def testOneTimeSeriesFeature(self):
# Build config.
feature_spec = {
"time_feature_1": {
"length": 10,
"is_time_series": True,
}
}
config = configurations.base()
config["inputs"]["features"] = feature_spec
config = configdict.ConfigDict(config)
# Build model.
features = input_ops.build_feature_placeholders(config.inputs.features)
labels = input_ops.build_labels_placeholder()
model = astro_model.AstroModel(features, labels, config.hparams,
tf.estimator.ModeKeys.TRAIN)
model.build()
# Validate hidden layers.
self.assertItemsEqual(["time_feature_1"],
model.time_series_hidden_layers.keys())
self.assertIs(model.time_series_features["time_feature_1"],
model.time_series_hidden_layers["time_feature_1"])
self.assertEqual(len(model.aux_hidden_layers), 0)
self.assertIs(model.time_series_features["time_feature_1"],
model.pre_logits_concat)
def testOneTimeSeriesFeature(self):
# Build config.
feature_spec = {
"time_feature_1": {
"length": 10,
"is_time_series": True,
}
}
config = configurations.base()
config["inputs"]["features"] = feature_spec
config = configdict.ConfigDict(config)
# Build model.
features = input_ops.build_feature_placeholders(config.inputs.features)
labels = input_ops.build_labels_placeholder()
model = astro_model.AstroModel(features, labels, config.hparams,
tf.estimator.ModeKeys.TRAIN)
model.build()
# Validate hidden layers.
self.assertItemsEqual(["time_feature_1"],
model.time_series_hidden_layers.keys())
self.assertIs(model.time_series_features["time_feature_1"],
model.time_series_hidden_layers["time_feature_1"])
self.assertEqual(len(model.aux_hidden_layers), 0)
self.assertIs(model.time_series_features["time_feature_1"],
model.pre_logits_concat)
def testZeroHiddenLayers(self):
# Build config.
feature_spec = {
"time_feature_1": {
"length": 10,
"is_time_series": True,
},
"time_feature_2": {
"length": 10,
"is_time_series": True,
},
"aux_feature_1": {
"length": 1,
"is_time_series": False,
},
}
config = configurations.base()
config["inputs"]["features"] = feature_spec
config = configdict.ConfigDict(config)
config.hparams.output_dim = 1
config.hparams.num_pre_logits_hidden_layers = 0
# Build model.
features = input_ops.build_feature_placeholders(config.inputs.features)
labels = input_ops.build_labels_placeholder()
model = astro_model.AstroModel(features, labels, config.hparams,
tf.estimator.ModeKeys.TRAIN)
model.build()
# Validate Tensor shapes.
self.assertShapeEquals((None, 21), model.pre_logits_concat)
logits_w = testing.get_variable_by_name("logits/kernel")
self.assertShapeEquals((21, 1), logits_w)
def testZeroHiddenLayers(self):
# Build config.
feature_spec = {
"time_feature_1": {
"length": 10,
"is_time_series": True,
},
"time_feature_2": {
"length": 10,
"is_time_series": True,
},
"aux_feature_1": {
"length": 1,
"is_time_series": False,
},
}
config = configurations.base()
config["inputs"]["features"] = feature_spec
config = configdict.ConfigDict(config)
config.hparams.output_dim = 1
config.hparams.num_pre_logits_hidden_layers = 0
# Build model.
features = input_ops.build_feature_placeholders(config.inputs.features)
labels = input_ops.build_labels_placeholder()
model = astro_model.AstroModel(features, labels, config.hparams,
tf.estimator.ModeKeys.TRAIN)
model.build()
# Validate Tensor shapes.
self.assertShapeEquals((None, 21), model.pre_logits_concat)
logits_w = testing.get_variable_by_name("logits/kernel")
self.assertShapeEquals((21, 1), logits_w)
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": {
"rnn_num_layers": 2,
"rnn_num_units": 16,
"rnn_memory_cells": "lstm",
"rnn_activation": "tanh",
"rnn_dropout": 0.0,
"rnn_direction": "bi"
},
"time_feature_2": {
"rnn_num_layers": 1,
"rnn_num_units": 4,
"rnn_memory_cells": "lstm",
"rnn_activation": "tanh",
"rnn_dropout": 0.0,
"rnn_direction": "bi"
}
}
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_rnn_model.AstroRNNModel(features, labels, config.hparams,
tf.estimator.ModeKeys.TRAIN)
model.build()
# 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 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 testInvalidModeRaisesError(self):
# Build config.
config = configdict.ConfigDict(configurations.base())
