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Change scalar_summary to summary.scalar #8883

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merged 2 commits into from Apr 8, 2017
Merged

Change scalar_summary to summary.scalar #8883

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8c48eca
Change scalar_summary to summary.scalar
nitbix Mar 31, 2017
3ae1536
Fix head_test to reflect new naming
nitbix Apr 6, 2017
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2 changes: 1 addition & 1 deletion tensorflow/contrib/learn/python/learn/estimators/head.py
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Expand Up @@ -611,7 +611,7 @@ def _create_model_fn_ops(features,
if (mode != model_fn.ModeKeys.INFER) and (labels is not None):
weight_tensor = _weight_tensor(features, weight_column_name)
loss, weighted_average_loss = loss_fn(labels, logits, weight_tensor)
logging_ops.scalar_summary(
summary.scalar(
_summary_key(head_name, mkey.LOSS), weighted_average_loss)

if mode == model_fn.ModeKeys.TRAIN:
Expand Down
80 changes: 41 additions & 39 deletions tensorflow/contrib/learn/python/learn/estimators/head_test.py
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Open in desktop
Expand Up @@ -124,7 +124,7 @@ def testPoissonWithLogits(self):
train_op_fn=head_lib.no_op_train_fn,
logits=logits)
self._assert_output_alternatives(model_fn_ops)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["regression_head/loss"])
_assert_no_variables(self)
loss = self._log_poisson_loss(logits, labels)
_assert_metrics(self, loss, {"loss": loss}, model_fn_ops)
Expand All @@ -150,7 +150,7 @@ def testRegressionWithLogits(self):
train_op_fn=head_lib.no_op_train_fn,
logits=((1.,), (1.,), (3.,)))
self._assert_output_alternatives(model_fn_ops)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["regression_head/loss"])
_assert_no_variables(self)
_assert_metrics(self, 5. / 3, {"loss": 5. / 3}, model_fn_ops)

Expand Down Expand Up @@ -180,7 +180,7 @@ def testRegressionWithLogitsInput(self):
_assert_variables(
self, expected_global=w, expected_model=w, expected_trainable=w)
variables.global_variables_initializer().run()
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["regression_head/loss"])
_assert_metrics(self, 2. / 3, {"loss": 2. / 3}, model_fn_ops)

def testRegressionWithLogitsAndLogitsInput(self):
Expand Down Expand Up @@ -208,7 +208,7 @@ def testRegressionEvalMode(self):
self._assert_output_alternatives(model_fn_ops)
self.assertIsNone(model_fn_ops.train_op)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["regression_head/loss"])
_assert_metrics(self, 5. / 3, {"loss": 5. / 3}, model_fn_ops)

def testRegressionWithLabelName(self):
Expand All @@ -223,7 +223,7 @@ def testRegressionWithLabelName(self):
logits=((1.,), (1.,), (3.,)))
self._assert_output_alternatives(model_fn_ops)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["regression_head/loss"])
_assert_metrics(self, 5. / 3, {"loss": 5. / 3}, model_fn_ops)

def testRegressionWithWeights(self):
Expand All @@ -238,7 +238,7 @@ def testRegressionWithWeights(self):
logits=((1.,), (1.,), (3.,)))
self._assert_output_alternatives(model_fn_ops)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["regression_head/loss"])
_assert_metrics(self, 2. / len(weights), {"loss": 2. / np.sum(weights)},
model_fn_ops)

Expand All @@ -261,7 +261,7 @@ def testRegressionWithCenteredBias(self):
expected_trainable=("regression_head/centered_bias_weight:0",))
variables.global_variables_initializer().run()
_assert_summary_tags(
self, ["loss", "regression_head/centered_bias/bias_0"])
self, ["regression_head/loss", "regression_head/centered_bias/bias_0"])
_assert_metrics(self, 5. / 3, {"loss": 5. / 3}, model_fn_ops)

