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Unverified Commit 2c181308 authored by Chris Shallue's avatar Chris Shallue Committed by GitHub
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Merge pull request #5862 from cshallue/master 

Move tensorflow_models/research/astronet to google-research/exoplanet-ml
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research/astronet/astronet/ops/dataset_ops.py deleted 100644 → 0
View file @ caafb6d1
# Copyright 2018 The TensorFlow Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Functions to build an input pipeline that reads from TFRecord files."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import six
import tensorflow as tf
def pad_tensor_to_batch_size(tensor, batch_size):
"""Pads a Tensor along the batch dimension to the desired batch size."""
if batch_size < 2:
raise ValueError("Cannot pad along batch dimension with batch_size < 2.")
ndims = len(tensor.shape)
if ndims < 1:
raise ValueError("Cannot pad a 0-dimensional Tensor")
num_pad_examples = batch_size - tf.shape(tensor)[0]
# paddings is a 2D Tensor with shape [ndims, 2]. Every element is zero except
# for paddings[0][1], which is the number of values to add along the 0-th
# dimension (the batch dimension) after the contents of the input tensor.
paddings = tf.sparse_to_dense(
sparse_indices=[[0, 1]],
output_shape=[ndims, 2],
sparse_values=num_pad_examples)
padded_tensor = tf.pad(tensor, paddings, name=tensor.op.name + "/pad")
# Set the new shape.
output_shape = tensor.shape.as_list()
output_shape[0] = batch_size
padded_tensor.set_shape(output_shape)
return padded_tensor
def _recursive_pad_to_batch_size(tensor_or_collection, batch_size):
"""Recursively pads to the batch size in a Tensor or collection of Tensors."""
if isinstance(tensor_or_collection, tf.Tensor):
return pad_tensor_to_batch_size(tensor_or_collection, batch_size)
if isinstance(tensor_or_collection, dict):
return {
name: _recursive_pad_to_batch_size(t, batch_size)
for name, t in tensor_or_collection.items()
}
if isinstance(tensor_or_collection, collections.Iterable):
return [
_recursive_pad_to_batch_size(t, batch_size)
for t in tensor_or_collection
]
raise ValueError("Unknown input type: {}".format(tensor_or_collection))
def pad_dataset_to_batch_size(dataset, batch_size):
"""Pads Tensors in a dataset along the batch dimension to batch_size.
The output contains a 'weights' Tensor, which is a 0/1 indicator of padded
elements. If a 'weights' Tensor already exists in the input dataset, then that
Tensor is padded with zeros. If a 'weights' Tensor does not already exist,
then the input dataset is assumed to have a 'labels' Tensor which is used to
construct the weights.
Args:
dataset: A tf.data.Dataset.
batch_size: Integer batch size.
Returns:
A tf.data.Dataset.
"""
def map_fn(tensors):
"""Pads Tensors along the batch dimension to the desired batch size."""
if not isinstance(tensors, dict):
raise ValueError(
"pad_dataset_to_batch_size requires a dictionary of named Tensors.")
outputs = _recursive_pad_to_batch_size(tensors, batch_size)
if "weights" not in outputs:
weights = tf.ones_like(tensors["labels"], dtype=tf.float32)
outputs["weights"] = pad_tensor_to_batch_size(weights, batch_size)
return outputs
return dataset.map(map_fn)
def _recursive_set_batch_size(tensor_or_collection, batch_size):
"""Recursively sets the batch size in a Tensor or collection of Tensors."""
if isinstance(tensor_or_collection, tf.Tensor):
t = tensor_or_collection
shape = t.shape.as_list()
shape[0] = batch_size
t.set_shape(t.shape.merge_with(shape))
elif isinstance(tensor_or_collection, dict):
for t in six.itervalues(tensor_or_collection):
_recursive_set_batch_size(t, batch_size)
elif isinstance(tensor_or_collection, collections.Iterable):
for t in tensor_or_collection:
_recursive_set_batch_size(t, batch_size)
else:
raise ValueError("Unknown input type: {}".format(tensor_or_collection))
return tensor_or_collection
def set_batch_size(dataset, batch_size):
"""Sets the batch dimension in all Tensors to batch_size."""
return dataset.map(lambda t: _recursive_set_batch_size(t, batch_size))
def build_dataset(file_pattern,
input_config,
batch_size,
include_labels=True,
reverse_time_series_prob=0,
shuffle_filenames=False,
shuffle_values_buffer=0,
repeat=1,
use_tpu=False):
"""Builds an input pipeline that reads a dataset from sharded TFRecord files.
Args:
file_pattern: File pattern matching input TFRecord files, e.g.
"/tmp/train-?????-of-00100". May also be a comma-separated list of file
patterns.
input_config: ConfigDict containing feature and label specifications.
batch_size: The number of examples per batch.
include_labels: Whether to read labels from the input files.
reverse_time_series_prob: If > 0, the time series features will be randomly
reversed with this probability. Within a given example, either all time
series features will be reversed, or none will be reversed.
shuffle_filenames: Whether to shuffle the order of TFRecord files between
epochs.
shuffle_values_buffer: If > 0, shuffle examples using a buffer of this size.
repeat: The number of times to repeat the dataset. If None or -1 the dataset
will repeat indefinitely.
use_tpu: Whether to build the dataset for TPU.
Raises:
ValueError: If an input file pattern does not match any files, or if the
label IDs in input_config.label_map are not contiguous integers starting
at 0.
Returns:
A tf.data.Dataset object.
"""
file_patterns = file_pattern.split(",")
filenames = []
for p in file_patterns:
matches = tf.gfile.Glob(p)
if not matches:
raise ValueError("Found no input files matching {}".format(p))
filenames.extend(matches)
tf.logging.info("Building input pipeline from %d files matching patterns: %s",
len(filenames), file_patterns)
if include_labels:
# Ensure that the label ids are contiguous integers starting at 0.
label_ids = set(input_config.label_map.values())
if label_ids != set(range(len(label_ids))):
raise ValueError(
"Label IDs must be contiguous integers starting at 0. Got: {}".format(
label_ids))
# Create a HashTable mapping label strings to integer ids.
table_initializer = tf.contrib.lookup.KeyValueTensorInitializer(
keys=list(input_config.label_map.keys()),
values=list(input_config.label_map.values()),
key_dtype=tf.string,
value_dtype=tf.int32)
label_to_id = tf.contrib.lookup.HashTable(
table_initializer, default_value=-1)
def _example_parser(serialized_example):
"""Parses a single tf.Example into feature and label tensors."""
# Set specifications for parsing the features.
data_fields = {
feature_name: tf.FixedLenFeature([feature.length], tf.float32)
for feature_name, feature in input_config.features.items()
}
if include_labels:
data_fields[input_config.label_feature] = tf.FixedLenFeature([],
tf.string)
# Parse the features.
parsed_features = tf.parse_single_example(
serialized_example, features=data_fields)
if reverse_time_series_prob > 0:
# Randomly reverse time series features with probability
# reverse_time_series_prob.
should_reverse = tf.less(
tf.random_uniform([], 0, 1),
reverse_time_series_prob,
name="should_reverse")
# Reorganize outputs.
output = {}
for feature_name, value in parsed_features.items():
if include_labels and feature_name == input_config.label_feature:
label_id = label_to_id.lookup(value)
# Ensure that the label_id is nonnegative to verify a successful hash
# map lookup.
assert_known_label = tf.Assert(
tf.greater_equal(label_id, tf.to_int32(0)),
["Unknown label string:", value])
with tf.control_dependencies([assert_known_label]):
label_id = tf.identity(label_id)
# We use the plural name "labels" in the output due to batching.
output["labels"] = label_id
elif input_config.features[feature_name].is_time_series:
# Possibly reverse.
if reverse_time_series_prob > 0:
# pylint:disable=cell-var-from-loop
value = tf.cond(should_reverse, lambda: tf.reverse(value, axis=[0]),
lambda: tf.identity(value))
# pylint:enable=cell-var-from-loop
if "time_series_features" not in output:
output["time_series_features"] = {}
output["time_series_features"][feature_name] = value
else:
if "aux_features" not in output:
output["aux_features"] = {}
output["aux_features"][feature_name] = value
return output
# Create a string dataset of filenames, and possibly shuffle.
filename_dataset = tf.data.Dataset.from_tensor_slices(filenames)
if len(filenames) > 1 and shuffle_filenames:
filename_dataset = filename_dataset.shuffle(len(filenames))
# Read serialized Example protos.
dataset = filename_dataset.flat_map(tf.data.TFRecordDataset)
# Possibly shuffle. Note that we shuffle before repeat(), so we only shuffle
# elements among each "epoch" of data, and not across epochs of data.
if shuffle_values_buffer > 0:
dataset = dataset.shuffle(shuffle_values_buffer)
# Repeat.
if repeat != 1:
dataset = dataset.repeat(repeat)
# Map the parser over the dataset.
dataset = dataset.map(_example_parser, num_parallel_calls=4)
# Batch results by up to batch_size.
dataset = dataset.batch(batch_size)
if repeat == -1 or repeat is None:
# The dataset repeats infinitely before batching, so each batch has the
# maximum number of elements.
dataset = set_batch_size(dataset, batch_size)
elif use_tpu:
# TPU requires all dimensions to be fixed. Since the dataset does not repeat
# infinitely before batching, the final batch may have fewer than batch_size
# elements. Therefore we pad to ensure that the final batch has batch_size
# elements.
dataset = pad_dataset_to_batch_size(dataset, batch_size)
# Prefetch a few batches.
dataset = dataset.prefetch(max(1, int(256 / batch_size)))
return dataset
research/astronet/astronet/ops/dataset_ops_test.py deleted 100644 → 0
View file @ caafb6d1
# Copyright 2018 The TensorFlow Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for dataset_ops.py."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
from absl import flags
import numpy as np
import tensorflow as tf
from astronet.ops import dataset_ops
from tf_util import configdict
FLAGS = flags.FLAGS
flags.DEFINE_string("test_srcdir", "", "Test source directory.")
