diff --git a/tensorflow/python/keras/engine/training_arrays_test.py b/tensorflow/python/keras/engine/training_arrays_test.py
index a4647ad34da..097d5eef36d 100644
--- a/tensorflow/python/keras/engine/training_arrays_test.py
+++ b/tensorflow/python/keras/engine/training_arrays_test.py
@@ -63,7 +63,8 @@ class ValidationDatasetNoLimitTest(keras_parameterized.TestCase):
                            evaluation[-1], places=5)
 
 
-class PrintTrainingInfoTest(parameterized.TestCase):
+class PrintTrainingInfoTest(keras_parameterized.TestCase,
+                            parameterized.TestCase):
 
   @test_util.run_v1_only("Only relevant in graph mode.")
   def test_print_info_with_datasets(self):
@@ -110,6 +111,38 @@ class PrintTrainingInfoTest(parameterized.TestCase):
     if do_validation:
       self.assertIn(", validate on 50 samples", mock_stdout.getvalue())
 
+  @keras_parameterized.run_all_keras_modes
+  def test_dict_float64_input(self):
+
+    class MyModel(keras.Model):
+
+      def __init__(self):
+        super(MyModel, self).__init__(self)
+        self.dense1 = keras.layers.Dense(10, activation="relu")
+        self.dense2 = keras.layers.Dense(10, activation="relu")
+        self.concat = keras.layers.Concatenate()
+        self.dense3 = keras.layers.Dense(1, activation="sigmoid")
+
+      def call(self, inputs):
+        d1 = self.dense1(inputs["one"])
+        d2 = self.dense2(inputs["two"])
+        concat = self.concat([d1, d2])
+        return self.dense3(concat)
+
+    model = MyModel()
+    model.compile(
+        loss="mae",
+        optimizer="adam",
+        run_eagerly=testing_utils.should_run_eagerly(),
+        experimental_run_tf_function=testing_utils.should_run_tf_function())
+
+    model.fit(
+        x={
+            "one": np.random.rand(100, 10, 1),
+            "two": np.random.rand(100, 10, 1)
+        },
+        y=np.random.rand(100, 10, 1))
+
   def test_dict_validation_input(self):
     """Test case for GitHub issue 30122."""
     train_input_0 = np.random.rand(1000, 1)
diff --git a/tensorflow/python/keras/engine/training_eager.py b/tensorflow/python/keras/engine/training_eager.py
index 3e1a03076e8..cd1fd8c6b2d 100644
--- a/tensorflow/python/keras/engine/training_eager.py
+++ b/tensorflow/python/keras/engine/training_eager.py
@@ -31,7 +31,6 @@ from tensorflow.python.ops import math_ops
 from tensorflow.python.ops.losses import util as tf_losses_utils
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util import nest
-from tensorflow.python.util.compat import collections_abc
 
 
 def _eager_loss_fn(outputs, targets, loss_fn, output_name):
@@ -287,10 +286,9 @@ def train_on_batch(model,
   Returns:
       total loss and the loss associated with each output.
   """
-  if isinstance(inputs, collections_abc.Sequence):
-    inputs = training_utils.cast_to_model_input_dtypes(inputs, model)
-    if targets:
-      targets = training_utils.cast_if_floating_dtype(targets)
+  inputs = training_utils.cast_to_model_input_dtypes(inputs, model)
+  if targets:
+    targets = training_utils.cast_if_floating_dtype(targets)
   if sample_weights:
     sample_weights = [
         training_utils.cast_if_floating_dtype(ops.convert_to_tensor(val))
@@ -333,10 +331,9 @@ def test_on_batch(model,
   Returns:
       total loss, loss and metrics associated with each output.
   """
-  if isinstance(inputs, collections_abc.Sequence):
-    inputs = training_utils.cast_to_model_input_dtypes(inputs, model)
-    if targets:
-      targets = training_utils.cast_if_floating_dtype(targets)
+  inputs = training_utils.cast_to_model_input_dtypes(inputs, model)
+  if targets:
+    targets = training_utils.cast_if_floating_dtype(targets)
   if sample_weights:
     sample_weights = [
         training_utils.cast_if_floating_dtype(ops.convert_to_tensor(val))