Re: [Dev] TensorFlow Stream Processor Extension

2017-11-10 Thread Sriskandarajah Suhothayan 
Can we agree on a conversion on naming the output.
Please share what you have decided so far.

Regards
Suho

On Fri, Nov 10, 2017 at 6:48 AM Niruhan Viswarupan  wrote:

> I could get the extension to make predictions from the KMeans saved model
> I prepared. Please find the extension here
> 
>  and
> the test case here
> 
>
> Regards,
>
>
> *Niruhan Viswarupan*
> Engineering Intern | WSO2
> Phone: +94776468013
>
> On Fri, Nov 10, 2017 at 10:42 AM, Niruhan Viswarupan 
> wrote:
>
>> [-Analytics-group, -engineering-group, + dev]
>>
>> The output tensor can be a single scalar value or a multidimensional
>> array. It totally *depends on the model* and how its output is
>> configured.
>>
>> For example,
>>
>>1. if we have a model for classification and its prediction is
>>configured to send a string of the class name then we will get a *scalar
>>(A Tensor of 0 dimension)* string tensor.
>>2. If we have a clustering model it can output the coordinates of the
>>closest centroid as a tensor which can be a *vector (A tensor of 1
>>dimension) *with any length (For example vector length will be 2 in
>>the case of Cartesian plane, 3 in the case of 3D coordinate system etc).
>>
>> *(Note:* The above examples have only a few values in output Tensors.
>> But it is possible for someone to build a TensorFlow model that sends out a
>> Tensor with thousands of dimensions and thousands of length per dimension.)
>>
>> *Terminology:*
>> eg. Consider a 3x4 *matrix (a tensor of 2 dimensions)* as below,
>>
>> 1 2 3 4
>> 5 6 7 8
>> 9 1 2 3
>> This has 2 dimensions and length of 1st dimension is 3 and the 2nd
>> dimension is 4.
>>
>> Also *problems identified in sending out multidimensional arrays in the
>> output stream* are,
>>
>>1. Its a *challenge to create primitive type multidimensional arrays*
>>(This is the type supported by Tensors copying method) whose *dimensions
>>are unknown at compile time* in Java (I welcome any suggestion as to
>>how this could be done)
>>2. As pointed out by Tishan *other extensions of Siddhi are not
>>configured to handle arrays*. So cannot process the output arrays of
>>this extension further in Siddhi
>>
>> Considering the above issues we decided to flatten the Tensor and send
>> all the values in a tensor one by one in primitive type. *In most cases
>> output Tensor will have a small number of values*.
>>
>> Regards,
>>
>> *Niruhan Viswarupan*
>> Engineering Intern | WSO2
>> Phone: +94776468013 <+94%2077%20646%208013>
>>
>> On Thu, Nov 9, 2017 at 11:41 PM, Sriskandarajah Suhothayan > > wrote:
>>
>>> Does Tensor always output multidimensional array or only for some cases?
>>> If it returns a multidimensional array what sort of processing the user
>>> should be doing to get sense out of it?
>>>
>>> My doubt is having a way to send multidimensional array out will solve
>>> the user's problem.
>>>
>>> Regards
>>> Suho
>>>
>>> On Thu, Nov 9, 2017 at 8:45 AM, Niruhan Viswarupan 
>>> wrote:
>>>
 Hi,

 This is regarding supporting predictions from TensorFlow saved models
 using stream processor.

 The *predictions (outputs) from a TensorFlow model in Java API will be
 a Tensor
 
 object*. This can have any number of dimensions and any shape. This
 means that *we will not know the shape of the output multidimensional
 array in the compile time*. This will only come to light in the run
 time when the user input model is loaded. I was stuck with creating
 multidimensional arrays of run time specified shape. It is solvable for
 object arrays since they can be recursively built. But the copyTo()
 
  method
 of Tensor works only for primitive type arrays.

 So after a discussion with Tishan we *decided to flatten the
 multidimensional Tensor* and output them as one-by-one primitive types
 that Siddhi handles. For example a 2x3 matrix Tensor [ [1,2,3], [4,5,6] ]
 will be output as 6 separate int into the event.

 Please find the *user story here
 *
  and
 the preliminary *coding works here
 *
 .

