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.
>
>
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