diff options
Diffstat (limited to 'tensorflow/contrib/estimator/python/estimator/rnn_test.py')
| -rw-r--r-- | tensorflow/contrib/estimator/python/estimator/rnn_test.py | 41 |
1 files changed, 27 insertions, 14 deletions
diff --git a/tensorflow/contrib/estimator/python/estimator/rnn_test.py b/tensorflow/contrib/estimator/python/estimator/rnn_test.py index 959b40371a..1aebed348d 100644 --- a/tensorflow/contrib/estimator/python/estimator/rnn_test.py +++ b/tensorflow/contrib/estimator/python/estimator/rnn_test.py @@ -713,7 +713,7 @@ class RNNClassifierTrainingTest(test.TestCase): # Uses same checkpoint and examples as testBinaryClassEvaluationMetrics. # See that test for loss calculation. - mock_optimizer = self._mock_optimizer(expected_loss=1.119661) + mock_optimizer = self._mock_optimizer(expected_loss=0.559831) sequence_feature_columns = [ seq_fc.sequence_numeric_column('price', shape=(1,))] @@ -748,7 +748,7 @@ class RNNClassifierTrainingTest(test.TestCase): # Uses same checkpoint and examples as testMultiClassEvaluationMetrics. # See that test for loss calculation. - mock_optimizer = self._mock_optimizer(expected_loss=2.662932) + mock_optimizer = self._mock_optimizer(expected_loss=1.331465) sequence_feature_columns = [ seq_fc.sequence_numeric_column('price', shape=(1,))] @@ -812,20 +812,32 @@ class RNNClassifierEvaluationTest(test.TestCase): # probability = exp(logits) / (1 + exp(logits)) = [[0.353593], [0.504930]] # loss = -label * ln(p) - (1 - label) * ln(1 - p) # = [[0.436326], [0.683335]] + # sum_over_batch_size = (0.436326 + 0.683335)/2 expected_metrics = { - ops.GraphKeys.GLOBAL_STEP: global_step, - metric_keys.MetricKeys.LOSS: 1.119661, - metric_keys.MetricKeys.LOSS_MEAN: 0.559831, - metric_keys.MetricKeys.ACCURACY: 1.0, - metric_keys.MetricKeys.PREDICTION_MEAN: 0.429262, - metric_keys.MetricKeys.LABEL_MEAN: 0.5, - metric_keys.MetricKeys.ACCURACY_BASELINE: 0.5, + ops.GraphKeys.GLOBAL_STEP: + global_step, + metric_keys.MetricKeys.LOSS: + 0.559831, + metric_keys.MetricKeys.LOSS_MEAN: + 0.559831, + metric_keys.MetricKeys.ACCURACY: + 1.0, + metric_keys.MetricKeys.PREDICTION_MEAN: + 0.429262, + metric_keys.MetricKeys.LABEL_MEAN: + 0.5, + metric_keys.MetricKeys.ACCURACY_BASELINE: + 0.5, # With default threshold of 0.5, the model is a perfect classifier. - metric_keys.MetricKeys.RECALL: 1.0, - metric_keys.MetricKeys.PRECISION: 1.0, + metric_keys.MetricKeys.RECALL: + 1.0, + metric_keys.MetricKeys.PRECISION: + 1.0, # Positive example is scored above negative, so AUC = 1.0. - metric_keys.MetricKeys.AUC: 1.0, - metric_keys.MetricKeys.AUC_PR: 1.0, + metric_keys.MetricKeys.AUC: + 1.0, + metric_keys.MetricKeys.AUC_PR: + 1.0, } self.assertAllClose( sorted_key_dict(expected_metrics), sorted_key_dict(eval_metrics)) @@ -871,9 +883,10 @@ class RNNClassifierEvaluationTest(test.TestCase): # [0.059494, 0.572639, 0.367866]] # loss = -1. * log(softmax[label]) # = [[2.105432], [0.557500]] + # sum_over_batch_size = (2.105432 + 0.557500)/2 expected_metrics = { ops.GraphKeys.GLOBAL_STEP: global_step, - metric_keys.MetricKeys.LOSS: 2.662932, + metric_keys.MetricKeys.LOSS: 1.331465, metric_keys.MetricKeys.LOSS_MEAN: 1.331466, metric_keys.MetricKeys.ACCURACY: 0.5, } |