def start_bundle(self):
# Build the model.
g = tf.Graph()
with g.as_default():
example_placeholder = tf.placeholder(tf.string, shape=[])
parsed_features = kepler_light_curves.parse_example(
example_placeholder)
parsed_example_id = parsed_features.pop("example_id")
parsed_time = parsed_features.pop("time")
features = {
# Add extra dimensions: [length] -> [1, length, 1].
feature_name: tf.reshape(value, [1, -1, 1])
for feature_name, value in parsed_features.items()
}
model = astrowavenet_model.AstroWaveNet(
features=features,
hparams=self.config.hparams,
mode=tf.estimator.ModeKeys.PREDICT)
model.build()
saver = tf.train.Saver()
sess = tf.Session(graph=g)
saver.restore(sess, self.checkpoint_file)
tf.logging.info("Successfully loaded checkpoint %s at global step %d.",
self.checkpoint_file, sess.run(model.global_step))
self.example_placeholder = example_placeholder
self.parsed_example_id = parsed_example_id
self.parsed_time = parsed_time
self.model = model
self.session = sess
def test_causality(self):
time_series_length = 7
input_num_features = 1
context_num_features = 1
input_placeholder = tf.placeholder(
dtype=tf.float32,
shape=[None, time_series_length, input_num_features],
name="input")
context_placeholder = tf.placeholder(
dtype=tf.float32,
shape=[None, time_series_length, context_num_features],
name="context")
features = {
"autoregressive_input": input_placeholder,
"conditioning_stack": context_placeholder
}
mode = tf.estimator.ModeKeys.TRAIN
hparams = configdict.ConfigDict({
"dilation_kernel_width": 1,
"skip_output_dim": 1,
"preprocess_output_size": 1,
"preprocess_kernel_width": 1,
"num_residual_blocks": 1,
"dilation_rates": [1],
"output_distribution": {
"type": "normal",
"min_scale": 0.001,
}
})
model = astrowavenet_model.AstroWaveNet(features, hparams, mode)
model.build()
scaffold = tf.train.Scaffold()
scaffold.finalize()
with self.cached_session() as sess:
sess.run([scaffold.init_op, scaffold.local_init_op])
step = sess.run(model.global_step)
self.assertEqual(0, step)
feed_dict = {
input_placeholder: [
[[0], [0], [0], [0], [0], [0], [0]],
[[1], [0], [0], [0], [0], [0], [0]],
[[0], [0], [0], [1], [0], [0], [0]],
[[0], [0], [0], [0], [0], [0], [1]],
[[0], [0], [0], [0], [0], [0], [0]],
[[0], [0], [0], [0], [0], [0], [0]],
[[0], [0], [0], [0], [0], [0], [0]],
],
context_placeholder: [
[[0], [0], [0], [0], [0], [0], [0]],
[[0], [0], [0], [0], [0], [0], [0]],
[[0], [0], [0], [0], [0], [0], [0]],
[[0], [0], [0], [0], [0], [0], [0]],
[[1], [0], [0], [0], [0], [0], [0]],
[[0], [0], [0], [1], [0], [0], [0]],
[[0], [0], [0], [0], [0], [0], [1]],
],
}
network_output = sess.run(model.network_output,
feed_dict=feed_dict)
