Merge pull request #39134 from yongtang:36790-tf.metrics.Recall-backend-float64

PiperOrigin-RevId: 313606803
Change-Id: Idbb4244c62fe797815ea40ae1297c0346c65983e

tensorflow/python/keras/metrics_test.py

@@ -33,6 +33,7 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors_impl
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import test_util
+from tensorflow.python.keras import backend
 from tensorflow.python.keras import combinations
 from tensorflow.python.keras import keras_parameterized
 from tensorflow.python.keras import layers
@@ -2247,6 +2248,23 @@ class ResetStatesTest(keras_parameterized.TestCase):
     self.assertArrayNear(self.evaluate(m_obj.total_cm)[0], [1, 0], 1e-1)
     self.assertArrayNear(self.evaluate(m_obj.total_cm)[1], [3, 0], 1e-1)
 
+  def test_reset_states_recall_float64(self):
+    # Test case for GitHub issue 36790.
+    try:
+      backend.set_floatx('float64')
+      r_obj = metrics.Recall()
+      model = _get_model([r_obj])
+      x = np.concatenate((np.ones((50, 4)), np.zeros((50, 4))))
+      y = np.concatenate((np.ones((50, 1)), np.ones((50, 1))))
+      model.evaluate(x, y)
+      self.assertEqual(self.evaluate(r_obj.true_positives), 50.)
+      self.assertEqual(self.evaluate(r_obj.false_negatives), 50.)
+      model.evaluate(x, y)
+      self.assertEqual(self.evaluate(r_obj.true_positives), 50.)
+      self.assertEqual(self.evaluate(r_obj.false_negatives), 50.)
+    finally:
+      backend.set_floatx('float32')
+
 
 if __name__ == '__main__':
   test.main()

tensorflow/python/keras/utils/metrics_utils.py

@@ -299,9 +299,19 @@ def update_confusion_matrix_variables(variables_to_update,
                      '`multi_label` is True.')
   if variables_to_update is None:
     return
-  y_true = math_ops.cast(y_true, dtype=dtypes.float32)
+  if not any(
-  y_pred = math_ops.cast(y_pred, dtype=dtypes.float32)
+      key for key in variables_to_update if key in list(ConfusionMatrix)):
-  thresholds = ops.convert_to_tensor_v2(thresholds, dtype=dtypes.float32)
+    raise ValueError(
+        'Please provide at least one valid confusion matrix '
+        'variable to update. Valid variable key options are: "{}". '
+        'Received: "{}"'.format(
+            list(ConfusionMatrix), variables_to_update.keys()))
+
+  variable_dtype = list(variables_to_update.values())[0].dtype
+
+  y_true = math_ops.cast(y_true, dtype=variable_dtype)
+  y_pred = math_ops.cast(y_pred, dtype=variable_dtype)
+  thresholds = ops.convert_to_tensor_v2(thresholds, dtype=variable_dtype)
   num_thresholds = thresholds.shape[0]
   if multi_label:
     one_thresh = math_ops.equal(
@@ -314,14 +324,6 @@ def update_confusion_matrix_variables(variables_to_update,
                                                                sample_weight)
     one_thresh = math_ops.cast(True, dtype=dtypes.bool)
 
-  if not any(
-      key for key in variables_to_update if key in list(ConfusionMatrix)):
-    raise ValueError(
-        'Please provide at least one valid confusion matrix '
-        'variable to update. Valid variable key options are: "{}". '
-        'Received: "{}"'.format(
-            list(ConfusionMatrix), variables_to_update.keys()))
-
   invalid_keys = [
       key for key in variables_to_update if key not in list(ConfusionMatrix)
   ]
@@ -401,7 +403,7 @@ def update_confusion_matrix_variables(variables_to_update,
 
   if sample_weight is not None:
     sample_weight = weights_broadcast_ops.broadcast_weights(
-        math_ops.cast(sample_weight, dtype=dtypes.float32), y_pred)
+        math_ops.cast(sample_weight, dtype=variable_dtype), y_pred)
     weights_tiled = array_ops.tile(
         array_ops.reshape(sample_weight, thresh_tiles), data_tiles)
   else:
@@ -422,9 +424,9 @@ def update_confusion_matrix_variables(variables_to_update,
 
   def weighted_assign_add(label, pred, weights, var):
     label_and_pred = math_ops.cast(
-        math_ops.logical_and(label, pred), dtype=dtypes.float32)
+        math_ops.logical_and(label, pred), dtype=var.dtype)
     if weights is not None:
-      label_and_pred *= weights
+      label_and_pred *= math_ops.cast(weights, dtype=var.dtype)
     return var.assign_add(math_ops.reduce_sum(label_and_pred, 1))
 
   loop_vars = {