diff --git a/tensorflow/python/keras/metrics.py b/tensorflow/python/keras/metrics.py
index 7abd44b692b..db8f897ec67 100644
--- a/tensorflow/python/keras/metrics.py
+++ b/tensorflow/python/keras/metrics.py
@@ -2997,12 +2997,25 @@ def top_k_categorical_accuracy(y_true, y_pred, k=5):
 
 @keras_export('keras.metrics.sparse_top_k_categorical_accuracy')
 def sparse_top_k_categorical_accuracy(y_true, y_pred, k=5):
+  """Computes how often integer targets are in the top `K` predictions.
+
+  Args:
+    y_true: tensor of true targets.
+    y_pred: tensor of predicted targets.
+    k: (Optional) Number of top elements to look at for computing accuracy.
+      Defaults to 5.
+
+  Returns:
+    Sparse top K categorical accuracy value.
+  """
   y_pred_rank = ops.convert_to_tensor(y_pred).shape.ndims
   y_true_rank = ops.convert_to_tensor(y_true).shape.ndims
-  # If the shape of y_true is (num_samples, 1), squeeze to (num_samples,)
-  if (y_true_rank is not None) and (y_pred_rank is not None) and (len(
-      K.int_shape(y_true)) == len(K.int_shape(y_pred))):
-    y_true = array_ops.squeeze(y_true, [-1])
+  # Flatten y_pred to (batch_size, num_samples) and y_true to (num_samples,)
+  if (y_true_rank is not None) and (y_pred_rank is not None):
+    if y_pred_rank > 2:
+      y_pred = array_ops.reshape(y_pred, [-1, y_pred.shape[-1]])
+    if y_true_rank > 1:
+      y_true = array_ops.reshape(y_true, [-1])
 
   return math_ops.cast(
       nn.in_top_k(y_pred, math_ops.cast(y_true, 'int32'), k), K.floatx())
diff --git a/tensorflow/python/keras/metrics_functional_test.py b/tensorflow/python/keras/metrics_functional_test.py
index 40478d28f24..1082ac939ff 100644
--- a/tensorflow/python/keras/metrics_functional_test.py
+++ b/tensorflow/python/keras/metrics_functional_test.py
@@ -46,15 +46,21 @@ class KerasFunctionalMetricsTest(test.TestCase):
       # Test correctness if the shape of y_true is (num_samples,)
       y_true = K.variable([1., 0., 0., 0.])
       y_pred = K.variable([[0.8, 0.2], [0.6, 0.4], [0.7, 0.3], [0.9, 0.1]])
-      print(K.eval(metric(y_true, y_pred)))
       self.assertAllEqual(K.eval(metric(y_true, y_pred)), [0., 1., 1., 1.])
 
       # Test correctness if the shape of y_true is (num_samples, 1)
       y_true = K.variable([[1.], [0.], [0.], [0.]])
       y_pred = K.variable([[0.8, 0.2], [0.6, 0.4], [0.7, 0.3], [0.9, 0.1]])
-      print(K.eval(metric(y_true, y_pred)))
       self.assertAllEqual(K.eval(metric(y_true, y_pred)), [0., 1., 1., 1.])
 
+      # Test correctness if the shape of y_true is (batch_size, seq_length) and
+      # y_pred is (batch_size, seq_length, num_classes)
+      y_pred = K.variable(
+          np.array([[[0.2, 0.3, 0.1], [0.1, 0.2, 0.7]],
+                    [[0.3, 0.2, 0.1], [0.7, 0.2, 0.1]]]))
+      y_true = K.variable(np.array([[1, 0], [1, 0]]))
+      self.assertAllEqual(K.eval(metric(y_true, y_pred)), [[1., 0.], [0., 1.]])
+
   def test_sparse_categorical_accuracy_float(self):
     with self.cached_session():
       metric = metrics.sparse_categorical_accuracy
@@ -106,6 +112,22 @@ class KerasFunctionalMetricsTest(test.TestCase):
           metrics.sparse_top_k_categorical_accuracy(y_true, y_pred, k=1))
       self.assertEqual(np.mean(result), 0.)
 
+      # Test correctness if the shape of y_true is (batch_size, seq_length) and
+      # y_pred is (batch_size, seq_length, num_classes)
+      y_pred = K.variable(
+          np.array([[[0.3, 0.2, 0.1], [0.1, 0.2, 0.7], [0.1, 0.2, 0.7]],
+                    [[0.3, 0.2, 0.1], [0.1, 0.2, 0.7], [0.3, 0.2, 0.1]]]))
+      y_true = K.variable(np.array([[1, 0, 0], [1, 0, 1]]))
+      result = K.eval(
+          metrics.sparse_top_k_categorical_accuracy(y_true, y_pred, k=3))
+      self.assertEqual(np.mean(result), 1)
+      result = K.eval(
+          metrics.sparse_top_k_categorical_accuracy(y_true, y_pred, k=2))
+      self.assertEqual(np.mean(result), 0.5)
+      result = K.eval(
+          metrics.sparse_top_k_categorical_accuracy(y_true, y_pred, k=1))
+      self.assertEqual(np.mean(result), 0.)
+
   def test_top_k_categorical_accuracy(self):
     with self.cached_session():
       y_pred = K.variable(np.array([[0.3, 0.2, 0.1], [0.1, 0.2, 0.7]]))