Using new data
nargesam opened this issue · 2 comments
Hi,
I created a sample function in generator.py to pass a random valued array like this:
def sample(self, nb_samples): example_inputs = np.random.rand(16, 50, 20 * 20) example_outputs = np.random.randint(5, size=(16, 50)).astype('int32') return example_inputs, example_outputs
When I run the model it gives me an error:
IndexError: index 10 is out of bounds for axis 1 with size 10 Apply node that caused the error: AdvancedIncSubtensor{inplace=False, set_instead_of_inc=False}(<TensorType(float64, matrix)>, <TensorType(bool, vector)>, TensorConstant{[ 0 1 2 .. 13 14 15]}, AdvancedSubtensor.0)
You can see the complete output bellow. I cannot understand what goes wrong. It is the same shape as the main data just random values.
Could you please tell me what I am doing wrong?
Thanks.
`Compiling the model...
Done
Training...
Traceback (most recent call last):
File "omniglot.py", line 83, in
omniglot()
File "omniglot.py", line 69, in omniglot
acc = accuracy_fn(example_input, example_output)
File "/Users/narges/Dropbox/OneShotLearningPaper/ntm-one-shot-master/venv/lib/python2.7/site-packages/theano/compile/function_module.py", line 898, in call
storage_map=getattr(self.fn, 'storage_map', None))
File "/Users/narges/Dropbox/OneShotLearningPaper/ntm-one-shot-master/venv/lib/python2.7/site-packages/theano/gof/link.py", line 325, in raise_with_op
reraise(exc_type, exc_value, exc_trace)
File "/Users/narges/Dropbox/OneShotLearningPaper/ntm-one-shot-master/venv/lib/python2.7/site-packages/theano/compile/function_module.py", line 884, in call
self.fn() if output_subset is None else
File "/Users/narges/Dropbox/OneShotLearningPaper/ntm-one-shot-master/venv/lib/python2.7/site-packages/theano/scan_module/scan_op.py", line 989, in rval
r = p(n, [x[0] for x in i], o)
File "/Users/narges/Dropbox/OneShotLearningPaper/ntm-one-shot-master/venv/lib/python2.7/site-packages/theano/scan_module/scan_op.py", line 978, in p
self, node)
File "theano/scan_module/scan_perform.pyx", line 405, in theano.scan_module.scan_perform.perform (/Users/narges/.theano/compiledir_Darwin-16.6.0-x86_64-i386-64bit-i386-2.7.10-64/scan_perform/mod.cpp:4606)
File "/Users/narges/Dropbox/OneShotLearningPaper/ntm-one-shot-master/venv/lib/python2.7/site-packages/theano/gof/link.py", line 325, in raise_with_op
reraise(exc_type, exc_value, exc_trace)
File "theano/scan_module/scan_perform.pyx", line 397, in theano.scan_module.scan_perform.perform (/Users/narges/.theano/compiledir_Darwin-16.6.0-x86_64-i386-64bit-i386-2.7.10-64/scan_perform/mod.cpp:4490)
File "/Users/narges/Dropbox/OneShotLearningPaper/ntm-one-shot-master/venv/lib/python2.7/site-packages/theano/gof/op.py", line 872, in rval
r = p(n, [x[0] for x in i], o)
File "/Users/narges/Dropbox/OneShotLearningPaper/ntm-one-shot-master/venv/lib/python2.7/site-packages/theano/tensor/subtensor.py", line 2244, in perform
inplace_increment(out[0], tuple(inputs[2:]), inputs[1])
IndexError: index 10 is out of bounds for axis 1 with size 10
Apply node that caused the error: AdvancedIncSubtensor{inplace=False, set_instead_of_inc=False}(<TensorType(float64, matrix)>, <TensorType(bool, vector)>, TensorConstant{[ 0 1 2 .. 13 14 15]}, AdvancedSubtensor.0)
Toposort index: 2
Inputs types: [TensorType(float64, matrix), TensorType(bool, vector), TensorType(int64, vector), TensorType(int32, vector)]
Inputs shapes: [(16, 10), (16,), (16,), (16,)]
Inputs strides: [(80, 8), (50,), (8,), (4,)]
Inputs values: ['not shown', 'not shown', 'not shown', 'not shown']
Outputs clients: [['output']]
Backtrace when the node is created(use Theano flag traceback.limit=N to make it longer):
File "omniglot.py", line 83, in
omniglot()
File "omniglot.py", line 47, in omniglot
accuracies = accuracy_instance(T.argmax(output_var, axis=2), target_var, batch_size=generator.batch_size)
File "/Users/narges/Dropbox/OneShotLearningPaper/ntm-one-shot-master/examples/mann/utils/metrics.py", line 28, in accuracy_instance
targets.dimshuffle(1, 0)], outputs_info=[accuracy_0, indices_0])
File "/Users/narges/Dropbox/OneShotLearningPaper/ntm-one-shot-master/venv/lib/python2.7/site-packages/theano/scan_module/scan_views.py", line 158, in foldl
name=name)
File "/Users/narges/Dropbox/OneShotLearningPaper/ntm-one-shot-master/venv/lib/python2.7/site-packages/theano/scan_module/scan_views.py", line 115, in reduce
name=name)
File "/Users/narges/Dropbox/OneShotLearningPaper/ntm-one-shot-master/venv/lib/python2.7/site-packages/theano/scan_module/scan.py", line 773, in scan
condition, outputs, updates = scan_utils.get_updates_and_outputs(fn(*args))
File "/Users/narges/Dropbox/OneShotLearningPaper/ntm-one-shot-master/examples/mann/utils/metrics.py", line 22, in step_
acc = T.inc_subtensor(acc[batch_range, idx[batch_range, t]], T.eq(p, t))
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.
Apply node that caused the error: forall_inplace,cpu,scan_fn}(Elemwise{Maximum}[(0, 0)].0, Elemwise{eq,no_inplace}.0, Subtensor{int64:int64:int8}.0, IncSubtensor{InplaceSet;:int64:}.0, IncSubtensor{InplaceSet;:int64:}.0)
Toposort index: 159
Inputs types: [TensorType(int64, scalar), TensorType(bool, matrix), TensorType(int32, matrix), TensorType(float64, 3D), TensorType(int32, (True, False, False))]
Inputs shapes: [(), (50, 16), (50, 16), (2, 16, 10), (1, 16, 5)]
Inputs strides: [(), (1, 50), (4, 200), (1280, 80, 8), (320, 20, 4)]
Inputs values: [array(50), 'not shown', 'not shown', 'not shown', 'not shown']
Outputs clients: [[Subtensor{int64}(forall_inplace,cpu,scan_fn}.0, ScalarFromTensor.0)], []]
HINT: Re-running with most Theano optimization disabled could give you a back-trace of when this node was created. This can be done with by setting the Theano flag 'optimizer=fast_compile'. If that does not work, Theano optimizations can be disabled with 'optimizer=None'.
HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and storage map footprint of this apply node.`
Hello! This error is due to an imbalance in your targets example_outputs. For the omniglot example, each episode has an equal number of samples per class (called nb_samples_per_class in the generator), which is not the case here if you define your example_outputs as random labels. The function computing the accuracy expects an equal number of samples per classes.
To make it work in a random setup, you could do something like
def sample(self, nb_samples):
example_inputs = np.random.rand(16, 50, 20 * 20)
example_outputs = np.zeros((16, 50), dtype='int32')
for i in range(16):
example_outputs[i] = np.random.permutation(range(5) * 10)
return example_inputs, example_outputsThank you!