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| author |  Yan Facai (颜发才) <facai.yan@gmail.com> | 2018-08-23 16:23:03 +0800 |
|---|---|---|
| committer | Yan Facai (颜发才) <facai.yan@gmail.com> | 2018-08-23 16:54:59 +0800 |
| commit | 38f811077dd52820eaa3d5c684f41142de01c7eb (patch) | |
| tree | 56791f8875cb4dffe56cbe2bf5a7c34e71ddacd0 /tensorflow/contrib/metrics | |
| parent | c05bb4efcaf53d4cbc315ef6d12de822f2557a13 (diff) | |
CLN: remove negative_to_zero argument
Diffstat (limited to 'tensorflow/contrib/metrics')
| -rw-r--r-- | tensorflow/contrib/metrics/python/ops/metric_ops.py | 20 |
1 files changed, 10 insertions, 10 deletions
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), |