np.testing.assert_array_equal(
[
[[0], [0], [0], [0], [0], [0], [0]],
# Input elements are used to predict the next timestamp.
[[0], [1], [0], [0], [0], [0], [0]],
[[0], [0], [0], [0], [1], [0], [0]],
[[0], [0], [0], [0], [0], [0], [0]],
# Context elements are used to predict the current timestamp.
[[1], [0], [0], [0], [0], [0], [0]],
[[0], [0], [0], [1], [0], [0], [0]],
[[0], [0], [0], [0], [0], [0], [1]],
],
np.greater(np.abs(network_output), 0))
def test_output_weighted(self):
time_series_length = 6
input_num_features = 2
context_num_features = 7
input_placeholder = tf.placeholder(
dtype=tf.float32,
shape=[None, time_series_length, input_num_features],
name="input")
weights_placeholder = tf.placeholder(
dtype=tf.float32,
shape=[None, time_series_length, input_num_features],
name="input")
context_placeholder = tf.placeholder(
dtype=tf.float32,
shape=[None, time_series_length, context_num_features],
name="context")
features = {
"autoregressive_input": input_placeholder,
"weights": weights_placeholder,
"conditioning_stack": context_placeholder
}
mode = tf.estimator.ModeKeys.TRAIN
hparams = configdict.ConfigDict({
"dilation_kernel_width": 2,
"skip_output_dim": 6,
"preprocess_output_size": 3,
"preprocess_kernel_width": 5,
"num_residual_blocks": 2,
"dilation_rates": [1, 2, 4],
"output_distribution": {
"type": "normal",
"min_scale": 0,
}
})
model = astrowavenet_model.AstroWaveNet(features, hparams, mode)
model.build()
scaffold = tf.train.Scaffold()
scaffold.finalize()
with self.cached_session() as sess:
sess.run([scaffold.init_op, scaffold.local_init_op])
step = sess.run(model.global_step)
self.assertEqual(0, step)
feed_dict = {
input_placeholder: [
[[1, 9], [1, 9], [1, 9], [1, 9], [1, 9], [1, 9]],
[[2, 8], [2, 8], [2, 8], [2, 8], [2, 8], [2, 8]],
[[3, 7], [3, 7], [3, 7], [3, 7], [3, 7], [3, 7]],
],
weights_placeholder: [
[[1, 1], [1, 1], [1, 1], [1, 1], [1, 1], [1, 1]],
[[1, 0], [1, 1], [1, 1], [0, 1], [0, 1], [0, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0]],
],
# Context is not needed since we explicitly feed the dist params.
model.dist_params["loc"]: [
[[1, 8], [1, 8], [1, 8], [1, 8], [1, 8], [1, 8]],
[[2, 9], [2, 9], [2, 9], [2, 9], [2, 9], [2, 9]],
[[3, 6], [3, 6], [3, 6], [3, 6], [3, 6], [3, 6]],
],
model.dist_params["scale"]: [
[[0.1, 0.1], [0.2, 0.2], [0.5, 0.5], [1, 1], [2, 2],
[5, 5]],
[[0.1, 0.1], [0.2, 0.2], [0.5, 0.5], [1, 1], [2, 2],
[5, 5]],
[[0.1, 0.1], [0.2, 0.2], [0.5, 0.5], [1, 1], [2, 2],
[5, 5]],
],
}
batch_losses, per_example_loss, num_examples, total_loss = sess.run(
[
model.batch_losses, model.per_example_loss,
model.num_nonzero_weight_examples, model.total_loss
],
feed_dict=feed_dict)
np.testing.assert_array_almost_equal(
[[[-1.38364656, 48.61635344], [-0.69049938, 11.80950062],
[0.22579135, 2.22579135], [0.91893853, 1.41893853],
[1.61208571, 1.73708571], [2.52837645, 2.54837645]],
[[-1.38364656, 0], [-0.69049938, 11.80950062],
[0.22579135, 2.22579135], [0, 1.41893853], [0, 1.73708571],
[0, 0]], [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]],
batch_losses)
np.testing.assert_array_almost_equal([5.96392435, 2.19185166, 0],
per_example_loss)
np.testing.assert_almost_equal(2, num_examples)
np.testing.assert_almost_equal(4.07788801, total_loss)
def test_build_model(self):
time_series_length = 9
input_num_features = 8
context_num_features = 7
input_placeholder = tf.placeholder(
dtype=tf.float32,
shape=[None, time_series_length, input_num_features],
name="input")
context_placeholder = tf.placeholder(
dtype=tf.float32,
shape=[None, time_series_length, context_num_features],
name="context")
features = {
"autoregressive_input": input_placeholder,
"conditioning_stack": context_placeholder
}
mode = tf.estimator.ModeKeys.TRAIN
hparams = configdict.ConfigDict({
"dilation_kernel_width": 2,
"skip_output_dim": 6,
"preprocess_output_size": 3,
"preprocess_kernel_width": 5,
"num_residual_blocks": 2,
"dilation_rates": [1, 2, 4],
"output_distribution": {
"type": "normal",
"min_scale": 0.001,
}
})
model = astrowavenet_model.AstroWaveNet(features, hparams, mode)
model.build()
