Hi Pascal,
How would I find out which version of Theano I have?
Can I use the following command to install the latest development version?
<sudo> pip install <--user> <--no-deps>
git+https://github.com/Theano/Theano
Thanks.
Chi
-----Original Message-----
From: [email protected] [mailto:[email protected]] On
Behalf Of Pascal Lamblin
Sent: Monday, November 14, 2016 12:37 PM
To: [email protected]
Subject: Re: [theano-users] TypeError: Cannot convert Type TensorType(float64,
3D) (of Variable Subtensor{:int64:}.0) into Type TensorType(float64, (False,
True, False)). You can try to manually convert Subtensor{:int64:}.0 into a
TensorType(float64, (False, Tr...
Which version of Theano are you using?
If you are using the 0.8.2 release, can you try the latest development version?
On Sat, Nov 12, 2016, [email protected] wrote:
>
> I got the following errors when calling tensor.grad() to compute the
> symbolic gradient of the finetune_cost of a hybrid DBN-RNN model.
> I tried changing the way this expression is formed in several ways without
> any success. I need help from experts. A tar file of the source code
> and data is attached here.
>
> The pretraining code for file hybrid_array.py ran for 0.48m
> ... getting the finetuning functions
> Traceback (most recent call last):
> File "/usr/lib/python2.7/pdb.py", line 1314, in main
> pdb._runscript(mainpyfile)
> File "/usr/lib/python2.7/pdb.py", line 1233, in _runscript
> self.run(statement)
> File "/usr/lib/python2.7/bdb.py", line 400, in run
> exec cmd in globals, locals
> File "<string>", line 1, in <module>
> File "hybrid_array.py", line 494, in <module>
> test_DBN()
> File "hybrid_array.py", line 412, in test_DBN
> learning_rate=finetune_lr
> File "hybrid_array.py", line 253, in build_finetune_functions
> gparams = T.grad(self.finetune_cost, self.params)
> File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py", line
> 561, in grad
> grad_dict, wrt, cost_name)
> File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py", line
> 1324, 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
> 1279, in access_grad_cache
> term = access_term_cache(node)[idx]
> File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py", line
> 973, 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
> 1279, in access_grad_cache
> term = access_term_cache(node)[idx]
> File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py", line
> 973, in access_term_cache
> output_grads = [access_grad_cache(var) for var in node.outputs]
> output_grads = [access_grad_cache(var) for var in node.outputs]
> File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py", line
> 1279, in access_grad_cache
> term = access_term_cache(node)[idx]
> File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py", line
> 973, 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
> 1279, in access_grad_cache
> term = access_term_cache(node)[idx]
> File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py", line
> 973, 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
> 1279, in access_grad_cache
> term = access_term_cache(node)[idx]
> File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py", line
> 973, 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
> 1279, in access_grad_cache
> term = access_term_cache(node)[idx]
> File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py", line
> 973, 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
> 1279, in access_grad_cache
> term = access_term_cache(node)[idx]
> File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py", line
> 973, 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
> 1279, in access_grad_cache
> term = access_term_cache(node)[idx]
> File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py", line
> 973, in access_term_cache
> output_gra term = access_term_cache(node)[idx]
> File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py", line
> 973, 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
> 1279, in access_grad_cache
> term = access_term_cache(node)[idx]
> File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py", line
> 973, 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
> 1279, in access_grad_cache
> term = access_term_cache(node)[idx]
> File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py", line
> 973, 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
> 1279, in access_grad_cache
> term = access_term_cache(node)[idx]
> File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py", line
> 973, 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
> 1279, in access_grad_cache
> term = access_term_cache(node)[idx]
> File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py", line
> 973, 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
> 1279, in access_grad_cache
> term = access_term_cache(node)[idx]
> File "/usr/local/lib/python2.7/dist-packages/theano/gradient.py", line
> 1113, in access_term_cache
> input_grads = node.op.grad(inputs, new_output_grads)
> File
> "/usr/local/lib/python2.7/dist-packages/theano/scan_module/scan_op.py",
> line 2523, in grad
> outputs = local_op(*outer_inputs)
> File "/usr/local/lib/python2.7/dist-packages/theano/gof/op.py", line 611,
> in __call__
> node = self.make_node(*inputs, **kwargs)
> File
> "/usr/local/lib/python2.7/dist-packages/theano/scan_module/scan_op.py",
> line 430, in make_node
> new_inputs.append(format(outer_seq, as_var=inner_seq))
> File
> "/usr/local/lib/python2.7/dist-packages/theano/scan_module/scan_op.py",
> line 422, in format
> rval = tmp.filter_variable(rval)
> File "/usr/local/lib/python2.7/dist-packages/theano/tensor/type.py", line
> 233, in filter_variable
> self=self))
> TypeError: Cannot convert Type TensorType(float64, 3D) (of Variable
> Subtensor{:int64:}.0) into Type TensorType(float64, (False, True, False)).
> You can try to manually convert Subtensor{:int64:}.0 into a
> TensorType(float64, (False, True, False)).
> Uncaught exception. Entering post mortem debugging
> Running 'cont' or 'step' will restart the program
> >
> /usr/local/lib/python2.7/dist-packages/theano/tensor/type.py(233)filter_variable()
> -> self=self))
>
>
>
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--
Pascal
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