Small improvements to handling of Datasets in Keras.

* Allow sparse labels to work with Datasets.
* Allow sample_weights to be passed as the third output of a Dataset (like how
generator input is treated).

PiperOrigin-RevId: 211834259

tensorflow/contrib/distribute/python/keras_test.py

@@ -446,8 +446,7 @@ class TestWithDistributionStrategy(test.TestCase):
       dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets))
       dataset = dataset.repeat(100)
 
-      with self.assertRaisesRegexp(ValueError,
+      with self.assertRaisesRegexp(ValueError, 'expected input to have shape'):
-                                   'expected input to have 2 dimensions'):
         model.fit(dataset, epochs=1, steps_per_epoch=2, verbose=0)
 
       # Wrong input shape

tensorflow/python/keras/engine/training.py

@@ -928,11 +928,16 @@ class Model(Network):
                            'Make sure that your dataset can generate '
                            'required number of samples.')
 
-      if not isinstance(next_element, (list, tuple)) or len(next_element) != 2:
+      if (not isinstance(next_element, (list, tuple)) or
-        raise ValueError('Please provide model inputs as a list or tuple of 2 '
+          len(next_element) not in [2, 3]):
-                         'elements: input and target pair. '
+        raise ValueError(
-                         'Received %s' % next_element)
+            'Please provide model inputs as a list or tuple of 2  or 3'
-      x, y = next_element
+            'elements: (input, target) or (input, target, sample_weights)'
+            'Received %s' % next_element)
+      if len(next_element) == 2:
+        x, y = next_element
+      else:
+        x, y, sample_weight = next_element
     x, y, sample_weights = self._standardize_weights(x, y, sample_weight,
                                                      class_weight, batch_size)
     return x, y, sample_weights
@@ -1331,7 +1336,8 @@ class Model(Network):
             (in case the model has multiple inputs).
           - A dict mapping input names to the corresponding array/tensors,
             if the model has named inputs.
-          - A `tf.data` dataset or a dataset iterator.
+          - A `tf.data` dataset or a dataset iterator. Should return a tuple
+            of either (inputs, targets) or (inputs, targets, sample_weights).
         y: Target data. Like the input data `x`,
           it could be either Numpy array(s) or TensorFlow tensor(s).
           It should be consistent with `x` (you cannot have Numpy inputs and
@@ -1396,7 +1402,8 @@ class Model(Network):
             to apply a different weight to every timestep of every sample.
             In this case you should make sure to specify
             `sample_weight_mode="temporal"` in `compile()`. This argument is not
-            supported when `x` is a dataset or a dataset iterator.
+            supported when `x` is a dataset or a dataset iterator, instead
+            provide the sample_weights as the third element of `x`.
         initial_epoch: Integer.
             Epoch at which to start training
             (useful for resuming a previous training run).

tensorflow/python/keras/engine/training_eager.py

@@ -417,11 +417,12 @@ def iterator_predict_loop(model, inputs, steps, verbose=0):
   """
   assert isinstance(inputs, iterator_ops.EagerIterator)
   if not isinstance(inputs.output_shapes,
-                    (list, tuple)) or len(inputs.output_shapes) > 2:
+                    (list, tuple)) or len(inputs.output_shapes) > 3:
     raise ValueError(
-        'Please provide data as a list or tuple of 1 or 2 elements '
+        'Please provide data as a list or tuple of 1, 2, or 3 elements '
-        ' - input or input and target pair. Received %s. We do not use the '
+        ' - `(input)`, or `(input, target)`, or `(input, target,'
-        '`target` value here.' % inputs.output_shapes)
+        'sample_weights)`. Received %s. We do not use the `target` or'
+        '`sample_weights` value here.' % inputs.output_shapes)
   outs = []
   if verbose == 1:
     progbar = generic_utils.Progbar(target=steps)

tensorflow/python/keras/engine/training_test.py

@@ -2097,6 +2097,43 @@ class TestTrainingWithDataset(test.TestCase):
                                  'you should specify the `steps` argument'):
       model.predict(dataset, verbose=0)
 
