diff options
Diffstat (limited to 'tensorflow/contrib')
| -rw-r--r-- | tensorflow/contrib/losses/python/losses/loss_ops.py | 9 | ||||
| -rw-r--r-- | tensorflow/contrib/metrics/python/ops/metric_ops.py | 20 | ||||
| -rw-r--r-- | tensorflow/contrib/rate/rate.py | 4 |
3 files changed, 16 insertions, 17 deletions
diff --git a/tensorflow/contrib/losses/python/losses/loss_ops.py b/tensorflow/contrib/losses/python/losses/loss_ops.py index 29f7953c3b..8a0932c376 100644 --- a/tensorflow/contrib/losses/python/losses/loss_ops.py +++ b/tensorflow/contrib/losses/python/losses/loss_ops.py @@ -78,8 +78,9 @@ def _safe_mean(losses, num_present): then zero is returned. """ total_loss = math_ops.reduce_sum(losses) - return math_ops.div_no_nan(total_loss, num_present, - negative_to_zero=True, name="value") + return math_ops.div_no_nan(total_loss, + math_ops.maximum(num_present, 0), + name="value") @deprecated("2016-12-30", "Use tf.losses.compute_weighted_loss instead.") @@ -585,14 +586,12 @@ def mean_pairwise_squared_error(predictions, num_present_per_batch = _num_present(diffs, weights, per_batch=True) term1 = 2.0 * math_ops.div_no_nan(sum_squares_diff_per_batch, - num_present_per_batch, - negative_to_zero=True, + math_ops.maximum(num_present_per_batch), name="value") sum_diff = math_ops.reduce_sum(diffs, reduction_indices=reduction_indices) term2 = 2.0 * math_ops.div_no_nan(math_ops.square(sum_diff), math_ops.square(num_present_per_batch), - negative_to_zero=True, name="value") loss = _scale_losses(term1 - term2, weights) diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops.py b/tensorflow/contrib/metrics/python/ops/metric_ops.py index d972e7da53..bfef0816aa 100644 --- a/tensorflow/contrib/metrics/python/ops/metric_ops.py +++ b/tensorflow/contrib/metrics/python/ops/metric_ops.py @@ -3188,12 +3188,12 @@ def streaming_covariance(predictions, # We update the means by Delta=Error*BatchCount/(BatchCount+PrevCount) # batch_mean_prediction is E[x_B] in the update equation batch_mean_prediction = math_ops.div_no_nan( - math_ops.reduce_sum(weighted_predictions), batch_count, - negative_to_zero=True, + math_ops.reduce_sum(weighted_predictions), + math_ops.maximum(batch_count, 0), name='batch_mean_prediction') delta_mean_prediction = math_ops.div_no_nan( - (batch_mean_prediction - mean_prediction) * batch_count, update_count, - negative_to_zero=True, + (batch_mean_prediction - mean_prediction) * batch_count, + math_ops.maximum(update_count, 0), name='delta_mean_prediction') update_mean_prediction = state_ops.assign_add(mean_prediction, delta_mean_prediction) @@ -3202,12 +3202,12 @@ def streaming_covariance(predictions, # batch_mean_label is E[y_B] in the update equation batch_mean_label = math_ops.div_no_nan( - math_ops.reduce_sum(weighted_labels), batch_count, - negative_to_zero=True, + math_ops.reduce_sum(weighted_labels), + math_ops.maximum(batch_count, 0), name='batch_mean_label') delta_mean_label = math_ops.div_no_nan( - (batch_mean_label - mean_label) * batch_count, update_count, - negative_to_zero=True, + (batch_mean_label - mean_label) * batch_count, + math_ops.maximum(update_count, 0), name='delta_mean_label') update_mean_label = state_ops.assign_add(mean_label, delta_mean_label) # prev_mean_label is E[y_A] in the update equation @@ -3871,8 +3871,8 @@ def cohen_kappa(labels, total = math_ops.reduce_sum(pe_row) pe_sum = math_ops.reduce_sum( math_ops.div_no_nan( - pe_row * pe_col, total, - negative_to_zero=True, + pe_row * pe_col, + math_ops.maximum(total, 0), name=None)) po_sum, pe_sum, total = (math_ops.to_double(po_sum), math_ops.to_double(pe_sum), diff --git a/tensorflow/contrib/rate/rate.py b/tensorflow/contrib/rate/rate.py index 68f5a6e58a..489d5cce78 100644 --- a/tensorflow/contrib/rate/rate.py +++ b/tensorflow/contrib/rate/rate.py @@ -141,6 +141,6 @@ class Rate(object): state_ops.assign(self.prev_values, values) state_ops.assign(self.prev_denominator, denominator) - return math_ops.div_no_nan(self.numer, self.denom, - negative_to_zero=True, + return math_ops.div_no_nan(self.numer, + math_op.maximum(self.denom, 0), name="safe_rate") |