_TEST_TFRECORD_FILE = "astronet/ops/test_data/test_dataset.tfrecord"
class DatasetOpsTest(tf.test.TestCase):
def testPadTensorToBatchSize(self):
with self.test_session():
# Cannot pad a 0-dimensional Tensor.
tensor_0d = tf.constant(1)
with self.assertRaises(ValueError):
dataset_ops.pad_tensor_to_batch_size(tensor_0d, 10)
# 1-dimensional Tensor. Un-padded batch size is 5.
tensor_1d = tf.range(5, dtype=tf.int32)
self.assertEqual([5], tensor_1d.shape)
self.assertAllEqual([0, 1, 2, 3, 4], tensor_1d.eval())
tensor_1d_pad5 = dataset_ops.pad_tensor_to_batch_size(tensor_1d, 5)
self.assertEqual([5], tensor_1d_pad5.shape)
self.assertAllEqual([0, 1, 2, 3, 4], tensor_1d_pad5.eval())
tensor_1d_pad8 = dataset_ops.pad_tensor_to_batch_size(tensor_1d, 8)
self.assertEqual([8], tensor_1d_pad8.shape)
self.assertAllEqual([0, 1, 2, 3, 4, 0, 0, 0], tensor_1d_pad8.eval())
# 2-dimensional Tensor. Un-padded batch size is 3.
tensor_2d = tf.reshape(tf.range(9, dtype=tf.int32), [3, 3])
self.assertEqual([3, 3], tensor_2d.shape)
self.assertAllEqual([[0, 1, 2], [3, 4, 5], [6, 7, 8]], tensor_2d.eval())
tensor_2d_pad3 = dataset_ops.pad_tensor_to_batch_size(tensor_2d, 3)
self.assertEqual([3, 3], tensor_2d_pad3.shape)
self.assertAllEqual([[0, 1, 2], [3, 4, 5], [6, 7, 8]],
tensor_2d_pad3.eval())
tensor_2d_pad4 = dataset_ops.pad_tensor_to_batch_size(tensor_2d, 4)
self.assertEqual([4, 3], tensor_2d_pad4.shape)
self.assertAllEqual([[0, 1, 2], [3, 4, 5], [6, 7, 8], [0, 0, 0]],
tensor_2d_pad4.eval())
def testPadDatasetToBatchSizeNoWeights(self):
values = {"labels": np.arange(10, dtype=np.int32)}
dataset = tf.data.Dataset.from_tensor_slices(values).batch(4)
self.assertItemsEqual(["labels"], dataset.output_shapes.keys())
self.assertFalse(dataset.output_shapes["labels"].is_fully_defined())
dataset_pad = dataset_ops.pad_dataset_to_batch_size(dataset, 4)
self.assertItemsEqual(["labels", "weights"],
dataset_pad.output_shapes.keys())
self.assertEqual([4], dataset_pad.output_shapes["labels"])
self.assertEqual([4], dataset_pad.output_shapes["weights"])
next_batch = dataset_pad.make_one_shot_iterator().get_next()
next_labels = next_batch["labels"]
next_weights = next_batch["weights"]
with self.test_session() as sess:
labels, weights = sess.run([next_labels, next_weights])
self.assertAllEqual([0, 1, 2, 3], labels)
self.assertAllClose([1, 1, 1, 1], weights)
labels, weights = sess.run([next_labels, next_weights])
self.assertAllEqual([4, 5, 6, 7], labels)
self.assertAllClose([1, 1, 1, 1], weights)
labels, weights = sess.run([next_labels, next_weights])
self.assertAllEqual([8, 9, 0, 0], labels)
self.assertAllClose([1, 1, 0, 0], weights)
with self.assertRaises(tf.errors.OutOfRangeError):
sess.run([next_labels, next_weights])
def testPadDatasetToBatchSizeWithWeights(self):
values = {
"labels": np.arange(10, dtype=np.int32),
"weights": 100 + np.arange(10, dtype=np.int32)
}
dataset = tf.data.Dataset.from_tensor_slices(values).batch(4)
self.assertItemsEqual(["labels", "weights"], dataset.output_shapes.keys())
self.assertFalse(dataset.output_shapes["labels"].is_fully_defined())
self.assertFalse(dataset.output_shapes["weights"].is_fully_defined())
dataset_pad = dataset_ops.pad_dataset_to_batch_size(dataset, 4)
self.assertItemsEqual(["labels", "weights"],
dataset_pad.output_shapes.keys())
self.assertEqual([4], dataset_pad.output_shapes["labels"])
self.assertEqual([4], dataset_pad.output_shapes["weights"])
next_batch = dataset_pad.make_one_shot_iterator().get_next()
next_labels = next_batch["labels"]
next_weights = next_batch["weights"]
with self.test_session() as sess:
labels, weights = sess.run([next_labels, next_weights])
self.assertAllEqual([0, 1, 2, 3], labels)
self.assertAllEqual([100, 101, 102, 103], weights)
labels, weights = sess.run([next_labels, next_weights])
self.assertAllEqual([4, 5, 6, 7], labels)
self.assertAllEqual([104, 105, 106, 107], weights)
labels, weights = sess.run([next_labels, next_weights])
self.assertAllEqual([8, 9, 0, 0], labels)
self.assertAllEqual([108, 109, 0, 0], weights)
with self.assertRaises(tf.errors.OutOfRangeError):
sess.run([next_labels, next_weights])
def testSetBatchSizeSingleTensor1d(self):
dataset = tf.data.Dataset.range(4).batch(2)
self.assertFalse(dataset.output_shapes.is_fully_defined())
dataset = dataset_ops.set_batch_size(dataset, 2)
self.assertEqual([2], dataset.output_shapes)
next_batch = dataset.make_one_shot_iterator().get_next()
with self.test_session() as sess:
batch_value = sess.run(next_batch)
self.assertAllEqual([0, 1], batch_value)
batch_value = sess.run(next_batch)
self.assertAllEqual([2, 3], batch_value)
with self.assertRaises(tf.errors.OutOfRangeError):
sess.run(next_batch)
def testSetBatchSizeSingleTensor2d(self):
values = np.arange(12, dtype=np.int32).reshape([4, 3])
dataset = tf.data.Dataset.from_tensor_slices(values).batch(2)
self.assertFalse(dataset.output_shapes.is_fully_defined())
dataset = dataset_ops.set_batch_size(dataset, 2)
self.assertEqual([2, 3], dataset.output_shapes)
next_batch = dataset.make_one_shot_iterator().get_next()
with self.test_session() as sess:
batch_value = sess.run(next_batch)
self.assertAllEqual([[0, 1, 2], [3, 4, 5]], batch_value)
batch_value = sess.run(next_batch)
self.assertAllEqual([[6, 7, 8], [9, 10, 11]], batch_value)
with self.assertRaises(tf.errors.OutOfRangeError):
sess.run(next_batch)
def testSetBatchSizeNested(self):
values = {
"a": 100 + np.arange(4, dtype=np.int32),
"nest": {
"b": np.arange(12, dtype=np.int32).reshape([4, 3]),
"c": np.arange(4, dtype=np.int32)
}
}
dataset = tf.data.Dataset.from_tensor_slices(values).batch(2)
self.assertItemsEqual(["a", "nest"], dataset.output_shapes.keys())
self.assertItemsEqual(["b", "c"], dataset.output_shapes["nest"].keys())
self.assertFalse(dataset.output_shapes["a"].is_fully_defined())
self.assertFalse(dataset.output_shapes["nest"]["b"].is_fully_defined())
self.assertFalse(dataset.output_shapes["nest"]["c"].is_fully_defined())
dataset = dataset_ops.set_batch_size(dataset, 2)
self.assertItemsEqual(["a", "nest"], dataset.output_shapes.keys())
self.assertItemsEqual(["b", "c"], dataset.output_shapes["nest"].keys())
self.assertEqual([2], dataset.output_shapes["a"])
self.assertEqual([2, 3], dataset.output_shapes["nest"]["b"])
self.assertEqual([2], dataset.output_shapes["nest"]["c"])
next_batch = dataset.make_one_shot_iterator().get_next()
next_a = next_batch["a"]
next_b = next_batch["nest"]["b"]
next_c = next_batch["nest"]["c"]
with self.test_session() as sess:
a, b, c = sess.run([next_a, next_b, next_c])
self.assertAllEqual([100, 101], a)
self.assertAllEqual([[0, 1, 2], [3, 4, 5]], b)
self.assertAllEqual([0, 1], c)
a, b, c = sess.run([next_a, next_b, next_c])
self.assertAllEqual([102, 103], a)
self.assertAllEqual([[6, 7, 8], [9, 10, 11]], b)
self.assertAllEqual([2, 3], c)
with self.assertRaises(tf.errors.OutOfRangeError):
sess.run(next_batch)
class BuildDatasetTest(tf.test.TestCase):
def setUp(self):
super(BuildDatasetTest, self).setUp()
# The test dataset contains 10 tensorflow.Example protocol buffers. The i-th
# Example contains the following features:
# global_view = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]
# local_view = [0.0, 1.0, 2.0, 3.0]
# aux_feature = 100 + i
# label_str = "PC" if i % 3 == 0 else "AFP" if i % 3 == 1 else "NTP"
self._file_pattern = os.path.join(FLAGS.test_srcdir, _TEST_TFRECORD_FILE)
self._input_config = configdict.ConfigDict({
"features": {
"global_view": {
"is_time_series": True,
"length": 8
},
"local_view": {
"is_time_series": True,
"length": 4
},
"aux_feature": {
"is_time_series": False,
"length": 1
}
}
})
def testNonExistentFileRaisesValueError(self):
with self.assertRaises(ValueError):
dataset_ops.build_dataset(
file_pattern="nonexistent",
input_config=self._input_config,
batch_size=4)
def testBuildWithoutLabels(self):
dataset = dataset_ops.build_dataset(
file_pattern=self._file_pattern,
input_config=self._input_config,
batch_size=4,
include_labels=False)
# We can use a one-shot iterator without labels because we don't have the
# stateful hash map for label ids.
iterator = dataset.make_one_shot_iterator()
features = iterator.get_next()
# Expect features only.
self.assertItemsEqual(["time_series_features", "aux_features"],
features.keys())
with self.test_session() as sess:
# Batch 1.
f = sess.run(features)
np.testing.assert_array_almost_equal([
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7],
], f["time_series_features"]["global_view"])
np.testing.assert_array_almost_equal([
[0, 1, 2, 3],
[0, 1, 2, 3],
[0, 1, 2, 3],
[0, 1, 2, 3],
], f["time_series_features"]["local_view"])
np.testing.assert_array_almost_equal([[100], [101], [102], [103]],
f["aux_features"]["aux_feature"])
# Batch 2.
f = sess.run(features)
np.testing.assert_array_almost_equal([
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7],
], f["time_series_features"]["global_view"])
np.testing.assert_array_almost_equal([
[0, 1, 2, 3],
[0, 1, 2, 3],
[0, 1, 2, 3],
[0, 1, 2, 3],
], f["time_series_features"]["local_view"])
np.testing.assert_array_almost_equal([[104], [105], [106], [107]],
f["aux_features"]["aux_feature"])