 Regards,

Re: [Dev] TensorFlow Stream Processor Extension

2017-11-09 Thread Niruhan Viswarupan 
I could get the extension to make predictions from the KMeans saved model I
prepared. Please find the extension here

and
the test case here


Regards,


*Niruhan Viswarupan*
Engineering Intern | WSO2
Phone: +94776468013

On Fri, Nov 10, 2017 at 10:42 AM, Niruhan Viswarupan 
wrote:

> [-Analytics-group, -engineering-group, + dev]
>
> The output tensor can be a single scalar value or a multidimensional
> array. It totally *depends on the model* and how its output is configured.
>
> For example,
>
>1. if we have a model for classification and its prediction is
>configured to send a string of the class name then we will get a *scalar
>(A Tensor of 0 dimension)* string tensor.
>2. If we have a clustering model it can output the coordinates of the
>closest centroid as a tensor which can be a *vector (A tensor of 1
>dimension) *with any length (For example vector length will be 2 in
>the case of Cartesian plane, 3 in the case of 3D coordinate system etc).
>
> *(Note:* The above examples have only a few values in output Tensors. But
> it is possible for someone to build a TensorFlow model that sends out a
> Tensor with thousands of dimensions and thousands of length per dimension.)
>
> *Terminology:*
> eg. Consider a 3x4 *matrix (a tensor of 2 dimensions)* as below,
>
> 1 2 3 4
> 5 6 7 8
> 9 1 2 3
> This has 2 dimensions and length of 1st dimension is 3 and the 2nd
> dimension is 4.
>
> Also *problems identified in sending out multidimensional arrays in the
> output stream* are,
>
>1. Its a *challenge to create primitive type multidimensional arrays*
>(This is the type supported by Tensors copying method) whose *dimensions
>are unknown at compile time* in Java (I welcome any suggestion as to
>how this could be done)
>2. As pointed out by Tishan *other extensions of Siddhi are not
>configured to handle arrays*. So cannot process the output arrays of
>this extension further in Siddhi
>
> Considering the above issues we decided to flatten the Tensor and send all
> the values in a tensor one by one in primitive type. *In most cases
> output Tensor will have a small number of values*.
>
> Regards,
>
> *Niruhan Viswarupan*
> Engineering Intern | WSO2
> Phone: +94776468013 <+94%2077%20646%208013>
>
> On Thu, Nov 9, 2017 at 11:41 PM, Sriskandarajah Suhothayan 
> wrote:
>
>> Does Tensor always output multidimensional array or only for some cases?
>> If it returns a multidimensional array what sort of processing the user
>> should be doing to get sense out of it?
>>
>> My doubt is having a way to send multidimensional array out will solve
>> the user's problem.
>>
>> Regards
>> Suho
>>
>> On Thu, Nov 9, 2017 at 8:45 AM, Niruhan Viswarupan 
>> wrote:
>>
>>> Hi,
>>>
>>> This is regarding supporting predictions from TensorFlow saved models
>>> using stream processor.
>>>
>>> The *predictions (outputs) from a TensorFlow model in Java API will be
>>> a Tensor
>>> 
>>> object*. This can have any number of dimensions and any shape. This
>>> means that *we will not know the shape of the output multidimensional
>>> array in the compile time*. This will only come to light in the run
>>> time when the user input model is loaded. I was stuck with creating
>>> multidimensional arrays of run time specified shape. It is solvable for
>>> object arrays since they can be recursively built. But the copyTo()
>>> 
>>>  method
>>> of Tensor works only for primitive type arrays.
>>>
>>> So after a discussion with Tishan we *decided to flatten the
>>> multidimensional Tensor* and output them as one-by-one primitive types
>>> that Siddhi handles. For example a 2x3 matrix Tensor [ [1,2,3], [4,5,6] ]
>>> will be output as 6 separate int into the event.
>>>
>>> Please find the *user story here
>>> *
>>>  and
>>> the preliminary *coding works here
>>> *
>>> .
>>>
>>> Regards,
>>>
>>> *Niruhan Viswarupan*
>>> Engineering Intern | WSO2
>>> Phone: +94776468013 <077%20646%208013>
>>>
>>
>>
>>
>> --
>>
>> *S. Suhothayan*
>> Associate Director / Architect
>> *WSO2 Inc. *http://wso2.com
>> * *
>> lean . enterprise . middleware
>>
>>
>> *cell: (+94) 779 756 757 <+94%2077%20975%206757> |

Re: [Dev] TensorFlow Stream Processor Extension

2017-11-09 Thread Niruhan Viswarupan 
[-Analytics-group, -engineering-group, + dev]

The output tensor can be a single scalar value or a multidimensional array.
It totally *depends on the model* and how its output is configured.

