Thank you, I was overloaded and I have not realized the mistake.
El martes, 6 de septiembre de 2016, 19:46:32 (UTC+2), Jesse Livezey
escribió:
>
> One of your labels is too large, or possibly too small. Are your labels
> from 0 to n-1 or 1 to n? They should be 0 to n-1.
>
> On Tuesday, September 6, 2016 at 2:19:18 AM UTC-7, Beatriz G. wrote:
>>
>> HI everyone
>>
>> I am trying to use 4 dimension image, but I get the following error and I
>> do not know what it means:
>>
>> ValueError: y_i value out of bounds
>> Apply node that caused the error:
>> CrossentropySoftmaxArgmax1HotWithBias(Dot22.0, b, Subtensor{int64:int64:}.0)
>> Toposort index: 34
>> Inputs types: [TensorType(float64, matrix), TensorType(float64, vector),
>> TensorType(int32, vector)]
>> Inputs shapes: [(20, 4), (4,), (20,)]
>> Inputs strides: [(32, 8), (8,), (4,)]
>> Inputs values: ['not shown', array([ 0., 0., 0., 0.]), 'not shown']
>> Outputs clients:
>> [[Sum{acc_dtype=float64}(CrossentropySoftmaxArgmax1HotWithBias.0)],
>> [CrossentropySoftmax1HotWithBiasDx(Elemwise{Inv}[(0, 0)].0,
>> CrossentropySoftmaxArgmax1HotWithBias.1, Subtensor{int64:int64:}.0)], []]
>>
>> Backtrace when the node is created(use Theano flag traceback.limit=N to
>> make it longer):
>> File "/home/beaa/Escritorio/Theano/Separando_Lenet.py", line 446, in
>> <module>
>> evaluate_lenet5()
>> File "/home/beaa/Escritorio/Theano/Separando_Lenet.py", line 257, in
>> evaluate_lenet5
>> cost = layer3.negative_log_likelihood(y)
>> File "/home/beaa/Escritorio/Theano/logistic_sgd.py", line 146, in
>> negative_log_likelihood
>> return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])
>>
>> HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and
>> storage map footprint of this apply node.
>>
>>
>> Here is how I give the data to the layers:
>>
>>
>> layer0 = LeNetConvPoolLayer(
>> rng,
>> input=layer0_input,
>> image_shape=(batch_size, 4, 104, 52),
>> filter_shape=(nkerns[0], 4, 5, 5),
>> poolsize=(2, 2)
>> )
>>
>>
>> layer1 = LeNetConvPoolLayer(
>> rng,
>> input=layer0.output,
>> image_shape=(batch_size, nkerns[0], 50, 24),
>> filter_shape=(nkerns[1], nkerns[0], 5, 5),
>> poolsize=(2, 2)
>>
>>
>>
>> My data is 104*52*4.
>>
>>
>> Thanks in advance. Regards.
>>
>>
--
---
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 [email protected].
For more options, visit https://groups.google.com/d/optout.
