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)