Improve error messages and doc strings for eager-mode tf.keras.Model.fit() + tf.data objects

- Previously, when validation_steps was missing, the error message incorrectly says "please provide either batch_size or steps_per_epoch". Now it reads "please provide either batch_size or validation_steps".
- Some whitespace-related fixes.

PiperOrigin-RevId: 215503991

4 files changed, 49 insertions, 8 deletions

diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py
index c842b8192e..85233de9b1 100644
--- a/tensorflow/python/keras/engine/training.py
+++ b/tensorflow/python/keras/engine/training.py
@@ -1419,6 +1419,8 @@ class Model(Network):
               - tuple `(x_val, y_val)` of Numpy arrays or tensors
               - tuple `(x_val, y_val, val_sample_weights)` of Numpy arrays
               - dataset or a dataset iterator
+            For the first two cases, `batch_size` must be provided.
+            For the last case, `validation_steps` must be provided.
         shuffle: Boolean (whether to shuffle the training data
             before each epoch) or str (for 'batch').
             'batch' is a special option for dealing with the
@@ -1454,9 +1456,10 @@ class Model(Network):
             TensorFlow data tensors, the default `None` is equal to
             the number of samples in your dataset divided by
             the batch size, or 1 if that cannot be determined.
-        validation_steps: Only relevant if `steps_per_epoch`
-            is specified. Total number of steps (batches of samples)
-            to validate before stopping.
+        validation_steps: Only relevant if `validation_data` is provided and
+            is a dataset or dataset iterator. Total number of steps (batches of
+            samples) to draw before stopping when performing validation
+            at the end of every epoch.
         max_queue_size: Integer. Used for generator or `keras.utils.Sequence`
             input only. Maximum size for the generator queue.
             If unspecified, `max_queue_size` will default to 10.
diff --git a/tensorflow/python/keras/engine/training_eager.py b/tensorflow/python/keras/engine/training_eager.py
index fb71bf2596..2a62edd698 100644
--- a/tensorflow/python/keras/engine/training_eager.py
+++ b/tensorflow/python/keras/engine/training_eager.py
@@ -739,7 +739,8 @@ def test_loop(model, inputs, targets,
       y=targets,
       sample_weights=sample_weights,
       batch_size=batch_size,
-      steps_per_epoch=steps)
+      steps_per_epoch=steps,
+      is_validation=True)
   with backend.learning_phase_scope(0):
     return iterator_test_loop(model, inputs, steps, verbose=verbose)
 
diff --git a/tensorflow/python/keras/engine/training_eager_test.py b/tensorflow/python/keras/engine/training_eager_test.py
index 1f5176c4d7..943ede1be9 100644
--- a/tensorflow/python/keras/engine/training_eager_test.py
+++ b/tensorflow/python/keras/engine/training_eager_test.py
@@ -125,6 +125,36 @@ class TrainingTest(test.TestCase):
     model.train_on_batch(inputs, targets)
     model.test_on_batch(inputs, targets)
 
+  def test_model_fit_and_validation_with_missing_arg_errors(self):
+    x = keras.layers.Input(shape=(3,), name='input')
+    y = keras.layers.Dense(4, name='dense')(x)
+    model = keras.Model(x, y)
+    model.compile(optimizer=RMSPropOptimizer(learning_rate=0.001), loss='mse')
+
+    x = keras.backend.zeros(shape=(10, 3))
+    y = keras.backend.zeros(shape=(10, 4))
+    dataset = dataset_ops.Dataset.from_tensor_slices((x, y)).repeat(10).batch(5)
+    iterator = dataset.make_one_shot_iterator()
+    validation_dataset = dataset_ops.Dataset.from_tensor_slices(
+        (x, y)).repeat(10).batch(5)
+    validation_iterator = validation_dataset.make_one_shot_iterator()
+
+    with self.assertRaisesRegexp(
+        ValueError, r'specify .* `steps_per_epoch`'):
+      model.fit(iterator, epochs=1, verbose=0)
+    with self.assertRaisesRegexp(
+        ValueError, r'provide either `batch_size` or `validation_steps`'):
+      model.fit(iterator, steps_per_epoch=2, epochs=1, verbose=0,
+                validation_data=(x, y))
+    with self.assertRaisesRegexp(
+        ValueError, r'provide either `batch_size` or `validation_steps`'):
+      model.fit(iterator, steps_per_epoch=2, epochs=1, verbose=0,
+                validation_data=validation_dataset)
+    with self.assertRaisesRegexp(
+        ValueError, r'provide either `batch_size` or `validation_steps`'):
+      model.fit(iterator, steps_per_epoch=2, epochs=1, verbose=0,
+                validation_data=validation_iterator)
+
   def test_generator_methods(self):
     model = keras.Sequential()
     model.add(keras.layers.Dense(4, input_shape=(3,)))
diff --git a/tensorflow/python/keras/engine/training_utils.py b/tensorflow/python/keras/engine/training_utils.py
index 9c303f4bed..dd2a7f16ec 100644
--- a/tensorflow/python/keras/engine/training_utils.py
+++ b/tensorflow/python/keras/engine/training_utils.py
@@ -106,7 +106,8 @@ def convert_to_iterator(x=None,
                         batch_size=None,
                         steps_per_epoch=None,
                         epochs=1,
-                        shuffle=False):
+                        shuffle=False,
+                        is_validation=False):
   """Converts NumPy arrays or EagerTensors to an EagerIterator.
 
   Combines all provided data into a single EagerIterator.
@@ -124,6 +125,9 @@ def convert_to_iterator(x=None,
         epoch.
       epochs: Epochs to repeat iterator for.
       shuffle: Whether to shuffle data after each epoch.
+      is_validation: Whether this call is for validation during a training
+        (e.g., `fit()`) call. This info is used to construct error messages
+        (if any).
 
   Raises:
       ValueError: if steps_per_epoch cannot be calculated from the data
@@ -151,9 +155,12 @@ def convert_to_iterator(x=None,
     steps_per_epoch = int(math.ceil(num_samples / batch_size))
 
   if steps_per_epoch is None:
-    raise ValueError('Could not determine steps_per_epoch.'
-                     'Please provide either batch_size or'
-                     'steps_per_epoch.')
+    alternative_arg_name = (
+        'validation_steps' if is_validation else 'steps_per_epoch')
+    raise ValueError(
+        'Could not determine how to convert EagerTensors into EagerIterator. '
+        'Please provide either `batch_size` or '
+        '`%s`.' % alternative_arg_name)
 
   # TODO(omalleyt) for NumPy arrays in graph mode
   # placeholder ops should be used