Initial move of tf.contrib.distributions to core:
* Move distribution_utils * Move base distribution classes * Move Bijector class * Move a unit test just to establish the directory structure. Change: 154426175
This commit is contained in:
Eugene Brevdo
TensorFlower Gardener
parent
15a275969a
commit
0db6371fd8
@ -307,6 +307,8 @@ filegroup(
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"//tensorflow/python/debug:all_files",
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"//tensorflow/python/estimator:all_files",
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"//tensorflow/python/kernel_tests:all_files",
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"//tensorflow/python/kernel_tests/distributions:all_files",
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"//tensorflow/python/ops/distributions:all_files",
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"//tensorflow/python/saved_model:all_files",
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"//tensorflow/python/tools:all_files",
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"//tensorflow/tensorboard:all_files",
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@ -48,9 +48,9 @@ import threading
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import six
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from tensorflow.contrib.bayesflow.python.ops import stochastic_gradient_estimators as sge
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from tensorflow.contrib.distributions.python.ops import distribution
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops.distributions import distribution
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STOCHASTIC_TENSOR_COLLECTION = "_stochastic_tensor_collection_"
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@ -28,10 +28,10 @@ from __future__ import print_function
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from tensorflow.contrib.bayesflow.python.ops import stochastic_graph_impl as sg
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from tensorflow.contrib.bayesflow.python.ops import stochastic_tensor_impl as st
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from tensorflow.contrib.distributions.python.ops import distribution
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from tensorflow.contrib.distributions.python.ops import kullback_leibler
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops.distributions import distribution
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from tensorflow.python.platform import tf_logging as logging
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VI_PRIORS = "__vi_priors__"
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@ -206,11 +206,13 @@ add_python_module("tensorflow/python/estimator/inputs/queues")
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add_python_module("tensorflow/python/framework")
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add_python_module("tensorflow/python/grappler")
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add_python_module("tensorflow/python/kernel_tests")
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add_python_module("tensorflow/python/kernel_tests/distributions")
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add_python_module("tensorflow/python/layers")
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add_python_module("tensorflow/python/lib")
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add_python_module("tensorflow/python/lib/core")
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add_python_module("tensorflow/python/lib/io")
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add_python_module("tensorflow/python/ops")
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add_python_module("tensorflow/python/ops/distributions")
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add_python_module("tensorflow/python/ops/losses")
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add_python_module("tensorflow/python/platform")
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add_python_module("tensorflow/python/platform/default")
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@ -29,6 +29,7 @@ py_library(
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"//tensorflow/python:nn_ops",
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"//tensorflow/python:random_ops",
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"//tensorflow/python:special_math_ops",
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"//tensorflow/python/ops/distributions",
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"//third_party/py/numpy",
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"@six_archive//:six",
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],
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@ -54,6 +55,7 @@ py_library(
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"//tensorflow/python:nn_ops",
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"//tensorflow/python:random_ops",
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"//tensorflow/python:special_math_ops",
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"//tensorflow/python/ops/distributions",
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"//third_party/py/numpy",
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"@six_archive//:six",
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],
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@ -813,25 +815,6 @@ filegroup(
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# === Bijector Tests ==========================================================
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cuda_py_test(
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name = "bijector_test",
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size = "small",
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srcs = ["python/kernel_tests/bijectors/bijector_test.py"],
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additional_deps = [
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":bijectors_py",
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":distributions_py",
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"//third_party/py/numpy",
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"@six_archive//:six",
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"//tensorflow/contrib/linalg:linalg_py",
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"//tensorflow/python:array_ops",
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"//tensorflow/python:client_testlib",
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"//tensorflow/python:framework_for_generated_wrappers",
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"//tensorflow/python:framework_test_lib",
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"//tensorflow/python:math_ops",
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"//tensorflow/python:platform_test",
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],
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)
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cuda_py_test(
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name = "conditional_bijector_test",
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size = "small",
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@ -96,12 +96,10 @@ from tensorflow.contrib.distributions.python.ops.beta import *
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from tensorflow.contrib.distributions.python.ops.binomial import *
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from tensorflow.contrib.distributions.python.ops.categorical import *
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from tensorflow.contrib.distributions.python.ops.chi2 import *
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from tensorflow.contrib.distributions.python.ops.conditional_distribution import *
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from tensorflow.contrib.distributions.python.ops.conditional_transformed_distribution import *
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from tensorflow.contrib.distributions.python.ops.deterministic import *
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from tensorflow.contrib.distributions.python.ops.dirichlet import *
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from tensorflow.contrib.distributions.python.ops.dirichlet_multinomial import *
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from tensorflow.contrib.distributions.python.ops.distribution import *
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from tensorflow.contrib.distributions.python.ops.distribution_util import matrix_diag_transform
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from tensorflow.contrib.distributions.python.ops.distribution_util import softplus_inverse
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from tensorflow.contrib.distributions.python.ops.exponential import *
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@ -129,6 +127,8 @@ from tensorflow.contrib.distributions.python.ops.student_t import *
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from tensorflow.contrib.distributions.python.ops.transformed_distribution import *
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from tensorflow.contrib.distributions.python.ops.uniform import *
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from tensorflow.contrib.distributions.python.ops.wishart import *
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from tensorflow.python.ops.distributions.conditional_distribution import *
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from tensorflow.python.ops.distributions.distribution import *
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# pylint: enable=unused-import,wildcard-import,line-too-long,g-importing-member
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@ -20,10 +20,10 @@ from __future__ import print_function
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import numpy as np
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from scipy import stats
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from tensorflow.contrib.distributions.python.ops import distribution
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from tensorflow.contrib.distributions.python.ops import logistic
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.ops.distributions import distribution
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from tensorflow.python.platform import test
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@ -19,16 +19,14 @@ from __future__ import print_function
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import numpy as np
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from tensorflow.contrib import distributions as distributions_lib
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from tensorflow.contrib.distributions.python.ops import distribution_util
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from tensorflow.contrib import distributions
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from tensorflow.contrib.distributions.python.ops import operator_pd_cholesky
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import nn_ops
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from tensorflow.python.ops.distributions import util as distribution_util
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from tensorflow.python.platform import test
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distributions = distributions_lib
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def softplus(x):
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return np.log(1 + np.exp(x))
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@ -18,8 +18,6 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.contrib.distributions.python.ops import distribution
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from tensorflow.contrib.distributions.python.ops import distribution_util
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from tensorflow.contrib.distributions.python.ops import kullback_leibler
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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@ -30,6 +28,8 @@ from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import nn
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from tensorflow.python.ops import random_ops
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from tensorflow.python.ops.distributions import distribution
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from tensorflow.python.ops.distributions import util as distribution_util
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class Bernoulli(distribution.Distribution):
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@ -20,8 +20,6 @@ from __future__ import print_function
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import numpy as np
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from tensorflow.contrib.distributions.python.ops import distribution
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from tensorflow.contrib.distributions.python.ops import distribution_util
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from tensorflow.contrib.distributions.python.ops import kullback_leibler
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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@ -33,6 +31,8 @@ from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import nn
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from tensorflow.python.ops import random_ops
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from tensorflow.python.ops.distributions import distribution
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from tensorflow.python.ops.distributions import util as distribution_util
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__all__ = [
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@ -39,7 +39,6 @@ from __future__ import print_function
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from tensorflow.contrib.distributions.python.ops.bijectors.affine import *
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from tensorflow.contrib.distributions.python.ops.bijectors.affine_linear_operator import *
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from tensorflow.contrib.distributions.python.ops.bijectors.bijector import *
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from tensorflow.contrib.distributions.python.ops.bijectors.chain import *
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from tensorflow.contrib.distributions.python.ops.bijectors.cholesky_outer_product import *
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from tensorflow.contrib.distributions.python.ops.bijectors.conditional_bijector import *
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@ -52,6 +51,7 @@ from tensorflow.contrib.distributions.python.ops.bijectors.sigmoid import *
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from tensorflow.contrib.distributions.python.ops.bijectors.sigmoid_centered import *
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from tensorflow.contrib.distributions.python.ops.bijectors.softmax_centered import *
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from tensorflow.contrib.distributions.python.ops.bijectors.softplus import *
