Set metric variable initializers as lambda.

PiperOrigin-RevId: 174100686

1 files changed, 14 insertions, 25 deletions

diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops.py b/tensorflow/contrib/metrics/python/ops/metric_ops.py
index c524da4309..c328b03707 100644
--- a/tensorflow/contrib/metrics/python/ops/metric_ops.py
+++ b/tensorflow/contrib/metrics/python/ops/metric_ops.py
@@ -92,8 +92,7 @@ def _count_condition(values,
       or tuple.
   """
   check_ops.assert_type(values, dtypes.bool)
-  count_ = metrics_impl.metric_variable(
-      array_ops.zeros([], dtype=dtypes.float32), name='count')
+  count_ = metrics_impl.metric_variable([], dtypes.float32, name='count')
 
   values = math_ops.to_float(values)
   if weights is not None:
@@ -916,8 +915,7 @@ def _streaming_confusion_matrix_at_thresholds(predictions,
 
   if 'tp' in includes:
     true_positives = metrics_impl.metric_variable(
-        array_ops.zeros([num_thresholds], dtype=dtypes.float32),
-        name='true_positives')
+        [num_thresholds], dtypes.float32, name='true_positives')
     is_true_positive = math_ops.to_float(
         math_ops.logical_and(label_is_pos, pred_is_pos))
     if weights_tiled is not None:
@@ -929,8 +927,7 @@ def _streaming_confusion_matrix_at_thresholds(predictions,
 
   if 'fn' in includes:
     false_negatives = metrics_impl.metric_variable(
-        array_ops.zeros([num_thresholds], dtype=dtypes.float32),
-        name='false_negatives')
+        [num_thresholds], dtypes.float32, name='false_negatives')
     is_false_negative = math_ops.to_float(
         math_ops.logical_and(label_is_pos, pred_is_neg))
     if weights_tiled is not None:
@@ -942,8 +939,7 @@ def _streaming_confusion_matrix_at_thresholds(predictions,
 
   if 'tn' in includes:
     true_negatives = metrics_impl.metric_variable(
-        array_ops.zeros([num_thresholds], dtype=dtypes.float32),
-        name='true_negatives')
+        [num_thresholds], dtypes.float32, name='true_negatives')
     is_true_negative = math_ops.to_float(
         math_ops.logical_and(label_is_neg, pred_is_neg))
     if weights_tiled is not None:
@@ -955,8 +951,7 @@ def _streaming_confusion_matrix_at_thresholds(predictions,
 
   if 'fp' in includes:
     false_positives = metrics_impl.metric_variable(
-        array_ops.zeros([num_thresholds], dtype=dtypes.float32),
-        name='false_positives')
+        [num_thresholds], dtypes.float32, name='false_positives')
     is_false_positive = math_ops.to_float(
         math_ops.logical_and(label_is_neg, pred_is_pos))
     if weights_tiled is not None:
@@ -1317,9 +1312,9 @@ def streaming_precision_recall_at_equal_thresholds(predictions,
 
     with ops.name_scope('variables'):
       tp_buckets_v = metrics_impl.metric_variable(
-          array_ops.zeros([num_thresholds], dtype=dtype), name='tp_buckets')
+          [num_thresholds], dtype, name='tp_buckets')
       fp_buckets_v = metrics_impl.metric_variable(
-          array_ops.zeros([num_thresholds], dtype=dtype), name='fp_buckets')
+          [num_thresholds], dtype, name='fp_buckets')
 
     with ops.name_scope('update_op'):
       update_tp = state_ops.scatter_add(
@@ -2582,15 +2577,13 @@ def streaming_covariance(predictions,
     predictions, labels, weights = metrics_impl._remove_squeezable_dimensions(  # pylint: disable=protected-access
         predictions, labels, weights)
     predictions.get_shape().assert_is_compatible_with(labels.get_shape())
-    count_ = metrics_impl.metric_variable(
-        array_ops.zeros([], dtype=dtypes.float32), name='count')
+    count_ = metrics_impl.metric_variable([], dtypes.float32, name='count')
     mean_prediction = metrics_impl.metric_variable(
-        array_ops.zeros([], dtype=dtypes.float32), name='mean_prediction')
+        [], dtypes.float32, name='mean_prediction')
     mean_label = metrics_impl.metric_variable(
-        array_ops.zeros([], dtype=dtypes.float32), name='mean_label')
+        [], dtypes.float32, name='mean_label')
     comoment = metrics_impl.metric_variable(  # C_A in update equation
-        array_ops.zeros([], dtype=dtypes.float32),
-        name='comoment')
+        [], dtypes.float32, name='comoment')
 
     if weights is None:
       batch_count = math_ops.to_float(array_ops.size(labels))  # n_B in eqn
@@ -3011,11 +3004,8 @@ def streaming_concat(values,
     init_size = 0 if max_size is None else max_size
     init_shape = [init_size] + fixed_shape
     array = metrics_impl.metric_variable(
-        array_ops.zeros(init_shape, dtype=values.dtype),
-        validate_shape=False,
-        name='array')
-    size = metrics_impl.metric_variable(
-        array_ops.zeros([], dtype=dtypes.int32), name='size')
+        init_shape, values.dtype, validate_shape=False, name='array')
+    size = metrics_impl.metric_variable([], dtypes.int32, name='size')
 
     perm = [0 if n == axis else n + 1 if n < axis else n for n in range(ndim)]
     valid_array = array[:size]
@@ -3149,8 +3139,7 @@ def count(values,
   """
 
   with variable_scope.variable_scope(name, 'count', (values, weights)):
-    count_ = metrics_impl.metric_variable(
-        array_ops.zeros([], dtype=dtypes.float32), name='count')
+    count_ = metrics_impl.metric_variable([], dtypes.float32, name='count')
 
     if weights is None:
       num_values = math_ops.to_float(array_ops.size(values))