tensorflow.patch

From fb7dd5c67d70f86082efca7cd3f29061b808467f Mon Sep 17 00:00:00 2001
From: Georg Wiese <georgwiese@gmail.com>
Date: Thu, 2 Apr 2020 15:38:00 +0200
Subject: [PATCH] Fix keras handling of targets with no loss

---
 tensorflow/python/keras/engine/training_eager.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/tensorflow/python/keras/engine/training_eager.py b/tensorflow/python/keras/engine/training_eager.py
index be1b2e89d9..e39a571a91 100644
--- a/tensorflow/python/keras/engine/training_eager.py
+++ b/tensorflow/python/keras/engine/training_eager.py
@@ -143,10 +143,11 @@ def _model_loss(model,
   output_losses = []
 
   with backend.name_scope('loss'):
-    loss_fns = [
-        loss_fn for loss_fn in model.loss_functions if loss_fn is not None
-    ]
-    for i, loss_fn in enumerate(loss_fns):
+    i_target = 0
+    for i, loss_fn in enumerate(model.loss_functions):
+      if loss_fn is None:
+        # Output i has no loss.
+        continue
       weights = sample_weights[i] if sample_weights else None
       mask = masks[i]
       with backend.name_scope(model.output_names[i] + '_loss'):
@@ -163,7 +164,7 @@ def _model_loss(model,
             weights *= mask
 
         if hasattr(loss_fn, 'reduction'):
-          per_sample_losses = loss_fn.call(targets[i], outs[i])
+          per_sample_losses = loss_fn.call(targets[i_target], outs[i])
           weighted_losses = losses_utils.compute_weighted_loss(
               per_sample_losses,
               sample_weight=weights,
@@ -193,13 +194,14 @@ def _model_loss(model,
       # as part of the loss_metrics.
       if len(model.outputs) > 1:
         # Keep track of the stateful output loss result.
-        output_losses.append(output_loss_metrics[i](output_loss))
+        output_losses.append(output_loss_metrics[i_target](output_loss))
 
       # Scale output loss for distribution. For custom losses we assume
       # reduction was mean.
       if loss_reduction == losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE:
         output_loss = losses_utils.scale_loss_for_distribution(output_loss)
       total_loss += model._loss_weights_list[i] * output_loss
+      i_target += 1
 
     # Add regularization losses
     custom_losses = model.losses
-- 
2.21.1 (Apple Git-122.3)