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from tensorflow.python.ops.distributions.bijector import *
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# pylint: enable=unused-import,wildcard-import,line-too-long,g-importing-member
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@ -22,7 +22,6 @@ from tensorflow.contrib.distributions.python.ops import operator_pd_cholesky
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from tensorflow.contrib.distributions.python.ops import operator_pd_diag
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from tensorflow.contrib.distributions.python.ops import operator_pd_identity
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from tensorflow.contrib.distributions.python.ops import operator_pd_vdvt_update
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from tensorflow.contrib.distributions.python.ops.bijectors import bijector
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from tensorflow.contrib.distributions.python.ops.shape import _DistributionShape
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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@ -32,6 +31,7 @@ from tensorflow.python.ops import check_ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import linalg_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops.distributions import bijector
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__all__ = [
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@ -18,7 +18,6 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.contrib.distributions.python.ops.bijectors import bijector
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from tensorflow.contrib.distributions.python.ops.shape import _DistributionShape
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from tensorflow.contrib.linalg.python.ops import linear_operator
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from tensorflow.python.framework import constant_op
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@ -27,6 +26,7 @@ from tensorflow.python.framework import ops
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from tensorflow.python.framework import tensor_util
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from tensorflow.python.ops import check_ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops.distributions import bijector
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__all__ = [
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@ -20,8 +20,8 @@ from __future__ import print_function
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import itertools
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from tensorflow.contrib.distributions.python.ops.bijectors import bijector
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from tensorflow.python.framework import constant_op
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from tensorflow.python.ops.distributions import bijector
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__all__ = [
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@ -20,8 +20,6 @@ from __future__ import print_function
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import numpy as np
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from tensorflow.contrib.distributions.python.ops import distribution_util
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from tensorflow.contrib.distributions.python.ops.bijectors import bijector
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import tensor_util
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from tensorflow.python.ops import array_ops
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@ -29,6 +27,8 @@ from tensorflow.python.ops import check_ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import linalg_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops.distributions import bijector
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from tensorflow.python.ops.distributions import util as distribution_util
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__all__ = [
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@ -18,8 +18,8 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.contrib.distributions.python.ops import distribution_util
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from tensorflow.contrib.distributions.python.ops.bijectors import bijector
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from tensorflow.python.ops.distributions import bijector
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from tensorflow.python.ops.distributions import util as distribution_util
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__all__ = ["ConditionalBijector"]
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@ -18,8 +18,8 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.contrib.distributions.python.ops.bijectors import bijector
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from tensorflow.python.framework import constant_op
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from tensorflow.python.ops.distributions import bijector
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__all__ = [
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@ -18,7 +18,7 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.contrib.distributions.python.ops.bijectors import bijector
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from tensorflow.python.ops.distributions import bijector
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__all__ = [
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@ -18,7 +18,7 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.contrib.distributions.python.ops.bijectors import bijector as bijector_lib
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from tensorflow.python.ops.distributions import bijector as bijector_lib
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__all__ = [
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"Invert",
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@ -18,12 +18,12 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.contrib.distributions.python.ops.bijectors import bijector
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import tensor_util
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from tensorflow.python.ops import check_ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops.distributions import bijector
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__all__ = [
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@ -18,9 +18,9 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.contrib.distributions.python.ops.bijectors import bijector
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import nn_ops
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from tensorflow.python.ops.distributions import bijector
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__all__ = [
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@ -20,7 +20,6 @@ from __future__ import print_function
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import numpy as np
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from tensorflow.contrib.distributions.python.ops.bijectors import bijector
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import tensor_shape
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@ -30,6 +29,7 @@ from tensorflow.python.ops import check_ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import nn_ops
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from tensorflow.python.ops.distributions import bijector
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__all__ = [
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@ -18,10 +18,10 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.contrib.distributions.python.ops import distribution_util
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from tensorflow.contrib.distributions.python.ops.bijectors import bijector
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import nn_ops
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from tensorflow.python.ops.distributions import bijector
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from tensorflow.python.ops.distributions import util as distribution_util
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__all__ = [
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@ -17,8 +17,6 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.contrib.distributions.python.ops import distribution
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from tensorflow.contrib.distributions.python.ops import distribution_util
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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@ -27,6 +25,8 @@ from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import check_ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops.distributions import distribution
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from tensorflow.python.ops.distributions import util as distribution_util
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_binomial_sample_note = """
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@ -18,8 +18,6 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.contrib.distributions.python.ops import distribution
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from tensorflow.contrib.distributions.python.ops import distribution_util
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from tensorflow.contrib.distributions.python.ops import kullback_leibler
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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@ -29,6 +27,8 @@ from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import nn_ops
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from tensorflow.python.ops import random_ops
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from tensorflow.python.ops.distributions import distribution
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from tensorflow.python.ops.distributions import util as distribution_util
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class Categorical(distribution.Distribution):
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@ -17,10 +17,10 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.contrib.distributions.python.ops import conditional_distribution
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from tensorflow.contrib.distributions.python.ops import distribution_util
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from tensorflow.contrib.distributions.python.ops import transformed_distribution
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops.distributions import conditional_distribution
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from tensorflow.python.ops.distributions import util as distribution_util
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# pylint: disable=protected-access
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@ -22,7 +22,6 @@ import abc
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import six
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from tensorflow.contrib.distributions.python.ops import distribution
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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@ -32,6 +31,7 @@ from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import check_ops
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from tensorflow.python.ops import control_flow_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops.distributions import distribution
|
||||
|
||||
__all__ = [
|
||||
"Deterministic",
|
||||
|
||||
@ -20,8 +20,6 @@ from __future__ import print_function
|
||||
|
||||
import numpy as np
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import distribution
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
from tensorflow.python.framework import ops
|
||||
from tensorflow.python.ops import array_ops
|
||||
from tensorflow.python.ops import check_ops
|
||||
@ -29,6 +27,8 @@ from tensorflow.python.ops import control_flow_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import random_ops
|
||||
from tensorflow.python.ops import special_math_ops
|
||||
from tensorflow.python.ops.distributions import distribution
|
||||
from tensorflow.python.ops.distributions import util as distribution_util
|
||||
|
||||
|
||||
__all__ = [
|
||||
|
||||
@ -18,8 +18,6 @@ from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import distribution
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
from tensorflow.python.ops import array_ops
|
||||
@ -28,6 +26,8 @@ from tensorflow.python.ops import control_flow_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import random_ops
|
||||
from tensorflow.python.ops import special_math_ops
|
||||
from tensorflow.python.ops.distributions import distribution
|
||||
from tensorflow.python.ops.distributions import util as distribution_util
|
||||
|
||||
|
||||
__all__ = [
|
||||
|
||||
@ -18,619 +18,14 @@ from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import functools
|
||||
import hashlib
|
||||
import math
|
||||
import numpy as np
|
||||
|
||||
from tensorflow.contrib import linalg
|
||||
from tensorflow.python.framework import constant_op
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
from tensorflow.python.framework import tensor_shape
|
||||
from tensorflow.python.framework import tensor_util
|
||||
from tensorflow.python.ops import array_ops
|
||||
from tensorflow.python.ops import check_ops
|
||||
from tensorflow.python.ops import control_flow_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import nn
|
||||
|
||||
|
||||
def assert_close(
|
||||
x, y, data=None, summarize=None, message=None, name="assert_close"):
|
||||
"""Assert that that x and y are within machine epsilon of each other.
|
||||
|
||||
Args:
|
||||
x: Floating-point `Tensor`
|
||||
y: Floating-point `Tensor`
|
||||
data: The tensors to print out if the condition is `False`. Defaults to
|
||||
error message and first few entries of `x` and `y`.
|
||||
summarize: Print this many entries of each tensor.
|
||||
message: A string to prefix to the default message.
|
||||
name: A name for this operation (optional).
|
||||
|
||||
Returns:
|
||||
Op raising `InvalidArgumentError` if |x - y| > machine epsilon.
|
||||
"""
|
||||
message = message or ""
|
||||
x = ops.convert_to_tensor(x, name="x")
|
||||
y = ops.convert_to_tensor(y, name="y")
|
||||
|
||||
if data is None:
|
||||
data = [
|
||||
message,
|
||||
"Condition x ~= y did not hold element-wise: x = ", x.name, x, "y = ",
|
||||
y.name, y
|
||||
]
|
||||
|
||||
if x.dtype.is_integer:
|
||||
return check_ops.assert_equal(
|
||||
x, y, data=data, summarize=summarize, message=message, name=name)
|
||||
|
||||
with ops.name_scope(name, "assert_close", [x, y, data]):
|
||||
tol = np.finfo(x.dtype.as_numpy_dtype).eps
|
||||
condition = math_ops.reduce_all(math_ops.less_equal(math_ops.abs(x-y), tol))
|
||||
return control_flow_ops.Assert(
|
||||
condition, data, summarize=summarize)
|
||||
|
||||
|
||||
def assert_integer_form(
|
||||
x, data=None, summarize=None, message=None, name="assert_integer_form"):
|
||||
"""Assert that x has integer components (or floats equal to integers).
|
||||
|
||||
Args:
|
||||
x: Floating-point `Tensor`
|
||||
data: The tensors to print out if the condition is `False`. Defaults to
|
||||
error message and first few entries of `x` and `y`.