# Batch 3.
f = sess.run(features)
np.testing.assert_array_almost_equal([
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7],
], f["time_series_features"]["global_view"])
np.testing.assert_array_almost_equal([
[0, 1, 2, 3],
[0, 1, 2, 3],
], f["time_series_features"]["local_view"])
np.testing.assert_array_almost_equal([[108], [109]],
f["aux_features"]["aux_feature"])
# No more batches.
with self.assertRaises(tf.errors.OutOfRangeError):
sess.run(features)
def testLabels1(self):
self._input_config["label_feature"] = "label_str"
self._input_config["label_map"] = {"PC": 0, "AFP": 1, "NTP": 2}
dataset = dataset_ops.build_dataset(
file_pattern=self._file_pattern,
input_config=self._input_config,
batch_size=4)
# We need an initializable iterator when using labels because of the
# stateful label id hash table.
iterator = dataset.make_initializable_iterator()
inputs = iterator.get_next()
init_op = tf.tables_initializer()
# Expect features and labels.
self.assertItemsEqual(["time_series_features", "aux_features", "labels"],
inputs.keys())
labels = inputs["labels"]
with self.test_session() as sess:
sess.run([init_op, iterator.initializer])
# Fetch 3 batches.
np.testing.assert_array_equal([0, 1, 2, 0], sess.run(labels))
np.testing.assert_array_equal([1, 2, 0, 1], sess.run(labels))
np.testing.assert_array_equal([2, 0], sess.run(labels))
# No more batches.
with self.assertRaises(tf.errors.OutOfRangeError):
sess.run(labels)
def testLabels2(self):
self._input_config["label_feature"] = "label_str"
self._input_config["label_map"] = {"PC": 1, "AFP": 0, "NTP": 0}
dataset = dataset_ops.build_dataset(
file_pattern=self._file_pattern,
input_config=self._input_config,
batch_size=4)
# We need an initializable iterator when using labels because of the
# stateful label id hash table.
iterator = dataset.make_initializable_iterator()
inputs = iterator.get_next()
init_op = tf.tables_initializer()
# Expect features and labels.
self.assertItemsEqual(["time_series_features", "aux_features", "labels"],
inputs.keys())
labels = inputs["labels"]
with self.test_session() as sess:
sess.run([init_op, iterator.initializer])
# Fetch 3 batches.
np.testing.assert_array_equal([1, 0, 0, 1], sess.run(labels))
np.testing.assert_array_equal([0, 0, 1, 0], sess.run(labels))
np.testing.assert_array_equal([0, 1], sess.run(labels))
# No more batches.
with self.assertRaises(tf.errors.OutOfRangeError):
sess.run(labels)
def testBadLabelIdsRaisesValueError(self):
self._input_config["label_feature"] = "label_str"
# Label ids should be contiguous integers starting at 0.
self._input_config["label_map"] = {"PC": 1, "AFP": 2, "NTP": 3}
with self.assertRaises(ValueError):
dataset_ops.build_dataset(
file_pattern=self._file_pattern,
input_config=self._input_config,
batch_size=4)
def testUnknownLabel(self):
self._input_config["label_feature"] = "label_str"
# label_map does not include "NTP".
self._input_config["label_map"] = {"PC": 1, "AFP": 0}
dataset = dataset_ops.build_dataset(
file_pattern=self._file_pattern,
input_config=self._input_config,
batch_size=4)
# We need an initializable iterator when using labels because of the
# stateful label id hash table.
iterator = dataset.make_initializable_iterator()
inputs = iterator.get_next()
init_op = tf.tables_initializer()
# Expect features and labels.
self.assertItemsEqual(["time_series_features", "aux_features", "labels"],
inputs.keys())
labels = inputs["labels"]
with self.test_session() as sess:
sess.run([init_op, iterator.initializer])
# Unknown label "NTP".
with self.assertRaises(tf.errors.InvalidArgumentError):
sess.run(labels)
def testReverseTimeSeries(self):
dataset = dataset_ops.build_dataset(
file_pattern=self._file_pattern,
input_config=self._input_config,
batch_size=4,
reverse_time_series_prob=1,
include_labels=False)
# We can use a one-shot iterator without labels because we don't have the
# stateful hash map for label ids.
iterator = dataset.make_one_shot_iterator()
features = iterator.get_next()
# Expect features only.
self.assertItemsEqual(["time_series_features", "aux_features"],
features.keys())
with self.test_session() as sess:
# Batch 1.
f = sess.run(features)
np.testing.assert_array_almost_equal([
[7, 6, 5, 4, 3, 2, 1, 0],
[7, 6, 5, 4, 3, 2, 1, 0],
[7, 6, 5, 4, 3, 2, 1, 0],
[7, 6, 5, 4, 3, 2, 1, 0],
], f["time_series_features"]["global_view"])
np.testing.assert_array_almost_equal([
[3, 2, 1, 0],
[3, 2, 1, 0],
[3, 2, 1, 0],
[3, 2, 1, 0],
], f["time_series_features"]["local_view"])
np.testing.assert_array_almost_equal([[100], [101], [102], [103]],
f["aux_features"]["aux_feature"])
# Batch 2.
f = sess.run(features)
np.testing.assert_array_almost_equal([
[7, 6, 5, 4, 3, 2, 1, 0],
[7, 6, 5, 4, 3, 2, 1, 0],
[7, 6, 5, 4, 3, 2, 1, 0],
[7, 6, 5, 4, 3, 2, 1, 0],
], f["time_series_features"]["global_view"])
np.testing.assert_array_almost_equal([
[3, 2, 1, 0],
[3, 2, 1, 0],
[3, 2, 1, 0],
[3, 2, 1, 0],
], f["time_series_features"]["local_view"])
np.testing.assert_array_almost_equal([[104], [105], [106], [107]],
f["aux_features"]["aux_feature"])
# Batch 3.
f = sess.run(features)
np.testing.assert_array_almost_equal([
[7, 6, 5, 4, 3, 2, 1, 0],
[7, 6, 5, 4, 3, 2, 1, 0],
], f["time_series_features"]["global_view"])
np.testing.assert_array_almost_equal([
[3, 2, 1, 0],
[3, 2, 1, 0],
], f["time_series_features"]["local_view"])
np.testing.assert_array_almost_equal([[108], [109]],
f["aux_features"]["aux_feature"])
# No more batches.
with self.assertRaises(tf.errors.OutOfRangeError):
sess.run(features)
def testRepeat(self):
dataset = dataset_ops.build_dataset(
file_pattern=self._file_pattern,
input_config=self._input_config,
batch_size=4,
include_labels=False)
# We can use a one-shot iterator without labels because we don't have the
# stateful hash map for label ids.
iterator = dataset.make_one_shot_iterator()
features = iterator.get_next()
# Expect features only.
self.assertItemsEqual(["time_series_features", "aux_features"],
features.keys())
with self.test_session() as sess:
# Batch 1.
f = sess.run(features)
np.testing.assert_array_almost_equal([
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7],
], f["time_series_features"]["global_view"])
np.testing.assert_array_almost_equal([
[0, 1, 2, 3],
[0, 1, 2, 3],
[0, 1, 2, 3],
[0, 1, 2, 3],
], f["time_series_features"]["local_view"])
np.testing.assert_array_almost_equal([[100], [101], [102], [103]],
f["aux_features"]["aux_feature"])
# Batch 2.
f = sess.run(features)
np.testing.assert_array_almost_equal([
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7],
], f["time_series_features"]["global_view"])
np.testing.assert_array_almost_equal([
[0, 1, 2, 3],
[0, 1, 2, 3],
[0, 1, 2, 3],
[0, 1, 2, 3],
], f["time_series_features"]["local_view"])
np.testing.assert_array_almost_equal([[104], [105], [106], [107]],
f["aux_features"]["aux_feature"])
# Batch 3.
f = sess.run(features)
np.testing.assert_array_almost_equal([
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7],
], f["time_series_features"]["global_view"])
np.testing.assert_array_almost_equal([
[0, 1, 2, 3],
[0, 1, 2, 3],
], f["time_series_features"]["local_view"])
np.testing.assert_array_almost_equal([[108], [109]],
f["aux_features"]["aux_feature"])
# No more batches.
with self.assertRaises(tf.errors.OutOfRangeError):
sess.run(features)
def testTPU(self):
dataset = dataset_ops.build_dataset(
file_pattern=self._file_pattern,
input_config=self._input_config,
batch_size=4,
include_labels=False)
# We can use a one-shot iterator without labels because we don't have the
# stateful hash map for label ids.
iterator = dataset.make_one_shot_iterator()
features = iterator.get_next()
# Expect features only.
self.assertItemsEqual(["time_series_features", "aux_features"],
features.keys())
with self.test_session() as sess:
# Batch 1.
f = sess.run(features)
np.testing.assert_array_almost_equal([
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7],
], f["time_series_features"]["global_view"])
np.testing.assert_array_almost_equal([
[0, 1, 2, 3],
[0, 1, 2, 3],
[0, 1, 2, 3],
[0, 1, 2, 3],
], f["time_series_features"]["local_view"])
np.testing.assert_array_almost_equal([[100], [101], [102], [103]],
f["aux_features"]["aux_feature"])
# Batch 2.
f = sess.run(features)
np.testing.assert_array_almost_equal([
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7],
], f["time_series_features"]["global_view"])
np.testing.assert_array_almost_equal([
[0, 1, 2, 3],
[0, 1, 2, 3],
[0, 1, 2, 3],
[0, 1, 2, 3],
], f["time_series_features"]["local_view"])
np.testing.assert_array_almost_equal([[104], [105], [106], [107]],
f["aux_features"]["aux_feature"])
# Batch 3.
f = sess.run(features)
np.testing.assert_array_almost_equal([
[0, 1, 2, 3, 4, 5, 6, 7],
[0, 1, 2, 3, 4, 5, 6, 7],
], f["time_series_features"]["global_view"])
np.testing.assert_array_almost_equal([
[0, 1, 2, 3],
[0, 1, 2, 3],
], f["time_series_features"]["local_view"])
np.testing.assert_array_almost_equal([[108], [109]],
f["aux_features"]["aux_feature"])
# No more batches.
with self.assertRaises(tf.errors.OutOfRangeError):
sess.run(features)
if __name__ == "__main__":
tf.test.main()
research/astronet/astronet/ops/input_ops.py deleted 100644 → 0
View file @ caafb6d1
# Copyright 2018 The TensorFlow Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Operations for feeding input data using TensorFlow placeholders."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
def prepare_feed_dict(model, features, labels=None, is_training=None):
"""Prepares a feed_dict for sess.run() given a batch of features and labels.
