[GitHub] szha commented on a change in pull request #9583: use nd for accuracy calculation
szha commented on a change in pull request #9583: use nd for accuracy
calculation
URL: https://github.com/apache/incubator-mxnet/pull/9583#discussion_r175583728
##
File path: python/mxnet/metric.py
##
@@ -380,23 +380,27 @@ def update(self, labels, preds):
Parameters
--
labels : list of `NDArray`
-The labels of the data.
+The labels of the data with class indices as values, one per
sample.
preds : list of `NDArray`
-Predicted values.
+Prediction values for samples. Each prediction value can either be
the class index,
+or a vector of likelihoods for all classes.
"""
check_label_shapes(labels, preds)
for label, pred_label in zip(labels, preds):
if pred_label.shape != label.shape:
pred_label = ndarray.argmax(pred_label, axis=self.axis)
-pred_label = pred_label.asnumpy().astype('int32')
-label = label.asnumpy().astype('int32')
+pred_label = pred_label.astype('int32')
+label = label.astype('int32')
check_label_shapes(label, pred_label)
-self.sum_metric += (pred_label.flat == label.flat).sum()
-self.num_inst += len(pred_label.flat)
+if pred_label.context != label.context:
+pred_label = pred_label.as_in_context(label.context)
+
+self.sum_metric += (pred_label.flatten() ==
label.flatten()).sum().asscalar()
Review comment:
Computation happens before asnumpy() happens, so nothing is happening in the
numpy world other than passing out a scalar value.
May I ask what your interest is in this PR? Do you have a use case that
benefits from using ndarray for metric?
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[GitHub] szha commented on a change in pull request #9583: use nd for accuracy calculation
szha commented on a change in pull request #9583: use nd for accuracy
calculation
URL: https://github.com/apache/incubator-mxnet/pull/9583#discussion_r164257186
##
File path: python/mxnet/metric.py
##
@@ -380,23 +380,24 @@ def update(self, labels, preds):
Parameters
--
labels : list of `NDArray`
-The labels of the data.
+The labels of the data with class indices as values, one per
sample.
preds : list of `NDArray`
-Predicted values.
+Prediction values for samples. Each prediction value can either be
the class index,
+or a vector of likelihoods for all classes.
"""
check_label_shapes(labels, preds)
for label, pred_label in zip(labels, preds):
if pred_label.shape != label.shape:
pred_label = ndarray.argmax(pred_label, axis=self.axis)
-pred_label = pred_label.asnumpy().astype('int32')
-label = label.asnumpy().astype('int32')
+pred_label = pred_label.astype('int32')
Review comment:
This requires larger space, which can show when the prediction class is
large (such as in NLP applications). Should I make it an option?
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To respond to the message, please log on GitHub and use the
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[GitHub] szha commented on a change in pull request #9583: use nd for accuracy calculation
szha commented on a change in pull request #9583: use nd for accuracy
calculation
URL: https://github.com/apache/incubator-mxnet/pull/9583#discussion_r164257186
##
File path: python/mxnet/metric.py
##
@@ -380,23 +380,24 @@ def update(self, labels, preds):
Parameters
--
labels : list of `NDArray`
-The labels of the data.
+The labels of the data with class indices as values, one per
sample.
preds : list of `NDArray`
-Predicted values.
+Prediction values for samples. Each prediction value can either be
the class index,
+or a vector of likelihoods for all classes.
"""
check_label_shapes(labels, preds)
for label, pred_label in zip(labels, preds):
if pred_label.shape != label.shape:
pred_label = ndarray.argmax(pred_label, axis=self.axis)
-pred_label = pred_label.asnumpy().astype('int32')
-label = label.asnumpy().astype('int32')
+pred_label = pred_label.astype('int32')
Review comment:
This requires larger space can show when the prediction class is large (such
as in NLP applications). Should I make it an option?
This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
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With regards,
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