[theano-users] Particle Swarm Optimisation using Theano

[bdhariyal94]

Hi!

Does theano provides PSO implementation in CUDA? Any other library can also 
be specified.

Thanks in advance

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Re: [theano-users] ImageNet ILSVRC2012 training and validation data sets

[Frédéric Bastien]

Thanks to forward the confirmation.

Frédéric

On Mon, Aug 28, 2017 at 2:54 PM ephi5757 via theano-users <
theano-users@googlegroups.com> wrote:

> FYI, see email below.
>
>
>
> - Forwarded Message -
>
> From: E Park 
>
> To:  
>
> Cc: ImageNet Support ; ilsvrc2...@image-net.org <
> ilsvrc2...@image-net.org>
>
> Sent: ‎Thursday‎, ‎August‎ ‎24‎, ‎2017‎ ‎03‎:‎48‎:‎08‎ ‎PM
>
> Subject: Re: ImageNet ILSVRC2012 validation dataset
>
>
>
> Hi, validation dataset is not overlapped with training images. Thanks!
>
>
>
> Best Regards,
>
> E Park
>
>
> On Monday, August 28, 2017 at 9:06:02 AM UTC-4, nouiz wrote:
>
>> I didn't used this dataset myself recently. But it would be a very big
>> error that the validation set is a subset of the training set. This should
>> never be the case.
>>
>> Fred
>>
>> On mer. 23 août 2017 18:47 ephi5757 via theano-users <
>> theano...@googlegroups.com> wrote:
>>
> Is the ImageNet ILSVRC2012 validation dataset (50,000 images) a subset of
>>> the training dataset (1.2 million images) or are the validation
>>> images new/independent of the training images?
>>> .
>>> Best,
>>> Arnold
>>>
>>> --
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Re: [theano-users] ImageNet ILSVRC2012 training and validation data sets

[ephi5757 via theano-users]

 

FYI, see email below.

 

- Forwarded Message -

From: E Park 

To:  

Cc: ImageNet Support ; ilsvrc2...@image-net.org 


Sent: ‎Thursday‎, ‎August‎ ‎24‎, ‎2017‎ ‎03‎:‎48‎:‎08‎ ‎PM

Subject: Re: ImageNet ILSVRC2012 validation dataset

 

Hi, validation dataset is not overlapped with training images. Thanks!

 

Best Regards,

E Park


On Monday, August 28, 2017 at 9:06:02 AM UTC-4, nouiz wrote:
>
> I didn't used this dataset myself recently. But it would be a very big 
> error that the validation set is a subset of the training set. This should 
> never be the case.
>
> Fred
>
> On mer. 23 août 2017 18:47 ephi5757 via theano-users <
> theano...@googlegroups.com > wrote:
>
>> Is the ImageNet ILSVRC2012 validation dataset (50,000 images) a subset of 
>> the training dataset (1.2 million images) or are the validation 
>> images new/independent of the training images?
>> .
>> Best,
>> Arnold
>>
>> -- 
>>
>> --- 
>> You received this message because you are subscribed to the Google Groups 
>> "theano-users" group.
>> To unsubscribe from this group and stop receiving emails from it, send an 
>> email to theano-users...@googlegroups.com .
>> For more options, visit https://groups.google.com/d/optout.
>>
>

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Re: [theano-users] ImageNet ILSVRC2012 training and validation data sets

[Frédéric Bastien]

I didn't used this dataset myself recently. But it would be a very big
error that the validation set is a subset of the training set. This should
never be the case.

Fred

On mer. 23 août 2017 18:47 ephi5757 via theano-users <
theano-users@googlegroups.com> wrote:

> Is the ImageNet ILSVRC2012 validation dataset (50,000 images) a subset of
> the training dataset (1.2 million images) or are the validation
> images new/independent of the training images?
> .
> Best,
> Arnold
>
> --
>
> ---
> You received this message because you are subscribed to the Google Groups
> "theano-users" group.
> To unsubscribe from this group and stop receiving emails from it, send an
> email to theano-users+unsubscr...@googlegroups.com.
> For more options, visit https://groups.google.com/d/optout.
>

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Re: [theano-users] Differences between theano.shared and numpy.ndarray.

[Frédéric Bastien]

It is not a bug. Currently, by default the shape of a shared variable can
changes during the execution. So we can't use the shape to determine the
broadcasting pattern.

You can pass the parameter broadcastable=() where you put the broadcast
pattern. By default it is never broadcastable.

Fred

On ven. 25 août 2017 00:58 佐藤優  wrote:

> I saw follown defferences:
> import theano
> import theano.tensor as T
> import numpy as np
>
> o = np.ones((1,2,3))
> o2= np.ones((2,1))
> o2_shared = theano.shared(ones((2, 1)))
>
> print((o2 + o).shape)
> print((o2_shared + o).shape)
>
> result is
>
> (1, 2, 3)
> [1 2 1]
>
>
> Maybe, broadcasting result is difference.
>
> But changing the order of calculation:
> import theano
> import theano.tensor as T
> import numpy as np
>
>
> o = np.ones((1,2,3))
> o2= np.ones((2,1))
> o2_shared = theano.shared(ones((2, 1)))
>
> print((o + o2).shape)
> print((o + o2_shared).shape.eval())
>
>
>
> result is same as follows:
>
> (1, 2, 3)
> [1 2 3]
>
>
> Is this theano.shared bug?
>
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