|
||||
summarize: Print this many entries of each tensor.
|
||||
message: A string to prefix to the default message.
|
||||
name: A name for this operation (optional).
|
||||
|
||||
Returns:
|
||||
Op raising `InvalidArgumentError` if round(x) != x.
|
||||
"""
|
||||
|
||||
message = message or "x has non-integer components"
|
||||
x = ops.convert_to_tensor(x, name="x")
|
||||
casted_x = math_ops.to_int64(x)
|
||||
return check_ops.assert_equal(
|
||||
x, math_ops.cast(math_ops.round(casted_x), x.dtype),
|
||||
data=data, summarize=summarize, message=message, name=name)
|
||||
|
||||
|
||||
def assert_symmetric(matrix):
|
||||
matrix_t = array_ops.matrix_transpose(matrix)
|
||||
return control_flow_ops.with_dependencies(
|
||||
[check_ops.assert_equal(matrix, matrix_t)], matrix)
|
||||
|
||||
|
||||
def embed_check_nonnegative_discrete(x, check_integer=True):
|
||||
"""Assert x is a non-negative tensor, and optionally of integers."""
|
||||
assertions = [check_ops.assert_non_negative(
|
||||
x, message="x must be non-negative.")]
|
||||
if check_integer:
|
||||
assertions += [assert_integer_form(
|
||||
x, message="x cannot contain fractional components.")]
|
||||
return control_flow_ops.with_dependencies(assertions, x)
|
||||
|
||||
|
||||
def same_dynamic_shape(a, b):
|
||||
"""Returns whether a and b have the same dynamic shape.
|
||||
|
||||
Args:
|
||||
a: `Tensor`
|
||||
b: `Tensor`
|
||||
|
||||
Returns:
|
||||
`bool` `Tensor` representing if both tensors have the same shape.
|
||||
"""
|
||||
a = ops.convert_to_tensor(a, name="a")
|
||||
b = ops.convert_to_tensor(b, name="b")
|
||||
|
||||
# Here we can't just do math_ops.equal(a.shape, b.shape), since
|
||||
# static shape inference may break the equality comparison between
|
||||
# shape(a) and shape(b) in math_ops.equal.
|
||||
def all_shapes_equal():
|
||||
return math_ops.reduce_all(math_ops.equal(
|
||||
array_ops.concat([array_ops.shape(a), array_ops.shape(b)], 0),
|
||||
array_ops.concat([array_ops.shape(b), array_ops.shape(a)], 0)))
|
||||
|
||||
# One of the shapes isn't fully defined, so we need to use the dynamic
|
||||
# shape.
|
||||
return control_flow_ops.cond(
|
||||
math_ops.equal(array_ops.rank(a), array_ops.rank(b)),
|
||||
all_shapes_equal,
|
||||
lambda: constant_op.constant(False))
|
||||
|
||||
|
||||
def get_logits_and_probs(logits=None,
|
||||
probs=None,
|
||||
multidimensional=False,
|
||||
validate_args=False,
|
||||
name="get_logits_and_probs"):
|
||||
"""Converts logit to probabilities (or vice-versa), and returns both.
|
||||
|
||||
Args:
|
||||
logits: Floating-point `Tensor` representing log-odds.
|
||||
probs: Floating-point `Tensor` representing probabilities.
|
||||
multidimensional: Python `bool`, default `False`.
|
||||
If `True`, represents whether the last dimension of `logits` or `probs`,
|
||||
a `[N1, N2, ... k]` dimensional tensor, representing the
|
||||
logit or probability of `shape[-1]` classes.
|
||||
validate_args: Python `bool`, default `False`. When `True`, either assert
|
||||
`0 <= probs <= 1` (if not `multidimensional`) or that the last dimension
|
||||
of `probs` sums to one.
|
||||
name: A name for this operation (optional).
|
||||
|
||||
Returns:
|
||||
logits, probs: Tuple of `Tensor`s. If `probs` has an entry that is `0` or
|
||||
`1`, then the corresponding entry in the returned logit will be `-Inf` and
|
||||
`Inf` respectively.
|
||||
|
||||
Raises:
|
||||
ValueError: if neither `probs` nor `logits` were passed in, or both were.
|
||||
"""
|
||||
with ops.name_scope(name, values=[probs, logits]):
|
||||
if (probs is None) == (logits is None):
|
||||
raise ValueError("Must pass probs or logits, but not both.")
|
||||
|
||||
if probs is None:
|
||||
logits = ops.convert_to_tensor(logits, name="logits")
|
||||
if multidimensional:
|
||||
return logits, nn.softmax(logits, name="probs")
|
||||
return logits, math_ops.sigmoid(logits, name="probs")
|
||||
|
||||
probs = ops.convert_to_tensor(probs, name="probs")
|
||||
if validate_args:
|
||||
with ops.name_scope("validate_probs"):
|
||||
one = constant_op.constant(1., probs.dtype)
|
||||
dependencies = [check_ops.assert_non_negative(probs)]
|
||||
if multidimensional:
|
||||
dependencies += [assert_close(math_ops.reduce_sum(probs, -1), one,
|
||||
message="probs does not sum to 1.")]
|
||||
else:
|
||||
dependencies += [check_ops.assert_less_equal(
|
||||
probs, one, message="probs has components greater than 1.")]
|
||||
probs = control_flow_ops.with_dependencies(dependencies, probs)
|
||||
|
||||
with ops.name_scope("logits"):
|
||||
if multidimensional:
|
||||
# Here we don't compute the multidimensional case, in a manner
|
||||
# consistent with respect to the unidimensional case. We do so
|
||||
# following the TF convention. Typically, you might expect to see
|
||||
# logits = log(probs) - log(probs[pivot]). A side-effect of
|
||||
# being consistent with the TF approach is that the unidimensional case
|
||||
# implicitly handles the second dimension but the multidimensional case
|
||||
# explicitly keeps the pivot dimension.
|
||||
return math_ops.log(probs), probs
|
||||
return math_ops.log(probs) - math_ops.log1p(-1. * probs), probs
|
||||
|
||||
|
||||
def log_combinations(n, counts, name="log_combinations"):
|
||||
"""Multinomial coefficient.
|
||||
|
||||
Given `n` and `counts`, where `counts` has last dimension `k`, we compute
|
||||
the multinomial coefficient as:
|
||||
|
||||
```n! / sum_i n_i!```
|
||||
|
||||
where `i` runs over all `k` classes.
|
||||
|
||||
Args:
|
||||
n: Floating-point `Tensor` broadcastable with `counts`. This represents `n`
|
||||
outcomes.
|
||||
counts: Floating-point `Tensor` broadcastable with `n`. This represents
|
||||
counts in `k` classes, where `k` is the last dimension of the tensor.
|
||||
name: A name for this operation (optional).
|
||||
|
||||
Returns:
|
||||
`Tensor` representing the multinomial coefficient between `n` and `counts`.
|
||||
"""
|
||||
# First a bit about the number of ways counts could have come in:
|
||||
# E.g. if counts = [1, 2], then this is 3 choose 2.
|
||||
# In general, this is (sum counts)! / sum(counts!)
|
||||
# The sum should be along the last dimension of counts. This is the
|
||||
# "distribution" dimension. Here n a priori represents the sum of counts.
|
||||
with ops.name_scope(name, values=[n, counts]):
|
||||
n = ops.convert_to_tensor(n, name="n")
|
||||
counts = ops.convert_to_tensor(counts, name="counts")
|
||||
total_permutations = math_ops.lgamma(n + 1)
|
||||
counts_factorial = math_ops.lgamma(counts + 1)
|
||||
redundant_permutations = math_ops.reduce_sum(counts_factorial, axis=[-1])
|
||||
return total_permutations - redundant_permutations
|
||||
|
||||
|
||||
def matrix_diag_transform(matrix, transform=None, name=None):
|
||||
"""Transform diagonal of [batch-]matrix, leave rest of matrix unchanged.
|
||||
|
||||
Create a trainable covariance defined by a Cholesky factor:
|
||||
|
||||
```python
|
||||
# Transform network layer into 2 x 2 array.
|
||||
matrix_values = tf.contrib.layers.fully_connected(activations, 4)
|
||||
matrix = tf.reshape(matrix_values, (batch_size, 2, 2))
|
||||
|
||||
# Make the diagonal positive. If the upper triangle was zero, this would be a
|
||||
# valid Cholesky factor.
|
||||
chol = matrix_diag_transform(matrix, transform=tf.nn.softplus)
|
||||
|
||||
# OperatorPDCholesky ignores the upper triangle.
|
||||
operator = OperatorPDCholesky(chol)
|
||||
```
|
||||
|
||||
Example of heteroskedastic 2-D linear regression.
