Set metric variable initializers as lambda.
PiperOrigin-RevId: 174100686
This commit is contained in:
Mustafa Ispir
TensorFlower Gardener
parent
453dd5848f
commit
b242a7988c
@ -92,8 +92,7 @@ def _count_condition(values,
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or tuple.
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"""
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check_ops.assert_type(values, dtypes.bool)
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count_ = metrics_impl.metric_variable(
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array_ops.zeros([], dtype=dtypes.float32), name='count')
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count_ = metrics_impl.metric_variable([], dtypes.float32, name='count')
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values = math_ops.to_float(values)
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if weights is not None:
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@ -916,8 +915,7 @@ def _streaming_confusion_matrix_at_thresholds(predictions,
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if 'tp' in includes:
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true_positives = metrics_impl.metric_variable(
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array_ops.zeros([num_thresholds], dtype=dtypes.float32),
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name='true_positives')
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[num_thresholds], dtypes.float32, name='true_positives')
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is_true_positive = math_ops.to_float(
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math_ops.logical_and(label_is_pos, pred_is_pos))
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if weights_tiled is not None:
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@ -929,8 +927,7 @@ def _streaming_confusion_matrix_at_thresholds(predictions,
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if 'fn' in includes:
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false_negatives = metrics_impl.metric_variable(
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array_ops.zeros([num_thresholds], dtype=dtypes.float32),
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name='false_negatives')
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[num_thresholds], dtypes.float32, name='false_negatives')
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is_false_negative = math_ops.to_float(
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math_ops.logical_and(label_is_pos, pred_is_neg))
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if weights_tiled is not None:
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@ -942,8 +939,7 @@ def _streaming_confusion_matrix_at_thresholds(predictions,
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if 'tn' in includes:
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true_negatives = metrics_impl.metric_variable(
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array_ops.zeros([num_thresholds], dtype=dtypes.float32),
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name='true_negatives')
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[num_thresholds], dtypes.float32, name='true_negatives')
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is_true_negative = math_ops.to_float(
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math_ops.logical_and(label_is_neg, pred_is_neg))
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if weights_tiled is not None:
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@ -955,8 +951,7 @@ def _streaming_confusion_matrix_at_thresholds(predictions,
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if 'fp' in includes:
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false_positives = metrics_impl.metric_variable(
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array_ops.zeros([num_thresholds], dtype=dtypes.float32),
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name='false_positives')
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[num_thresholds], dtypes.float32, name='false_positives')
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is_false_positive = math_ops.to_float(
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math_ops.logical_and(label_is_neg, pred_is_pos))
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if weights_tiled is not None:
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@ -1317,9 +1312,9 @@ def streaming_precision_recall_at_equal_thresholds(predictions,
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with ops.name_scope('variables'):
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tp_buckets_v = metrics_impl.metric_variable(
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array_ops.zeros([num_thresholds], dtype=dtype), name='tp_buckets')
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[num_thresholds], dtype, name='tp_buckets')
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fp_buckets_v = metrics_impl.metric_variable(
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array_ops.zeros([num_thresholds], dtype=dtype), name='fp_buckets')
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[num_thresholds], dtype, name='fp_buckets')
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with ops.name_scope('update_op'):
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update_tp = state_ops.scatter_add(
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@ -2582,15 +2577,13 @@ def streaming_covariance(predictions,
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predictions, labels, weights = metrics_impl._remove_squeezable_dimensions( # pylint: disable=protected-access
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predictions, labels, weights)
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predictions.get_shape().assert_is_compatible_with(labels.get_shape())
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count_ = metrics_impl.metric_variable(
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array_ops.zeros([], dtype=dtypes.float32), name='count')
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count_ = metrics_impl.metric_variable([], dtypes.float32, name='count')
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mean_prediction = metrics_impl.metric_variable(
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array_ops.zeros([], dtype=dtypes.float32), name='mean_prediction')
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[], dtypes.float32, name='mean_prediction')
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mean_label = metrics_impl.metric_variable(
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array_ops.zeros([], dtype=dtypes.float32), name='mean_label')
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[], dtypes.float32, name='mean_label')
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comoment = metrics_impl.metric_variable( # C_A in update equation
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array_ops.zeros([], dtype=dtypes.float32),
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name='comoment')
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[], dtypes.float32, name='comoment')
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if weights is None:
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batch_count = math_ops.to_float(array_ops.size(labels)) # n_B in eqn
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@ -3011,11 +3004,8 @@ def streaming_concat(values,
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init_size = 0 if max_size is None else max_size
