I encountered something similar a few days ago. Turned out float32 divided
by int32 gave float64. This is consistent with numpy behavior, a bit weird,
but true. No sure if related.
In general, traversing the graph (prior to compilation) to find the
problematic nodes can be helpful.
On Sunday, April 16, 2017 at 10:44:49 PM UTC+8, Justin Wilson wrote:
>
> I've set THEANO_FLAGS such that floatX=float32 and type casted to float32
> throughout the code. Is there a way to set allow_convert = True so that can
> convert from float64 to float32 for Variable Elemwise{add,no_inplace}.0). I'm
> new to group and first post so let me know if any additional info would be
> helpful.
>
>
> ---------------------------------------------------------------------------TypeError
> Traceback (most recent call
> last)/home/wilson/anaconda3/lib/python3.5/site-packages/theano/compile/pfunc.py
> in rebuild_collect_shared(outputs, inputs, replace, updates, rebuild_strict,
> copy_inputs_over, no_default_updates) 192 update_val =
> store_into.type.filter_variable(update_val,--> 193
> allow_convert=False) 194 except
> TypeError:
> /home/wilson/anaconda3/lib/python3.5/site-packages/theano/tensor/type.py in
> filter_variable(self, other, allow_convert) 234
> other=other,--> 235 self=self)) 236
> TypeError: Cannot convert Type TensorType(float64, matrix) (of Variable
> Elemwise{add,no_inplace}.0) into Type TensorType(float32, matrix). You can
> try to manually convert Elemwise{add,no_inplace}.0 into a TensorType(float32,
> matrix).
>
> During handling of the above exception, another exception occurred:
> TypeError Traceback (most recent call
> last)<ipython-input-137-40e9aa6eac9d> in <module>() 78 79 t =
> time()---> 80 train_g = theano.function([X, Z], cost, updates=g_updates)
> 81 train_d = theano.function([X, Z], cost, updates=d_updates) 82 gen =
> theano.function([Z], gX)
> /home/wilson/anaconda3/lib/python3.5/site-packages/theano/compile/function.py
> in function(inputs, outputs, mode, updates, givens, no_default_updates,
> accept_inplace, name, rebuild_strict, allow_input_downcast, profile,
> on_unused_input) 324 on_unused_input=on_unused_input,
> 325 profile=profile,--> 326
> output_keys=output_keys) 327 # We need to add the flag check_aliased
> inputs if we have any mutable or 328 # borrowed used defined inputs
> /home/wilson/anaconda3/lib/python3.5/site-packages/theano/compile/pfunc.py in
> pfunc(params, outputs, mode, updates, givens, no_default_updates,
> accept_inplace, name, rebuild_strict, allow_input_downcast, profile,
> on_unused_input, output_keys) 447
> rebuild_strict=rebuild_strict, 448
> copy_inputs_over=True,--> 449
> no_default_updates=no_default_updates) 450 # extracting the arguments
> 451 input_variables, cloned_extended_outputs, other_stuff = output_vars
> /home/wilson/anaconda3/lib/python3.5/site-packages/theano/compile/pfunc.py in
> rebuild_collect_shared(outputs, inputs, replace, updates, rebuild_strict,
> copy_inputs_over, no_default_updates) 206 '
> function to remove broadcastable dimensions.') 207 --> 208
> raise TypeError(err_msg, err_sug) 209 assert update_val.type ==
> store_into.type 210
> TypeError: ('An update must have the same type as the original shared
> variable (shared_var=<TensorType(float32, matrix)>,
> shared_var.type=TensorType(float32, matrix),
> update_val=Elemwise{add,no_inplace}.0, update_val.type=TensorType(float64,
> matrix)).', 'If the difference is related to the broadcast pattern, you can
> call the tensor.unbroadcast(var, axis_to_unbroadcast[, ...]) function to
> remove broadcastable dimensions.')
>
>
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