def testRegressionErrorInSparseTensorLabels(self):
Expand Down Expand Up @@ -326,7 +326,7 @@ def testMultiLabelWithLogits(self):
logits=self._logits)
self._assert_output_alternatives(model_fn_ops)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["multi_label_head/loss"])
expected_loss = .89985204
_assert_metrics(self, expected_loss,
self._expected_eval_metrics(expected_loss), model_fn_ops)
Expand All @@ -343,7 +343,7 @@ def testMultiLabelTwoClasses(self):
train_op_fn=head_lib.no_op_train_fn, logits=logits)
self._assert_output_alternatives(model_fn_ops)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["multi_label_head/loss"])
expected_loss = 1.00320443
_assert_metrics(self, expected_loss, {
"accuracy": 0.,
Expand Down Expand Up @@ -383,7 +383,7 @@ def testMultiLabelWithLogitsInput(self):
_assert_variables(
self, expected_global=w, expected_model=w, expected_trainable=w)
variables.global_variables_initializer().run()
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["multi_label_head/loss"])
expected_loss = .69314718
_assert_metrics(self, expected_loss, {
"accuracy": 2. / 3,
Expand Down Expand Up @@ -428,7 +428,7 @@ def testMultiLabelEvalMode(self):
self._assert_output_alternatives(model_fn_ops)
self.assertIsNone(model_fn_ops.train_op)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["multi_label_head/loss"])
expected_loss = .89985204
_assert_metrics(self, expected_loss,
self._expected_eval_metrics(expected_loss), model_fn_ops)
Expand All @@ -447,7 +447,7 @@ def testMultiClassEvalModeWithLargeLogits(self):
self._assert_output_alternatives(model_fn_ops)
self.assertIsNone(model_fn_ops.train_op)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["multi_label_head/loss"])
expected_loss = 1.377779
expected_eval_metrics = {
"accuracy": 1. / 3,
Expand Down Expand Up @@ -485,7 +485,7 @@ def testMultiLabelWithLabelName(self):
head_lib.no_op_train_fn, logits=self._logits)
self._assert_output_alternatives(model_fn_ops)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["multi_label_head/loss"])
expected_loss = .89985204
_assert_metrics(self, expected_loss,
self._expected_eval_metrics(expected_loss), model_fn_ops)
Expand All @@ -505,7 +505,7 @@ def testMultiLabelWithWeight(self):
logits=self._logits)
self._assert_output_alternatives(model_fn_ops)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["multi_label_head/loss"])
_assert_metrics(self, .089985214,
self._expected_eval_metrics(2.69956), model_fn_ops)

Expand All @@ -525,7 +525,7 @@ def testMultiLabelWithCustomLoss(self):
logits=self._logits)
self._assert_output_alternatives(model_fn_ops)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["multi_label_head/loss"])
_assert_metrics(self, 0.089985214,
self._expected_eval_metrics(0.089985214), model_fn_ops)