variables = {v.op.name: v for v in tf.trainable_variables()}
# Verify variable shapes in two residual blocks.
var = variables["preprocess/causal_conv/kernel"]
self.assertShapeEquals((5, 8, 3), var)
var = variables["preprocess/causal_conv/bias"]
self.assertShapeEquals((3, ), var)
var = variables["block_0/dilation_1/filter/causal_conv/kernel"]
self.assertShapeEquals((2, 3, 3), var)
var = variables["block_0/dilation_1/filter/causal_conv/bias"]
self.assertShapeEquals((3, ), var)
var = variables["block_0/dilation_1/filter/conv1x1/kernel"]
self.assertShapeEquals((1, 7, 3), var)
var = variables["block_0/dilation_1/filter/conv1x1/bias"]
self.assertShapeEquals((3, ), var)
var = variables["block_0/dilation_1/gate/causal_conv/kernel"]
self.assertShapeEquals((2, 3, 3), var)
var = variables["block_0/dilation_1/gate/causal_conv/bias"]
self.assertShapeEquals((3, ), var)
var = variables["block_0/dilation_1/gate/conv1x1/kernel"]
self.assertShapeEquals((1, 7, 3), var)
var = variables["block_0/dilation_1/gate/conv1x1/bias"]
self.assertShapeEquals((3, ), var)
var = variables["block_0/dilation_1/residual/conv1x1/kernel"]
self.assertShapeEquals((1, 3, 3), var)
var = variables["block_0/dilation_1/residual/conv1x1/bias"]
self.assertShapeEquals((3, ), var)
var = variables["block_0/dilation_1/skip/conv1x1/kernel"]
self.assertShapeEquals((1, 3, 6), var)
var = variables["block_0/dilation_1/skip/conv1x1/bias"]
self.assertShapeEquals((6, ), var)
var = variables["block_1/dilation_4/filter/causal_conv/kernel"]
self.assertShapeEquals((2, 3, 3), var)
var = variables["block_1/dilation_4/filter/causal_conv/bias"]
self.assertShapeEquals((3, ), var)
var = variables["block_1/dilation_4/filter/conv1x1/kernel"]
self.assertShapeEquals((1, 7, 3), var)
var = variables["block_1/dilation_4/filter/conv1x1/bias"]
self.assertShapeEquals((3, ), var)
var = variables["block_1/dilation_4/gate/causal_conv/kernel"]
self.assertShapeEquals((2, 3, 3), var)
var = variables["block_1/dilation_4/gate/causal_conv/bias"]
self.assertShapeEquals((3, ), var)
var = variables["block_1/dilation_4/gate/conv1x1/kernel"]
self.assertShapeEquals((1, 7, 3), var)
var = variables["block_1/dilation_4/gate/conv1x1/bias"]
self.assertShapeEquals((3, ), var)
var = variables["block_1/dilation_4/residual/conv1x1/kernel"]
self.assertShapeEquals((1, 3, 3), var)
var = variables["block_1/dilation_4/residual/conv1x1/bias"]
self.assertShapeEquals((3, ), var)
var = variables["block_1/dilation_4/skip/conv1x1/kernel"]
self.assertShapeEquals((1, 3, 6), var)
var = variables["block_1/dilation_4/skip/conv1x1/bias"]
self.assertShapeEquals((6, ), var)
var = variables["postprocess/conv1x1/kernel"]
self.assertShapeEquals((1, 6, 6), var)
var = variables["postprocess/conv1x1/bias"]
self.assertShapeEquals((6, ), var)
var = variables["dist_params/conv1x1/kernel"]
self.assertShapeEquals((1, 6, 16), var)
var = variables["dist_params/conv1x1/bias"]
self.assertShapeEquals((16, ), var)
# Verify total number of trainable parameters.
num_preprocess_params = (
hparams.preprocess_kernel_width * input_num_features *
hparams.preprocess_output_size + hparams.preprocess_output_size)
num_gated_params = (
hparams.dilation_kernel_width * hparams.preprocess_output_size *
hparams.preprocess_output_size + hparams.preprocess_output_size +
1 * context_num_features * hparams.preprocess_output_size +
hparams.preprocess_output_size) * 2
num_residual_params = (1 * hparams.preprocess_output_size *
hparams.preprocess_output_size +
hparams.preprocess_output_size)
num_skip_params = (
1 * hparams.preprocess_output_size * hparams.skip_output_dim +
hparams.skip_output_dim)
num_block_params = (
num_gated_params + num_residual_params + num_skip_params) * len(
hparams.dilation_rates) * hparams.num_residual_blocks
num_postprocess_params = (
1 * hparams.skip_output_dim * hparams.skip_output_dim +
hparams.skip_output_dim)
num_dist_params = (
1 * hparams.skip_output_dim * 2 * input_num_features +
2 * input_num_features)
total_params = (num_preprocess_params + num_block_params +
num_postprocess_params + num_dist_params)
total_retrieved_params = 0
for v in tf.trainable_variables():
total_retrieved_params += np.prod(v.shape)
self.assertEqual(total_params, total_retrieved_params)
