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    </span><span style="color: #6666cc;">Nelem   </span><span style=""> : </span><span style="color: #dd6;">1806336</span><span style="">
    </span><span style="color: #6666cc;">Min     </span><span style=""> : </span><span style="color: #f66;">0</span><span style="">
    </span><span style="color: #6666cc;">Max     </span><span style=""> : </span><span style="color: #99f;">1</span><span style="">
    </span><span style="color: #6666cc;">Badflag </span><span style=""> : </span><span style="color: #2c2;">No</span><span style="">
    </span><span style="color: #6666cc;">Has Bads</span><span style=""> : </span><span style="color: #2c2;">No</span><span style="">
</span><span style="color: #33ccff;">}</span><span style="">
</span><span style="color: #cc66cc;">AI::TensorFlow::Libtensorflow::Tensor</span><span style=""> </span><span style="color: #33ccff;">{</span><span style="">
    </span><span style="color: #6666cc;">Type           </span><span style=""> </span><span style="color: #cc66cc;">FLOAT</span><span style="">
    </span><span style="color: #6666cc;">Dims           </span><span style=""> </span><span style="color: #33ccff;">[</span><span style=""> </span><span style="color: #ff6633;">12</span><span style=""> </span><span style="color: #ff6633;">224</span><...
    </span><span style="color: #6666cc;">NumDims        </span><span style=""> </span><span style="color: #ff6633;">4</span><span style="">
    </span><span style="color: #6666cc;">ElementCount   </span><span style=""> </span><span style="color: #ff6633;">1806336</span><span style="">
</span><span style="color: #33ccff;">}</span><span style="">
</span></code></pre></span>

=head2 Run the model for inference

We can use the C<Run> method to run the session and get the output C<TFTensor>.

First, we send a single random input to warm up the model.

  my $RunSession = sub {
      my ($session, $t) = @_;
      my @outputs_t;
  
      $session->Run(
          undef,
          $outputs{in}, [$t],
          $outputs{out}, \@outputs_t,
          undef,
          undef,
          $s
      );
      AssertOK($s);
  
      return $outputs_t[0];
  };
  
  say "Warming up the model";
  use PDL::GSL::RNG;
  my $rng = PDL::GSL::RNG->new('default');
  my $image_size = $model_name_to_params{$model_name}{image_size};
  my $warmup_input = zeros(float, 3, @$image_size, 1 );
  $rng->get_uniform($warmup_input);
  
  p $RunSession->($session, FloatPDLTOTFTensor($warmup_input));

B<STREAM (STDOUT)>:

  Warming up the model

B<STREAM (STDERR)>:

=for html <span style="display:inline-block;margin-left:1em;"><pre style="display: block"><code><span style="color: #cc66cc;">AI::TensorFlow::Libtensorflow::Tensor</span><span style=""> </span><span style="color: #33ccff;">{</span><span style="">
    </span><span style="color: #6666cc;">Type           </span><span style=""> </span><span style="color: #cc66cc;">FLOAT</span><span style="">
    </span><span style="color: #6666cc;">Dims           </span><span style=""> </span><span style="color: #33ccff;">[</span><span style=""> </span><span style="color: #ff6633;">1</span><span style=""> </span><span style="color: #ff6633;">1001</span><...
    </span><span style="color: #6666cc;">NumDims        </span><span style=""> </span><span style="color: #ff6633;">2</span><span style="">
    </span><span style="color: #6666cc;">ElementCount   </span><span style=""> </span><span style="color: #ff6633;">1001</span><span style="">
</span><span style="color: #33ccff;">}</span><span style="">
</span></code></pre></span>

Then we send the batched image data. The returned scores need to by normalised using the L<softmax function|https://en.wikipedia.org/wiki/Softmax_function> with the following formula (taken from Wikipedia):

$$ {\displaystyle \sigma (\mathbf {z} )I<{i}={\frac {e^{z>{i}}}{\sum I<{j=1}^{K}e^{z>{j}}}}\ \ {\text{ for }}i=1,\dotsc ,K{\text{ and }}\mathbf {z} =(zI<{1},\dotsc ,z>{K})\in \mathbb {R} ^{K}.} $$

  my $output_pdl_batched = FloatTFTensorToPDL($RunSession->($session, $t));
  my $softmax = sub { ( map $_/sumover($_)->dummy(0), exp($_[0]) )[0] };
  my $probabilities_batched = $softmax->($output_pdl_batched);
  p $probabilities_batched;

B<STREAM (STDERR)>:

=for html <span style="display:inline-block;margin-left:1em;"><pre style="display: block"><code><span style="color: #cc66cc;">PDL</span><span style="color: #33ccff;"> {</span><span style="">
    </span><span style="color: #6666cc;">Data    </span><span style=""> : </span><span style="color: #669933;">too long to print</span><span style="">
    </span><span style="color: #6666cc;">Type    </span><span style=""> : </span><span style="color: #cc66cc;">float</span><span style="">
    </span><span style="color: #6666cc;">Shape   </span><span style=""> : </span><span style="color: #33ccff;">[</span><span style="color: #9999cc;">1001 12</span><span style="color: #33ccff;">]</span><span style="">
    </span><span style="color: #6666cc;">Nelem   </span><span style=""> : </span><span style="color: #dd6;">12012</span><span style="">
    </span><span style="color: #6666cc;">Min     </span><span style=""> : </span><span style="color: #f66;">2.73727380317723e-07</span><span style="">
    </span><span style="color: #6666cc;">Max     </span><span style=""> : </span><span style="color: #99f;">0.980696022510529</span><span style="">
    </span><span style="color: #6666cc;">Badflag </span><span style=""> : </span><span style="color: #2c2;">No</span><span style="">
    </span><span style="color: #6666cc;">Has Bads</span><span style=""> : </span><span style="color: #2c2;">No</span><span style="">
</span><span style="color: #33ccff;">}</span><span style="">
</span></code></pre></span>

=head2 Results summary

Then select the top 5 of those and find their class labels.

  my $N = 5; # number to select
  
  my $top_batched = $probabilities_batched->qsorti->slice([-1, -$N]);
  
  my @top_lists   = dog($top_batched);
  
  my $includes_background_class = $probabilities_batched->dim(0) == IMAGENET_LABEL_COUNT_WITH_BG;
  
  if( IN_IPERL ) {
      my $html = IPerl->html(
          my_table( [0..$#image_names], sub {
              my ($batch_idx, $h) = @_;
              my $image_name = $image_names[$batch_idx];
              my @top_for_image = $top_lists[$batch_idx]->list;
              (
                      $h->tt($image_name),
                      $h->a( { href => $images_for_test_to_uri{$image_name} },
                          $h->img({
                              src => $images_for_test_to_uri{$image_name},
                              alt => $image_name,
                              width => '50%',
                          })
                      ),
                      do {
                          my @tr;
                          push @tr, [ $h->th('Rank', 'Label No', 'Label', 'Prob') ];
                          while( my ($i, $label_index) = each @top_for_image ) {
                              my $class_index = $includes_background_class ? $label_index : $label_index + 1;
                              push @tr, [ $h->td(
                                      $i + 1,
                                      $class_index,
                                      $labels[$class_index],
                                      $probabilities_batched->at($label_index,$batch_idx),
                              ) ];