|
||||
|
||||
```python
|
||||
# Get a trainable Cholesky factor.
|
||||
matrix_values = tf.contrib.layers.fully_connected(activations, 4)
|
||||
matrix = tf.reshape(matrix_values, (batch_size, 2, 2))
|
||||
chol = matrix_diag_transform(matrix, transform=tf.nn.softplus)
|
||||
|
||||
# Get a trainable mean.
|
||||
mu = tf.contrib.layers.fully_connected(activations, 2)
|
||||
|
||||
# This is a fully trainable multivariate normal!
|
||||
dist = tf.contrib.distributions.MVNCholesky(mu, chol)
|
||||
|
||||
# Standard log loss. Minimizing this will "train" mu and chol, and then dist
|
||||
# will be a distribution predicting labels as multivariate Gaussians.
|
||||
loss = -1 * tf.reduce_mean(dist.log_prob(labels))
|
||||
```
|
||||
|
||||
Args:
|
||||
matrix: Rank `R` `Tensor`, `R >= 2`, where the last two dimensions are
|
||||
equal.
|
||||
transform: Element-wise function mapping `Tensors` to `Tensors`. To
|
||||
be applied to the diagonal of `matrix`. If `None`, `matrix` is returned
|
||||
unchanged. Defaults to `None`.
|
||||
name: A name to give created ops.
|
||||
Defaults to "matrix_diag_transform".
|
||||
|
||||
Returns:
|
||||
A `Tensor` with same shape and `dtype` as `matrix`.
|
||||
"""
|
||||
with ops.name_scope(name, "matrix_diag_transform", [matrix]):
|
||||
matrix = ops.convert_to_tensor(matrix, name="matrix")
|
||||
if transform is None:
|
||||
return matrix
|
||||
# Replace the diag with transformed diag.
|
||||
diag = array_ops.matrix_diag_part(matrix)
|
||||
transformed_diag = transform(diag)
|
||||
transformed_mat = array_ops.matrix_set_diag(matrix, transformed_diag)
|
||||
|
||||
return transformed_mat
|
||||
|
||||
|
||||
def rotate_transpose(x, shift, name="rotate_transpose"):
|
||||
"""Circularly moves dims left or right.
|
||||
|
||||
Effectively identical to:
|
||||
|
||||
```python
|
||||
numpy.transpose(x, numpy.roll(numpy.arange(len(x.shape)), shift))
|
||||
```
|
||||
|
||||
When `validate_args=False` additional graph-runtime checks are
|
||||
performed. These checks entail moving data from to GPU to CPU.
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
x = ... # Tensor of shape [1, 2, 3, 4].
|
||||
rotate_transpose(x, -1) # result shape: [2, 3, 4, 1]
|
||||
rotate_transpose(x, -2) # result shape: [3, 4, 1, 2]
|
||||
rotate_transpose(x, 1) # result shape: [4, 1, 2, 3]
|
||||
rotate_transpose(x, 2) # result shape: [3, 4, 1, 2]
|
||||
rotate_transpose(x, 7) == rotate_transpose(x, 3)
|
||||
rotate_transpose(x, -7) == rotate_transpose(x, -3)
|
||||
```
|
||||
|
||||
Args:
|
||||
x: `Tensor`.
|
||||
shift: `Tensor`. Number of dimensions to transpose left (shift<0) or
|
||||
transpose right (shift>0).
|
||||
name: Python `str`. The name to give this op.
|
||||
|
||||
Returns:
|
||||
rotated_x: Input `Tensor` with dimensions circularly rotated by shift.
|
||||
|
||||
Raises:
|
||||
TypeError: if shift is not integer type.
|
||||
"""
|
||||
with ops.name_scope(name, values=[x, shift]):
|
||||
x = ops.convert_to_tensor(x, name="x")
|
||||
shift = ops.convert_to_tensor(shift, name="shift")
|
||||
# We do not assign back to preserve constant-ness.
|
||||
check_ops.assert_integer(shift)
|
||||
shift_value_static = tensor_util.constant_value(shift)
|
||||
ndims = x.get_shape().ndims
|
||||
if ndims is not None and shift_value_static is not None:
|
||||
if ndims < 2: return x
|
||||
shift_value_static = np.sign(shift_value_static) * (
|
||||
abs(shift_value_static) % ndims)
|
||||
if shift_value_static == 0: return x
|
||||
perm = np.roll(np.arange(ndims), shift_value_static)
|
||||
return array_ops.transpose(x, perm=perm)
|
||||
else:
|
||||
# Consider if we always had a positive shift, and some specified
|
||||
# direction.
|
||||
# When shifting left we want the new array:
|
||||
# last(x, n-shift) + first(x, shift)
|
||||
# and if shifting right then we want:
|
||||
# last(x, shift) + first(x, n-shift)
|
||||
# Observe that last(a) == slice(a, n) and first(a) == slice(0, a).
|
||||
# Also, we can encode direction and shift as one: direction * shift.
|
||||
# Combining these facts, we have:
|
||||
# a = cond(shift<0, -shift, n-shift)
|
||||
# last(x, n-a) + first(x, a) == x[a:n] + x[0:a]
|
||||
# Finally, we transform shift by modulo length so it can be specified
|
||||
# independently from the array upon which it operates (like python).
|
||||
ndims = array_ops.rank(x)
|
||||
shift = array_ops.where(math_ops.less(shift, 0),
|
||||
math_ops.mod(-shift, ndims),
|
||||
ndims - math_ops.mod(shift, ndims))
|
||||
first = math_ops.range(0, shift)
|
||||
last = math_ops.range(shift, ndims)
|
||||
perm = array_ops.concat([last, first], 0)
|
||||
return array_ops.transpose(x, perm=perm)
|
||||
|
||||
|
||||
def pick_vector(cond,
|
||||
true_vector,
|
||||
false_vector,
|
||||
name="pick_vector"):
|
||||
"""Picks possibly different length row `Tensor`s based on condition.
|
||||
|
||||
Value `Tensor`s should have exactly one dimension.
|
||||
|
||||
If `cond` is a python Boolean or `tf.constant` then either `true_vector` or
|
||||
`false_vector` is immediately returned. I.e., no graph nodes are created and
|
||||
no validation happens.
|
||||
|
||||
Args:
|
||||
cond: `Tensor`. Must have `dtype=tf.bool` and be scalar.
|
||||
true_vector: `Tensor` of one dimension. Returned when cond is `True`.
|
||||
false_vector: `Tensor` of one dimension. Returned when cond is `False`.
|
||||
name: Python `str`. The name to give this op.
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
pick_vector(tf.less(0, 5), tf.range(10, 12), tf.range(15, 18))
|
||||
# result is tensor: [10, 11].
|
||||
pick_vector(tf.less(5, 0), tf.range(10, 12), tf.range(15, 18))
|
||||
# result is tensor: [15, 16, 17].
|
||||
```
|
||||
|
||||
Returns:
|
||||
true_or_false_vector: `Tensor`.
|
||||
|
||||
Raises:
|
||||
TypeError: if `cond.dtype != tf.bool`
|
||||
TypeError: if `cond` is not a constant and
|
||||
`true_vector.dtype != false_vector.dtype`
|
||||
"""
|
||||
with ops.name_scope(name, values=(cond, true_vector, false_vector)):
|
||||
cond = ops.convert_to_tensor(cond, name="cond")
|
||||
if cond.dtype != dtypes.bool:
|
||||
raise TypeError("%s.dtype=%s which is not %s" %
|
||||
(cond.name, cond.dtype, dtypes.bool))
|
||||
cond_value_static = tensor_util.constant_value(cond)
|
||||
if cond_value_static is not None:
|
||||
return true_vector if cond_value_static else false_vector
|
||||
true_vector = ops.convert_to_tensor(true_vector, name="true_vector")
|
||||
false_vector = ops.convert_to_tensor(false_vector, name="false_vector")
|
||||
if true_vector.dtype != false_vector.dtype:
|
||||
raise TypeError(
|
||||
"%s.dtype=%s does not match %s.dtype=%s"
|
||||
% (true_vector.name, true_vector.dtype,
|
||||
false_vector.name, false_vector.dtype))
|
||||
n = array_ops.shape(true_vector)[0]
|
||||
return array_ops.slice(
|
||||
array_ops.concat([true_vector, false_vector], 0),
|
||||
[array_ops.where(cond, 0, n)], [array_ops.where(cond, n, -1)])
|
||||
|
||||
|
||||
def gen_new_seed(seed, salt):
|
||||
"""Generate a new seed, from the given seed and salt."""
