Fix: When sample_weight_mode is a list/dict set default sample_weight values so that we do not require sample_weight to be set during training/eval

PiperOrigin-RevId: 206242625

3 files changed, 87 insertions, 42 deletions

diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py
index 4df739254b..39d207cc6b 100644
--- a/tensorflow/python/keras/engine/training.py
+++ b/tensorflow/python/keras/engine/training.py
@@ -24,7 +24,6 @@ import numpy as np
 from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.data.ops import iterator_ops
 from tensorflow.python.eager import context
-from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
@@ -374,21 +373,14 @@ class Model(Network):
               'sample_weight_mode dictionary: "' + name + '". '
               'Only expected the following keys: ' + str(self.output_names))
       for i, name in enumerate(self.output_names):
-        if i in skip_target_weighing_indices:
-          weight = None
-          sample_weight_modes.append(None)
-        else:
-          if name not in sample_weight_mode:
-            raise ValueError(
-                'Output "' + name + '" missing from sample_weight_modes '
-                'dictionary')
-          if sample_weight_mode.get(name) == 'temporal':
-            weight = K.placeholder(ndim=2, name=name + '_sample_weights')
-            sample_weight_modes.append('temporal')
-          else:
-            weight = K.placeholder(ndim=1, name=name + 'sample_weights')
-            sample_weight_modes.append(None)
+        if (i not in skip_target_weighing_indices and
+            name not in sample_weight_mode):
+          raise ValueError('Output "' + name +
+                           '" missing from sample_weight_modes dictionary')
+        weight, mode = training_utils.get_output_sample_weight_and_mode(
+            skip_target_weighing_indices, sample_weight_mode.get(name), name, i)
         sample_weights.append(weight)
+        sample_weight_modes.append(mode)
     elif isinstance(sample_weight_mode, list):
       if len(sample_weight_mode) != len(self.outputs):
         raise ValueError('When passing a list as sample_weight_mode, '
@@ -396,36 +388,17 @@ class Model(Network):
                          'The model has ' + str(len(self.outputs)) +
                          ' outputs, but you passed '
                          'sample_weight_mode=' + str(sample_weight_mode))
-      for i in range(len(self.output_names)):
-        if i in skip_target_weighing_indices:
-          weight = None
-          sample_weight_modes.append(None)
-        else:
-          mode = sample_weight_mode[i]
-          name = self.output_names[i]
-          if mode == 'temporal':
-            weight = K.placeholder(ndim=2, name=name + '_sample_weights')
-            sample_weight_modes.append('temporal')
-          else:
-            weight = K.placeholder(ndim=1, name=name + '_sample_weights')
-            sample_weight_modes.append(None)
+      for i, name in enumerate(self.output_names):
+        weight, mode = training_utils.get_output_sample_weight_and_mode(
+            skip_target_weighing_indices, sample_weight_mode[i], name, i)
         sample_weights.append(weight)
+        sample_weight_modes.append(mode)
     else:
       for i, name in enumerate(self.output_names):
-        if i in skip_target_weighing_indices:
-          sample_weight_modes.append(None)
-          sample_weights.append(None)
-        else:
-          if sample_weight_mode == 'temporal':
-            sample_weights.append(array_ops.placeholder_with_default(
-                constant_op.constant([[1.]], dtype=K.floatx()),
-                shape=[None, None], name=name + '_sample_weights'))
-            sample_weight_modes.append('temporal')
-          else:
-            sample_weights.append(array_ops.placeholder_with_default(
-                constant_op.constant([1.], dtype=K.floatx()),
-                shape=[None], name=name + '_sample_weights'))
-            sample_weight_modes.append(None)
+        weight, mode = training_utils.get_output_sample_weight_and_mode(
+            skip_target_weighing_indices, sample_weight_mode, name, i)
+        sample_weights.append(weight)
+        sample_weight_modes.append(mode)
     self.sample_weight_modes = sample_weight_modes
     self._feed_sample_weight_modes = []
     for i in range(len(self.outputs)):
diff --git a/tensorflow/python/keras/engine/training_test.py b/tensorflow/python/keras/engine/training_test.py
index 301a6ca866..129441d159 100644
--- a/tensorflow/python/keras/engine/training_test.py
+++ b/tensorflow/python/keras/engine/training_test.py
@@ -731,6 +731,54 @@ class LossWeightingTest(test.TestCase):
         model.fit(x_np, [y_np, y_np], epochs=1,
                   sample_weight={'1': bad_w_np})
 
