Stefan Thaler stefanthaler

## group_trunc_seq_length.py
#
import tensorflow as tf
import numpy as np

from tensorflow.python.framework import ops
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.util.tf_export import tf_export
from tensorflow.python.data.experimental import group_by_window

from tensorflow.python.framework import constant_op

## python_xlib_select_spike.py
#!/usr/bin/python3

import sys
import os
from Xlib import X, display, Xutil
import select

# http://python-xlib.sourceforge.net/doc/html/python-xlib_11.html

# Application window (only one)

## ensure_gradient_flow.py
import tensorflow as tf # 1.3.0
import numpy as np

# model + input
w = tf.Variable(1, name="w", dtype=tf.float32 ) # parameter to optimize for
x = tf.placeholder(shape=(),  dtype=tf.float32, name="x") # input

# graph operations
diff_op = tf.multiply(w, x) # an operation that is differentiable
non_diff_op = tf.cast(tf.equal(diff_op, 0), dtype=tf.float32) # operation that is non differentiable

## check_reference_links.py
# needs     bibtexparser==0.6.2, argparse
"""
    Parses a Bib file and checks if all links are available
"""

# imports
import bibtexparser
import argparse
import re
import requests

## tensorflow-dynamic-rnn-explained.ipynb

      
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                stefanthaler
                / tensorflow-dynamic-rnn-explained.ipynb
            
            
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              February 22, 2017 16:13
            
              
                Tensorflow Dynamic RNN explained
              
          
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## colorize_word_embeddings.py
"""
    Simple example to demostrate the embedding visualization for word embeddings in tensorflow / tensorboard
    https://www.tensorflow.org/how_tos/embedding_viz/
"""

import tensorflow as tf
import os
assert tf.__version__ == '1.0.0-rc0' # if code breaks, check tensorflow version
from tensorflow.contrib.tensorboard.plugins import projector

## lstm_backwards_embedding.py
from keras.models import Sequential # model used
from keras.layers import Dense, Embedding, LSTM # layers used

batch_size = 2 # how many sequence to process in parallel
time_steps = 3 # lstm length, number of cells, etc.
input_dim = 1 # number of features
embedding_size = 5 # size of embedding vector

model = Sequential()
model.add(Embedding(

## stateful_lstm_embedding.py
"""
Learning Task:
    Given a sequence, predict a label based on the first value of the sequence

Explanation of stateful LSTM and setup:
    http://philipperemy.github.io/keras-stateful-lstm/

Exmple:
    given a sequence [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], predict 1
    given a sequence [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], predict 0

## fischer_wagner_explained.py
import numpy as np
from scipy.stats import entropy # calculate kl divergence #http://scipy.github.io/devdocs/generated/scipy.stats.entropy.html
np.set_printoptions(precision=2)
# https://en.wikipedia.org/wiki/Wagner%E2%80%93Fischer_algorithm
# Original Paper http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.367.5281&rep=rep1&type=pdf

deletion_cost = 1
insertion_cost = 1
substitution_cost = 1

## nutritients_lp.py
# sudo pip install pulp
# sudo apt-get install python-glpk gplk-utils
from pulp import *

optimization_problem = LpProblem("Nutrients Optimization", LpMinimize)

# Cost Parameters for Selecting a food
# diversity (minimum amount of certain foods)
# diversity (minimum amount of vegetables)
# multiple cost factors (money, co2, water)
	#
	import tensorflow as tf
	import numpy as np

	from tensorflow.python.framework import ops
	from tensorflow.python.data.ops import dataset_ops
	from tensorflow.python.util.tf_export import tf_export
	from tensorflow.python.data.experimental import group_by_window

	from tensorflow.python.framework import constant_op
	#!/usr/bin/python3

	import sys
	import os
	from Xlib import X, display, Xutil
	import select

	# http://python-xlib.sourceforge.net/doc/html/python-xlib_11.html

	# Application window (only one)
	import tensorflow as tf # 1.3.0
	import numpy as np

	# model + input
	w = tf.Variable(1, name="w", dtype=tf.float32 ) # parameter to optimize for
	x = tf.placeholder(shape=(), dtype=tf.float32, name="x") # input

	# graph operations
	diff_op = tf.multiply(w, x) # an operation that is differentiable
	non_diff_op = tf.cast(tf.equal(diff_op, 0), dtype=tf.float32) # operation that is non differentiable
	# needs bibtexparser==0.6.2, argparse
	"""
	Parses a Bib file and checks if all links are available
	"""

	# imports
	import bibtexparser
	import argparse
	import re
	import requests
	"""
	Simple example to demostrate the embedding visualization for word embeddings in tensorflow / tensorboard
	https://www.tensorflow.org/how_tos/embedding_viz/
	"""

	import tensorflow as tf
	import os
	assert tf.__version__ == '1.0.0-rc0' # if code breaks, check tensorflow version
	from tensorflow.contrib.tensorboard.plugins import projector
	from keras.models import Sequential # model used
	from keras.layers import Dense, Embedding, LSTM # layers used

	batch_size = 2 # how many sequence to process in parallel
	time_steps = 3 # lstm length, number of cells, etc.
	input_dim = 1 # number of features
	embedding_size = 5 # size of embedding vector

	model = Sequential()
	model.add(Embedding(
	"""
	Learning Task:
	Given a sequence, predict a label based on the first value of the sequence

	Explanation of stateful LSTM and setup:
	http://philipperemy.github.io/keras-stateful-lstm/

	Exmple:
	given a sequence [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], predict 1
	given a sequence [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], predict 0
	import numpy as np
	from scipy.stats import entropy # calculate kl divergence #http://scipy.github.io/devdocs/generated/scipy.stats.entropy.html
	np.set_printoptions(precision=2)
	# https://en.wikipedia.org/wiki/Wagner%E2%80%93Fischer_algorithm
	# Original Paper http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.367.5281&rep=rep1&type=pdf

	deletion_cost = 1
	insertion_cost = 1
	substitution_cost = 1
	# sudo pip install pulp
	# sudo apt-get install python-glpk gplk-utils
	from pulp import *

	optimization_problem = LpProblem("Nutrients Optimization", LpMinimize)

	# Cost Parameters for Selecting a food
	# diversity (minimum amount of certain foods)
	# diversity (minimum amount of vegetables)
	# multiple cost factors (money, co2, water)