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init_shape = [init_size] + fixed_shape
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array = metrics_impl.metric_variable(
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array_ops.zeros(init_shape, dtype=values.dtype),
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validate_shape=False,
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name='array')
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size = metrics_impl.metric_variable(
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array_ops.zeros([], dtype=dtypes.int32), name='size')
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init_shape, values.dtype, validate_shape=False, name='array')
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size = metrics_impl.metric_variable([], dtypes.int32, name='size')
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perm = [0 if n == axis else n + 1 if n < axis else n for n in range(ndim)]
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valid_array = array[:size]
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@ -3149,8 +3139,7 @@ def count(values,
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"""
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with variable_scope.variable_scope(name, 'count', (values, weights)):
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count_ = metrics_impl.metric_variable(
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array_ops.zeros([], dtype=dtypes.float32), name='count')
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count_ = metrics_impl.metric_variable([], dtypes.float32, name='count')
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if weights is None:
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num_values = math_ops.to_float(array_ops.size(values))
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@ -35,18 +35,11 @@ from tensorflow.python.ops import variable_scope
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from tensorflow.python.ops import weights_broadcast_ops
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def metric_variable(initial_value, validate_shape=True, name=None):
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"""Create variable in `GraphKeys.(LOCAL|METRIC_VARIABLES`) collections.
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def metric_variable(shape, dtype, validate_shape=True, name=None):
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"""Create variable in `GraphKeys.(LOCAL|METRIC_VARIABLES`) collections."""
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Args:
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initial_value: See variables.Variable.__init__.
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validate_shape: See variables.Variable.__init__.
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name: See variables.Variable.__init__.
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Returns:
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New variable.
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"""
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return variable_scope.variable(
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initial_value,
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lambda: array_ops.zeros(shape, dtype),
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trainable=False,
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collections=[
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ops.GraphKeys.LOCAL_VARIABLES, ops.GraphKeys.METRIC_VARIABLES
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@ -244,8 +237,7 @@ def _streaming_confusion_matrix(labels, predictions, num_classes, weights=None):
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"""
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# Local variable to accumulate the predictions in the confusion matrix.
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total_cm = metric_variable(
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array_ops.zeros([num_classes, num_classes], dtype=dtypes.float64),
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name='total_confusion_matrix')
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[num_classes, num_classes], dtypes.float64, name='total_confusion_matrix')
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# Cast the type to int64 required by confusion_matrix_ops.
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predictions = math_ops.to_int64(predictions)
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@ -315,10 +307,8 @@ def mean(values, weights=None, metrics_collections=None,
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with variable_scope.variable_scope(name, 'mean', (values, weights)):
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values = math_ops.to_float(values)
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total = metric_variable(
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array_ops.zeros([], dtype=dtypes.float32), name='total')
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count = metric_variable(
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array_ops.zeros([], dtype=dtypes.float32), name='count')
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total = metric_variable([], dtypes.float32, name='total')
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count = metric_variable([], dtypes.float32, name='count')
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if weights is None:
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num_values = math_ops.to_float(array_ops.size(values))
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@ -516,8 +506,7 @@ def _confusion_matrix_at_thresholds(
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if 'tp' in includes:
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true_p = metric_variable(
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array_ops.zeros([num_thresholds], dtype=dtypes.float32),
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name='true_positives')
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[num_thresholds], dtypes.float32, name='true_positives')
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is_true_positive = math_ops.to_float(
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math_ops.logical_and(label_is_pos, pred_is_pos))
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if weights_tiled is not None:
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@ -528,8 +517,7 @@ def _confusion_matrix_at_thresholds(
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if 'fn' in includes:
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false_n = metric_variable(
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array_ops.zeros([num_thresholds], dtype=dtypes.float32),
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name='false_negatives')
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[num_thresholds], dtypes.float32, name='false_negatives')
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is_false_negative = math_ops.to_float(
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math_ops.logical_and(label_is_pos, pred_is_neg))
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if weights_tiled is not None:
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@ -540,8 +528,7 @@ def _confusion_matrix_at_thresholds(
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if 'tn' in includes:
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true_n = metric_variable(
