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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