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

The top five results are 
golden retriever
0.7964490652084351
Labrador retriever
0.0944252461194992
kuvasz
0.03383997827768326
clumber
0.005355054046958685
Great Pyrenees

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

require 'tensorflow'
graph_def = Tensorflow::GraphDef.new
node_a = Tensorflow::NodeDef.new(name: "Placeholder", op: "Placeholder", attr: [])
node_a.attr.push(Tensorflow::NodeDef::AttrEntry.new(key: "dtype" ,value: Tensorflow::AttrValue.new(type: 1)))
node_a.attr.push(Tensorflow::NodeDef::AttrEntry.new(key: "shape" , value: Tensorflow::AttrValue.new(shape: Tensorflow::TensorShapeProto.new(dim: [Tensorflow::TensorShapeProto::Dim.new(size: 2)]))))
graph_def.node.push(node_a)

node_b = Tensorflow::NodeDef.new(name: "Placeholder_1", op: "Placeholder", attr: [])
node_b.attr.push(Tensorflow::NodeDef::AttrEntry.new(key: "dtype" ,value: Tensorflow::AttrValue.new(type: 1)))
node_b.attr.push(Tensorflow::NodeDef::AttrEntry.new(key: "shape" , value: Tensorflow::AttrValue.new(shape: Tensorflow::TensorShapeProto.new(dim: [Tensorflow::TensorShapeProto::Dim.new(size: 2)]))))

arafatkatze

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

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

require 'tensorflow'
graph = Tensorflow::Graph.new
tensor_1 = Tensorflow::Tensor.new([[      [2.0,5.0],
                          [1.0,-20.0]],

                          [[124.0,5.0],
                           [53.0,-2.0]],

                          [[1.0,0.0],
                           [0.0,1.0]]

arafatkatze

require 'tensorflow'
graph = Tensorflow::Graph.new
tensor_1 = Tensorflow::Tensor.new([[2, 2.3], [ 10, 6.2]])
tensor_2 = Tensorflow::Tensor.new([[4, 3.2], [ 47, 1.2]])
placeholder_1 = graph.placeholder('tensor1', tensor_1.type_num)
placeholder_2 = graph.placeholder('tensor2', tensor_2.type_num)
opspec = Tensorflow::OpSpec.new('Addition_of_tensors', 'Add', nil, [placeholder_1, placeholder_2])

op = graph.AddOperation(opspec)
session_op = Tensorflow::Session_options.new

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

/////////////////////////////////////////////////////////////////////
// = 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