def traces_gen(nsamples):
# Delaying computation of this value because we dont know nsamples yet
return asl.prodsample(stack_optspace(),
to_enum=["tracegen", "nrounds", "nitems"],
to_sample=["init", "batch_size"],
to_sample_merge=["arch_opt", "optim_args"],
nsamples=nsamples)
def runoptsgen(nsamples):
# Delaying computation of this value because we dont know nsamples yet
return asl.prodsample(set_optspace(),
to_enum=[],
to_sample=["init", "batch_size", "lr", "accum", "learn_constants"],
to_sample_merge=["arch_opt", "optim_args"],
nsamples=nsamples)
def traces_gen(nsamples):
# Delaying computation of this value because we dont know nsamples yet
return asl.prodsample(stack_optspace(),
to_enum=["tracegen", "dataset", "nrounds", "nitems"],
to_sample=["init",
"batch_size"],
to_sample_merge=["arch_opt", "optim_args"],
nsamples=nsamples)
def traces_gen(nsamples):
# Delaying computation of this value because we dont know nsamples yet
return asl.prodsample(stack_optspace(),
to_enum=[],
to_sample=["init",
"batch_size",
"lr",
"learn_constants",
"normalize"],
to_sample_merge=["optim_args"],
nsamples=nsamples)
def runoptsgen(nsamples):
# Delaying computation of this value because we dont know nsamples yet
return asl.prodsample(stack_optspace(),
to_enum=["nitems", "dataset"],
to_sample=["init",
"nrounds",
"batch_size",
"lr",
"accum",
"learn_constants",
"normalize"],
to_sample_merge=["arch_opt", "optim_args"],
nsamples=nsamples)