Args:
model: An instance of AstroModel.
features: Dictionary containing "time_series_features" and "aux_features".
Each is a dictionary of named numpy arrays of shape [batch_size, length].
labels: (Optional). Numpy array of shape [batch_size].
is_training: (Optional). Python boolean to feed to the model.is_training
Tensor (if None, no value is fed).
Returns:
feed_dict: A dictionary of input Tensor to numpy array.
"""
feed_dict = {}
for feature, tensor in model.time_series_features.items():
feed_dict[tensor] = features["time_series_features"][feature]
for feature, tensor in model.aux_features.items():
feed_dict[tensor] = features["aux_features"][feature]
if labels is not None:
feed_dict[model.labels] = labels
if is_training is not None:
feed_dict[model.is_training] = is_training
return feed_dict
def build_feature_placeholders(config):
"""Builds tf.Placeholder ops for feeding model features and labels.
Args:
config: ConfigDict containing the feature configurations.
Returns:
features: A dictionary containing "time_series_features" and "aux_features",
each of which is a dictionary of tf.Placeholders of features from the
input configuration. All features have dtype float32 and shape
[batch_size, length].
"""
batch_size = None # Batch size will be dynamically specified.
features = {"time_series_features": {}, "aux_features": {}}
for feature_name, feature_spec in config.items():
placeholder = tf.placeholder(
dtype=tf.float32,
shape=[batch_size, feature_spec.length],
name=feature_name)
if feature_spec.is_time_series:
features["time_series_features"][feature_name] = placeholder
else:
features["aux_features"][feature_name] = placeholder
return features
def build_labels_placeholder():
"""Builds a tf.Placeholder op for feeding model labels.
Returns:
labels: An int64 tf.Placeholder with shape [batch_size].
"""
batch_size = None # Batch size will be dynamically specified.
return tf.placeholder(dtype=tf.int64, shape=[batch_size], name="labels")
research/astronet/astronet/ops/input_ops_test.py deleted 100644 → 0
View file @ caafb6d1
# Copyright 2018 The TensorFlow Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for input_ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from astronet.ops import input_ops
from tf_util import configdict
class InputOpsTest(tf.test.TestCase):
def assertFeatureShapesEqual(self, expected_shapes, features):
"""Asserts that a dict of feature placeholders has the expected shapes.
Args:
expected_shapes: Dictionary of expected Tensor shapes, as lists,
corresponding to the structure of 'features'.
features: Dictionary of feature placeholders of the format returned by
input_ops.build_feature_placeholders().
"""
actual_shapes = {}
for feature_type in features:
actual_shapes[feature_type] = {
feature: tensor.shape.as_list()
for feature, tensor in features[feature_type].items()
}
self.assertDictEqual(expected_shapes, actual_shapes)
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)
if __name__ == "__main__":
tf.test.main()
research/astronet/astronet/ops/metrics.py deleted 100644 → 0
View file @ caafb6d1
# Copyright 2018 The TensorFlow Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Functions for computing evaluation metrics."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
def _metric_variable(name, shape, dtype):
"""Creates a Variable in LOCAL_VARIABLES and METRIC_VARIABLES collections."""
return tf.get_variable(
name,
initializer=tf.zeros(shape, dtype),
trainable=False,
collections=[tf.GraphKeys.LOCAL_VARIABLES, tf.GraphKeys.METRIC_VARIABLES])
def _build_metrics(labels, predictions, weights, batch_losses, output_dim=1):
"""Builds TensorFlow operations to compute model evaluation metrics.
Args:
labels: Tensor with shape [batch_size].
predictions: Tensor with shape [batch_size, output_dim].
weights: Tensor with shape [batch_size].
batch_losses: Tensor with shape [batch_size].
output_dim: Dimension of model output
Returns:
A dictionary {metric_name: (metric_value, update_op).
"""
# Compute the predicted labels.
assert len(predictions.shape) == 2
binary_classification = output_dim == 1
if binary_classification:
assert predictions.shape[1] == 1
predictions = tf.squeeze(predictions, axis=[1])
predicted_labels = tf.to_int32(
tf.greater(predictions, 0.5), name="predicted_labels")
else:
predicted_labels = tf.argmax(
predictions, 1, name="predicted_labels", output_type=tf.int32)
metrics = {}
with tf.variable_scope("metrics"):
# Total number of examples.
num_examples = _metric_variable("num_examples", [], tf.float32)
update_num_examples = tf.assign_add(num_examples, tf.reduce_sum(weights))
metrics["num_examples"] = (num_examples.read_value(), update_num_examples)
# Accuracy metrics.
num_correct = _metric_variable("num_correct", [], tf.float32)
is_correct = weights * tf.to_float(tf.equal(labels, predicted_labels))
update_num_correct = tf.assign_add(num_correct, tf.reduce_sum(is_correct))
metrics["accuracy/num_correct"] = (num_correct.read_value(),
update_num_correct)
accuracy = tf.div(num_correct, num_examples, name="accuracy")
metrics["accuracy/accuracy"] = (accuracy, tf.no_op())
# Weighted cross-entropy loss.
metrics["losses/weighted_cross_entropy"] = tf.metrics.mean(
batch_losses, weights=weights, name="cross_entropy_loss")
def _count_condition(name, labels_value, predicted_value):
"""Creates a counter for given values of predictions and labels."""
count = _metric_variable(name, [], tf.float32)
is_equal = tf.to_float(
tf.logical_and(
tf.equal(labels, labels_value),
tf.equal(predicted_labels, predicted_value)))
update_op = tf.assign_add(count, tf.reduce_sum(weights * is_equal))
return count.read_value(), update_op
# Confusion matrix metrics.
num_labels = 2 if binary_classification else output_dim
for gold_label in range(num_labels):
for pred_label in range(num_labels):
metric_name = "confusion_matrix/label_{}_pred_{}".format(
gold_label, pred_label)
metrics[metric_name] = _count_condition(
metric_name, labels_value=gold_label, predicted_value=pred_label)
# Possibly create AUC metric for binary classification.
if binary_classification:
labels = tf.cast(labels, dtype=tf.bool)
metrics["auc"] = tf.metrics.auc(
labels, predictions, weights=weights, num_thresholds=1000)
return metrics
def create_metric_fn(model):
"""Creates a tuple (metric_fn, metric_fn_inputs).
This function is primarily used for creating a TPUEstimator.
The result of calling metric_fn(**metric_fn_inputs) is a dictionary
{metric_name: (metric_value, update_op)}.
Args:
model: Instance of AstroModel.
Returns:
A tuple (metric_fn, metric_fn_inputs).
"""
weights = model.weights
if weights is None:
weights = tf.ones_like(model.labels, dtype=tf.float32)
metric_fn_inputs = {
"labels": model.labels,
"predictions": model.predictions,
"weights": weights,
"batch_losses": model.batch_losses,
}
def metric_fn(labels, predictions, weights, batch_losses):
return _build_metrics(
labels,
predictions,
weights,
batch_losses,
output_dim=model.hparams.output_dim)
return metric_fn, metric_fn_inputs
def create_metrics(model):
"""Creates a dictionary {metric_name: (metric_value, update_op)}.
This function is primarily used for creating an Estimator.
Args:
model: Instance of AstroModel.
Returns:
A dictionary {metric_name: (metric_value, update_op).
"""
metric_fn, metric_fn_inputs = create_metric_fn(model)
return metric_fn(**metric_fn_inputs)
research/astronet/astronet/ops/metrics_test.py deleted 100644 → 0
View file @ caafb6d1
# Copyright 2018 The TensorFlow Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for metrics.py."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from astronet.ops import metrics
def _unpack_metric_map(names_to_tuples):
"""Unpacks {metric_name: (metric_value, update_op)} into separate dicts."""
metric_names = names_to_tuples.keys()
value_ops, update_ops = zip(*names_to_tuples.values())
return dict(zip(metric_names, value_ops)), dict(zip(metric_names, update_ops))
class _MockHparams(object):
"""Mock Hparams class to support accessing with dot notation."""
pass
class _MockModel(object):
"""Mock model for testing."""