|
||||
if seed is None:
|
||||
return None
|
||||
string = (str(seed) + salt).encode("utf-8")
|
||||
return int(hashlib.md5(string).hexdigest()[:8], 16) & 0x7FFFFFFF
|
||||
|
||||
|
||||
def fill_lower_triangular(x, validate_args=False, name="fill_lower_triangular"):
|
||||
"""Creates a (batch of) lower triangular matrix from a vector of inputs.
|
||||
|
||||
If `x.get_shape()` is `[b1, b2, ..., bK, d]` then the output shape is `[b1,
|
||||
b2, ..., bK, n, n]` where `n` is such that `d = n(n+1)/2`, i.e.,
|
||||
`n = int(0.5 * (math.sqrt(1. + 8. * d) - 1.))`.
|
||||
|
||||
Although the non-batch complexity is O(n**2), large constants and sub-optimal
|
||||
vectorization means the complexity of this function is 5x slower than zeroing
|
||||
out the upper triangular, i.e., `tf.matrix_band_part(X, -1, 0)`. This
|
||||
function becomes competitive only when several matmul/cholesky/etc ops can be
|
||||
ellided in constructing the input. Example: wiring a fully connected layer as
|
||||
a covariance matrix; this function reduces the final layer by 2x and possibly
|
||||
reduces the network arch complexity considerably. In most cases it is better
|
||||
to simply build a full matrix and zero out the upper triangular elements,
|
||||
e.g., `tril = tf.matrix_band_part(full, -1, 0)`, rather than directly
|
||||
construct a lower triangular.
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
fill_lower_triangular([1, 2, 3, 4, 5, 6])
|
||||
# Returns: [[1, 0, 0],
|
||||
# [2, 3, 0],
|
||||
# [4, 5, 6]]
|
||||
```
|
||||
|
||||
For comparison, a pure numpy version of this function can be found in
|
||||
`distribution_util_test.py`, function `_fill_lower_triangular`.
|
||||
|
||||
Args:
|
||||
x: `Tensor` representing lower triangular elements.
|
||||
validate_args: Python `bool`, default `False`. Whether to ensure the shape
|
||||
of `x` can be mapped to a lower triangular matrix (controls non-static
|
||||
checks only).
|
||||
name: Python `str`. The name to give this op.
|
||||
|
||||
Returns:
|
||||
tril: `Tensor` with lower triangular elements filled from `x`.
|
||||
|
||||
Raises:
|
||||
ValueError: if shape if `x` has static shape which cannot be mapped to a
|
||||
lower triangular matrix.
|
||||
"""
|
||||
# TODO(jvdillon): Replace this code with dedicated op when it exists.
|
||||
with ops.name_scope(name, values=[x]):
|
||||
x = ops.convert_to_tensor(x, name="x")
|
||||
if (x.get_shape().ndims is not None and
|
||||
x.get_shape()[-1].value is not None):
|
||||
d = x.get_shape()[-1].value
|
||||
# d = n(n+1)/2 implies n is:
|
||||
n = int(0.5 * (math.sqrt(1. + 8. * d) - 1.))
|
||||
d_inferred = n * (n + 1) /2
|
||||
if d != d_inferred:
|
||||
raise ValueError("Input cannot be mapped to a lower triangular; "
|
||||
"n*(n+1)/2 = %d != %d" % (d_inferred, d))
|
||||
final_shape = x.get_shape()[:-1].concatenate(
|
||||
tensor_shape.TensorShape([n, n]))
|
||||
else:
|
||||
d = math_ops.cast(array_ops.shape(x)[-1], dtype=dtypes.float32)
|
||||
# d = n(n+1)/2 implies n is:
|
||||
n = math_ops.cast(0.5 * (dtypes.sqrt(1. + 8. * d) - 1.),
|
||||
dtype=dtypes.int32)
|
||||
if validate_args:
|
||||
is_valid_input_shape = check_ops.assert_equal(
|
||||
n * (n + 1) / 2, d,
|
||||
message="Input cannot be mapped to a lower triangular.")
|
||||
n = control_flow_ops.with_dependencies([is_valid_input_shape], n)
|
||||
final_shape = x.get_shape()[:-1].concatenate(
|
||||
tensor_shape.TensorShape([None, None]))
|
||||
|
||||
def tril_ids(n):
|
||||
"""Internal helper to create vector of linear indices into y."""
|
||||
# Build the ids statically; chose 512 because it implies 1MiB.
|
||||
if not tensor_util.is_tensor(n) and n <= 512:
|
||||
ids = np.arange(n**2, dtype=np.int32)
|
||||
rows = (ids / n).astype(np.int32) # Implicit floor.
|
||||
# We need to stop incrementing the index when we encounter
|
||||
# upper-triangular elements. The idea here is to compute the
|
||||
# lower-right number of zeros then by "symmetry" subtract this from the
|
||||
# total number of zeros, n(n-1)/2.
|
||||
# Then we note that: n(n-1)/2 - (n-r)*(n-r-1)/2 = r(2n-r-1)/2
|
||||
offset = (rows * (2 * n - rows - 1) / 2).astype(np.int32)
|
||||
# We could also zero out when (rows < cols) == (rows < ids-n*rows).
|
||||
# mask = (ids <= (n + 1) * rows).astype(np.int32)
|
||||
else:
|
||||
ids = math_ops.range(n**2)
|
||||
rows = math_ops.cast(ids / n, dtype=dtypes.int32)
|
||||
offset = math_ops.cast(rows * (2 * n - rows - 1) / 2,
|
||||
dtype=dtypes.int32)
|
||||
return ids - offset
|
||||
|
||||
# Special-case non-batch case.
|
||||
if x.get_shape().ndims == 1:
|
||||
y = array_ops.gather(x, array_ops.reshape(tril_ids(n), [n, n]))
|
||||
y = array_ops.matrix_band_part(y, -1, 0)
|
||||
y.set_shape(y.get_shape().merge_with(final_shape))
|
||||
return y
|
||||
|
||||
# Make ids for each batch dim.
|
||||
if (x.get_shape().ndims is not None and
|
||||
x.get_shape()[:-1].is_fully_defined()):
|
||||
batch_shape = np.asarray(x.get_shape()[:-1].as_list(), dtype=np.int32)
|
||||
m = np.prod(batch_shape).astype(np.int32)
|
||||
else:
|
||||
batch_shape = array_ops.shape(x)[:-1]
|
||||
m = array_ops.reduce_prod(array_ops.shape(x)[:-1])
|
||||
batch_ids = math_ops.range(m)
|
||||
|
||||
# Assemble the tril_ids into batch,tril_id pairs.
|
||||
idx = array_ops.stack([
|
||||
array_ops.tile(array_ops.expand_dims(batch_ids, 1), [1, n * n]),
|
||||
array_ops.tile(array_ops.expand_dims(tril_ids(n), 0), [m, 1])
|
||||
])
|
||||
idx = array_ops.transpose(idx, [1, 2, 0])
|
||||
|
||||
# Gather up, reshape, and return.
|
||||
y = array_ops.reshape(x, [-1, d])
|
||||
y = array_ops.gather_nd(y, idx)
|
||||
y = array_ops.reshape(y, array_ops.concat([batch_shape, [n, n]], 0))
|
||||
y = array_ops.matrix_band_part(y, -1, 0)
|
||||
y.set_shape(y.get_shape().merge_with(final_shape))
|
||||
return y
|
||||
|
||||
|
||||
# TODO(jvdillon): Merge this test back into:
|
||||
# tensorflow/python/ops/softplus_op_test.py
|
||||
# once TF core is accepting new ops.
|
||||
def softplus_inverse(x, name=None):
|
||||
"""Computes the inverse softplus, i.e., x = softplus_inverse(softplus(x)).
|
||||
|
||||
Mathematically this op is equivalent to:
|
||||
|
||||
```none
|
||||
softplus_inverse = log(exp(x) - 1.)
|
||||
```
|
||||
|
||||
Args:
|
||||
x: `Tensor`. Non-negative (not enforced), floating-point.
|
||||
name: A name for the operation (optional).
|
||||
|
||||
Returns:
|
||||
`Tensor`. Has the same type/shape as input `x`.
