diff options
Diffstat (limited to 'tensorflow/contrib/estimator/python/estimator/multi_head_test.py')
| -rw-r--r-- | tensorflow/contrib/estimator/python/estimator/multi_head_test.py | 188 |
1 files changed, 1 insertions, 187 deletions
diff --git a/tensorflow/contrib/estimator/python/estimator/multi_head_test.py b/tensorflow/contrib/estimator/python/estimator/multi_head_test.py index 8d51a298b2..16177aebd5 100644 --- a/tensorflow/contrib/estimator/python/estimator/multi_head_test.py +++ b/tensorflow/contrib/estimator/python/estimator/multi_head_test.py @@ -106,8 +106,7 @@ class MultiHeadTest(test.TestCase): multi_head = multi_head_lib.multi_head([head1, head2]) self.assertEqual('head1_head2', multi_head.name) - def test_predict_two_heads_logits_dict(self): - """Tests predict with logits as dict.""" + def test_predict_two_heads(self): head1 = head_lib.multi_label_head(n_classes=2, name='head1') head2 = head_lib.multi_label_head(n_classes=3, name='head2') multi_head = multi_head_lib.multi_head([head1, head2]) @@ -159,111 +158,6 @@ class MultiHeadTest(test.TestCase): expected_probabilities['head2'], sess.run(spec.export_outputs['head2'].scores)) - def test_predict_two_heads_logits_tensor(self): - """Tests predict with logits as Tensor.""" - head1 = head_lib.multi_label_head(n_classes=2, name='head1') - head2 = head_lib.multi_label_head(n_classes=3, name='head2') - multi_head = multi_head_lib.multi_head([head1, head2]) - - logits = np.array( - [[-1., 1., 2., -2., 2.], [-1.5, 1., -3., 2., -2.]], dtype=np.float32) - expected_logits1 = np.array([[-1., 1.], [-1.5, 1.]], dtype=np.float32) - expected_logits2 = np.array([[2., -2., 2.], [-3., 2., -2.]], - dtype=np.float32) - expected_probabilities = { - 'head1': _sigmoid(expected_logits1), - 'head2': _sigmoid(expected_logits2), - } - - spec = multi_head.create_estimator_spec( - features={'x': np.array(((42,),), dtype=np.int32)}, - mode=model_fn.ModeKeys.PREDICT, - logits=logits) - - self.assertItemsEqual( - (_DEFAULT_SERVING_KEY, 'head1', 'classification/head1', 'predict/head1', - 'head2', 'classification/head2', 'predict/head2'), - spec.export_outputs.keys()) - - # Assert predictions and export_outputs. - with self.test_session() as sess: - _initialize_variables(self, spec.scaffold) - self.assertIsNone(spec.scaffold.summary_op) - predictions = sess.run(spec.predictions) - self.assertAllClose( - expected_logits1, - predictions[('head1', prediction_keys.PredictionKeys.LOGITS)]) - self.assertAllClose( - expected_logits2, - predictions[('head2', prediction_keys.PredictionKeys.LOGITS)]) - self.assertAllClose( - expected_probabilities['head1'], - predictions[('head1', prediction_keys.PredictionKeys.PROBABILITIES)]) - self.assertAllClose( - expected_probabilities['head2'], - predictions[('head2', prediction_keys.PredictionKeys.PROBABILITIES)]) - - self.assertAllClose( - expected_probabilities['head1'], - sess.run(spec.export_outputs[_DEFAULT_SERVING_KEY].scores)) - self.assertAllClose( - expected_probabilities['head1'], - sess.run(spec.export_outputs['head1'].scores)) - self.assertAllClose( - expected_probabilities['head2'], - sess.run(spec.export_outputs['head2'].scores)) - - def test_predict_two_heads_logits_tensor_multi_dim(self): - """Tests predict with multi-dimensional logits of shape [2, 2, 5].""" - head1 = head_lib.regression_head(label_dimension=2, name='head1') - head2 = head_lib.regression_head(label_dimension=3, name='head2') - multi_head = multi_head_lib.multi_head([head1, head2]) - - logits = np.array( - [[[-1., 1., 2., -2., 2.], [-1., 1., 2., -2., 2.]], - [[-1.5, 1., -3., 2., -2.], [-1.5, 1., -3., 2., -2.]]], - dtype=np.float32) - expected_logits1 = np.array( - [[[-1., 1.], [-1., 1.]], - [[-1.5, 1.], [-1.5, 1.]]], - dtype=np.float32) - expected_logits2 = np.array( - [[[2., -2., 2.], [2., -2., 2.]], - [[-3., 2., -2.], [-3., 2., -2.]]], - dtype=np.float32) - - spec = multi_head.create_estimator_spec( - features={'x': np.array(((42,),), dtype=np.int32)}, - mode=model_fn.ModeKeys.PREDICT, - logits=logits) - - self.assertItemsEqual( - (_DEFAULT_SERVING_KEY, 'head1', 'regression/head1', 'predict/head1', - 'head2', 'regression/head2', 'predict/head2'), - spec.export_outputs.keys()) - - # Assert predictions and export_outputs. - with self.test_session() as sess: - _initialize_variables(self, spec.scaffold) - self.assertIsNone(spec.scaffold.summary_op) - predictions = sess.run(spec.predictions) - self.assertAllClose( - expected_logits1, - predictions[('head1', prediction_keys.PredictionKeys.PREDICTIONS)]) - self.assertAllClose( - expected_logits2, - predictions[('head2', prediction_keys.PredictionKeys.PREDICTIONS)]) - - self.assertAllClose( - expected_logits1, - sess.run(spec.export_outputs[_DEFAULT_SERVING_KEY].value)) - self.assertAllClose( - expected_logits1, - sess.run(spec.export_outputs['head1'].value)) - self.assertAllClose( - expected_logits2, - sess.run(spec.export_outputs['head2'].value)) - def test_eval_two_heads_with_weights(self): head1 = head_lib.multi_label_head(n_classes=2, name='head1') head2 = head_lib.multi_label_head(n_classes=3, name='head2') @@ -390,84 +284,6 @@ class MultiHeadTest(test.TestCase): # example_weight_sum = 1 * (1 + 2) + 2 * (2 + 3) = 13 self.assertAllClose(13., example_weight_sum.eval(), rtol=tol, atol=tol) - def test_train_create_loss_logits_tensor(self): - """Tests create_loss with logits Tensor.""" - weights1 = np.array([[1.], [2.]], dtype=np.float32) - weights2 = np.array([[2.], [3.]]) - head1 = head_lib.multi_label_head(n_classes=2, name='head1', - weight_column='weights1') - head2 = head_lib.multi_label_head(n_classes=3, name='head2', - weight_column='weights2') - multi_head = multi_head_lib.multi_head( - [head1, head2], head_weights=[1., 2.]) - - logits = np.array([[-10., 10., 20., -20., 20.], - [-15., 10., -30., 20., -20.]], dtype=np.float32) - labels = { - 'head1': np.array([[1, 0], [1, 1]], dtype=np.int64), - 'head2': np.array([[0, 1, 0], [1, 1, 0]], dtype=np.int64), - } - weighted_sum_loss, example_weight_sum, _ = multi_head.create_loss( - features={ - 'x': np.array(((42,),), dtype=np.int32), - 'weights1': weights1, - 'weights2': weights2 - }, - mode=model_fn.ModeKeys.TRAIN, - logits=logits, - labels=labels) - tol = 1e-3 - with self.test_session(): - # loss of the first head is [[(10 + 10) / 2], [(15 + 0) / 2]] - # = [10, 7.5] - # weighted_sum_loss = 1 * 10 + 2 * 7.5 = 25 - # loss of the second head is [[(20 + 20 + 20) / 3], [(30 + 0 + 0) / 3]] - # = [20, 10] - # weighted_sum_loss = 2 * 20 + 3 * 10 = 70 - # head-weighted merge = 1 * 25 + 2 * 70 = 165 - self.assertAllClose(165, weighted_sum_loss.eval(), rtol=tol, atol=tol) - # example_weight_sum = 1 * (1 + 2) + 2 * (2 + 3) = 13 - self.assertAllClose(13., example_weight_sum.eval(), rtol=tol, atol=tol) - - def test_train_create_loss_logits_tensor_multi_dim(self): - """Tests create_loss with multi-dimensional logits of shape [2, 2, 5].""" - head1 = head_lib.regression_head(label_dimension=2, name='head1') - head2 = head_lib.regression_head(label_dimension=3, name='head2') - multi_head = multi_head_lib.multi_head([head1, head2]) - - logits = np.array( - [[[-1., 1., 2., -2., 2.], [-1., 1., 2., -2., 2.]], - [[-1.5, 1.5, -2., 2., -2.], [-1.5, 1.5, -2., 2., -2.]]], - dtype=np.float32) - labels = { - 'head1': np.array([[[1., 0.], [1., 0.]], - [[1.5, 1.5], [1.5, 1.5]]], dtype=np.float32), - 'head2': np.array([[[0., 1., 0.], [0., 1., 0.]], - [[2., 2., 0.], [2., 2., 0.]]], dtype=np.float32), - } - # Loss for the first head: - # loss1 = (1+1)^2 + (0-1)^2 + (1+1)^2 + (0-1)^2 + - # (1.5+1.5)^2 + (1.5-1.5)^2 + (1.5+1.5)^2 + (1.5-1.5)^2 - # = 28 - # Loss for the second head: - # loss2 = (0-2)^2 + (1+2)^2 + (0-2)^2 + (0-2)^2 + (1+2)^2 + (0-2)^2 + - # (2+2)^2 + (2-2)^2 + (0+2)^2 + (2+2)^2 + (2-2)^2 + (0+2)^2 - # = 74 - expected_weighted_sum_loss = 28. + 74. - - weighted_sum_loss, example_weight_sum, _ = multi_head.create_loss( - features={}, - mode=model_fn.ModeKeys.TRAIN, - logits=logits, - labels=labels) - tol = 1e-3 - with self.test_session(): - self.assertAllClose( - expected_weighted_sum_loss, weighted_sum_loss.eval(), - rtol=tol, atol=tol) - self.assertAllClose( - 2. * 2. * 5., example_weight_sum.eval(), rtol=tol, atol=tol) - def test_train_one_head(self): head1 = head_lib.multi_label_head(n_classes=2, name='head1') multi_head = multi_head_lib.multi_head([head1]) @@ -511,7 +327,6 @@ class MultiHeadTest(test.TestCase): six.b('{0:s}{1:.3f}'.format(expected_train_result, expected_loss)), train_result) _assert_simple_summaries(self, { - metric_keys.MetricKeys.LOSS: expected_loss, metric_keys.MetricKeys.LOSS + '/head1': expected_loss, # Average loss over examples. metric_keys.MetricKeys.LOSS_MEAN + '/head1': expected_loss / 2, @@ -572,7 +387,6 @@ class MultiHeadTest(test.TestCase): six.b('{0:s}{1:.3f}'.format(expected_train_result, expected_loss)), train_result) _assert_simple_summaries(self, { - metric_keys.MetricKeys.LOSS: expected_loss, metric_keys.MetricKeys.LOSS + '/head1': expected_loss_head1, metric_keys.MetricKeys.LOSS + '/head2': expected_loss_head2, # Average loss over examples. |