Expand All @@ -549,7 +549,7 @@ def testMultiLabelWithCenteredBias(self):
expected_trainable=("multi_label_head/centered_bias_weight:0",))
variables.global_variables_initializer().run()
_assert_summary_tags(self, (
"loss",
"multi_label_head/loss",
"multi_label_head/centered_bias/bias_0",
"multi_label_head/centered_bias/bias_1",
"multi_label_head/centered_bias/bias_2"
Expand All @@ -574,7 +574,7 @@ def testMultiLabelSparseTensorLabels(self):
train_op_fn=head_lib.no_op_train_fn,
logits=self._logits)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["multi_label_head/loss"])
expected_loss = .89985204
_assert_metrics(self, expected_loss,
self._expected_eval_metrics(expected_loss), model_fn_ops)
Expand Down Expand Up @@ -639,7 +639,7 @@ def testBinaryClassificationWithLogits(self):
logits=self._logits)
self._assert_output_alternatives(model_fn_ops)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["binary_logistic_head/loss"])
expected_loss = .81326175
_assert_metrics(self, expected_loss,
self._expected_eval_metrics(expected_loss), model_fn_ops)
Expand Down Expand Up @@ -667,7 +667,7 @@ def testBinaryClassificationWithLogitsInput(self):
_assert_variables(
self, expected_global=w, expected_model=w, expected_trainable=w)
variables.global_variables_initializer().run()
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["binary_logistic_head/loss"])
expected_loss = .69314718
label_mean = np.mean(self._labels)
_assert_metrics(self, expected_loss, {
Expand Down Expand Up @@ -703,7 +703,7 @@ def testBinaryClassificationEvalMode(self):
self._assert_output_alternatives(model_fn_ops)
self.assertIsNone(model_fn_ops.train_op)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["binary_logistic_head/loss"])
expected_loss = .81326175
_assert_metrics(self, expected_loss,
self._expected_eval_metrics(expected_loss), model_fn_ops)
Expand Down Expand Up @@ -768,7 +768,7 @@ def testBinaryClassificationWithLabelName(self):
logits=self._logits)
self._assert_output_alternatives(model_fn_ops)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["binary_logistic_head/loss"])
expected_loss = .81326175
_assert_metrics(self, expected_loss,
self._expected_eval_metrics(expected_loss), model_fn_ops)
Expand All @@ -789,7 +789,7 @@ def testBinaryClassificationWithWeights(self):
logits=self._logits)
self._assert_output_alternatives(model_fn_ops)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["binary_logistic_head/loss"])
expected_total_loss = .31326166
_assert_metrics(
self,
Expand Down Expand Up @@ -822,7 +822,7 @@ def testBinaryClassificationWithCustomLoss(self):
logits=self._logits)
self._assert_output_alternatives(model_fn_ops)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["binary_logistic_head/loss"])
# logloss: z:label, x:logit
# z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
# expected_loss is (total_weighted_loss)/1 since htere is 1 nonzero
Expand Down Expand Up @@ -862,7 +862,8 @@ def testBinaryClassificationWithCenteredBias(self):
expected_trainable=("binary_logistic_head/centered_bias_weight:0",))
variables.global_variables_initializer().run()
_assert_summary_tags(
self, ["loss", "binary_logistic_head/centered_bias/bias_0"])
self, ["binary_logistic_head/loss",
"binary_logistic_head/centered_bias/bias_0"])
expected_loss = .81326175
_assert_metrics(self, expected_loss,
self._expected_eval_metrics(expected_loss), model_fn_ops)
Expand Down Expand Up @@ -911,7 +912,7 @@ def testMultiClassWithLogits(self):
logits=self._logits)
self._assert_output_alternatives(model_fn_ops)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["multi_class_head/loss"])
expected_loss = 1.5514447
_assert_metrics(self, expected_loss,
self._expected_eval_metrics(expected_loss), model_fn_ops)
Expand Down Expand Up @@ -950,7 +951,7 @@ def testMultiClassWithLogitsInput(self):
_assert_variables(
self, expected_global=w, expected_model=w, expected_trainable=w)
variables.global_variables_initializer().run()
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["multi_class_head/loss"])
expected_loss = 1.0986123
_assert_metrics(self, expected_loss, {
"accuracy": 0.,
Expand Down Expand Up @@ -1001,7 +1002,7 @@ def testMultiClassEnableCenteredBias(self):
expected_trainable=("multi_class_head/centered_bias_weight:0",))
variables.global_variables_initializer().run()
_assert_summary_tags(self,
["loss",
["multi_class_head/loss",