|
||||
"""
|
||||
with ops.name_scope(name, "softplus_inverse", values=[x]):
|
||||
x = ops.convert_to_tensor(x, name="x")
|
||||
# We begin by deriving a more numerically stable softplus_inverse:
|
||||
# x = softplus(y) = Log[1 + exp{y}], (which means x > 0).
|
||||
# ==> exp{x} = 1 + exp{y} (1)
|
||||
# ==> y = Log[exp{x} - 1] (2)
|
||||
# = Log[(exp{x} - 1) / exp{x}] + Log[exp{x}]
|
||||
# = Log[(1 - exp{-x}) / 1] + Log[exp{x}]
|
||||
# = Log[1 - exp{-x}] + x (3)
|
||||
# (2) is the "obvious" inverse, but (3) is more stable than (2) for large x.
|
||||
# For small x (e.g. x = 1e-10), (3) will become -inf since 1 - exp{-x} will
|
||||
# be zero. To fix this, we use 1 - exp{-x} approx x for small x > 0.
|
||||
#
|
||||
# In addition to the numerically stable derivation above, we clamp
|
||||
# small/large values to be congruent with the logic in:
|
||||
# tensorflow/core/kernels/softplus_op.h
|
||||
#
|
||||
# Finally, we set the input to one whenever the input is too large or too
|
||||
# small. This ensures that no unchosen codepath is +/- inf. This is
|
||||
# necessary to ensure the gradient doesn't get NaNs. Recall that the
|
||||
# gradient of `where` behaves like `pred*pred_true + (1-pred)*pred_false`
|
||||
# thus an `inf` in an unselected path results in `0*inf=nan`. We are careful
|
||||
# to overwrite `x` with ones only when we will never actually use this
|
||||
# value. Note that we use ones and not zeros since `log(expm1(0.)) = -inf`.
|
||||
threshold = np.log(np.finfo(x.dtype.as_numpy_dtype).eps) + 2.
|
||||
is_too_small = math_ops.less(x, np.exp(threshold))
|
||||
is_too_large = math_ops.greater(x, -threshold)
|
||||
too_small_value = math_ops.log(x)
|
||||
too_large_value = x
|
||||
# This `where` will ultimately be a NOP because we won't select this
|
||||
# codepath whenever we used the surrogate `ones_like`.
|
||||
x = array_ops.where(math_ops.logical_or(is_too_small, is_too_large),
|
||||
array_ops.ones_like(x), x)
|
||||
y = x + math_ops.log(-math_ops.expm1(-x)) # == log(expm1(x))
|
||||
return array_ops.where(is_too_small, too_small_value,
|
||||
array_ops.where(is_too_large, too_large_value, y))
|
||||
|
||||
|
||||
# TODO(b/35290280): Add unit-tests.
|
||||
def dimension_size(x, axis):
|
||||
"""Returns the size of a specific dimension."""
|
||||
# Since tf.gather isn't "constant-in, constant-out", we must first check the
|
||||
# static shape or fallback to dynamic shape.
|
||||
num_rows = (None if x.get_shape().ndims is None
|
||||
else x.get_shape()[axis].value)
|
||||
if num_rows is not None:
|
||||
return num_rows
|
||||
return array_ops.shape(x)[axis]
|
||||
from tensorflow.python.ops.distributions import util
|
||||
from tensorflow.python.ops.distributions.util import * # pylint: disable=wildcard-import
|
||||
|
||||
|
||||
# TODO(b/35290280): Add unit-tests.
|
||||
@ -678,7 +73,7 @@ def make_diag_scale(loc, scale_diag, scale_identity_multiplier,
|
||||
raise ValueError(
|
||||
"Cannot infer `event_shape` unless `loc` is specified.")
|
||||
|
||||
num_rows = dimension_size(loc, -1)
|
||||
num_rows = util.dimension_size(loc, -1)
|
||||
|
||||
if scale_identity_multiplier is None:
|
||||
return linalg.LinearOperatorIdentity(
|
||||
@ -695,64 +90,3 @@ def make_diag_scale(loc, scale_diag, scale_identity_multiplier,
|
||||
is_self_adjoint=True,
|
||||
is_positive_definite=assert_positive,
|
||||
assert_proper_shapes=validate_args)
|
||||
|
||||
|
||||
class AppendDocstring(object):
|
||||
"""Helper class to promote private subclass docstring to public counterpart.
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
class TransformedDistribution(Distribution):
|
||||
@distribution_util.AppendDocstring(
|
||||
additional_note="A special note!",
|
||||
kwargs_dict={"foo": "An extra arg."})
|
||||
def _prob(self, y, foo=None):
|
||||
pass
|
||||
```
|
||||
|
||||
In this case, the `AppendDocstring` decorator appends the `additional_note` to
|
||||
the docstring of `prob` (not `_prob`) and adds a new `kwargs`
|
||||
section with each dictionary item as a bullet-point.
|
||||
|
||||
For a more detailed example, see `TransformedDistribution`.
|
||||
"""
|
||||
|
||||
def __init__(self, additional_note="", kwargs_dict=None):
|
||||
"""Initializes the AppendDocstring object.
|
||||
|
||||
Args:
|
||||
additional_note: Python string added as additional docstring to public
|
||||
version of function.
|
||||
kwargs_dict: Python string/string dictionary representing
|
||||
specific kwargs expanded from the **kwargs input.
|
||||
|
||||
Raises:
|
||||
ValueError: if kwargs_dict.key contains whitespace.
|
||||
ValueError: if kwargs_dict.value contains newlines.
|
||||
"""
|
||||
self._additional_note = additional_note
|
||||
if kwargs_dict:
|
||||
bullets = []
|
||||
for key in sorted(kwargs_dict.keys()):
|
||||
value = kwargs_dict[key]
|
||||
if any(x.isspace() for x in key):
|
||||
raise ValueError(
|
||||
"Parameter name \"%s\" contains whitespace." % key)
|
||||
value = value.lstrip()
|
||||
if "\n" in value:
|
||||
raise ValueError(
|
||||
"Parameter description for \"%s\" contains newlines." % key)
|
||||
bullets.append("* `%s`: %s" % (key, value))
|
||||
self._additional_note += ("\n\n##### `kwargs`:\n\n" +
|
||||
"\n".join(bullets))
|
||||
|
||||
def __call__(self, fn):
|
||||
@functools.wraps(fn)
|
||||
def _fn(*args, **kwargs):
|
||||
return fn(*args, **kwargs)
|
||||
if _fn.__doc__ is None:
|
||||
_fn.__doc__ = self._additional_note
|
||||
else:
|
||||
_fn.__doc__ += "\n%s" % self._additional_note
|
||||
return _fn
|
||||
|
||||
@ -20,8 +20,6 @@ from __future__ import print_function
|
||||
|
||||
import numpy as np
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import distribution
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
from tensorflow.contrib.distributions.python.ops import kullback_leibler
|
||||
from tensorflow.python.framework import constant_op
|
||||
from tensorflow.python.framework import dtypes
|
||||
@ -33,6 +31,8 @@ from tensorflow.python.ops import control_flow_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import nn
|
||||
from tensorflow.python.ops import random_ops
|
||||
from tensorflow.python.ops.distributions import distribution
|
||||
from tensorflow.python.ops.distributions import util as distribution_util
|
||||
|
||||
|
||||
__all__ = [
|
||||
|
||||
@ -19,8 +19,6 @@ from __future__ import print_function
|
||||
|
||||
import numpy as np
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import distribution
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
from tensorflow.python.framework import constant_op
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
@ -31,6 +29,8 @@ from tensorflow.python.ops import control_flow_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import nn
|
||||
from tensorflow.python.ops import random_ops
|
||||
from tensorflow.python.ops.distributions import distribution
|
||||
from tensorflow.python.ops.distributions import util as distribution_util
|
||||
|
||||
|
||||
class Geometric(distribution.Distribution):
|
||||
|
||||
@ -20,7 +20,6 @@ from __future__ import print_function
|
||||
|
||||
import math
|
||||
import numpy as np
|
||||
from tensorflow.contrib.distributions.python.ops import distribution
|
||||
from tensorflow.python.framework import constant_op
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
@ -29,6 +28,7 @@ from tensorflow.python.ops import array_ops
|
||||
from tensorflow.python.ops import check_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import random_ops
|
||||
from tensorflow.python.ops.distributions import distribution
|
||||
|
||||
|
||||
class _Gumbel(distribution.Distribution):
|
||||
|
||||
@ -20,8 +20,6 @@ from __future__ import print_function
|
||||
|
||||
import numpy as np
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import distribution
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
from tensorflow.python.framework import constant_op
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
@ -32,6 +30,8 @@ from tensorflow.python.ops import control_flow_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import nn
|
||||
from tensorflow.python.ops import random_ops
|
||||
from tensorflow.python.ops.distributions import distribution
|
||||
from tensorflow.python.ops.distributions import util as distribution_util
|
||||
|
||||
|
||||
__all__ = [
|
||||
|
||||
@ -22,7 +22,6 @@ import math
|
||||
|
||||
import numpy as np
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import distribution
|
||||
from tensorflow.contrib.distributions.python.ops import special_math
|
||||
from tensorflow.python.framework import constant_op
|
||||
from tensorflow.python.framework import dtypes
|
||||
@ -33,6 +32,7 @@ from tensorflow.python.ops import check_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import nn
|
||||
from tensorflow.python.ops import random_ops
|
||||
from tensorflow.python.ops.distributions import distribution
|
||||
|
||||
|
||||
__all__ = [
|
||||
|
||||
@ -21,7 +21,6 @@ from __future__ import print_function
|
||||
import math
|
||||
import numpy as np
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import distribution
|
||||
from tensorflow.python.framework import constant_op
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
@ -31,6 +30,7 @@ from tensorflow.python.ops import check_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import nn_ops
|
||||
from tensorflow.python.ops import random_ops
|
||||
from tensorflow.python.ops.distributions import distribution
|
||||
|
||||
|
||||
