diff options
Diffstat (limited to 'tensorflow/python/estimator/canned/dnn_testing_utils.py')
| -rw-r--r-- | tensorflow/python/estimator/canned/dnn_testing_utils.py | 109 |
1 files changed, 109 insertions, 0 deletions
diff --git a/tensorflow/python/estimator/canned/dnn_testing_utils.py b/tensorflow/python/estimator/canned/dnn_testing_utils.py index cd66d0a3bd..71d7e54783 100644 --- a/tensorflow/python/estimator/canned/dnn_testing_utils.py +++ b/tensorflow/python/estimator/canned/dnn_testing_utils.py @@ -34,6 +34,7 @@ from tensorflow.python.estimator.canned import metric_keys from tensorflow.python.estimator.canned import prediction_keys from tensorflow.python.estimator.inputs import numpy_io from tensorflow.python.feature_column import feature_column +from tensorflow.python.feature_column import feature_column_v2 from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -479,6 +480,60 @@ class BaseDNNModelFnTest(object): else: self.fail('Invalid mode: {}'.format(mode)) + def test_multi_feature_column_mix_multi_dim_logits(self): + """Tests multiple feature columns and multi-dimensional logits. + + All numbers are the same as test_multi_dim_input_multi_dim_logits. The only + difference is that the input consists of two 1D feature columns, instead of + one 2D feature column. + """ + base_global_step = 100 + create_checkpoint(( + ([[.6, .5], [-.6, -.5]], [.1, -.1]), + ([[1., .8], [-.8, -1.]], [.2, -.2]), + ([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]), + ), base_global_step, self._model_dir) + hidden_units = (2, 2) + logits_dimension = 3 + inputs = ([[10.]], [[8.]]) + expected_logits = [[-0.48, 0.48, 0.39]] + + for mode in [ + model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL, + model_fn.ModeKeys.PREDICT + ]: + with ops.Graph().as_default(): + training_util.create_global_step() + head = mock_head( + self, + hidden_units=hidden_units, + logits_dimension=logits_dimension, + expected_logits=expected_logits) + estimator_spec = self._dnn_model_fn( + features={ + 'age': constant_op.constant(inputs[0]), + 'height': constant_op.constant(inputs[1]) + }, + labels=constant_op.constant([[1]]), + mode=mode, + head=head, + hidden_units=hidden_units, + feature_columns=[ + feature_column.numeric_column('age'), + feature_column_v2.numeric_column('height') + ], + optimizer=mock_optimizer(self, hidden_units)) + with monitored_session.MonitoredTrainingSession( + checkpoint_dir=self._model_dir) as sess: + if mode == model_fn.ModeKeys.TRAIN: + sess.run(estimator_spec.train_op) + elif mode == model_fn.ModeKeys.EVAL: + sess.run(estimator_spec.loss) + elif mode == model_fn.ModeKeys.PREDICT: + sess.run(estimator_spec.predictions) + else: + self.fail('Invalid mode: {}'.format(mode)) + def test_features_tensor_raises_value_error(self): """Tests that passing a Tensor for features raises a ValueError.""" hidden_units = (2, 2) @@ -806,6 +861,60 @@ class BaseDNNLogitFnTest(object): checkpoint_dir=self._model_dir) as sess: self.assertAllClose(expected_logits, sess.run(logits)) + def test_multi_feature_column_mix_multi_dim_logits(self): + """Tests multiple feature columns and multi-dimensional logits. + + All numbers are the same as test_multi_dim_input_multi_dim_logits. The only + difference is that the input consists of two 1D feature columns, instead of + one 2D feature column. + """ + base_global_step = 100 + create_checkpoint(( + ([[.6, .5], [-.6, -.5]], [.1, -.1]), + ([[1., .8], [-.8, -1.]], [.2, -.2]), + ([[-1., 1., .5], [-1., 1., .5]], [.3, -.3, .0]), + ), base_global_step, self._model_dir) + + hidden_units = (2, 2) + logits_dimension = 3 + inputs = ([[10.]], [[8.]]) + expected_logits = [[-0.48, 0.48, 0.39]] + + for mode in [ + model_fn.ModeKeys.TRAIN, model_fn.ModeKeys.EVAL, + model_fn.ModeKeys.PREDICT + ]: + with ops.Graph().as_default(): + # Global step needed for MonitoredSession, which is in turn used to + # explicitly set variable weights through a checkpoint. + training_util.create_global_step() + # Use a variable scope here with 'dnn', emulating the dnn model_fn, so + # the checkpoint naming is shared. + with variable_scope.variable_scope('dnn'): + input_layer_partitioner = ( + partitioned_variables.min_max_variable_partitioner( + max_partitions=0, min_slice_size=64 << 20)) + logit_fn = self._dnn_logit_fn_builder( + units=logits_dimension, + hidden_units=hidden_units, + feature_columns=[ + feature_column.numeric_column('age'), + feature_column_v2.numeric_column('height') + ], + activation_fn=nn.relu, + dropout=None, + input_layer_partitioner=input_layer_partitioner, + batch_norm=False) + logits = logit_fn( + features={ + 'age': constant_op.constant(inputs[0]), + 'height': constant_op.constant(inputs[1]) + }, + mode=mode) + with monitored_session.MonitoredTrainingSession( + checkpoint_dir=self._model_dir) as sess: + self.assertAllClose(expected_logits, sess.run(logits)) + class BaseDNNWarmStartingTest(object): |