def __init__(self, labels, predictions, weights, batch_losses, output_dim):
self.labels = tf.constant(labels, dtype=tf.int32)
self.predictions = tf.constant(predictions, dtype=tf.float32)
self.weights = None if weights is None else tf.constant(
weights, dtype=tf.float32)
self.batch_losses = tf.constant(batch_losses, dtype=tf.float32)
self.hparams = _MockHparams()
self.hparams.output_dim = output_dim
class MetricsTest(tf.test.TestCase):
def testMultiClassificationWithoutWeights(self):
labels = [0, 1, 2, 3]
predictions = [
[0.7, 0.2, 0.1, 0.0], # Predicted label = 0
[0.2, 0.4, 0.2, 0.2], # Predicted label = 1
[0.0, 0.0, 0.0, 1.0], # Predicted label = 3
[0.1, 0.1, 0.7, 0.1], # Predicted label = 2
]
weights = None
batch_losses = [0, 0, 4, 2]
model = _MockModel(labels, predictions, weights, batch_losses, output_dim=4)
metric_map = metrics.create_metrics(model)
value_ops, update_ops = _unpack_metric_map(metric_map)
initializer = tf.local_variables_initializer()
with self.test_session() as sess:
sess.run(initializer)
sess.run(update_ops)
self.assertAllClose({
"num_examples": 4,
"accuracy/num_correct": 2,
"accuracy/accuracy": 0.5,
"losses/weighted_cross_entropy": 1.5,
"confusion_matrix/label_0_pred_0": 1,
"confusion_matrix/label_0_pred_1": 0,
"confusion_matrix/label_0_pred_2": 0,
"confusion_matrix/label_0_pred_3": 0,
"confusion_matrix/label_1_pred_0": 0,
"confusion_matrix/label_1_pred_1": 1,
"confusion_matrix/label_1_pred_2": 0,
"confusion_matrix/label_1_pred_3": 0,
"confusion_matrix/label_2_pred_0": 0,
"confusion_matrix/label_2_pred_1": 0,
"confusion_matrix/label_2_pred_2": 0,
"confusion_matrix/label_2_pred_3": 1,
"confusion_matrix/label_3_pred_0": 0,
"confusion_matrix/label_3_pred_1": 0,
"confusion_matrix/label_3_pred_2": 1,
"confusion_matrix/label_3_pred_3": 0
}, sess.run(value_ops))
sess.run(update_ops)
self.assertAllClose({
"num_examples": 8,
"accuracy/num_correct": 4,
"accuracy/accuracy": 0.5,
"losses/weighted_cross_entropy": 1.5,
"confusion_matrix/label_0_pred_0": 2,
"confusion_matrix/label_0_pred_1": 0,
"confusion_matrix/label_0_pred_2": 0,
"confusion_matrix/label_0_pred_3": 0,
"confusion_matrix/label_1_pred_0": 0,
"confusion_matrix/label_1_pred_1": 2,
"confusion_matrix/label_1_pred_2": 0,
"confusion_matrix/label_1_pred_3": 0,
"confusion_matrix/label_2_pred_0": 0,
"confusion_matrix/label_2_pred_1": 0,
"confusion_matrix/label_2_pred_2": 0,
"confusion_matrix/label_2_pred_3": 2,
"confusion_matrix/label_3_pred_0": 0,
"confusion_matrix/label_3_pred_1": 0,
"confusion_matrix/label_3_pred_2": 2,
"confusion_matrix/label_3_pred_3": 0
}, sess.run(value_ops))
def testMultiClassificationWithWeights(self):
labels = [0, 1, 2, 3]
predictions = [
[0.7, 0.2, 0.1, 0.0], # Predicted label = 0
[0.2, 0.4, 0.2, 0.2], # Predicted label = 1
[0.0, 0.0, 0.0, 1.0], # Predicted label = 3
[0.1, 0.1, 0.7, 0.1], # Predicted label = 2
]
weights = [0, 1, 0, 1]
batch_losses = [0, 0, 4, 2]
model = _MockModel(labels, predictions, weights, batch_losses, output_dim=4)
metric_map = metrics.create_metrics(model)
value_ops, update_ops = _unpack_metric_map(metric_map)
initializer = tf.local_variables_initializer()
with self.test_session() as sess:
sess.run(initializer)
sess.run(update_ops)
self.assertAllClose({
"num_examples": 2,
"accuracy/num_correct": 1,
"accuracy/accuracy": 0.5,
"losses/weighted_cross_entropy": 1,
"confusion_matrix/label_0_pred_0": 0,
"confusion_matrix/label_0_pred_1": 0,
"confusion_matrix/label_0_pred_2": 0,
"confusion_matrix/label_0_pred_3": 0,
"confusion_matrix/label_1_pred_0": 0,
"confusion_matrix/label_1_pred_1": 1,
"confusion_matrix/label_1_pred_2": 0,
"confusion_matrix/label_1_pred_3": 0,
"confusion_matrix/label_2_pred_0": 0,
"confusion_matrix/label_2_pred_1": 0,
"confusion_matrix/label_2_pred_2": 0,
"confusion_matrix/label_2_pred_3": 0,
"confusion_matrix/label_3_pred_0": 0,
"confusion_matrix/label_3_pred_1": 0,
"confusion_matrix/label_3_pred_2": 1,
"confusion_matrix/label_3_pred_3": 0
}, sess.run(value_ops))
sess.run(update_ops)
self.assertAllClose({
"num_examples": 4,
"accuracy/num_correct": 2,
"accuracy/accuracy": 0.5,
"losses/weighted_cross_entropy": 1,
"confusion_matrix/label_0_pred_0": 0,
"confusion_matrix/label_0_pred_1": 0,
"confusion_matrix/label_0_pred_2": 0,
"confusion_matrix/label_0_pred_3": 0,
"confusion_matrix/label_1_pred_0": 0,
"confusion_matrix/label_1_pred_1": 2,
"confusion_matrix/label_1_pred_2": 0,
"confusion_matrix/label_1_pred_3": 0,
"confusion_matrix/label_2_pred_0": 0,
"confusion_matrix/label_2_pred_1": 0,
"confusion_matrix/label_2_pred_2": 0,
"confusion_matrix/label_2_pred_3": 0,
"confusion_matrix/label_3_pred_0": 0,
"confusion_matrix/label_3_pred_1": 0,
"confusion_matrix/label_3_pred_2": 2,
"confusion_matrix/label_3_pred_3": 0
}, sess.run(value_ops))
def testBinaryClassificationWithoutWeights(self):
labels = [0, 1, 1, 0]
predictions = [
[0.4], # Predicted label = 0
[0.6], # Predicted label = 1
[0.0], # Predicted label = 0
[1.0], # Predicted label = 1
]
weights = None
batch_losses = [0, 0, 4, 2]
model = _MockModel(labels, predictions, weights, batch_losses, output_dim=1)
metric_map = metrics.create_metrics(model)
value_ops, update_ops = _unpack_metric_map(metric_map)
initializer = tf.local_variables_initializer()
with self.test_session() as sess:
sess.run(initializer)
sess.run(update_ops)
self.assertAllClose({
"num_examples": 4,
"accuracy/num_correct": 2,
"accuracy/accuracy": 0.5,
"losses/weighted_cross_entropy": 1.5,
"auc": 0.25,
"confusion_matrix/label_0_pred_0": 1,
"confusion_matrix/label_0_pred_1": 1,
"confusion_matrix/label_1_pred_0": 1,
"confusion_matrix/label_1_pred_1": 1,
}, sess.run(value_ops))
sess.run(update_ops)
self.assertAllClose({
"num_examples": 8,
"accuracy/num_correct": 4,
"accuracy/accuracy": 0.5,
"losses/weighted_cross_entropy": 1.5,
"auc": 0.25,
"confusion_matrix/label_0_pred_0": 2,
"confusion_matrix/label_0_pred_1": 2,
"confusion_matrix/label_1_pred_0": 2,
"confusion_matrix/label_1_pred_1": 2,
}, sess.run(value_ops))
def testBinaryClassificationWithWeights(self):
labels = [0, 1, 1, 0]
predictions = [
[0.4], # Predicted label = 0
[0.6], # Predicted label = 1
[0.0], # Predicted label = 0
[1.0], # Predicted label = 1
]
weights = [0, 1, 0, 1]
batch_losses = [0, 0, 4, 2]
model = _MockModel(labels, predictions, weights, batch_losses, output_dim=1)
metric_map = metrics.create_metrics(model)
value_ops, update_ops = _unpack_metric_map(metric_map)
initializer = tf.local_variables_initializer()
with self.test_session() as sess:
sess.run(initializer)
sess.run(update_ops)
self.assertAllClose({
"num_examples": 2,
"accuracy/num_correct": 1,
"accuracy/accuracy": 0.5,
"losses/weighted_cross_entropy": 1,
"auc": 0,
"confusion_matrix/label_0_pred_0": 0,
"confusion_matrix/label_0_pred_1": 1,
"confusion_matrix/label_1_pred_0": 0,
"confusion_matrix/label_1_pred_1": 1,
}, sess.run(value_ops))
sess.run(update_ops)
self.assertAllClose({
"num_examples": 4,
"accuracy/num_correct": 2,
"accuracy/accuracy": 0.5,
"losses/weighted_cross_entropy": 1,
"auc": 0,
"confusion_matrix/label_0_pred_0": 0,
"confusion_matrix/label_0_pred_1": 2,
"confusion_matrix/label_1_pred_0": 0,
"confusion_matrix/label_1_pred_1": 2,
}, sess.run(value_ops))
if __name__ == "__main__":
tf.test.main()
research/astronet/astronet/ops/testing.py deleted 100644 → 0
View file @ caafb6d1
# Copyright 2018 The TensorFlow Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TensorFlow utilities for unit tests."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
def get_variable_by_name(name, scope=""):
"""Gets a tf.Variable by name.
Args:
name: Name of the Variable within the specified scope.
scope: Variable scope; use the empty string for top-level scope.
Returns:
The matching tf.Variable object.
"""
with tf.variable_scope(scope, reuse=True):
return tf.get_variable(name)
def fake_features(feature_spec, batch_size):
"""Creates random numpy arrays representing input features for unit testing.
Args:
feature_spec: Dictionary containing the feature specifications.
batch_size: Integer batch size.
Returns:
Dictionary containing "time_series_features" and "aux_features". Each is a
dictionary of named numpy arrays of shape [batch_size, length].
"""
features = {"time_series_features": {}, "aux_features": {}}
for name, spec in feature_spec.items():
ftype = "time_series_features" if spec["is_time_series"] else "aux_features"
features[ftype][name] = np.random.random([batch_size, spec["length"]])
return features
def fake_labels(output_dim, batch_size):
"""Creates a radom numpy array representing labels for unit testing.
Args:
output_dim: Number of output units in the classification model.
batch_size: Integer batch size.
Returns:
Numpy array of shape [batch_size].
"""
# Binary classification is denoted by output_dim == 1. In that case there are
# 2 label classes even though there is only 1 output prediction by the model.
# Otherwise, the classification task is multi-labeled with output_dim classes.
num_labels = 2 if output_dim == 1 else output_dim
return np.random.randint(num_labels, size=batch_size)
research/astronet/astronet/ops/training.py deleted 100644 → 0
View file @ caafb6d1
# Copyright 2018 The TensorFlow Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Functions for training an AstroNet model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
def create_learning_rate(hparams, global_step):
"""Creates a learning rate Tensor.
Args:
hparams: ConfigDict containing the learning rate configuration.
global_step: The global step Tensor.
Returns:
A learning rate Tensor.
"""
if hparams.get("learning_rate_decay_factor"):
learning_rate = tf.train.exponential_decay(
learning_rate=float(hparams.learning_rate),
global_step=global_step,
decay_steps=hparams.learning_rate_decay_steps,
decay_rate=hparams.learning_rate_decay_factor,
staircase=hparams.learning_rate_decay_staircase)
else:
learning_rate = tf.constant(hparams.learning_rate)
return learning_rate
def create_optimizer(hparams, learning_rate, use_tpu=False):
"""Creates a TensorFlow Optimizer.
Args:
hparams: ConfigDict containing the optimizer configuration.
learning_rate: A Python float or a scalar Tensor.
use_tpu: If True, the returned optimizer is wrapped in a
CrossShardOptimizer.
Returns:
A TensorFlow optimizer.
Raises:
ValueError: If hparams.optimizer is unrecognized.
"""
optimizer_name = hparams.optimizer.lower()
if optimizer_name == "momentum":
optimizer = tf.train.MomentumOptimizer(
learning_rate,
momentum=hparams.get("momentum", 0.9),
use_nesterov=hparams.get("use_nesterov", False))
elif optimizer_name == "sgd":
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
elif optimizer_name == "adagrad":
optimizer = tf.train.AdagradOptimizer(learning_rate)
elif optimizer_name == "adam":
optimizer = tf.train.AdamOptimizer(learning_rate)
elif optimizer_name == "rmsprop":
optimizer = tf.RMSPropOptimizer(learning_rate)
else:
raise ValueError("Unknown optimizer: {}".format(hparams.optimizer))
if use_tpu:
optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)
return optimizer
def create_train_op(model, optimizer):
"""Creates a Tensor to train the model.