class Logistic(distribution.Distribution):
|
||||
|
||||
@ -21,8 +21,6 @@ from __future__ import print_function
|
||||
import numpy as np
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import categorical
|
||||
from tensorflow.contrib.distributions.python.ops import distribution
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
from tensorflow.python.framework import ops
|
||||
from tensorflow.python.framework import tensor_shape
|
||||
from tensorflow.python.framework import tensor_util
|
||||
@ -31,6 +29,8 @@ from tensorflow.python.ops import check_ops
|
||||
from tensorflow.python.ops import data_flow_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import nn_ops
|
||||
from tensorflow.python.ops.distributions import distribution
|
||||
from tensorflow.python.ops.distributions import util as distribution_util
|
||||
|
||||
|
||||
class Mixture(distribution.Distribution):
|
||||
|
||||
@ -18,8 +18,6 @@ from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import distribution
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
from tensorflow.python.ops import array_ops
|
||||
@ -27,6 +25,8 @@ from tensorflow.python.ops import check_ops
|
||||
from tensorflow.python.ops import control_flow_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import random_ops
|
||||
from tensorflow.python.ops.distributions import distribution
|
||||
from tensorflow.python.ops.distributions import util as distribution_util
|
||||
|
||||
|
||||
__all__ = [
|
||||
|
||||
@ -20,7 +20,6 @@ from __future__ import print_function
|
||||
|
||||
from tensorflow.contrib import linalg
|
||||
from tensorflow.contrib.distributions.python.ops import bijectors
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
from tensorflow.contrib.distributions.python.ops import kullback_leibler
|
||||
from tensorflow.contrib.distributions.python.ops import normal
|
||||
from tensorflow.contrib.distributions.python.ops import transformed_distribution
|
||||
@ -30,6 +29,7 @@ from tensorflow.python.framework import tensor_util
|
||||
from tensorflow.python.ops import array_ops
|
||||
from tensorflow.python.ops import linalg_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops.distributions import util as distribution_util
|
||||
|
||||
|
||||
__all__ = [
|
||||
|
||||
@ -19,13 +19,13 @@ from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
from tensorflow.contrib import linalg
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
from tensorflow.contrib.distributions.python.ops import mvn_linear_operator as mvn_linop
|
||||
from tensorflow.python.framework import ops
|
||||
from tensorflow.python.ops import array_ops
|
||||
from tensorflow.python.ops import check_ops
|
||||
from tensorflow.python.ops import control_flow_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops.distributions import util as distribution_util
|
||||
|
||||
|
||||
__all__ = [
|
||||
|
||||
@ -18,8 +18,6 @@ from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import distribution
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
from tensorflow.python.framework import tensor_shape
|
||||
@ -27,6 +25,8 @@ from tensorflow.python.ops import array_ops
|
||||
from tensorflow.python.ops import check_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import random_ops
|
||||
from tensorflow.python.ops.distributions import distribution
|
||||
from tensorflow.python.ops.distributions import util as distribution_util
|
||||
|
||||
|
||||
class NegativeBinomial(distribution.Distribution):
|
||||
|
||||
@ -20,7 +20,6 @@ from __future__ import print_function
|
||||
|
||||
import math
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import distribution
|
||||
from tensorflow.contrib.distributions.python.ops import kullback_leibler
|
||||
from tensorflow.contrib.distributions.python.ops import special_math
|
||||
from tensorflow.python.framework import constant_op
|
||||
@ -32,6 +31,7 @@ from tensorflow.python.ops import check_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import nn
|
||||
from tensorflow.python.ops import random_ops
|
||||
from tensorflow.python.ops.distributions import distribution
|
||||
|
||||
|
||||
__all__ = [
|
||||
|
||||
@ -18,8 +18,6 @@ from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import distribution
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
from tensorflow.contrib.distributions.python.ops import kullback_leibler
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
@ -29,6 +27,8 @@ from tensorflow.python.ops import control_flow_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import nn_ops
|
||||
from tensorflow.python.ops import random_ops
|
||||
from tensorflow.python.ops.distributions import distribution
|
||||
from tensorflow.python.ops.distributions import util as distribution_util
|
||||
|
||||
|
||||
class OneHotCategorical(distribution.Distribution):
|
||||
|
||||
@ -18,10 +18,10 @@ from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
from tensorflow.contrib.distributions.python.ops import operator_pd_cholesky
|
||||
from tensorflow.python.framework import ops
|
||||
from tensorflow.python.ops import linalg_ops
|
||||
from tensorflow.python.ops.distributions import util as distribution_util
|
||||
|
||||
|
||||
__all__ = [
|
||||
|
||||
@ -18,8 +18,6 @@ from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import distribution
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
from tensorflow.python.framework import constant_op
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
@ -28,6 +26,8 @@ from tensorflow.python.ops import array_ops
|
||||
from tensorflow.python.ops import check_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import random_ops
|
||||
from tensorflow.python.ops.distributions import distribution
|
||||
from tensorflow.python.ops.distributions import util as distribution_util
|
||||
|
||||
__all__ = [
|
||||
"Poisson",
|
||||
|
||||
@ -20,13 +20,13 @@ from __future__ import print_function
|
||||
|
||||
import numpy as np
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import distribution as distributions
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
from tensorflow.python.framework import ops
|
||||
from tensorflow.python.ops import array_ops
|
||||
from tensorflow.python.ops import check_ops
|
||||
from tensorflow.python.ops import control_flow_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops.distributions import distribution as distributions
|
||||
from tensorflow.python.ops.distributions import util as distribution_util
|
||||
|
||||
__all__ = ["QuantizedDistribution"]
|
||||
|
||||
|
||||
@ -18,7 +18,6 @@ from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
from tensorflow.contrib.distributions.python.ops import logistic
|
||||
from tensorflow.contrib.distributions.python.ops import transformed_distribution
|
||||
# Bijectors must be directly imported because `remove_undocumented` prevents
|
||||
@ -28,6 +27,7 @@ from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
from tensorflow.python.ops import array_ops
|
||||
from tensorflow.python.ops import check_ops
|
||||
from tensorflow.python.ops.distributions import util as distribution_util
|
||||
|
||||
|
||||
class RelaxedBernoulli(transformed_distribution.TransformedDistribution):
|
||||
|
||||
@ -20,8 +20,6 @@ from __future__ import print_function
|
||||
|
||||
import numpy as np
|
||||
from tensorflow.contrib.distributions.python.ops import bijectors
|
||||
from tensorflow.contrib.distributions.python.ops import distribution
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
from tensorflow.contrib.distributions.python.ops import transformed_distribution
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
@ -31,6 +29,8 @@ from tensorflow.python.ops import control_flow_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import nn_ops
|
||||
from tensorflow.python.ops import random_ops
|
||||
from tensorflow.python.ops.distributions import distribution
|
||||
from tensorflow.python.ops.distributions import util as distribution_util
|
||||
|
||||
|
||||
class ExpRelaxedOneHotCategorical(distribution.Distribution):
|
||||
|
||||
@ -19,7 +19,6 @@ from __future__ import print_function
|
||||
|
||||
import contextlib
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
from tensorflow.python.framework import tensor_util
|
||||
@ -27,6 +26,7 @@ from tensorflow.python.ops import array_ops
|
||||
from tensorflow.python.ops import check_ops
|
||||
from tensorflow.python.ops import control_flow_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops.distributions import util as distribution_util
|
||||
|
||||
|
||||
class _DistributionShape(object):
|
||||
|
||||
@ -20,8 +20,6 @@ from __future__ import print_function
|
||||
|
||||
import numpy as np
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import distribution
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
from tensorflow.python.framework import constant_op
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
@ -33,6 +31,8 @@ from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import nn
|
||||
from tensorflow.python.ops import random_ops
|
||||
from tensorflow.python.ops import special_math_ops
|
||||
from tensorflow.python.ops.distributions import distribution
|
||||
from tensorflow.python.ops.distributions import util as distribution_util
|
||||
|
||||
|
||||
__all__ = [
|
||||
|
||||
@ -19,8 +19,6 @@ from __future__ import print_function
|
||||
|
||||
import numpy as np
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import distribution as distribution_lib
