Ok, I did that... Now it throws the error: TypeError: Cannot convert Type
TensorType(int32, matrix) (of Variable Subtensor{int64:int64:}.0) into Type
TensorType(int32, vector)
Error on the line
y: training_y[index * batch_size: (index + 1) * batch_size]
How to fix this? My training labels are nparrays casted into Tensor shared
variables.
Here is the link to my code:
*https://github.com/Layman806/ocr-cnn/blob/master/CNN.py*
On Monday, May 29, 2017 at 5:59:22 PM UTC+5:30, nouiz wrote:
>
> Python list don't know how to handle Theano variable. You can't miss them.
> Convert your list to a Theano tensor. Maybe theano.shared(your list) will
> work for your case.
>
> Le sam. 27 mai 2017 17:50, Bavani Sankar <[email protected]
> <javascript:>> a écrit :
>
>> I have a python list I want to splice... I also have the theano tensor
>> scalar 'i':
>>
>>> i = T.lscalar()
>>>
>>>
>> How do I use this scalar to splice the list?
>> This code that used to work, no longer works:
>>
>>> training_x[i * self.mini_batch_size: (i + 1) * self.mini_batch_size]
>>>
>>> I get the error: "TypeError: slice indices must be integers or None or
>> have an __index__ method"
>> Can you please help me out?
>>
>>
>> On Thursday, July 28, 2016 at 9:49:21 PM UTC+5:30, nouiz wrote:
>>
>>> You should compile 1 theano function per network. You can put them in a
>>> list and index that list to find the good funtion to call.
>>>
>>> Fred
>>>
>>> On Thu, Jul 28, 2016 at 11:42 AM Florin <[email protected]> wrote:
>>>
>>>> I have a similar problem:
>>>>
>>>> out = lasagne.layers.get_output(nets[index], x)
>>>> theano.function([index], out, givens= {x: inputs})
>>>>
>>>> I basically have a list of neural networks nets and want to dynamically
>>>> select which to evaluate, depending on index. How could I do this?
>>>>
>>>> Cris
>>>>
>>>> On Tuesday, March 10, 2015 at 9:10:58 AM UTC-4, nouiz wrote:
>>>>
>>>>> Do all element of the list have the same size? If so, I would
>>>>> recommand to make an ndarray with 1 extra dimensions instead of putting
>>>>> this in a list. Then you can put this ndarray as a shared variable
>>>>>
>>>>> example:
>>>>>
>>>>> nd_list = numpy.ndarray(list)
>>>>> shared_var = theano.shared(nd_list)
>>>>>
>>>>> then you can do:
>>>>>
>>>>> shared_var[index]
>>>>>
>>>>> And use that in a Theano function.
>>>>>
>>>>> Otherwise, you can use typed list:
>>>>>
>>>>> http://deeplearning.net/software/theano/library/typed_list.html
>>>>>
>>>>> Fred
>>>>>
>>>> On Tue, Mar 10, 2015 at 9:03 AM, Rob <[email protected]> wrote:
>>>>>
>>>> I have circumvented the problem. Rather than creating a list of tensor
>>>>>> on which I apply a single theano function, I just created a list of
>>>>>> theano
>>>>>> functions (one for each tensor).
>>>>>>
>>>>>> If there are better solutions I would still love to here them.
>>>>>>
>>>>>>
>>>>>> On Tuesday, 10 March 2015 12:46:05 UTC, Rob wrote:
>>>>>>>
>>>>>>> I want a theano function that, given a scalar input, will return a
>>>>>>> tensor stored in a list at the index of that scalar. i.e
>>>>>>>
>>>>>>> theano.function([index],list[index],givens= {x: <some function of
>>>>>>> index>})
>>>>>>>
>>>>>>> where the list contains different x-dependent tensors that I want to
>>>>>>> evaluate depending on the index given.
>>>>>>>
>>>>>>> This generates error:
>>>>>>>
>>>>>>> TypeError: list indices must be integers, not TensorVariable
>>>>>>>
>>>>>>>
>>>>>>> I'm very new to Theano so I'm probably thinking about this problem
>>>>>>> in the wrong way. I would really appreciate any advice.
>>>>>>>
>>>>>>> Rob
>>>>>>>
>>>>>> --
>>>>>>
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>>>>>>
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>>> --
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>
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