def testOneTimeSeriesFeature(self): # Build config. feature_spec = { "time_feature_1": { "length": 14, "is_time_series": True, } } hidden_spec = { "time_feature_1": { "num_local_layers": 2, "local_layer_size": 20, "translation_delta": 2, "pooling_type": "max", "dropout_rate": 0.5, } } config = configurations.base() config["inputs"]["features"] = feature_spec config["hparams"]["time_series_hidden"] = hidden_spec config = configdict.ConfigDict(config) # Build model. features = input_ops.build_feature_placeholders(config.inputs.features) labels = input_ops.build_labels_placeholder() model = astro_fc_model.AstroFCModel(features, labels, config.hparams, tf.estimator.ModeKeys.TRAIN) model.build() # Validate Tensor shapes. conv = testing.get_variable_by_name("time_feature_1_hidden/conv1d/kernel") self.assertShapeEquals((10, 1, 20), conv) fc_1 = testing.get_variable_by_name( "time_feature_1_hidden/fully_connected_1/weights") self.assertShapeEquals((20, 20), fc_1) self.assertItemsEqual(["time_feature_1"], model.time_series_hidden_layers.keys()) self.assertShapeEquals((None, 20), model.time_series_hidden_layers["time_feature_1"]) self.assertEqual(len(model.aux_hidden_layers), 0) self.assertIs(model.time_series_hidden_layers["time_feature_1"], model.pre_logits_concat) # Execute the TensorFlow graph. scaffold = tf.train.Scaffold() scaffold.finalize() with self.test_session() as sess: sess.run([scaffold.init_op, scaffold.local_init_op]) step = sess.run(model.global_step) self.assertEqual(0, step) # Fetch predictions. features = testing.fake_features(feature_spec, batch_size=16) labels = testing.fake_labels(config.hparams.output_dim, batch_size=16) feed_dict = input_ops.prepare_feed_dict(model, features, labels) predictions = sess.run(model.predictions, feed_dict=feed_dict) self.assertShapeEquals((16, 1), predictions)
def testOneTimeSeriesFeature(self): # Build config. feature_spec = { "time_feature_1": { "length": 14, "is_time_series": True, } } hidden_spec = { "time_feature_1": { "num_local_layers": 2, "local_layer_size": 20, "translation_delta": 2, "pooling_type": "max", "dropout_rate": 0.5, } } config = configurations.base() config["inputs"]["features"] = feature_spec config["hparams"]["time_series_hidden"] = hidden_spec config = configdict.ConfigDict(config) # Build model. features = input_ops.build_feature_placeholders(config.inputs.features) labels = input_ops.build_labels_placeholder() model = astro_fc_model.AstroFCModel(features, labels, config.hparams, tf.estimator.ModeKeys.TRAIN) model.build() # Validate Tensor shapes. conv = testing.get_variable_by_name("time_feature_1_hidden/conv1d/kernel") self.assertShapeEquals((10, 1, 20), conv) fc_1 = testing.get_variable_by_name( "time_feature_1_hidden/fully_connected_1/weights") self.assertShapeEquals((20, 20), fc_1) self.assertItemsEqual(["time_feature_1"], model.time_series_hidden_layers.keys()) self.assertShapeEquals((None, 20), model.time_series_hidden_layers["time_feature_1"]) self.assertEqual(len(model.aux_hidden_layers), 0) self.assertIs(model.time_series_hidden_layers["time_feature_1"], model.pre_logits_concat) # Execute the TensorFlow graph. scaffold = tf.train.Scaffold() scaffold.finalize() with self.test_session() as sess: sess.run([scaffold.init_op, scaffold.local_init_op]) step = sess.run(model.global_step) self.assertEqual(0, step) # Fetch predictions. features = testing.fake_features(feature_spec, batch_size=16) labels = testing.fake_labels(config.hparams.output_dim, batch_size=16) feed_dict = input_ops.prepare_feed_dict(model, features, labels) predictions = sess.run(model.predictions, feed_dict=feed_dict) self.assertShapeEquals((16, 1), predictions)
def testTwoTimeSeriesFeatures(self): # Build config. feature_spec = { "time_feature_1": { "length": 20, "is_time_series": True, }, "time_feature_2": { "length": 5, "is_time_series": True, }, "aux_feature_1": { "length": 1, "is_time_series": False, }, } hidden_spec = { "time_feature_1": { "num_local_layers": 1, "local_layer_size": 20, "translation_delta": 1, "pooling_type": "max", "dropout_rate": 0.5, }, "time_feature_2": { "num_local_layers": 2, "local_layer_size": 7, "translation_delta": 0, "dropout_rate": 0, } } config = configurations.base() config["inputs"]["features"] = feature_spec config["hparams"]["time_series_hidden"] = hidden_spec config = configdict.ConfigDict(config) # Build model. features = input_ops.build_feature_placeholders(config.inputs.features) labels = input_ops.build_labels_placeholder() model = astro_fc_model.AstroFCModel(features, labels, config.hparams, tf.estimator.ModeKeys.TRAIN) model.build() # TODO(shallue): TensorFlow 2.0 doesn't have global variable collections. # If we want to keep testing variable shapes in 2.0, we must keep track of # the individual Keras Layer objects in the model class. variables = {v.op.name: v for v in tf.global_variables()} # Validate Tensor shapes. conv = variables["time_feature_1_hidden/conv1d/kernel"] self.assertShapeEquals((18, 1, 20), conv) fc_1 = variables["time_feature_2_hidden/fully_connected_1/kernel"] self.assertShapeEquals((5, 7), fc_1) fc_2 = variables["time_feature_2_hidden/fully_connected_2/kernel"] self.assertShapeEquals((7, 7), fc_2) self.assertItemsEqual(["time_feature_1", "time_feature_2"], model.time_series_hidden_layers.keys()) self.assertShapeEquals( (None, 20), model.time_series_hidden_layers["time_feature_1"]) self.assertShapeEquals( (None, 7), model.time_series_hidden_layers["time_feature_2"]) self.assertItemsEqual(["aux_feature_1"], model.aux_hidden_layers.keys()) self.assertIs(model.aux_features["aux_feature_1"], model.aux_hidden_layers["aux_feature_1"]) self.assertShapeEquals((None, 28), model.pre_logits_concat) # Execute the TensorFlow graph. scaffold = tf.train.Scaffold() scaffold.finalize() with self.session() as sess: sess.run([scaffold.init_op, scaffold.local_init_op]) step = sess.run(model.global_step) self.assertEqual(0, step) # Fetch predictions. features = testing.fake_features(feature_spec, batch_size=16) labels = testing.fake_labels(config.hparams.output_dim, batch_size=16) feed_dict = input_ops.prepare_feed_dict(model, features, labels) predictions = sess.run(model.predictions, feed_dict=feed_dict) self.assertShapeEquals((16, 1), predictions)