# Verify model runs and outputs losses of correct shape.
scaffold = tf.train.Scaffold()
scaffold.finalize()
with self.cached_session() as sess:
sess.run([scaffold.init_op, scaffold.local_init_op])
step = sess.run(model.global_step)
self.assertEqual(0, step)
batch_size = 11
feed_dict = {
input_placeholder:
np.random.random(
(batch_size, time_series_length, input_num_features)),
context_placeholder:
np.random.random(
(batch_size, time_series_length, context_num_features))
}
batch_losses, per_example_loss, total_loss = sess.run(
[model.batch_losses, model.per_example_loss, model.total_loss],
feed_dict=feed_dict)
self.assertShapeEquals(
(batch_size, time_series_length, input_num_features),
batch_losses)
self.assertShapeEquals((batch_size, ), per_example_loss)
self.assertShapeEquals((), total_loss)
def test_output_categorical(self):
time_series_length = 3
input_num_features = 1
context_num_features = 7
num_classes = 4 # For quantized categorical output predictions.
input_placeholder = tf.placeholder(
dtype=tf.float32,
shape=[None, time_series_length, input_num_features],
name="input")
context_placeholder = tf.placeholder(
dtype=tf.float32,
shape=[None, time_series_length, context_num_features],
name="context")
features = {
"autoregressive_input": input_placeholder,
"conditioning_stack": context_placeholder
}
mode = tf.estimator.ModeKeys.TRAIN
hparams = configdict.ConfigDict({
"dilation_kernel_width": 2,
"skip_output_dim": 6,
"preprocess_output_size": 3,
"preprocess_kernel_width": 5,
"num_residual_blocks": 2,
"dilation_rates": [1, 2, 4],
"output_distribution": {
"type": "categorical",
"min_scale": 0,
"num_classes": num_classes,
"min_quantization_value": 0,
"max_quantization_value": 1
}
})
model = astrowavenet_model.AstroWaveNet(features, hparams, mode)
model.build()
self.assertItemsEqual(["logits"], model.dist_params.keys())
self.assertShapeEquals(
(None, time_series_length, input_num_features, num_classes),
model.dist_params["logits"])
scaffold = tf.train.Scaffold()
scaffold.finalize()
with self.cached_session() as sess:
sess.run([scaffold.init_op, scaffold.local_init_op])
step = sess.run(model.global_step)
self.assertEqual(0, step)
feed_dict = {
input_placeholder: [
[[0], [0], [0]], # min_quantization_value
[[0.2], [0.2], [0.2]], # Within bucket.
[[0.25], [0.25], [0.25]], # On bucket boundary.
[[0.5], [0.5], [0.5]], # On bucket boundary.
[[0.8], [0.8], [0.8]], # Within bucket.
[[1], [1], [1]], # max_quantization_value
[[-0.1], [1.5], [200]], # Outside range: will be clipped.
],
# Context is not needed since we explicitly feed the dist params.
model.dist_params["logits"]: [
[[[1, 0, 0, 0]], [[0, 1, 0, 0]], [[0, 0, 0, 1]]],
[[[1, 0, 0, 0]], [[0, 1, 0, 0]], [[0, 0, 0, 1]]],
[[[0, 1, 0, 0]], [[1, 0, 0, 0]], [[0, 0, 1, 0]]],
[[[0, 0, 1, 0]], [[0, 1, 0, 0]], [[0, 0, 0, 1]]],
[[[0, 0, 0, 1]], [[1, 0, 0, 0]], [[1, 0, 0, 0]]],
[[[0, 0, 0, 1]], [[0, 1, 0, 0]], [[0, 0, 1, 0]]],
[[[1, 0, 0, 0]], [[0, 0, 1, 0]], [[0, 1, 0, 0]]],
],
}
(target, batch_losses, per_example_loss, num_examples,
total_loss) = sess.run([
model.autoregressive_target, model.batch_losses,
model.per_example_loss, model.num_nonzero_weight_examples,
model.total_loss
],
feed_dict=feed_dict)
np.testing.assert_array_almost_equal([
[[0], [0], [0]],
[[0], [0], [0]],
[[1], [1], [1]],
[[2], [2], [2]],
[[3], [3], [3]],
[[3], [3], [3]],
[[0], [3], [3]],
], target)
np.testing.assert_array_almost_equal([
[[0.74366838], [1.74366838], [1.74366838]],