Args:
model: Instance of AstroModel.
optimizer: Instance of tf.train.Optimizer.
Returns:
A Tensor that runs a single training step and returns model.total_loss.
"""
# Maybe clip gradient norms.
transform_grads_fn = None
if model.hparams.get("clip_grad_norm"):
transform_grads_fn = tf.contrib.training.clip_gradient_norms_fn(
model.hparams.clip_gradient_norm)
# Create train op.
return tf.contrib.training.create_train_op(
total_loss=model.total_loss,
optimizer=optimizer,
global_step=model.global_step,
transform_grads_fn=transform_grads_fn)
research/astronet/astronet/predict.py deleted 100644 → 0
View file @ caafb6d1
# Copyright 2018 The TensorFlow Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Generate predictions for a Threshold Crossing Event using a trained model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import sys
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from astronet import models
from astronet.data import preprocess
from astronet.util import estimator_util
from tf_util import config_util
from tf_util import configdict
parser = argparse.ArgumentParser()
parser.add_argument(
"--model", type=str, required=True, help="Name of the model class.")
parser.add_argument(
"--config_name",
type=str,
help="Name of the model and training configuration. Exactly one of "
"--config_name or --config_json is required.")
parser.add_argument(
"--config_json",
type=str,
help="JSON string or JSON file containing the model and training "
"configuration. Exactly one of --config_name or --config_json is required.")
parser.add_argument(
"--model_dir",
type=str,
required=True,
help="Directory containing a model checkpoint.")
parser.add_argument(
"--kepler_data_dir",
type=str,
required=True,
help="Base folder containing Kepler data.")
parser.add_argument(
"--kepler_id",
type=int,
required=True,
help="Kepler ID of the target star.")
parser.add_argument(
"--period", type=float, required=True, help="Period of the TCE, in days.")
parser.add_argument("--t0", type=float, required=True, help="Epoch of the TCE.")
parser.add_argument(
"--duration",
type=float,
required=True,
help="Duration of the TCE, in days.")
parser.add_argument(
"--output_image_file",
type=str,
help="If specified, path to an output image file containing feature plots. "
"Must end in a valid image extension, e.g. png.")
def _process_tce(feature_config):
"""Reads and process the input features of a Threshold Crossing Event.
Args:
feature_config: ConfigDict containing the feature configurations.
Returns:
A dictionary of processed light curve features.
Raises:
ValueError: If feature_config contains features other than 'global_view'
and 'local_view'.
"""
if not {"global_view", "local_view"}.issuperset(feature_config.keys()):
raise ValueError(
"Only 'global_view' and 'local_view' features are supported.")
# Read and process the light curve.
all_time, all_flux = preprocess.read_light_curve(FLAGS.kepler_id,
FLAGS.kepler_data_dir)
time, flux = preprocess.process_light_curve(all_time, all_flux)
time, flux = preprocess.phase_fold_and_sort_light_curve(
time, flux, FLAGS.period, FLAGS.t0)
# Generate the local and global views.
features = {}
if "global_view" in feature_config:
global_view = preprocess.global_view(time, flux, FLAGS.period)
# Add a batch dimension.
features["global_view"] = np.expand_dims(global_view, 0)
if "local_view" in feature_config:
local_view = preprocess.local_view(time, flux, FLAGS.period, FLAGS.duration)
# Add a batch dimension.
features["local_view"] = np.expand_dims(local_view, 0)
# Possibly save plots.
if FLAGS.output_image_file:
ncols = len(features)
fig, axes = plt.subplots(1, ncols, figsize=(10 * ncols, 5), squeeze=False)
for i, name in enumerate(sorted(features)):
ax = axes[0][i]
ax.plot(features[name][0], ".")
ax.set_title(name)
ax.set_xlabel("Bucketized Time (days)")
ax.set_ylabel("Normalized Flux")
fig.tight_layout()
fig.savefig(FLAGS.output_image_file, bbox_inches="tight")
return features
def main(_):
model_class = models.get_model_class(FLAGS.model)
# Look up the model configuration.
assert (FLAGS.config_name is None) != (FLAGS.config_json is None), (
"Exactly one of --config_name or --config_json is required.")
config = (
models.get_model_config(FLAGS.model, FLAGS.config_name)
if FLAGS.config_name else config_util.parse_json(FLAGS.config_json))
config = configdict.ConfigDict(config)
# Create the estimator.
estimator = estimator_util.create_estimator(
model_class, config.hparams, model_dir=FLAGS.model_dir)
# Read and process the input features.
features = _process_tce(config.inputs.features)
# Create an input function.
def input_fn():
return tf.data.Dataset.from_tensors({"time_series_features": features})
# Generate the predictions.
for predictions in estimator.predict(input_fn):
assert len(predictions) == 1
print("Prediction:", predictions[0])
if __name__ == "__main__":
tf.logging.set_verbosity(tf.logging.INFO)
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
research/astronet/astronet/train.py deleted 100644 → 0
View file @ caafb6d1
# Copyright 2018 The TensorFlow Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Script for training an AstroNet model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import sys
import tensorflow as tf
from astronet import models
from astronet.util import estimator_util
from tf_util import config_util
from tf_util import configdict
from tf_util import estimator_runner
parser = argparse.ArgumentParser()
parser.add_argument(
"--model", type=str, required=True, help="Name of the model class.")
parser.add_argument(
"--config_name",
type=str,
help="Name of the model and training configuration. Exactly one of "
"--config_name or --config_json is required.")
parser.add_argument(
"--config_json",
type=str,
help="JSON string or JSON file containing the model and training "
"configuration. Exactly one of --config_name or --config_json is required.")
parser.add_argument(
"--train_files",
type=str,
required=True,
help="Comma-separated list of file patterns matching the TFRecord files in "
"the training dataset.")
parser.add_argument(
"--eval_files",
type=str,
help="Comma-separated list of file patterns matching the TFRecord files in "
"the validation dataset.")
parser.add_argument(
"--model_dir",
type=str,
required=True,
help="Directory for model checkpoints and summaries.")
parser.add_argument(
"--train_steps",
type=int,
default=625,
help="Total number of steps to train the model for.")
parser.add_argument(
"--shuffle_buffer_size",
type=int,
default=15000,
help="Size of the shuffle buffer for the training dataset.")
def main(_):
model_class = models.get_model_class(FLAGS.model)
# Look up the model configuration.
assert (FLAGS.config_name is None) != (FLAGS.config_json is None), (
"Exactly one of --config_name or --config_json is required.")
config = (
models.get_model_config(FLAGS.model, FLAGS.config_name)
if FLAGS.config_name else config_util.parse_json(FLAGS.config_json))
config = configdict.ConfigDict(config)
config_util.log_and_save_config(config, FLAGS.model_dir)
# Create the estimator.
run_config = tf.estimator.RunConfig(keep_checkpoint_max=1)
estimator = estimator_util.create_estimator(model_class, config.hparams,
run_config, FLAGS.model_dir)
# Create an input function that reads the training dataset. We iterate through
# the dataset once at a time if we are alternating with evaluation, otherwise
# we iterate infinitely.
train_input_fn = estimator_util.create_input_fn(
file_pattern=FLAGS.train_files,
input_config=config.inputs,
mode=tf.estimator.ModeKeys.TRAIN,
shuffle_values_buffer=FLAGS.shuffle_buffer_size,
repeat=1 if FLAGS.eval_files else None)
if not FLAGS.eval_files:
estimator.train(train_input_fn, max_steps=FLAGS.train_steps)
else:
eval_input_fn = estimator_util.create_input_fn(
file_pattern=FLAGS.eval_files,
input_config=config.inputs,
mode=tf.estimator.ModeKeys.EVAL)
eval_args = {
"val": (eval_input_fn, None) # eval_name: (input_fn, eval_steps)
}
for _ in estimator_runner.continuous_train_and_eval(
estimator=estimator,
train_input_fn=train_input_fn,
eval_args=eval_args,
train_steps=FLAGS.train_steps):
# continuous_train_and_eval() yields evaluation metrics after each
# training epoch. We don't do anything here.
pass
if __name__ == "__main__":
tf.logging.set_verbosity(tf.logging.INFO)
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
research/astronet/astronet/util/BUILD deleted 100644 → 0
View file @ caafb6d1
package(default_visibility = ["//visibility:public"])
licenses(["notice"]) # Apache 2.0
py_library(
name = "estimator_util",
srcs = ["estimator_util.py"],
srcs_version = "PY2AND3",
deps = [
"//astronet/ops:dataset_ops",
"//astronet/ops:metrics",
"//astronet/ops:training",
],
)
research/astronet/astronet/util/__init__.py deleted 100644 → 0
View file @ caafb6d1
# Copyright 2018 The TensorFlow Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
research/astronet/astronet/util/estimator_util.py deleted 100644 → 0
View file @ caafb6d1
# Copyright 2018 The TensorFlow Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Helper functions for creating a TensorFlow Estimator."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import tensorflow as tf
from astronet.ops import dataset_ops
from astronet.ops import metrics
from astronet.ops import training
class _InputFn(object):
"""Class that acts as a callable input function for Estimator train / eval."""
def __init__(self,
file_pattern,
input_config,
mode,
shuffle_values_buffer=0,
repeat=1):
"""Initializes the input function.
Args:
file_pattern: File pattern matching input TFRecord files, e.g.
"/tmp/train-?????-of-00100". May also be a comma-separated list of file
patterns.
input_config: ConfigDict containing feature and label specifications.
mode: A tf.estimator.ModeKeys.
shuffle_values_buffer: If > 0, shuffle examples using a buffer of this
size.
repeat: The number of times to repeat the dataset. If None or -1 the
elements will be repeated indefinitely.
"""
self._file_pattern = file_pattern
self._input_config = input_config
self._mode = mode
self._shuffle_values_buffer = shuffle_values_buffer
self._repeat = repeat
def __call__(self, config, params):
"""Builds the input pipeline."""
# Infer whether this input_fn was called by Estimator or TPUEstimator using
# the config type.
use_tpu = isinstance(config, tf.contrib.tpu.RunConfig)
mode = self._mode
include_labels = (
mode in [tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL])
reverse_time_series_prob = 0.5 if mode == tf.estimator.ModeKeys.TRAIN else 0
shuffle_filenames = (mode == tf.estimator.ModeKeys.TRAIN)
dataset = dataset_ops.build_dataset(
file_pattern=self._file_pattern,
input_config=self._input_config,
batch_size=params["batch_size"],
include_labels=include_labels,
reverse_time_series_prob=reverse_time_series_prob,
shuffle_filenames=shuffle_filenames,
shuffle_values_buffer=self._shuffle_values_buffer,
repeat=self._repeat,
use_tpu=use_tpu)
return dataset
def create_input_fn(file_pattern,
input_config,
mode,
shuffle_values_buffer=0,
repeat=1):
"""Creates an input_fn that reads a dataset from sharded TFRecord files.
