	
	An auto-encoder works as follows:

	Data of dimension k is reduced to a lower dimension j using a matrix multiplication:
	softmax(W*x + b)  = x'
	
	where W is matrix from R^k --> R^j

	A reconstruction matrix W' maps back from R^j --> R^k

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import numpy as np
import tensorflow as tf

# N, size of matrix. R, rank of data
N = 100
R = 5

# generate data
W_true = np.random.randn(N,R)
C_true = np.random.randn(R,N)

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import numpy as np
import tensorflow as tf

# N, size of matrix. R, rank of data
N = 100
R = 5

# generate data
W_true = np.random.randn(N,R)
C_true = np.random.randn(R,N)

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// memdjpeg - A super simple example of how to decode a jpeg in memory
// Kenneth Finnegan, 2012
// blog.thelifeofkenneth.com
//
// After installing jpeglib, compile with:
// cc memdjpeg.c -ljpeg -o memdjpeg
//
// Run with:
// ./memdjpeg filename.jpg
//

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# Check if SSE instructions are available on the machine where 
# the project is compiled.

MACRO (FindSSE)

IF(CMAKE_SYSTEM_NAME MATCHES "Linux")
   EXEC_PROGRAM(cat ARGS "/proc/cpuinfo" OUTPUT_VARIABLE CPUINFO)

   STRING(REGEX REPLACE "^.*(sse2).*$" "¥¥1" SSE_THERE ${CPUINFO})
   STRING(COMPARE EQUAL "sse2" "${SSE_THERE}" SSE2_TRUE)

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name	caffemodel	caffemodel_url	license	sha1	caffe_commit
Vanilla CNN Model	vanillaCNN.caffemodel	https://github.com/ishay2b/VanillaCNN/blob/master/ZOO/vanillaCNN.caffemodel?raw=true	unrestricted	b5e34ce75d078025e07452cb47e65d198fe27912	9c9f94e18a8909580a6b94c44dbb1e46f0ee8eb8

Implementation of the Vanilla CNN described in the paper: Yue Wu and Tal Hassner, "Facial Landmark Detection with Tweaked Convolutional Neural Networks", arXiv preprint arXiv:1511.04031, 12 Nov. 2015. See project page for more information about this project.