[[0.74366838], [1.74366838], [1.74366838]],
[[0.74366838], [1.74366838], [1.74366838]],
[[0.74366838], [1.74366838], [1.74366838]],
[[0.74366838], [1.74366838], [1.74366838]],
[[0.74366838], [1.74366838], [1.74366838]],
[[0.74366838], [1.74366838], [1.74366838]],
], batch_losses)
np.testing.assert_array_almost_equal([
1.41033504, 1.41033504, 1.41033504, 1.41033504, 1.41033504,
1.41033504, 1.41033504
], per_example_loss)
np.testing.assert_almost_equal(7, num_examples)
np.testing.assert_almost_equal(1.41033504, total_loss)
def test_build_model_categorical(self):
time_series_length = 9
input_num_features = 8
context_num_features = 7
input_placeholder = tf.placeholder(
dtype=tf.float32,
shape=[None, time_series_length, input_num_features],
name="input")
context_placeholder = tf.placeholder(
dtype=tf.float32,
shape=[None, time_series_length, context_num_features],
name="context")
features = {
"autoregressive_input": input_placeholder,
"conditioning_stack": context_placeholder
}
mode = tf.estimator.ModeKeys.TRAIN
hparams = configdict.ConfigDict({
"dilation_kernel_width": 2,
"skip_output_dim": 6,
"preprocess_output_size": 3,
"preprocess_kernel_width": 5,
"num_residual_blocks": 2,
"dilation_rates": [1, 2, 4],
"output_distribution": {
"type": "categorical",
"num_classes": 256,
"min_quantization_value": -1,
"max_quantization_value": 1
}
})
model = astrowavenet_model.AstroWaveNet(features, hparams, mode)
model.build()
variables = {v.op.name: v for v in tf.trainable_variables()}
var = variables["dist_params/conv1x1/kernel"]
self.assertShapeEquals(
(1, hparams.skip_output_dim,
hparams.output_distribution.num_classes * input_num_features),
var)
var = variables["dist_params/conv1x1/bias"]
self.assertShapeEquals(
(hparams.output_distribution.num_classes * input_num_features, ),
var)
# Verify model runs and outputs losses of correct shape.
scaffold = tf.train.Scaffold()
scaffold.finalize()
with self.cached_session() as sess:
sess.run([scaffold.init_op, scaffold.local_init_op])
step = sess.run(model.global_step)
self.assertEqual(0, step)
batch_size = 11
feed_dict = {
input_placeholder:
np.random.random(
(batch_size, time_series_length, input_num_features)),
context_placeholder:
np.random.random(
(batch_size, time_series_length, context_num_features))
}
batch_losses, per_example_loss, total_loss = sess.run(
[model.batch_losses, model.per_example_loss, model.total_loss],
feed_dict=feed_dict)
self.assertShapeEquals(
(batch_size, time_series_length, input_num_features),
batch_losses)
self.assertShapeEquals((batch_size, ), per_example_loss)
self.assertShapeEquals((), total_loss)
def test_output_normal_mixture(self):
time_series_length = 6
input_num_features = 2
context_num_features = 7
input_placeholder = tf.placeholder(
dtype=tf.float32,
shape=[None, time_series_length, input_num_features],
name="input")
context_placeholder = tf.placeholder(
dtype=tf.float32,
shape=[None, time_series_length, context_num_features],
name="context")
features = {
"autoregressive_input": input_placeholder,
"conditioning_stack": context_placeholder
}
mode = tf.estimator.ModeKeys.TRAIN
hparams = configdict.ConfigDict({
"dilation_kernel_width": 2,
"skip_output_dim": 6,
"preprocess_output_size": 3,
"preprocess_kernel_width": 5,
"num_residual_blocks": 2,
"dilation_rates": [1, 2, 4],
"output_distribution": {
"type": "normal",
"min_scale": 0,
"predict_outlier_distribution": True
}
})
model = astrowavenet_model.AstroWaveNet(features, hparams, mode)
model.build()