Args:
file_pattern: File pattern matching input TFRecord files, e.g.
"/tmp/train-?????-of-00100". May also be a comma-separated list of file
patterns.
input_config: ConfigDict containing feature and label specifications.
mode: A tf.estimator.ModeKeys.
shuffle_values_buffer: If > 0, shuffle examples using a buffer of this size.
repeat: The number of times to repeat the dataset. If None or -1 the
elements will be repeated indefinitely.
Returns:
A callable that builds the input pipeline and returns a tf.data.Dataset
object.
"""
return _InputFn(file_pattern, input_config, mode, shuffle_values_buffer,
repeat)
class _ModelFn(object):
"""Class that acts as a callable model function for Estimator train / eval."""
def __init__(self, model_class, hparams, use_tpu=False):
"""Initializes the model function.
Args:
model_class: Model class.
hparams: ConfigDict containing hyperparameters for building and training
the model.
use_tpu: If True, a TPUEstimator will be returned. Otherwise an Estimator
will be returned.
"""
self._model_class = model_class
self._base_hparams = hparams
self._use_tpu = use_tpu
def __call__(self, features, labels, mode, params):
"""Builds the model and returns an EstimatorSpec or TPUEstimatorSpec."""
hparams = copy.deepcopy(self._base_hparams)
if "batch_size" in params:
hparams.batch_size = params["batch_size"]
# Allow labels to be passed in the features dictionary.
if "labels" in features:
if labels is not None and labels is not features["labels"]:
raise ValueError(
"Conflicting labels: features['labels'] = {}, labels = {}".format(
features["labels"], labels))
labels = features.pop("labels")
model = self._model_class(features, labels, hparams, mode)
model.build()
# Possibly create train_op.
use_tpu = self._use_tpu
train_op = None
if mode == tf.estimator.ModeKeys.TRAIN:
learning_rate = training.create_learning_rate(hparams, model.global_step)
optimizer = training.create_optimizer(hparams, learning_rate, use_tpu)
train_op = training.create_train_op(model, optimizer)
# Possibly create evaluation metrics.
eval_metrics = None
if mode == tf.estimator.ModeKeys.EVAL:
eval_metrics = (
metrics.create_metric_fn(model)
if use_tpu else metrics.create_metrics(model))
if use_tpu:
estimator = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
predictions=model.predictions,
loss=model.total_loss,
train_op=train_op,
eval_metrics=eval_metrics)
else:
estimator = tf.estimator.EstimatorSpec(
mode=mode,
predictions=model.predictions,
loss=model.total_loss,
train_op=train_op,
eval_metric_ops=eval_metrics)
return estimator
def create_model_fn(model_class, hparams, use_tpu=False):
"""Wraps model_class as an Estimator or TPUEstimator model_fn.
Args:
model_class: AstroModel or a subclass.
hparams: ConfigDict of configuration parameters for building the model.
use_tpu: If True, a TPUEstimator model_fn is returned. Otherwise an
Estimator model_fn is returned.
Returns:
model_fn: A callable that constructs the model and returns a
TPUEstimatorSpec if use_tpu is True, otherwise an EstimatorSpec.
"""
return _ModelFn(model_class, hparams, use_tpu)
def create_estimator(model_class,
hparams,
run_config=None,
model_dir=None,
eval_batch_size=None):
"""Wraps model_class as an Estimator or TPUEstimator.
If run_config is None or a tf.estimator.RunConfig, an Estimator is returned.
If run_config is a tf.contrib.tpu.RunConfig, a TPUEstimator is returned.
Args:
model_class: AstroModel or a subclass.
hparams: ConfigDict of configuration parameters for building the model.
run_config: Optional tf.estimator.RunConfig or tf.contrib.tpu.RunConfig.
model_dir: Optional directory for saving the model. If not passed
explicitly, it must be specified in run_config.
eval_batch_size: Optional batch size for evaluation on TPU. Only applicable
if run_config is a tf.contrib.tpu.RunConfig. Defaults to
hparams.batch_size.
Returns:
An Estimator object if run_config is None or a tf.estimator.RunConfig, or a
TPUEstimator object if run_config is a tf.contrib.tpu.RunConfig.
Raises:
ValueError:
If model_dir is not passed explicitly or in run_config.model_dir, or if
eval_batch_size is specified and run_config is not a
tf.contrib.tpu.RunConfig.
"""
if run_config is None:
run_config = tf.estimator.RunConfig()
else:
run_config = copy.deepcopy(run_config)
if not model_dir and not run_config.model_dir:
raise ValueError(
"model_dir must be passed explicitly or specified in run_config")
use_tpu = isinstance(run_config, tf.contrib.tpu.RunConfig)
model_fn = create_model_fn(model_class, hparams, use_tpu)
if use_tpu:
eval_batch_size = eval_batch_size or hparams.batch_size
estimator = tf.contrib.tpu.TPUEstimator(
model_fn=model_fn,
model_dir=model_dir,
config=run_config,
train_batch_size=hparams.batch_size,
eval_batch_size=eval_batch_size)
else:
if eval_batch_size is not None:
raise ValueError("eval_batch_size can only be specified for TPU.")
estimator = tf.estimator.Estimator(
model_fn=model_fn,
model_dir=model_dir,
config=run_config,
params={"batch_size": hparams.batch_size})
return estimator
research/astronet/astrowavenet/BUILD deleted 100644 → 0
View file @ caafb6d1
"""A TensorFlow model for generative modeling of light curves."""
package(default_visibility = ["//visibility:public"])
licenses(["notice"]) # Apache 2.0
py_binary(
name = "trainer",
srcs = ["trainer.py"],
srcs_version = "PY2AND3",
deps = [
":astrowavenet_model",
":configurations",
"//astrowavenet/data:kepler_light_curves",
"//astrowavenet/data:synthetic_transits",
"//astrowavenet/util:estimator_util",
"//tf_util:config_util",
"//tf_util:configdict",
"//tf_util:estimator_runner",
],
)
py_library(
name = "configurations",
srcs = ["configurations.py"],
srcs_version = "PY2AND3",
)
py_library(
name = "astrowavenet_model",
srcs = [
"astrowavenet_model.py",
],
srcs_version = "PY2AND3",
)
py_test(
name = "astrowavenet_model_test",
size = "small",
srcs = [
"astrowavenet_model_test.py",
],
srcs_version = "PY2AND3",
deps = [
":astrowavenet_model",
":configurations",
"//tf_util:configdict",
],
)
research/astronet/astrowavenet/README.md deleted 100644 → 0
View file @ caafb6d1
# AstroWaveNet: A generative model for light curves.
Implementation based on "WaveNet: A Generative Model of Raw Audio":
https://arxiv.org/abs/1609.03499
## Code Authors
Alex Tamkin: [@atamkin](https://github.com/atamkin)
Chris Shallue: [@cshallue](https://github.com/cshallue)
## Pull Requests / Issues
Chris Shallue: [@cshallue](https://github.com/cshallue)
## Additional Dependencies
In addition to the [required packages](../README.md#required-packages) listed in
the top-level README, this package requires:
* **TensorFlow 1.12 or greater** ([instructions](https://www.tensorflow.org/install/))
* **TensorFlow Probability** ([instructions](https://www.tensorflow.org/probability/install))
* **Six** ([instructions](https://pypi.org/project/six/))
## Basic Usage
To train a model on synthetic transits:
```bash
bazel build astrowavenet/...
```
```bash
bazel-bin/astrowavenet/trainer \
--dataset=synthetic_transits \
--model_dir=/tmp/astrowavenet/ \
--config_overrides='{"hparams": {"batch_size": 16, "num_residual_blocks": 2}}' \
--schedule=train_and_eval \
--eval_steps=100 \
--save_checkpoints_steps=1000
```
research/astronet/astrowavenet/__init__.py deleted 100644 → 0
View file @ caafb6d1
# Copyright 2018 The TensorFlow Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
research/astronet/astrowavenet/astrowavenet_model.py deleted 100644 → 0
View file @ caafb6d1
# Copyright 2018 The TensorFlow Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""A TensorFlow WaveNet model for generative modeling of light curves.
Implementation based on "WaveNet: A Generative Model of Raw Audio":
https://arxiv.org/abs/1609.03499
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import tensorflow_probability as tfp
def _shift_right(x):
"""Shifts the input Tensor right by one index along the second dimension.
Pads the front with zeros and discards the last element.
Args:
x: Input three-dimensional tf.Tensor.
Returns:
Padded, shifted tensor of same shape as input.
"""
x_padded = tf.pad(x, [[0, 0], [1, 0], [0, 0]])
return x_padded[:, :-1, :]
class AstroWaveNet(object):
"""A TensorFlow model for generative modeling of light curves."""
def __init__(self, features, hparams, mode):
"""Basic setup.
The actual TensorFlow graph is constructed in build().
Args:
features: A dictionary containing "autoregressive_input" and
"conditioning_stack", each of which is a named input Tensor. All
features have dtype float32 and shape [batch_size, length, dim].
hparams: A ConfigDict of hyperparameters for building the model.
mode: A tf.estimator.ModeKeys to specify whether the graph should be built
for training, evaluation or prediction.
Raises:
ValueError: If mode is invalid.
"""
valid_modes = [
tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL,
tf.estimator.ModeKeys.PREDICT
]
if mode not in valid_modes:
raise ValueError("Expected mode in {}. Got: {}".format(valid_modes, mode))
self.hparams = hparams
self.mode = mode
self.autoregressive_input = features["autoregressive_input"]
self.conditioning_stack = features["conditioning_stack"]
self.weights = features.get("weights")
self.network_output = None # Sum of skip connections from dilation stack.
self.dist_params = None # Dict of predicted distribution parameters.
self.predicted_distributions = None # Predicted distribution for examples.
self.autoregressive_target = None # Autoregressive target predictions.
self.batch_losses = None # Loss for each predicted distribution in batch.
self.per_example_loss = None # Loss for each example in batch.
self.num_nonzero_weight_examples = None # Number of examples in batch.
self.total_loss = None # Overall loss for the batch.
self.global_step = None # Global step Tensor.
def causal_conv_layer(self, x, output_size, kernel_width, dilation_rate=1):
"""Applies a dialated causal convolution to the input.
