Can you open that trace in pdb, and let us know what the dtype of
`grad_steps` here is?
What about the dtype of `self.truncate_gradient`?
And the type of `s_`?
On 2018-03-03 03:12 AM, Siddhartha Saxena wrote:
Hi
I am training a custom LSTM model on theano with LSTM layers as in
(https://github.com/asheshjain399/NeuralModels/tree/master/neuralmodels/layers/LSTM.py)
and
(https://github.com/asheshjain399/NeuralModels/blob/master/neuralmodels/layers/multilayerLSTM.py).
Now the model that I have created makes a valid theano graph but at the
step the find the updates (RMSprop) of the model with a cost (mean
square) function via the theano.grad function, I keep getting the
following error. The source of the error is that a theano op
(Elemwise{minimum,no_inplace}.0) is being sent for slicing to the
subtensor.py file instead of a theano constant. The issue is that I am
not sure how it ends up there. The model works if I remove the LSTM
layer, but this LSTM layer works with some other model perfectly.
I'll be extremely thankful if someone can help me through this error
which is bugging me for days now.
Best
Siddhartha
-------------------------------------------------------------
File "trainGCNN_NoGraph.py", line 251, in trainGCNN
gcnn =
GCNNmodelRegression(preGraphNets,nodeList,nodeFeatureLength,temporalNodeFeatureLength,new_idx,featureRange)
File "trainGCNN_NoGraph.py", line 202, in GCNNmodelRegression
gcnn =
GCNN(graphLayers,finalLayer,preGraphNets,nodeNames,temporalNodeRNN,nodeRNNs,topLayer,euclidean_loss,nodeLabels,learning_rate,adjacency,new_idx,featureRange,clipnorm=args.clipnorm,update_type=gradient_method,weight_decay=args.weight_decay)
File "/home/siddhartha/Graph_CNNs/neuralmodels/models/GCNN.py", line
169, in __init__
[self.updates,self.grads] =
self.update_type.get_updates(self.params_all,self.cost)
File "/home/siddhartha/Graph_CNNs/neuralmodels/updates.py", line 108,
in get_updates
grads_unclipped = T.grad(cost, params)
File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py",
line 555, in grad
grad_dict, wrt, cost_name)
File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py",
line 1317, in _populate_grad_dict
rval = [access_grad_cache(elem) for elem in wrt]
File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py",
line 1272, in access_grad_cache
term = access_term_cache(node)[idx]
File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py",
line 967, in access_term_cache
output_grads = [access_grad_cache(var) for var in node.outputs]
File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py",
line 1272, in access_grad_cache
term = access_term_cache(node)[idx]
File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py",
line 967, in access_term_cache
output_grads = [access_grad_cache(var) for var in node.outputs]
File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py",
line 1272, in access_grad_cache
term = access_term_cache(node)[idx]
File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py",
line 967, in access_term_cache
output_grads = [access_grad_cache(var) for var in node.outputs]
File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py",
line 1272, in access_grad_cache
term = access_term_cache(node)[idx]
File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py",
line 967, in access_term_cache
output_grads = [access_grad_cache(var) for var in node.outputs]
File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py",
line 1272, in access_grad_cache
term = access_term_cache(node)[idx]
File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py",
line 967, in access_term_cache
output_grads = [access_grad_cache(var) for var in node.outputs]
File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py",
line 1272, in access_grad_cache
term = access_term_cache(node)[idx]
File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py",
line 967, in access_term_cache
output_grads = [access_grad_cache(var) for var in node.outputs]
File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py",
line 1272, in access_grad_cache
term = access_term_cache(node)[idx]
File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py",
line 967, in access_term_cache
output_grads = [access_grad_cache(var) for var in node.outputs]
File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py",
line 1272, in access_grad_cache
term = access_term_cache(node)[idx]
File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py",
line 1108, in access_term_cache
new_output_grads)
File
"/usr/local/lib/python2.7/dist-packages/theano/scan_module/scan_op.py",
line 2250, in L_op
outer_inp_seqs = [s_[:grad_steps] for s_ in outer_inp_seqs]
File "/usr/local/lib/python2.7/dist-packages/theano/tensor/var.py",
line 519, in __getitem__
theano.tensor.subtensor.Subtensor.convert(arg)
File
"/usr/local/lib/python2.7/dist-packages/theano/tensor/subtensor.py",
line 378, in convert
slice_b = Subtensor.convert(b, False)
File
"/usr/local/lib/python2.7/dist-packages/theano/tensor/subtensor.py",
line 348, in convert
raise TypeError("Expected an integer")
TypeError: Expected an integer
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