+  @tf_test_util.run_in_graph_and_eager_modes
+  def test_dataset_with_sample_weights(self):
+    model = testing_utils.get_small_functional_mlp(1, 4, input_dim=3)
+    optimizer = RMSPropOptimizer(learning_rate=0.001)
+    loss = 'mse'
+    metrics = ['mae', metrics_module.CategoricalAccuracy()]
+    model.compile(optimizer, loss, metrics=metrics)
+
+    inputs = np.zeros((10, 3), np.float32)
+    targets = np.zeros((10, 4), np.float32)
+    sample_weights = np.ones((10), np.float32)
+    dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets,
+                                                      sample_weights))
+    dataset = dataset.repeat(100)
+    dataset = dataset.batch(10)
+
+    model.fit(dataset, epochs=1, steps_per_epoch=2, verbose=1)
+    model.evaluate(dataset, steps=2, verbose=1)
+    model.predict(dataset, steps=2)
+    model.train_on_batch(dataset)
+    model.predict_on_batch(dataset)
+
+  @tf_test_util.run_in_graph_and_eager_modes
+  def test_dataset_with_sparse_labels(self):
+    model = testing_utils.get_small_functional_mlp(1, 4, input_dim=3)
+    optimizer = RMSPropOptimizer(learning_rate=0.001)
+    loss = 'sparse_categorical_crossentropy'
+    model.compile(optimizer, loss)
+
+    inputs = np.zeros((10, 3))
+    targets = np.random.randint(0, 4, size=10, dtype=np.int32)
+    dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets))
+    dataset = dataset.repeat(100)
+    dataset = dataset.batch(10)
+
+    model.fit(dataset, epochs=1, steps_per_epoch=2, verbose=1)
+
   def test_dataset_input_shape_validation(self):
     with self.test_session():
       model = testing_utils.get_small_functional_mlp(1, 4, input_dim=3)
@@ -2108,8 +2145,10 @@ class TestTrainingWithDataset(test.TestCase):
       dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets))
       dataset = dataset.repeat(100)
 
-      with self.assertRaisesRegexp(ValueError,
+      with self.assertRaisesRegexp(
-                                   r'expected (.*?) to have 2 dimensions'):
+          ValueError,
+          r'expected (.*?) to have shape \(3,\) but got array with shape \(1,\)'
+      ):
         model.train_on_batch(dataset)
 
       # Wrong input shape

tensorflow/python/keras/engine/training_utils.py

@@ -210,10 +210,11 @@ def check_num_samples(ins,
 def standardize_single_array(x):
   if x is None:
     return None
-  elif tensor_util.is_tensor(x):
+  if x.shape is not None and len(x.shape) == 1:
-    return x
+    if tensor_util.is_tensor(x):
-  elif x.ndim == 1:
+      return array_ops.expand_dims(x, axis=1)
-    x = np.expand_dims(x, 1)
+    else:
+      return np.expand_dims(x, 1)
   return x
 
 
@@ -341,7 +342,7 @@ def standardize_sample_or_class_weights(x_weight, output_names, weight_type):
   Raises:
       ValueError: In case of invalid user-provided argument.
   """
-  if x_weight is None or len(x_weight) == 0:  # pylint: disable=g-explicit-length-test
+  if x_weight is None or (isinstance(x_weight, list) and len(x_weight) == 0):  # pylint: disable=g-explicit-length-test
     return [None for _ in output_names]
   if len(output_names) == 1:
     if isinstance(x_weight, list) and len(x_weight) == 1:
@@ -675,7 +676,8 @@ def standardize_weights(y,
           'Expected sample_weight with rank '
           'less than or equal to ' + str(len(y.shape)))
 
-    if y.shape[:sample_weight.ndim] != sample_weight.shape:
+    if (not tensor_util.is_tensor(sample_weight) and
+        y.shape[:sample_weight.ndim] != sample_weight.shape):
       raise ValueError(
           'Found a sample_weight array with shape ' + str(sample_weight.shape) +
           ' for an input with shape ' + str(y.shape) + '. '
@@ -777,7 +779,9 @@ def validate_iterator_input(x, y, sample_weight, validation_split=None):
                      'Received: %s' % (x, y))
   if sample_weight is not None:
     raise ValueError('`sample_weight` argument is not supported when input '
-                     '`x` is a dataset or a dataset iterator. '
+                     '`x` is a dataset or a dataset iterator. Instead, you'
+                     'can provide sample_weight as the third element  of your'
+                     'dataset, i.e. (inputs, targets, sample_weight). '
                      'Received: x=%s, sample_weight=%s' % (x, sample_weight))
   if validation_split is not None and validation_split != 0.0:
     raise ValueError(