AI-TensorFlow-Libtensorflow
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lib/AI/TensorFlow/Libtensorflow/Manual/CAPI.pod view on Meta::CPAN
(i.e. `noutputs`) or be null. In the former case,
the names should match the regular expression for ArgDef
names - "[a-z][a-z0-9_]*". In the latter case,
names for outputs will be generated automatically.
opts - various options for the function, e.g. XLA's inlining control.
description - optional human-readable description of this function.
status - Set to OK on success and an appropriate error on failure.
Note that when the same TF_Output is listed as both an input and an output,
the corresponding function's output will equal to this input,
instead of the original node's output.
Callers must also satisfy the following constraints:
- `inputs` cannot refer to TF_Outputs within a control flow context. For
example, one cannot use the output of "switch" node as input.
- `inputs` and `outputs` cannot have reference types. Reference types are
not exposed through C API and are being replaced with Resources. We support
reference types inside function's body to support legacy code. Do not
use them in new code.
- Every node in the function's body must have all of its inputs (including
control inputs). In other words, for every node in the body, each input
lib/AI/TensorFlow/Libtensorflow/Manual/CAPI.pod view on Meta::CPAN
/* From <tensorflow/c/c_api_experimental.h> */
TF_CAPI_EXPORT extern void TF_EnableXLACompilation(TF_SessionOptions* options,
unsigned char enable);
=head2 TF_SetXlaEnableLazyCompilation
=over 2
Set XLA's internal BuildXlaOpsPassFlags.tf_xla_enable_lazy_compilation to the
value of 'enabled'. Also returns the original value of that flag.
Use in tests to allow XLA to fallback to TF classic. This has global effect.
=back
/* From <tensorflow/c/c_api_experimental.h> */
TF_CAPI_EXPORT unsigned char TF_SetXlaEnableLazyCompilation(
unsigned char enable);
=head2 TF_SetTfXlaCpuGlobalJit
lib/AI/TensorFlow/Libtensorflow/Manual/Notebook/InferenceUsingTFHubEnformerGeneExprPredModel.pod view on Meta::CPAN
$gp->close;
if( IN_IPERL ) {
IPerl->png( bytestream => path($plot_output_path)->slurp_raw );
}
B<DISPLAY>:
=for html <span style="display:inline-block;margin-left:1em;"><p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA+gAAAMgCAIAAAA/et9qAAAgAElEQVR4nOzdd2AUVeIH8Ddb0jshBAIEpSo1GjoIpyAgCOqd3uGdoGBBUQQFRUVBRbkTf9gOBQucqFiwUhSSgJQYCCSBkJBAet1k...
=head2 Parts of the original notebook that fall outside the scope
In the orignal notebook, there are several more steps that have not been ported here:
=over
=item 1.
"Compute contribution scores":
This task requires implementing C<@tf.function> to compile gradients.
maint/process-notebook.pl view on Meta::CPAN
##
## requires '...';
## requires '...';
scan-perl-prereqs-nqlite --cpanfile $DST | perl -M5';print qq|=head1 CPANFILE\n\n|' -plE '$_ = q| | . $_;' | sponge -a $DST ;
## Check output (if on TTY)
if [ -t 0 ]; then
perldoc $DST;
fi
## Check and run script in the directory of the original (e.g., to get data
## files).
perl -c $DST
#&& perl -MCwd -MPath::Tiny -E '
#my $nb = path(shift @ARGV);
#my $script = path(shift @ARGV)->absolute;
#chdir $nb->parent;
#do $script;
#' $SRC $DST
BASH
t/upstream/CAPI/018_ImportGraphDef.t view on Meta::CPAN
$opts->RemapControlDependency("imported/feed", $feed );
$graph->ImportGraphDef($graph_def, $opts, $s);
TF_Utils::AssertStatusOK($s);
ok my $scalar4 = $graph->OperationByName("imported4/imported3/scalar"),
"imported4/imported3/scalar";
ok my $feed4 = $graph->OperationByName("imported4/imported2/feed"),
"imported4/imported2/feed";
note q|Check that imported `imported3/scalar` has remapped control dep from
original graph and imported control dep|;
is $scalar4->GetControlInputs, array {
item object { call Name => $feed->Name };
item object { call Name => $feed4->Name };
end;
}, 'scalar4 control inputs';
undef $opts;
undef $graph_def;
note 'Can add nodes to the imported graph without trouble.';
( run in 0.270 second using v1.01-cache-2.11-cpan-f985c23238c )