diff options
Diffstat (limited to 'tensorflow/python/estimator/canned/linear_testing_utils.py')
| -rw-r--r-- | tensorflow/python/estimator/canned/linear_testing_utils.py | 141 |
1 files changed, 139 insertions, 2 deletions
diff --git a/tensorflow/python/estimator/canned/linear_testing_utils.py b/tensorflow/python/estimator/canned/linear_testing_utils.py index 0e6436b421..c3934c7a80 100644 --- a/tensorflow/python/estimator/canned/linear_testing_utils.py +++ b/tensorflow/python/estimator/canned/linear_testing_utils.py @@ -29,6 +29,7 @@ import six from tensorflow.core.example import example_pb2 from tensorflow.core.example import feature_pb2 from tensorflow.python.client import session as tf_session +from tensorflow.python.data.ops import dataset_ops from tensorflow.python.estimator import estimator from tensorflow.python.estimator import run_config from tensorflow.python.estimator.canned import linear @@ -260,6 +261,8 @@ class BaseLinearRegressorEvaluationTest(object): self.assertDictEqual({ metric_keys.MetricKeys.LOSS: 9., metric_keys.MetricKeys.LOSS_MEAN: 9., + metric_keys.MetricKeys.PREDICTION_MEAN: 13., + metric_keys.MetricKeys.LABEL_MEAN: 10., ops.GraphKeys.GLOBAL_STEP: 100 }, eval_metrics) @@ -285,6 +288,8 @@ class BaseLinearRegressorEvaluationTest(object): self.assertDictEqual({ metric_keys.MetricKeys.LOSS: 18., metric_keys.MetricKeys.LOSS_MEAN: 9., + metric_keys.MetricKeys.PREDICTION_MEAN: 13., + metric_keys.MetricKeys.LABEL_MEAN: 10., ops.GraphKeys.GLOBAL_STEP: 100 }, eval_metrics) @@ -315,6 +320,8 @@ class BaseLinearRegressorEvaluationTest(object): self.assertDictEqual({ metric_keys.MetricKeys.LOSS: 27., metric_keys.MetricKeys.LOSS_MEAN: 9., + metric_keys.MetricKeys.PREDICTION_MEAN: 13., + metric_keys.MetricKeys.LABEL_MEAN: 10., ops.GraphKeys.GLOBAL_STEP: 100 }, eval_metrics) @@ -345,7 +352,9 @@ class BaseLinearRegressorEvaluationTest(object): self.assertItemsEqual( (metric_keys.MetricKeys.LOSS, metric_keys.MetricKeys.LOSS_MEAN, - ops.GraphKeys.GLOBAL_STEP), eval_metrics.keys()) + metric_keys.MetricKeys.PREDICTION_MEAN, + metric_keys.MetricKeys.LABEL_MEAN, ops.GraphKeys.GLOBAL_STEP), + eval_metrics.keys()) # Logit is # [2., 4., 5.] * [1.0, 2.0] + [7.0, 8.0] = [39, 50] + [7.0, 8.0] @@ -382,7 +391,9 @@ class BaseLinearRegressorEvaluationTest(object): eval_metrics = est.evaluate(input_fn=input_fn, steps=1) self.assertItemsEqual( (metric_keys.MetricKeys.LOSS, metric_keys.MetricKeys.LOSS_MEAN, - ops.GraphKeys.GLOBAL_STEP), eval_metrics.keys()) + metric_keys.MetricKeys.PREDICTION_MEAN, + metric_keys.MetricKeys.LABEL_MEAN, ops.GraphKeys.GLOBAL_STEP), + eval_metrics.keys()) # Logit is [(20. * 10.0 + 4 * 2.0 + 5.0), (40. * 10.0 + 8 * 2.0 + 5.0)] = # [213.0, 421.0], while label is [213., 421.]. Loss = 0. @@ -484,6 +495,69 @@ class BaseLinearRegressorPredictTest(object): # x0 * weight0 + x1 * weight1 + bias = 2. * 10. + 3. * 20 + .2 = 80.2 self.assertAllClose([[80.2]], predicted_scores) + def testSparseCombiner(self): + w_a = 2.0 + w_b = 3.0 + w_c = 5.0 + bias = 5.0 + with ops.Graph().as_default(): + variables_lib.Variable([[w_a], [w_b], [w_c]], name=LANGUAGE_WEIGHT_NAME) + variables_lib.Variable([bias], name=BIAS_NAME) + variables_lib.Variable(1, name=ops.GraphKeys.GLOBAL_STEP, + dtype=dtypes.int64) + save_variables_to_ckpt(self._model_dir) + + def _input_fn(): + return dataset_ops.Dataset.from_tensors({ + 'language': sparse_tensor.SparseTensor( + values=['a', 'c', 'b', 'c'], + indices=[[0, 0], [0, 1], [1, 0], [1, 1]], + dense_shape=[2, 2]), + }) + + feature_columns = ( + feature_column_lib.categorical_column_with_vocabulary_list( + 'language', vocabulary_list=['a', 'b', 'c']),) + + # Check prediction for each sparse_combiner. + # With sparse_combiner = 'sum', we have + # logits_1 = w_a + w_c + bias + # = 2.0 + 5.0 + 5.0 = 12.0 + # logits_2 = w_b + w_c + bias + # = 3.0 + 5.0 + 5.0 = 13.0 + linear_regressor = self._linear_regressor_fn( + feature_columns=feature_columns, + model_dir=self._model_dir) + predictions = linear_regressor.predict(input_fn=_input_fn) + predicted_scores = list([x['predictions'] for x in predictions]) + self.assertAllClose([[12.0], [13.0]], predicted_scores) + + # With sparse_combiner = 'mean', we have + # logits_1 = 1/2 * (w_a + w_c) + bias + # = 1/2 * (2.0 + 5.0) + 5.0 = 8.5 + # logits_2 = 1/2 * (w_b + w_c) + bias + # = 1/2 * (3.0 + 5.0) + 5.0 = 9.0 + linear_regressor = self._linear_regressor_fn( + feature_columns=feature_columns, + model_dir=self._model_dir, + sparse_combiner='mean') + predictions = linear_regressor.predict(input_fn=_input_fn) + predicted_scores = list([x['predictions'] for x in predictions]) + self.assertAllClose([[8.5], [9.0]], predicted_scores) + + # With sparse_combiner = 'sqrtn', we have + # logits_1 = sqrt(2)/2 * (w_a + w_c) + bias + # = sqrt(2)/2 * (2.0 + 5.0) + 5.0 = 9.94974 + # logits_2 = sqrt(2)/2 * (w_b + w_c) + bias + # = sqrt(2)/2 * (3.0 + 5.0) + 5.0 = 10.65685 + linear_regressor = self._linear_regressor_fn( + feature_columns=feature_columns, + model_dir=self._model_dir, + sparse_combiner='sqrtn') + predictions = linear_regressor.predict(input_fn=_input_fn) + predicted_scores = list([x['predictions'] for x in predictions]) + self.assertAllClose([[9.94974], [10.65685]], predicted_scores) + class BaseLinearRegressorIntegrationTest(object): @@ -1636,6 +1710,69 @@ class BaseLinearClassifierPredictTest(object): for i in range(n_classes)], label_output_fn=lambda x: ('class_vocab_%s' % x).encode()) + def testSparseCombiner(self): + w_a = 2.0 + w_b = 3.0 + w_c = 5.0 + bias = 5.0 + with ops.Graph().as_default(): + variables_lib.Variable([[w_a], [w_b], [w_c]], name=LANGUAGE_WEIGHT_NAME) + variables_lib.Variable([bias], name=BIAS_NAME) + variables_lib.Variable(1, name=ops.GraphKeys.GLOBAL_STEP, + dtype=dtypes.int64) + save_variables_to_ckpt(self._model_dir) + + def _input_fn(): + return dataset_ops.Dataset.from_tensors({ + 'language': sparse_tensor.SparseTensor( + values=['a', 'c', 'b', 'c'], + indices=[[0, 0], [0, 1], [1, 0], [1, 1]], + dense_shape=[2, 2]), + }) + + feature_columns = ( + feature_column_lib.categorical_column_with_vocabulary_list( + 'language', vocabulary_list=['a', 'b', 'c']),) + + # Check prediction for each sparse_combiner. + # With sparse_combiner = 'sum', we have + # logits_1 = w_a + w_c + bias + # = 2.0 + 5.0 + 5.0 = 12.0 + # logits_2 = w_b + w_c + bias + # = 3.0 + 5.0 + 5.0 = 13.0 + linear_classifier = self._linear_classifier_fn( + feature_columns=feature_columns, + model_dir=self._model_dir) + predictions = linear_classifier.predict(input_fn=_input_fn) + predicted_scores = list([x['logits'] for x in predictions]) + self.assertAllClose([[12.0], [13.0]], predicted_scores) + + # With sparse_combiner = 'mean', we have + # logits_1 = 1/2 * (w_a + w_c) + bias + # = 1/2 * (2.0 + 5.0) + 5.0 = 8.5 + # logits_2 = 1/2 * (w_b + w_c) + bias + # = 1/2 * (3.0 + 5.0) + 5.0 = 9.0 + linear_classifier = self._linear_classifier_fn( + feature_columns=feature_columns, + model_dir=self._model_dir, + sparse_combiner='mean') + predictions = linear_classifier.predict(input_fn=_input_fn) + predicted_scores = list([x['logits'] for x in predictions]) + self.assertAllClose([[8.5], [9.0]], predicted_scores) + + # With sparse_combiner = 'sqrtn', we have + # logits_1 = sqrt(2)/2 * (w_a + w_c) + bias + # = sqrt(2)/2 * (2.0 + 5.0) + 5.0 = 9.94974 + # logits_2 = sqrt(2)/2 * (w_b + w_c) + bias + # = sqrt(2)/2 * (3.0 + 5.0) + 5.0 = 10.65685 + linear_classifier = self._linear_classifier_fn( + feature_columns=feature_columns, + model_dir=self._model_dir, + sparse_combiner='sqrtn') + predictions = linear_classifier.predict(input_fn=_input_fn) + predicted_scores = list([x['logits'] for x in predictions]) + self.assertAllClose([[9.94974], [10.65685]], predicted_scores) + class BaseLinearClassifierIntegrationTest(object): |