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array_ops.zeros([num_thresholds], dtype=dtypes.float32),
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name='true_negatives')
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[num_thresholds], dtypes.float32, name='true_negatives')
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is_true_negative = math_ops.to_float(
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math_ops.logical_and(label_is_neg, pred_is_neg))
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if weights_tiled is not None:
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@ -552,8 +539,7 @@ def _confusion_matrix_at_thresholds(
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if 'fp' in includes:
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false_p = metric_variable(
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array_ops.zeros([num_thresholds], dtype=dtypes.float32),
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name='false_positives')
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[num_thresholds], dtypes.float32, name='false_positives')
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is_false_positive = math_ops.to_float(
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math_ops.logical_and(label_is_neg, pred_is_pos))
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if weights_tiled is not None:
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@ -1183,11 +1169,9 @@ def mean_tensor(values, weights=None, metrics_collections=None,
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with variable_scope.variable_scope(name, 'mean', (values, weights)):
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values = math_ops.to_float(values)
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total = metric_variable(
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array_ops.zeros(values.get_shape(), dtype=dtypes.float32),
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name='total_tensor')
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values.get_shape(), dtypes.float32, name='total_tensor')
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count = metric_variable(
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array_ops.zeros(values.get_shape(), dtype=dtypes.float32),
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name='count_tensor')
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values.get_shape(), dtypes.float32, name='count_tensor')
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num_values = array_ops.ones_like(values)
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if weights is not None:
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@ -1300,8 +1284,7 @@ def _count_condition(values, weights=None, metrics_collections=None,
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or tuple.
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"""
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check_ops.assert_type(values, dtypes.bool)
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count = metric_variable(
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array_ops.zeros([], dtype=dtypes.float32), name='count')
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count = metric_variable([], dtypes.float32, name='count')
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values = math_ops.to_float(values)
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if weights is not None:
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@ -2082,7 +2065,7 @@ def _streaming_sparse_true_positive_at_k(labels,
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weights=weights)
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batch_total_tp = math_ops.to_double(math_ops.reduce_sum(tp))
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var = metric_variable(array_ops.zeros([], dtype=dtypes.float64), name=scope)
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var = metric_variable([], dtypes.float64, name=scope)
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return var, state_ops.assign_add(var, batch_total_tp, name='update')
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@ -2178,7 +2161,7 @@ def _streaming_sparse_false_negative_at_k(labels,
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weights=weights)
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batch_total_fn = math_ops.to_double(math_ops.reduce_sum(fn))
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var = metric_variable(array_ops.zeros([], dtype=dtypes.float64), name=scope)
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var = metric_variable([], dtypes.float64, name=scope)
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return var, state_ops.assign_add(var, batch_total_fn, name='update')
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@ -2829,8 +2812,7 @@ def _streaming_sparse_average_precision_at_top_k(labels,
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# - For the unweighted case, this is just the number of rows.
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# - For the weighted case, it's the sum of the weights broadcast across
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# `average_precision` rows.
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max_var = metric_variable(
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array_ops.zeros([], dtype=dtypes.float64), name=max_scope)
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max_var = metric_variable([], dtypes.float64, name=max_scope)
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if weights is None:
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batch_max = math_ops.to_double(
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array_ops.size(average_precision, name='batch_max'))
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@ -2838,8 +2820,7 @@ def _streaming_sparse_average_precision_at_top_k(labels,
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batch_max = math_ops.reduce_sum(weights, name='batch_max')
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max_update = state_ops.assign_add(max_var, batch_max, name='update')
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with ops.name_scope(None, 'total', (average_precision,)) as total_scope:
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total_var = metric_variable(
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array_ops.zeros([], dtype=dtypes.float64), name=total_scope)
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total_var = metric_variable([], dtypes.float64, name=total_scope)
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batch_total = math_ops.reduce_sum(average_precision, name='batch_total')
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total_update = state_ops.assign_add(total_var, batch_total, name='update')
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@ -3025,7 +3006,7 @@ def _streaming_sparse_false_positive_at_k(labels,
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weights=weights)
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batch_total_fp = math_ops.to_double(math_ops.reduce_sum(fp))
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var = metric_variable(array_ops.zeros([], dtype=dtypes.float64), name=scope)
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var = metric_variable([], dtypes.float64, name=scope)
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return var, state_ops.assign_add(var, batch_total_fp, name='update')
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