# Build model.
features = input_ops.build_feature_placeholders(config.inputs.features)
labels = input_ops.build_labels_placeholder()
with self.assertRaises(ValueError):
_ = astro_model.AstroModel(features, labels, config.hparams, "training")
def testInvalidModeRaisesError(self):
# Build config.
config = configdict.ConfigDict(configurations.base())
# Build model.
features = input_ops.build_feature_placeholders(config.inputs.features)
labels = input_ops.build_labels_placeholder()
with self.assertRaises(ValueError):
_ = astro_model.AstroModel(features, labels, config.hparams, "training")
def testEvalMode(self):
# Build config.
feature_spec = {
"time_feature_1": {
"length": 10,
"is_time_series": True,
},
"time_feature_2": {
"length": 10,
"is_time_series": True,
},
"aux_feature_1": {
"length": 1,
"is_time_series": False,
},
}
config = configurations.base()
config["inputs"]["features"] = feature_spec
config = configdict.ConfigDict(config)
config.hparams.output_dim = 1
# Build model.
features = input_ops.build_feature_placeholders(config.inputs.features)
labels = input_ops.build_labels_placeholder()
model = astro_model.AstroModel(features, labels, config.hparams,
tf.estimator.ModeKeys.TRAIN)
model.build()
# Validate Tensor shapes.
self.assertShapeEquals((None, 21), model.pre_logits_concat)
self.assertShapeEquals((None, 1), model.logits)
self.assertShapeEquals((None, 1), model.predictions)
self.assertShapeEquals((None, ), model.batch_losses)
self.assertShapeEquals((), model.total_loss)
# 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 total loss.
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)
total_loss = sess.run(model.total_loss, feed_dict=feed_dict)
self.assertShapeEquals((), total_loss)
def testZeroFeaturesRaisesError(self):
# Build config.
config = configurations.base()
config["inputs"]["features"] = {}
config = configdict.ConfigDict(config)
# Build model.
features = input_ops.build_feature_placeholders(config.inputs.features)
labels = input_ops.build_labels_placeholder()
model = astro_model.AstroModel(features, labels, config.hparams,
tf.estimator.ModeKeys.TRAIN)
with self.assertRaises(ValueError):
# Raises ValueError because at least one feature is required.
model.build()
def testZeroFeaturesRaisesError(self):
# Build config.
config = configurations.base()
config["inputs"]["features"] = {}
config = configdict.ConfigDict(config)
# Build model.
features = input_ops.build_feature_placeholders(config.inputs.features)
labels = input_ops.build_labels_placeholder()
model = astro_model.AstroModel(features, labels, config.hparams,
tf.estimator.ModeKeys.TRAIN)
with self.assertRaises(ValueError):
# Raises ValueError because at least one feature is required.
model.build()
def testEvalMode(self):
# Build config.
feature_spec = {
"time_feature_1": {
"length": 10,
"is_time_series": True,
},
"time_feature_2": {
"length": 10,
"is_time_series": True,
},
"aux_feature_1": {
"length": 1,
"is_time_series": False,
},
}
config = configurations.base()
config["inputs"]["features"] = feature_spec
config = configdict.ConfigDict(config)
config.hparams.output_dim = 1
# Build model.
features = input_ops.build_feature_placeholders(config.inputs.features)
labels = input_ops.build_labels_placeholder()
model = astro_model.AstroModel(features, labels, config.hparams,
tf.estimator.ModeKeys.TRAIN)
model.build()
# Validate Tensor shapes.
self.assertShapeEquals((None, 21), model.pre_logits_concat)
self.assertShapeEquals((None, 1), model.logits)
self.assertShapeEquals((None, 1), model.predictions)
self.assertShapeEquals((None,), model.batch_losses)
self.assertShapeEquals((), model.total_loss)
# 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 total loss.
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)
total_loss = sess.run(model.total_loss, feed_dict=feed_dict)
self.assertShapeEquals((), total_loss)
def testTwoHiddenLayers(self):
# Build config.
feature_spec = {
"time_feature_1": {
"length": 10,
"is_time_series": True,
},
"time_feature_2": {
"length": 10,
"is_time_series": True,
},
"aux_feature_1": {
"length": 1,
"is_time_series": False,
},
}
config = configurations.base()
config["inputs"]["features"] = feature_spec
config = configdict.ConfigDict(config)
config.hparams.output_dim = 1
config.hparams.num_pre_logits_hidden_layers = 2
config.hparams.pre_logits_hidden_layer_size = 5
# Build model.
features = input_ops.build_feature_placeholders(config.inputs.features)
labels = input_ops.build_labels_placeholder()
model = astro_model.AstroModel(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.
