arafatkatze

from sympy import *
import time
matrix = Matrix([   [ 1.0,  2.0, 1.0] ,  [-2.0, -3.0, 1.0] ,  [ 3.0,  5.0, 0.0]  ])
start = time.clock()
for x in range(0, 1000):
  matrix.berkowitz()
print time.clock() - start
#2.103344

arafatkatze

require 'nmatrix'
require 'time'
def timer n
  matrix = NMatrix.new([3,3], [1.0,  2.0, 1.0, -2.0, -3.0, 1.0 ,3.0,  5.0, 0.0])  
  now = Time.now.to_f  
  n.times do 
  	matrix.charpoly
  end
  endd = Time.now.to_f  
  endd-now

arafatkatze

/////////////////////////////////////////////////////////////////////
// = NMatrix
//
// A linear algebra library for scientific computation in Ruby.
// NMatrix is part of SciRuby.
//
// NMatrix was originally inspired by and derived from NArray, by
// Masahiro Tanaka: http://narray.rubyforge.org
//
// == Copyright Information

arafatkatze

import tensorflow as tf
import numpy as np
input1 = tf.placeholder(tf.int64, shape=(2, 2), name = "input1")
input2 = tf.placeholder(tf.int64, shape=(2, 2), name = "input2")
output = tf.add(input1, input2, name = "output")

with tf.Session() as sess:
 tf.train.write_graph(sess.graph_def, "model/", "graph.pb", as_text=False)

arafatkatze

import tensorflow as tf
a = tf.Graph()
input1 = tf.placeholder(tf.float32, shape=(2))
input2 = tf.placeholder(tf.float32, shape=(2))
output = tf.mul(input1, input2)

with tf.Session() as sess:
  print(sess.run([output], feed_dict={input1:[7,2], input2:[2,4]}))
  tf.train.write_graph(sess.graph_def, 'models/', 'test_graph_multi_dim.pb', as_text=True)
    

arafatkatze

node {
  name: "Placeholder"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {

arafatkatze

import tensorflow as tf
from tensorflow.python.platform import gfile
import sys

def converter(filename): 
  with gfile.FastGFile(filename,'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    tf.import_graph_def(graph_def, name='')
    tf.train.write_graph(graph_def, 'pbtxt/', 'protobuf.pbtxt', as_text=True)

arafatkatze

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arafatkatze

# This file is useful for reading the contents of the ops generated by ruby.
# You can read any graph defination in pb/pbtxt format generated by ruby
# or by python and then convert it back and forth from human readable to binary format.

import tensorflow as tf
from google.protobuf import text_format
from tensorflow.python.platform import gfile

def pbtxt_to_graphdef(filename):
  with open(filename, 'r') as f:

arafatkatze

3
input1Placeholder*
dtype0	*
shape:
3
input2Placeholder*
dtype0	*
shape:
&