For example,

   1. if we have a model for classification and its prediction is
   configured to send a string of the class name then we will get a *scalar
   (A Tensor of 0 dimension)* string tensor.
   2. If we have a clustering model it can output the coordinates of the
   closest centroid as a tensor which can be a *vector (A tensor of 1
   dimension) *with any length (For example vector length will be 2 in the
   case of Cartesian plane, 3 in the case of 3D coordinate system etc).

*(Note:* The above examples have only a few values in output Tensors. But
it is possible for someone to build a TensorFlow model that sends out a
Tensor with thousands of dimensions and thousands of length per dimension.)

*Terminology:*
eg. Consider a 3x4 *matrix (a tensor of 2 dimensions)* as below,

1 2 3 4
5 6 7 8
9 1 2 3
This has 2 dimensions and length of 1st dimension is 3 and the 2nd
dimension is 4.

Also *problems identified in sending out multidimensional arrays in the
output stream* are,

   1. Its a *challenge to create primitive type multidimensional arrays*
   (This is the type supported by Tensors copying method) whose *dimensions
   are unknown at compile time* in Java (I welcome any suggestion as to how
   this could be done)
   2. As pointed out by Tishan *other extensions of Siddhi are not
   configured to handle arrays*. So cannot process the output arrays of
   this extension further in Siddhi

Considering the above issues we decided to flatten the Tensor and send all
the values in a tensor one by one in primitive type. *In most cases output
Tensor will have a small number of values*.

Regards,

*Niruhan Viswarupan*
Engineering Intern | WSO2
Phone: +94776468013

On Thu, Nov 9, 2017 at 11:41 PM, Sriskandarajah Suhothayan 
wrote:

> Does Tensor always output multidimensional array or only for some cases?
> If it returns a multidimensional array what sort of processing the user
> should be doing to get sense out of it?
>
> My doubt is having a way to send multidimensional array out will solve the
> user's problem.
>
> Regards
> Suho
>
> On Thu, Nov 9, 2017 at 8:45 AM, Niruhan Viswarupan 
> wrote:
>
>> Hi,
>>
>> This is regarding supporting predictions from TensorFlow saved models
>> using stream processor.
>>
>> The *predictions (outputs) from a TensorFlow model in Java API will be a
>> Tensor
>> 
>> object*. This can have any number of dimensions and any shape. This
>> means that *we will not know the shape of the output multidimensional
>> array in the compile time*. This will only come to light in the run time
>> when the user input model is loaded. I was stuck with creating
>> multidimensional arrays of run time specified shape. It is solvable for
>> object arrays since they can be recursively built. But the copyTo()
>> 
>>  method
>> of Tensor works only for primitive type arrays.
>>
>> So after a discussion with Tishan we *decided to flatten the
>> multidimensional Tensor* and output them as one-by-one primitive types
>> that Siddhi handles. For example a 2x3 matrix Tensor [ [1,2,3], [4,5,6] ]
>> will be output as 6 separate int into the event.
>>
>> Please find the *user story here
>> *
>>  and
>> the preliminary *coding works here
>> *
>> .
>>
>> Regards,
>>
>> *Niruhan Viswarupan*
>> Engineering Intern | WSO2
>> Phone: +94776468013 <077%20646%208013>
>>
>
>
>
> --
>
> *S. Suhothayan*
> Associate Director / Architect
> *WSO2 Inc. *http://wso2.com
> * *
> lean . enterprise . middleware
>
>
> *cell: (+94) 779 756 757 <+94%2077%20975%206757> | blog:
> http://suhothayan.blogspot.com/ twitter:
> http://twitter.com/suhothayan  | linked-in:
> http://lk.linkedin.com/in/suhothayan *
>
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