+  def test_default_sample_weight(self):
+    """Verifies that fit works without having to set sample_weight."""
+
+    num_classes = 5
+    input_dim = 5
+    timesteps = 3
+    with self.test_session():
+      model = keras.models.Sequential()
+      model.add(
+          keras.layers.TimeDistributed(
+              keras.layers.Dense(num_classes),
+              input_shape=(timesteps, input_dim)))
+
+      x = np.random.random((10, timesteps, input_dim))
+      y = np.random.random((10, timesteps, num_classes))
+
+      # sample_weight_mode is a list and mode value is None
+      model.compile(loss='mse', optimizer='rmsprop', sample_weight_mode=[None])
+      model.fit(x, y, epochs=1, batch_size=10)
+
+      # sample_weight_mode is a list and mode value is `temporal`
+      model.compile(
+          loss='mse', optimizer='rmsprop', sample_weight_mode=['temporal'])
+      model.fit(x, y, epochs=1, batch_size=10)
+
+      # sample_weight_mode is a dict and mode value is None
+      model.compile(
+          loss='mse',
+          optimizer='rmsprop',
+          sample_weight_mode={'time_distributed': None})
+      model.fit(x, y, epochs=1, batch_size=10)
+
+      # sample_weight_mode is a dict and mode value is `temporal`
+      model.compile(
+          loss='mse',
+          optimizer='rmsprop',
+          sample_weight_mode={'time_distributed': 'temporal'})
+      model.fit(x, y, epochs=1, batch_size=10)
+
+      # sample_weight_mode is a not a list/dict and mode value is None
+      model.compile(loss='mse', optimizer='rmsprop', sample_weight_mode=None)
+      model.fit(x, y, epochs=1, batch_size=10)
+
+      # sample_weight_mode is a not a list/dict and mode value is `temporal`
+      model.compile(
+          loss='mse', optimizer='rmsprop', sample_weight_mode='temporal')
+      model.fit(x, y, epochs=1, batch_size=10)
+
 
 class LossMaskingTest(test.TestCase):
 
diff --git a/tensorflow/python/keras/engine/training_utils.py b/tensorflow/python/keras/engine/training_utils.py
index dbbc87daf9..21495fd0bd 100644
--- a/tensorflow/python/keras/engine/training_utils.py
+++ b/tensorflow/python/keras/engine/training_utils.py
@@ -26,10 +26,12 @@ import numpy as np
 from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.data.ops import iterator_ops
 from tensorflow.python.eager import context
+from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.keras import backend as K
 from tensorflow.python.keras import losses
 from tensorflow.python.keras import metrics as metrics_module
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 
 
@@ -856,3 +858,25 @@ def cast_if_floating_dtype(x):
         for val in x
     ]
   return math_ops.cast(x, dtype=K.floatx()) if x.dtype.is_floating else x
+
+
+def get_output_sample_weight_and_mode(skip_target_weighing_indices,
+                                      sample_weight_mode, output_name,
+                                      output_index):
+  """Returns the sample weight and weight mode for a single output."""
+  if output_index in skip_target_weighing_indices:
+    return None, None
+
+  if sample_weight_mode == 'temporal':
+    default_value = [[1.]]
+    shape = [None, None]
+    mode = 'temporal'
+  else:
+    default_value = [1.]
+    shape = [None]
+    mode = None
+  weight = array_ops.placeholder_with_default(
+      constant_op.constant(default_value, dtype=K.floatx()),
+      shape=shape,
+      name=output_name + '_sample_weights')
+  return weight, mode