On Sat, Oct 15, 2016, Pascal Lamblin wrote: > Another option, still experimental, may be the `map_variables` function > in scan_modules/scan_utils.

## Advertising

There seem to be some challenges regarding scalar constants with that function, but I was able to do the following: >>> theano.tensor.basic.constant.enable = False >>> v = theano.tensor.lscalar('v') >>> exp1 = 2 * v >>> exp1.name = 'exp1' >>> exp2 = 4 * exp1 >>> exp2.name = 'exp2' >>> exp3 = 6 * exp2 >>> exp3.name = 'exp3' >>> exp4 = 8 * exp3 >>> exp4.name = 'exp4' >>> replace_dict = {'exp1': (3*exp1), 'exp2': (5*exp2), 'exp3': (7*exp3)} >>> def replace(var): ... return replace_dict.get(var.name, var) >>> exp5, = theano.scan_module.scan_utils.map_variables(replace, [exp4]) >>> theano.printing.debugprint(exp5) Elemwise{mul,no_inplace} [id A] 'exp4' |TensorConstant{8} [id B] |Elemwise{mul,no_inplace} [id C] '' |TensorConstant{7} [id D] |Elemwise{mul,no_inplace} [id C] '' The issue is that it introduced a cycle in the graph: it replaced exp3 by 7*exp3, where exp3 is the new one... I guess that illustrates the challenge of getting replacements right. > > Finally, it is actually possible to replace Apply nodes inputs manually. > In your case, you could do something like: > > >>> exp2.owner.inputs[1] = 3*exp1 > >>> exp3.owner.inputs[1] = 5*exp2 > >>> exp4.owner.inputs[1] = 7*exp3 > >>> print(theano.pp(exp4)) > (TensorConstant{8} * (TensorConstant{7} * (TensorConstant{6} * > (TensorConstant{5} * (TensorConstant{4} * (TensorConstant{3} * > (TensorConstant{2} * <TensorType(int64, scalar)>))))))) > >>> exp4.eval({v: 1}) > array(40320) > > But it can get hard to get right if the same expression is re-used > several times. > > On Fri, Oct 14, 2016, John Coolidge wrote: > > Hello, > > > > I'm trying to use theano.clone to implement dropout in my MLP network. > > Because I want to apply dropout at multiple layers, I pass the clone call > > multiple key value pairs to its replacement parameter: > > replace={layer1:mask*layer1, layer2:mask*layer2, etc} however the graph > > that's returned seems to have only actually made one of the replacements. > > I suspect this is because clone is doing the replacements sequentially and > > once it's done one replacement it generates a new graph for which the other > > key value pairs no longer correspond. > > > > Here is some example code that demonstrates the unexpected behavior: > > > > v = T.lscalar() > > exp1 = 2*v > > exp2 = 4*exp1 > > exp3 = 6*exp2 > > exp4 = 8*exp3 > > > > print theano.pp(exp4) > > exp5 = theano.clone(exp4, replace={exp1:(3*exp1), exp2:(5*exp2), > > exp3:(7*exp3)}) > > print theano.pp(exp5) > > t = theano.function(inputs=[v], outputs=exp5) > > print t(1) > > > > > > The output is: > > (TensorConstant{8} * (TensorConstant{6} * (TensorConstant{4} * > > (TensorConstant{2} * <TensorType(int64, scalar)>)))) > > (TensorConstant{8} * (TensorConstant{7} * (TensorConstant{6} * > > (TensorConstant{4} * (TensorConstant{2} * <TensorType(int64, scalar)>))))) > > 2688 > > > > Although the clone adds the 7 factor to the new graph, it does not add the > > 3 or 5 factors such that the output for an input value of 1 is 8*7*6*4*2*1 > > instead of 8! as I would have expected. > > > > I'm guessing this is how the clone function is supposed to work, but does > > anyone see how to get the desired behavior I'm looking for? Perhaps I > > could apply the replacements one at a time and after each replacement > > update the remaining replacement key value pairs to point to corresponding > > points in the new graph, but I'm not sure how to find these corresponding > > points. Or perhaps there's a function like the clone but that actually > > makes the replacements in place so that the other replacement key value > > pairs would not be invalidated after the first replacement? Any ideas > > would be greatly appreciated! > > > > -- > > > > --- > > You received this message because you are subscribed to the Google Groups > > "theano-users" group. > > To unsubscribe from this group and stop receiving emails from it, send an > > email to theano-users+unsubscr...@googlegroups.com. > > For more options, visit https://groups.google.com/d/optout. > > > -- > Pascal > > -- > > --- > You received this message because you are subscribed to the Google Groups > "theano-users" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to theano-users+unsubscr...@googlegroups.com. > For more options, visit https://groups.google.com/d/optout. -- Pascal -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to theano-users+unsubscr...@googlegroups.com. For more options, visit https://groups.google.com/d/optout.