1 files changed, 4 insertions, 6 deletions
diff --git a/tensorflow/contrib/eager/python/metrics_impl.py b/tensorflow/contrib/eager/python/metrics_impl.py
index efa6ba0626..6efafccd6b 100644
--- a/tensorflow/contrib/eager/python/metrics_impl.py
+++ b/tensorflow/contrib/eager/python/metrics_impl.py
@@ -291,8 +291,6 @@ class Metric(checkpointable.CheckpointableBase):
 
 class Mean(Metric):
   """Computes the (weighted) mean of the given values."""
-  # TODO(josh11b): Maybe have a dtype argument that defaults to tf.float64?
-  # Or defaults to type of the input if it is tf.float32, else tf.float64?
 
   def __init__(self, name=None, dtype=dtypes.float64,
                use_global_variables=False):
@@ -377,7 +375,7 @@ class Accuracy(Mean):
         array_ops.shape(labels), array_ops.shape(predictions),
         message="Shapes of labels and predictions are unequal")
     matches = math_ops.equal(labels, predictions)
-    matches = math_ops.cast(matches, dtypes.float64)
+    matches = math_ops.cast(matches, self.dtype)
     super(Accuracy, self).call(matches, weights=weights)
     if weights is None:
       return labels, predictions
@@ -421,7 +419,7 @@ class CategoricalAccuracy(Mean):
     labels = math_ops.argmax(labels, axis=-1)
     predictions = math_ops.argmax(predictions, axis=-1)
     matches = math_ops.equal(labels, predictions)
-    matches = math_ops.cast(matches, dtypes.float64)
+    matches = math_ops.cast(matches, self.dtype)
     super(CategoricalAccuracy, self).call(matches, weights=weights)
     if weights is None:
       return labels, predictions
@@ -472,7 +470,7 @@ class BinaryAccuracy(Mean):
     predictions = ops.convert_to_tensor(predictions)
     predictions = predictions > self.threshold
     matches = math_ops.equal(labels, predictions)
-    matches = math_ops.cast(matches, dtypes.float64)
+    matches = math_ops.cast(matches, self.dtype)
     super(BinaryAccuracy, self).call(matches, weights=weights)
     if weights is None:
       return labels, predictions
@@ -520,7 +518,7 @@ class SparseAccuracy(Mean):
     predictions = math_ops.argmax(predictions, axis=-1)
     labels = math_ops.cast(labels, dtypes.int64)
     matches = math_ops.equal(labels, predictions)
-    matches = math_ops.cast(matches, dtypes.float64)
+    matches = math_ops.cast(matches, self.dtype)
     super(SparseAccuracy, self).call(matches, weights=weights)
     if weights is None:
       return labels, predictions