# Model predicts the loc and scale of the outlier and non-outlier Gaussian
# distributions, and the probability of being an outlier.
self.assertItemsEqual(
["loc", "scale", "outlier_prob", "outlier_loc", "outlier_scale"],
model.dist_params.keys())
self.assertShapeEquals((None, time_series_length, input_num_features),
model.dist_params["loc"])
self.assertShapeEquals((None, time_series_length, input_num_features),
model.dist_params["scale"])
self.assertShapeEquals((2, ), model.dist_params["outlier_prob"])
self.assertShapeEquals((2, ), model.dist_params["outlier_loc"])
self.assertShapeEquals((2, ), model.dist_params["outlier_scale"])
scaffold = tf.train.Scaffold()
scaffold.finalize()
with self.cached_session() as sess:
sess.run([scaffold.init_op, scaffold.local_init_op])
step = sess.run(model.global_step)
self.assertEqual(0, step)
feed_dict = {
input_placeholder: [
[[1, 9], [1, 9], [1, 9], [1, 9], [1, 9], [1, 9]],
[[2, 8], [2, 8], [2, 8], [2, 8], [2, 8], [2, 8]],
],
# Context is not needed since we explicitly feed the dist params.
model.dist_params["loc"]: [
[[1, 8], [1, 8], [1, 8], [1, 8], [1, 8], [1, 8]],
[[2, 9], [2, 9], [2, 9], [2, 9], [2, 9], [2, 9]],
],
model.dist_params["scale"]: [
[[0.1, 0.1], [0.2, 0.2], [0.5, 0.5], [1, 1], [2, 2],
[5, 5]],
[[0.1, 0.1], [0.2, 0.2], [0.5, 0.5], [1, 1], [2, 2],
[5, 5]],
],
model.dist_params["outlier_prob"]: [0, 0],
model.dist_params["outlier_loc"]: [1, 8],
model.dist_params["outlier_scale"]: [1, 0.1],
}
batch_losses, per_example_loss, num_examples, total_loss = sess.run(
[
model.batch_losses, model.per_example_loss,
model.num_nonzero_weight_examples, model.total_loss
],
feed_dict=feed_dict)
# Outlier probability is 0.0, so predictions are from the non-outlier
# distribution.
np.testing.assert_array_almost_equal(
[[[-1.38364656, 48.61635344], [-0.69049938, 11.80950062],
[0.22579135, 2.22579135], [0.91893853, 1.41893853],
[1.61208571, 1.73708571], [2.52837645, 2.54837645]],
[[-1.38364656, 48.61635344], [-0.69049938, 11.80950062],
[0.22579135, 2.22579135], [0.91893853, 1.41893853],
[1.61208571, 1.73708571], [2.52837645, 2.54837645]]],
batch_losses)
np.testing.assert_array_almost_equal([5.96392435, 5.96392435],
per_example_loss)
np.testing.assert_almost_equal(2, num_examples)
np.testing.assert_almost_equal(5.96392435, total_loss)
# Outlier probability is 1.0, so predictions are from the outlier
# distribution.
feed_dict[model.dist_params["outlier_prob"]] = [1, 1]
batch_losses, per_example_loss, num_examples, total_loss = sess.run(
[
model.batch_losses, model.per_example_loss,
model.num_nonzero_weight_examples, model.total_loss
],
feed_dict=feed_dict)
np.testing.assert_array_almost_equal(
[[[0.918939, 48.616352]] * 6, [[1.418939, -1.383647]] * 6],
batch_losses)
np.testing.assert_array_almost_equal([24.7676468, 0.017645916],
per_example_loss, 5)
np.testing.assert_almost_equal(2, num_examples)
np.testing.assert_almost_equal(12.392646358, total_loss, decimal=6)
# Predictions are weighted from the non-outlier and outlier distributions.
feed_dict[model.dist_params["outlier_prob"]] = [0.3, 0.5]
batch_losses, per_example_loss, num_examples, total_loss = sess.run(
[
model.batch_losses, model.per_example_loss,
model.num_nonzero_weight_examples, model.total_loss
],
feed_dict=feed_dict)
np.testing.assert_array_almost_equal(
[[[-1.06893575, 48.61635208], [-0.41606259, 12.5026474],
[0.38831028, 2.91893864], [0.91893858, 2.11208582],
[1.34972155, 2.430233], [1.73991919, 3.24152374]],
[[-1.05263364, -0.69049942], [-0.38450652, -0.69050133],
[0.46027452, -0.71720666], [1.04454803, -0.74938428],
[1.55012715, -0.73367846], [2.05226898, -0.70991373]]],
batch_losses)
np.testing.assert_array_almost_equal([6.227806, -0.051759],
per_example_loss)
np.testing.assert_almost_equal(2, num_examples)
np.testing.assert_almost_equal(3.0880234, total_loss, decimal=6)