self.assertShapeEquals((None, 21), model.pre_logits_concat)
fc1 = variables["pre_logits_hidden/fully_connected_1/kernel"]
self.assertShapeEquals((21, 5), fc1)
fc2 = variables["pre_logits_hidden/fully_connected_2/kernel"]
self.assertShapeEquals((5, 5), fc2)
logits_w = variables["logits/kernel"]
self.assertShapeEquals((5, 1), logits_w)
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)
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": {
"cnn_num_blocks": 2,
"cnn_block_size": 2,
"cnn_initial_num_filters": 4,
"cnn_block_filter_factor": 1.5,
"cnn_kernel_size": 3,
"convolution_padding": "same",
"pool_size": 2,
"pool_strides": 2,
},
"time_feature_2": {
"cnn_num_blocks": 1,
"cnn_block_size": 1,
"cnn_initial_num_filters": 5,
"cnn_block_filter_factor": 1,
"cnn_kernel_size": 2,
"convolution_padding": "same",
"pool_size": 0,
"pool_strides": 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_cnn_model.AstroCNNModel(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.
feature_1_block_1_conv_1 = variables[
"time_feature_1_hidden/block_1/conv_1/kernel"]
self.assertShapeEquals((3, 1, 4), feature_1_block_1_conv_1)
feature_1_block_1_conv_2 = variables[
"time_feature_1_hidden/block_1/conv_2/kernel"]
self.assertShapeEquals((3, 4, 4), feature_1_block_1_conv_2)
feature_1_block_2_conv_1 = variables[
"time_feature_1_hidden/block_2/conv_1/kernel"]
self.assertShapeEquals((3, 4, 6), feature_1_block_2_conv_1)
feature_1_block_2_conv_2 = variables[
"time_feature_1_hidden/block_2/conv_2/kernel"]
self.assertShapeEquals((3, 6, 6), feature_1_block_2_conv_2)
feature_2_block_1_conv_1 = variables[
"time_feature_2_hidden/block_1/conv_1/kernel"]
self.assertShapeEquals((2, 1, 5), feature_2_block_1_conv_1)
self.assertItemsEqual(["time_feature_1", "time_feature_2"],
model.time_series_hidden_layers.keys())
self.assertShapeEquals((None, 30),
model.time_series_hidden_layers["time_feature_1"])
self.assertShapeEquals((None, 25),
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, 56), 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)
def testOneTimeSeriesFeature(self):
# Build config.
feature_spec = {
"time_feature_1": {
"length": 20,
"is_time_series": True,
}
}
hidden_spec = {
"time_feature_1": {
"cnn_num_blocks": 2,
"cnn_block_size": 2,
"cnn_initial_num_filters": 4,
"cnn_block_filter_factor": 1.5,
"cnn_kernel_size": 3,
"convolution_padding": "same",
"pool_size": 2,
"pool_strides": 2,
}
}
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_cnn_model.AstroCNNModel(features, labels, config.hparams,
tf.estimator.ModeKeys.TRAIN)
model.build()
# Validate Tensor shapes.
block_1_conv_1 = testing.get_variable_by_name(
"time_feature_1_hidden/block_1/conv_1/kernel")
self.assertShapeEquals((3, 1, 4), block_1_conv_1)
block_1_conv_2 = testing.get_variable_by_name(
"time_feature_1_hidden/block_1/conv_2/kernel")
self.assertShapeEquals((3, 4, 4), block_1_conv_2)
block_2_conv_1 = testing.get_variable_by_name(
"time_feature_1_hidden/block_2/conv_1/kernel")
self.assertShapeEquals((3, 4, 6), block_2_conv_1)
block_2_conv_2 = testing.get_variable_by_name(
"time_feature_1_hidden/block_2/conv_2/kernel")
self.assertShapeEquals((3, 6, 6), block_2_conv_2)
self.assertItemsEqual(["time_feature_1"],
model.time_series_hidden_layers.keys())
self.assertShapeEquals((None, 30),
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 testBuildFeaturePlaceholders(self):
# One time series feature.
config = configdict.ConfigDict({
"time_feature_1": {
"length": 14,
"is_time_series": True,
}
})
expected_shapes = {
"time_series_features": {
"time_feature_1": [None, 14],
},
"aux_features": {}
}
features = input_ops.build_feature_placeholders(config)
self.assertFeatureShapesEqual(expected_shapes, features)
# Two time series features.
config = configdict.ConfigDict({
"time_feature_1": {
"length": 14,
"is_time_series": True,
},
"time_feature_2": {
"length": 5,
"is_time_series": True,
}
})
expected_shapes = {
"time_series_features": {
"time_feature_1": [None, 14],
"time_feature_2": [None, 5],
},
"aux_features": {}
}
features = input_ops.build_feature_placeholders(config)
self.assertFeatureShapesEqual(expected_shapes, features)