"multi_class_head/centered_bias/bias_0",
"multi_class_head/centered_bias/bias_1",
"multi_class_head/centered_bias/bias_2"])
Expand All @@ -1019,7 +1020,7 @@ def testMultiClassEvalMode(self):
self._assert_output_alternatives(model_fn_ops)
self.assertIsNone(model_fn_ops.train_op)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["multi_class_head/loss"])
expected_loss = 1.5514447
_assert_metrics(self, expected_loss,
self._expected_eval_metrics(expected_loss), model_fn_ops)
Expand All @@ -1038,7 +1039,7 @@ def testMultiClassEvalModeWithLargeLogits(self):
self._assert_output_alternatives(model_fn_ops)
self.assertIsNone(model_fn_ops.train_op)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["multi_class_head/loss"])
expected_loss = 3.1698461
expected_eval_metrics = {
"accuracy": 0.,
Expand Down Expand Up @@ -1077,7 +1078,7 @@ def testMultiClassWithWeight(self):
logits=self._logits)
self._assert_output_alternatives(model_fn_ops)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["multi_class_head/loss"])
expected_loss = 1.5514447
_assert_metrics(self, expected_loss * weight,
self._expected_eval_metrics(expected_loss), model_fn_ops)
Expand All @@ -1101,7 +1102,7 @@ def testMultiClassWithCustomLoss(self):
logits=self._logits)
self._assert_output_alternatives(model_fn_ops)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["multi_class_head/loss"])
expected_loss = 1.5514447 * weight
_assert_metrics(self, expected_loss,
self._expected_eval_metrics(expected_loss), model_fn_ops)
Expand Down Expand Up @@ -1208,7 +1209,7 @@ def testMultiClassWithLabelKeysEvalAccuracy0(self):
data_flow_ops.tables_initializer().run()
self.assertIsNone(model_fn_ops.train_op)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["multi_class_head/loss"])
expected_loss = 1.5514447
expected_eval_metrics = {
"accuracy": 0.,
Expand All @@ -1234,7 +1235,7 @@ def testMultiClassWithLabelKeysEvalAccuracy1(self):
data_flow_ops.tables_initializer().run()
self.assertIsNone(model_fn_ops.train_op)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["multi_class_head/loss"])
expected_loss = 0.5514447
expected_eval_metrics = {
"accuracy": 1.,
Expand Down Expand Up @@ -1273,7 +1274,7 @@ def testBinarySVMWithLogits(self):
logits=self._predictions)
self._assert_output_alternatives(model_fn_ops)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["binary_svm_head/loss"])
expected_loss = np.average(self._expected_losses)
_assert_metrics(self, expected_loss, {
"accuracy": 1.,
Expand Down Expand Up @@ -1303,7 +1304,7 @@ def testBinarySVMWithLogitsInput(self):
_assert_variables(
self, expected_global=w, expected_model=w, expected_trainable=w)
variables.global_variables_initializer().run()
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["binary_svm_head/loss"])
expected_loss = 1.
_assert_metrics(self, expected_loss, {
"accuracy": .5,
Expand Down Expand Up @@ -1335,7 +1336,7 @@ def testBinarySVMEvalMode(self):
self._assert_output_alternatives(model_fn_ops)
self.assertIsNone(model_fn_ops.train_op)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["binary_svm_head/loss"])
expected_loss = np.average(self._expected_losses)
_assert_metrics(self, expected_loss, {
"accuracy": 1.,
Expand All @@ -1354,7 +1355,7 @@ def testBinarySVMWithLabelName(self):
logits=self._predictions)
self._assert_output_alternatives(model_fn_ops)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["binary_svm_head/loss"])
expected_loss = np.average(self._expected_losses)
_assert_metrics(self, expected_loss, {
"accuracy": 1.,
Expand All @@ -1373,7 +1374,7 @@ def testBinarySVMWithWeights(self):
logits=self._predictions)
self._assert_output_alternatives(model_fn_ops)
_assert_no_variables(self)
_assert_summary_tags(self, ["loss"])
_assert_summary_tags(self, ["binary_svm_head/loss"])
expected_weighted_sum = np.sum(
np.multiply(weights, self._expected_losses))
_assert_metrics(self, expected_weighted_sum / len(weights), {
Expand Down Expand Up @@ -1401,7 +1402,8 @@ def testBinarySVMWithCenteredBias(self):
expected_trainable=("binary_svm_head/centered_bias_weight:0",))
variables.global_variables_initializer().run()
_assert_summary_tags(
self, ["loss", "binary_svm_head/centered_bias/bias_0"])
self, ["binary_svm_head/loss",
"binary_svm_head/centered_bias/bias_0"])
expected_loss = np.average(self._expected_losses)
_assert_metrics(self, expected_loss, {
"accuracy": 1.,
Expand Down