Args:
x: tf.Tensor; Input tensor.
output_size: int; Number of output filters for the convolution.
kernel_width: int; Width of the 1D convolution window.
dilation_rate: int; Dilation rate of the layer.
Returns:
Resulting tf.Tensor after applying the convolution.
"""
causal_conv_op = tf.keras.layers.Conv1D(
output_size,
kernel_width,
padding="causal",
dilation_rate=dilation_rate,
name="causal_conv")
return causal_conv_op(x)
def conv_1x1_layer(self, x, output_size, activation=None):
"""Applies a 1x1 convolution to the input.
Args:
x: tf.Tensor; Input tensor.
output_size: int; Number of output filters for the 1x1 convolution.
activation: Activation function to apply (e.g. 'relu').
Returns:
Resulting tf.Tensor after applying the 1x1 convolution.
"""
conv_1x1_op = tf.keras.layers.Conv1D(
output_size, 1, activation=activation, name="conv1x1")
return conv_1x1_op(x)
def gated_residual_layer(self, x, dilation_rate):
"""Creates a gated, dilated convolutional layer with a residual connnection.
Args:
x: tf.Tensor; Input tensor
dilation_rate: int; Dilation rate of the layer.
Returns:
skip_connection: tf.Tensor; Skip connection to network_output layer.
residual_connection: tf.Tensor; Sum of learned residual and input tensor.
"""
with tf.variable_scope("filter"):
x_filter_conv = self.causal_conv_layer(x, x.shape[-1].value,
self.hparams.dilation_kernel_width,
dilation_rate)
cond_filter_conv = self.conv_1x1_layer(self.conditioning_stack,
x.shape[-1].value)
with tf.variable_scope("gate"):
x_gate_conv = self.causal_conv_layer(x, x.shape[-1].value,
self.hparams.dilation_kernel_width,
dilation_rate)
cond_gate_conv = self.conv_1x1_layer(self.conditioning_stack,
x.shape[-1].value)
gated_activation = (
tf.tanh(x_filter_conv + cond_filter_conv) *
tf.sigmoid(x_gate_conv + cond_gate_conv))
with tf.variable_scope("residual"):
residual = self.conv_1x1_layer(gated_activation, x.shape[-1].value)
with tf.variable_scope("skip"):
skip_connection = self.conv_1x1_layer(gated_activation,
self.hparams.skip_output_dim)
return skip_connection, x + residual
def build_network(self):
"""Builds WaveNet network.
This consists of:
1) An initial causal convolution,
2) The dialation stack, and
3) Summing of skip connections
The network output can then be used to predict various output distributions.
Inputs:
self.autoregressive_input
self.conditioning_stack
Outputs:
self.network_output; tf.Tensor
"""
skip_connections = []
x = _shift_right(self.autoregressive_input)
with tf.variable_scope("preprocess"):
x = self.causal_conv_layer(x, self.hparams.preprocess_output_size,
self.hparams.preprocess_kernel_width)
for i in range(self.hparams.num_residual_blocks):
with tf.variable_scope("block_{}".format(i)):
for dilation_rate in self.hparams.dilation_rates:
with tf.variable_scope("dilation_{}".format(dilation_rate)):
skip_connection, x = self.gated_residual_layer(x, dilation_rate)
skip_connections.append(skip_connection)
self.network_output = tf.add_n(skip_connections)
def dist_params_layer(self, x, outputs_size):
"""Converts x to the correct shape for populating a distribution object.
Args:
x: A Tensor of shape [batch_size, time_series_length, num_features].
outputs_size: The number of parameters needed to specify all the
distributions in the output. E.g. 5*3=15 to specify 5 distributions with
3 parameters each.
Returns:
The parameters of each distribution, a tensor of shape [batch_size,
time_series_length, outputs_size].
"""
with tf.variable_scope("dist_params"):
conv_outputs = self.conv_1x1_layer(x, outputs_size)
return conv_outputs
def build_predictions(self):
"""Predicts output distribution from network outputs.
Runs the model through:
1) ReLU
2) 1x1 convolution
3) ReLU
4) 1x1 convolution
The result of the last convolution is used as the parameters of the
specified output distribution (currently either Categorical or Normal).
Inputs:
self.network_outputs
Outputs:
self.dist_params
self.predicted_distributions
Raises:
ValueError: If distribution type is neither 'categorical' nor 'normal'.
"""
with tf.variable_scope("postprocess"):
network_output = tf.keras.activations.relu(self.network_output)
network_output = self.conv_1x1_layer(
network_output,
output_size=network_output.shape[-1].value,
activation="relu")
num_dists = self.autoregressive_input.shape[-1].value
if self.hparams.output_distribution.type == "categorical":
num_classes = self.hparams.output_distribution.num_classes
logits = self.dist_params_layer(network_output, num_dists * num_classes)
logits_shape = tf.concat(
[tf.shape(network_output)[:-1], [num_dists, num_classes]], 0)
logits = tf.reshape(logits, logits_shape)
dist = tfp.distributions.Categorical(logits=logits)
dist_params = {"logits": logits}
elif self.hparams.output_distribution.type == "normal":
loc_scale = self.dist_params_layer(network_output, num_dists * 2)
loc, scale = tf.split(loc_scale, 2, axis=-1)
# Ensure scale is positive.
scale = tf.nn.softplus(scale) + self.hparams.output_distribution.min_scale
dist = tfp.distributions.Normal(loc, scale)
dist_params = {"loc": loc, "scale": scale}
else:
raise ValueError("Unsupported distribution type {}".format(
self.hparams.output_distribution.type))
self.dist_params = dist_params
self.predicted_distributions = dist
def build_losses(self):
"""Builds the training losses.
Inputs:
self.predicted_distributions
Outputs:
self.batch_losses
self.total_loss
"""
autoregressive_target = self.autoregressive_input
# Quantize the target if the output distribution is categorical.
if self.hparams.output_distribution.type == "categorical":
min_val = self.hparams.output_distribution.min_quantization_value
max_val = self.hparams.output_distribution.max_quantization_value
num_classes = self.hparams.output_distribution.num_classes
clipped_target = tf.keras.backend.clip(autoregressive_target, min_val,
max_val)
quantized_target = tf.floor(
(clipped_target - min_val) / (max_val - min_val) * num_classes)
# Deal with the corner case where clipped_target equals max_val by mapping
# the label num_classes to num_classes - 1. Essentially, this makes the
# final quantized bucket a closed interval while all the other quantized
# buckets are half-open intervals.
quantized_target = tf.where(
quantized_target >= num_classes,
tf.ones_like(quantized_target) * (num_classes - 1), quantized_target)
autoregressive_target = quantized_target
log_prob = self.predicted_distributions.log_prob(autoregressive_target)
weights = self.weights
if weights is None:
weights = tf.ones_like(log_prob)
weights_dim = len(weights.shape)
per_example_weight = tf.reduce_sum(
weights, axis=list(range(1, weights_dim)))
per_example_indicator = tf.to_float(tf.greater(per_example_weight, 0))
num_examples = tf.reduce_sum(per_example_indicator)
batch_losses = -log_prob * weights
losses_ndims = batch_losses.shape.ndims
per_example_loss_sum = tf.reduce_sum(
batch_losses, axis=list(range(1, losses_ndims)))
per_example_loss = tf.where(per_example_weight > 0,
per_example_loss_sum / per_example_weight,
tf.zeros_like(per_example_weight))
total_loss = tf.reduce_sum(per_example_loss) / num_examples
self.autoregressive_target = autoregressive_target
self.batch_losses = batch_losses
self.per_example_loss = per_example_loss
self.num_nonzero_weight_examples = num_examples
self.total_loss = total_loss
def build(self):
"""Creates all ops for training, evaluation or inference."""
self.global_step = tf.train.get_or_create_global_step()
self.build_network()
self.build_predictions()
if self.mode in [tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL]:
self.build_losses()
research/astronet/astrowavenet/astrowavenet_model_test.py deleted 100644 → 0
View file @ caafb6d1
# Copyright 2018 The TensorFlow Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for astrowavenet."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from astrowavenet import astrowavenet_model
from tf_util import configdict
class AstrowavenetTest(tf.test.TestCase):
def assertShapeEquals(self, shape, tensor_or_array):
"""Asserts that a Tensor or Numpy array has the expected shape.
Args:
shape: Numpy array or anything that can be converted to one.
tensor_or_array: tf.Tensor, tf.Variable, or Numpy array.
"""
if isinstance(tensor_or_array, (np.ndarray, np.generic)):
self.assertAllEqual(shape, tensor_or_array.shape)
elif isinstance(tensor_or_array, (tf.Tensor, tf.Variable)):
self.assertAllEqual(shape, tensor_or_array.shape.as_list())
else:
raise TypeError("tensor_or_array must be a Tensor or Numpy ndarray")
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_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(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,
}
})
model = astrowavenet_model.AstroWaveNet(features, hparams, mode)
model.build()
# Model predicts loc and scale.
self.assertItemsEqual(["loc", "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"])
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]],
],
}
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, 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)
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_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_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))
if __name__ == "__main__":
tf.test.main()
research/astronet/astrowavenet/configurations.py deleted 100644 → 0
View file @ caafb6d1
# Copyright 2018 The TensorFlow Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Configurations for model building, training and evaluation."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
def base():
"""Returns the base config for model building, training and evaluation."""
return {
# Hyperparameters for building and training the model.
"hparams": {
"batch_size": 64,
"dilation_kernel_width": 2,
"skip_output_dim": 10,
"preprocess_output_size": 3,
"preprocess_kernel_width": 10,
"num_residual_blocks": 4,
"dilation_rates": [1, 2, 4, 8, 16],
"output_distribution": {
"type": "normal",
"min_scale": 0.001
},
# Learning rate parameters.
"learning_rate": 1e-6,
"learning_rate_decay_steps": 0,
"learning_rate_decay_factor": 0,
"learning_rate_decay_staircase": True,
# Optimizer for training the model.
"optimizer": "adam",
# If not None, gradient norms will be clipped to this value.
"clip_gradient_norm": 1,
}
}
def categorical():
"""Returns a config for models with a categorical output distribution.
Input values will be clipped to {min,max}_value_for_quantization, then
linearly split into num_classes.
"""
config = base()
config["hparams"]["output_distribution"] = {
"type": "categorical",
"num_classes": 256,
"min_quantization_value": -1,
"max_quantization_value": 1
}
return config
def get_config(config_name):
"""Returns config correspnding to provided name."""
if config_name in ["base", "normal"]:
return base()
elif config_name == "categorical":
return categorical()
else:
raise ValueError("Unrecognized config name: {}".format(config_name))
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