# One aux feature.
config = configdict.ConfigDict({
"time_feature_1": {
"length": 14,
"is_time_series": True,
},
"aux_feature_1": {
"length": 1,
"is_time_series": False,
}
})
expected_shapes = {
"time_series_features": {
"time_feature_1": [None, 14],
},
"aux_features": {
"aux_feature_1": [None, 1]
}
}
features = input_ops.build_feature_placeholders(config)
self.assertFeatureShapesEqual(expected_shapes, features)
# Two aux features.
config = configdict.ConfigDict({
"time_feature_1": {
"length": 14,
"is_time_series": True,
},
"aux_feature_1": {
"length": 1,
"is_time_series": False,
},
"aux_feature_2": {
"length": 6,
"is_time_series": False,
},
})
expected_shapes = {
"time_series_features": {
"time_feature_1": [None, 14],
},
"aux_features": {
"aux_feature_1": [None, 1],
"aux_feature_2": [None, 6]
}
}
features = input_ops.build_feature_placeholders(config)
self.assertFeatureShapesEqual(expected_shapes, features)
def testOneTimeSeriesFeature(self):
# Build config.
feature_spec = {
"time_feature_1": {
"length": 20,
"is_time_series": True,
}
}
hidden_spec = {
"time_feature_1": {
"cnn_num_blocks": 2,
"cnn_block_size": 2,
"cnn_initial_num_filters": 4,
"cnn_block_filter_factor": 1.5,
"cnn_kernel_size": 3,
"convolution_padding": "same",
"pool_size": 2,
"pool_strides": 2,
}
}
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_cnn_model.AstroCNNModel(features, labels, config.hparams,
tf.estimator.ModeKeys.TRAIN)
model.build()
# Validate Tensor shapes.
block_1_conv_1 = testing.get_variable_by_name(
"time_feature_1_hidden/block_1/conv_1/kernel")
self.assertShapeEquals((3, 1, 4), block_1_conv_1)
block_1_conv_2 = testing.get_variable_by_name(
"time_feature_1_hidden/block_1/conv_2/kernel")
self.assertShapeEquals((3, 4, 4), block_1_conv_2)
block_2_conv_1 = testing.get_variable_by_name(
"time_feature_1_hidden/block_2/conv_1/kernel")
self.assertShapeEquals((3, 4, 6), block_2_conv_1)
block_2_conv_2 = testing.get_variable_by_name(
"time_feature_1_hidden/block_2/conv_2/kernel")
self.assertShapeEquals((3, 6, 6), block_2_conv_2)
self.assertItemsEqual(["time_feature_1"],
model.time_series_hidden_layers.keys())
self.assertShapeEquals((None, 30),
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 testBuildFeaturePlaceholders(self):
# One time series feature.
config = configdict.ConfigDict(
{"time_feature_1": {
"length": 14,
"is_time_series": True,
}})
expected_shapes = {
"time_series_features": {
"time_feature_1": [None, 14],
},
"aux_features": {}
}
features = input_ops.build_feature_placeholders(config)
self.assertFeatureShapesEqual(expected_shapes, features)
# Two time series features.
config = configdict.ConfigDict({
"time_feature_1": {
"length": 14,
"is_time_series": True,
},
"time_feature_2": {
"length": 5,
"is_time_series": True,
}
})
expected_shapes = {
"time_series_features": {
"time_feature_1": [None, 14],
"time_feature_2": [None, 5],
},
"aux_features": {}
}
features = input_ops.build_feature_placeholders(config)
self.assertFeatureShapesEqual(expected_shapes, features)
# One aux feature.
config = configdict.ConfigDict({
"time_feature_1": {
"length": 14,
"is_time_series": True,
},
"aux_feature_1": {
"length": 1,
"is_time_series": False,
}
})
expected_shapes = {
"time_series_features": {
"time_feature_1": [None, 14],
},
"aux_features": {
"aux_feature_1": [None, 1]
}
}
features = input_ops.build_feature_placeholders(config)
self.assertFeatureShapesEqual(expected_shapes, features)
# Two aux features.
config = configdict.ConfigDict({
"time_feature_1": {
"length": 14,
"is_time_series": True,
},
"aux_feature_1": {
"length": 1,
"is_time_series": False,
},
"aux_feature_2": {
"length": 6,
"is_time_series": False,
},
})
expected_shapes = {
"time_series_features": {
"time_feature_1": [None, 14],
},
"aux_features": {
"aux_feature_1": [None, 1],
"aux_feature_2": [None, 6]
}
}
features = input_ops.build_feature_placeholders(config)
self.assertFeatureShapesEqual(expected_shapes, features)