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
# Bijectors must be directly imported because `remove_undocumented` prevents
|
||||
# individual file imports.
|
||||
from tensorflow.contrib.distributions.python.ops.bijectors.identity import Identity
|
||||
@ -33,6 +31,8 @@ from tensorflow.python.ops import array_ops
|
||||
from tensorflow.python.ops import check_ops
|
||||
from tensorflow.python.ops import control_flow_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops.distributions import distribution as distribution_lib
|
||||
from tensorflow.python.ops.distributions import util as distribution_util
|
||||
|
||||
__all__ = [
|
||||
"TransformedDistribution",
|
||||
|
||||
@ -20,7 +20,6 @@ from __future__ import print_function
|
||||
|
||||
import math
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import distribution
|
||||
from tensorflow.python.framework import constant_op
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
@ -29,6 +28,7 @@ from tensorflow.python.ops import array_ops
|
||||
from tensorflow.python.ops import check_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import random_ops
|
||||
from tensorflow.python.ops.distributions import distribution
|
||||
|
||||
|
||||
class Uniform(distribution.Distribution):
|
||||
|
||||
@ -19,7 +19,6 @@ from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import bijectors
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
from tensorflow.contrib.distributions.python.ops import student_t
|
||||
from tensorflow.contrib.distributions.python.ops import transformed_distribution
|
||||
from tensorflow.python.framework import constant_op
|
||||
@ -27,6 +26,7 @@ from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
from tensorflow.python.framework import tensor_shape
|
||||
from tensorflow.python.ops import array_ops
|
||||
from tensorflow.python.ops.distributions import util as distribution_util
|
||||
|
||||
|
||||
# TODO(jvdillon): Add unittests for this once we know where will put this code
|
||||
|
||||
@ -21,8 +21,6 @@ from __future__ import print_function
|
||||
import math
|
||||
import numpy as np
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import distribution
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
from tensorflow.contrib.distributions.python.ops import operator_pd_cholesky
|
||||
from tensorflow.contrib.distributions.python.ops import operator_pd_full
|
||||
from tensorflow.python.framework import constant_op
|
||||
@ -35,6 +33,8 @@ from tensorflow.python.ops import control_flow_ops
|
||||
from tensorflow.python.ops import linalg_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import random_ops
|
||||
from tensorflow.python.ops.distributions import distribution
|
||||
from tensorflow.python.ops.distributions import util as distribution_util
|
||||
|
||||
|
||||
__all__ = [
|
||||
|
||||
@ -81,6 +81,7 @@ py_library(
|
||||
"//third_party/py/numpy",
|
||||
"//tensorflow/python/estimator:estimator_py",
|
||||
"//tensorflow/python/ops/losses",
|
||||
"//tensorflow/python/ops/distributions",
|
||||
"//tensorflow/python/saved_model",
|
||||
] + if_not_windows([
|
||||
"//tensorflow/contrib:contrib_py",
|
||||
|
||||
42
tensorflow/python/kernel_tests/distributions/BUILD
Normal file
42
tensorflow/python/kernel_tests/distributions/BUILD
Normal file
@ -0,0 +1,42 @@
|
||||
# Tests of TensorFlow kernels written using the Python API.
|
||||
|
||||
package(
|
||||
default_visibility = ["//tensorflow:internal"],
|
||||
features = [
|
||||
"-layering_check",
|
||||
"-parse_headers",
|
||||
],
|
||||
)
|
||||
|
||||
licenses(["notice"]) # Apache 2.0
|
||||
|
||||
load("//tensorflow:tensorflow.bzl", "cuda_py_test")
|
||||
|
||||
cuda_py_test(
|
||||
name = "bijector_test",
|
||||
size = "small",
|
||||
srcs = ["bijector_test.py"],
|
||||
additional_deps = [
|
||||
"//tensorflow/python/ops/distributions",
|
||||
"//third_party/py/numpy",
|
||||
"@six_archive//:six",
|
||||
"//tensorflow/python:array_ops",
|
||||
"//tensorflow/python:client_testlib",
|
||||
"//tensorflow/python:framework_for_generated_wrappers",
|
||||
"//tensorflow/python:framework_test_lib",
|
||||
"//tensorflow/python:math_ops",
|
||||
"//tensorflow/python:platform_test",
|
||||
],
|
||||
)
|
||||
|
||||
filegroup(
|
||||
name = "all_files",
|
||||
srcs = glob(
|
||||
["**/*"],
|
||||
exclude = [
|
||||
"**/METADATA",
|
||||
"**/OWNERS",
|
||||
],
|
||||
),
|
||||
visibility = ["//tensorflow:__subpackages__"],
|
||||
)
|
||||
18
tensorflow/python/kernel_tests/distributions/__init__.py
Normal file
18
tensorflow/python/kernel_tests/distributions/__init__.py
Normal file
@ -0,0 +1,18 @@
|
||||
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
|
||||
#
|
||||
# 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.
|
||||
# ==============================================================================
|
||||
"""Kernel tests for tf.distributions."""
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
@ -22,9 +22,9 @@ import abc
|
||||
|
||||
import six
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops.bijectors.bijector import Bijector
|
||||
from tensorflow.python.framework import constant_op
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops.distributions import bijector
|
||||
from tensorflow.python.platform import test
|
||||
|
||||
|
||||
@ -36,10 +36,10 @@ class BaseBijectorTest(test.TestCase):
|
||||
with self.assertRaisesRegexp(TypeError,
|
||||
("Can't instantiate abstract class Bijector "
|
||||
"with abstract methods __init__")):
|
||||
Bijector()
|
||||
bijector.Bijector() # pylint: disable=abstract-class-instantiated
|
||||
|
||||
def testDefaults(self):
|
||||
class _BareBonesBijector(Bijector):
|
||||
class _BareBonesBijector(bijector.Bijector):
|
||||
"""Minimal specification of a `Bijector`."""
|
||||
|
||||
def __init__(self):
|
||||
@ -80,7 +80,7 @@ class IntentionallyMissingError(Exception):
|
||||
pass
|
||||
|
||||
|
||||
class BrokenBijector(Bijector):
|
||||
class BrokenBijector(bijector.Bijector):
|
||||
"""Forward and inverse are not inverses of each other."""
|
||||
|
||||
def __init__(self, forward_missing=False, inverse_missing=False):
|
||||
41
tensorflow/python/ops/distributions/BUILD
Normal file
41
tensorflow/python/ops/distributions/BUILD
Normal file
@ -0,0 +1,41 @@
|
||||
package(
|
||||
default_visibility = [
|
||||
"//tensorflow:internal",
|
||||
],
|
||||
features = [
|
||||
"-layering_check",
|
||||
"-parse_headers",
|
||||
],
|
||||
)
|
||||
|
||||
load("//tensorflow:tensorflow.bzl", "py_test")
|
||||
|
||||
licenses(["notice"]) # Apache 2.0
|
||||
|
||||
py_library(
|
||||
name = "distributions",
|
||||
srcs = glob(["*.py"]),
|
||||
srcs_version = "PY2AND3",
|
||||
deps = [
|
||||
"//tensorflow/python:array_ops",
|
||||
"//tensorflow/python:control_flow_ops",
|
||||
"//tensorflow/python:framework_for_generated_wrappers",
|
||||
"//tensorflow/python:math_ops",
|
||||
"//tensorflow/python:nn",
|
||||
"//tensorflow/python:nn_ops",
|
||||
"//tensorflow/python:platform",
|
||||
"//tensorflow/python:util",
|
||||
],
|
||||
)
|
||||
|
||||
filegroup(
|
||||
name = "all_files",
|
||||
srcs = glob(
|
||||
["**/*"],
|
||||
exclude = [
|
||||
"**/METADATA",
|
||||
"**/OWNERS",
|
||||
],
|
||||
),
|
||||
visibility = ["//tensorflow:__subpackages__"],
|
||||
)
|
||||
18
tensorflow/python/ops/distributions/__init__.py
Normal file
18
tensorflow/python/ops/distributions/__init__.py
Normal file
@ -0,0 +1,18 @@
|
||||
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
|
||||
#
|
||||
# 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.
|
||||
# ==============================================================================
|
||||
"""Core module for TensorFlow distribution objects and helpers."""
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
@ -20,7 +20,7 @@ from __future__ import print_function
|
||||
|
||||
# go/tf-wildcard-import
|
||||
# pylint: disable=wildcard-import
|
||||
from tensorflow.contrib.distributions.python.ops.bijectors.bijector_impl import *
|
||||
from tensorflow.python.ops.distributions.bijector_impl import *
|
||||
# pylint: enable=wildcard-import
|
||||
from tensorflow.python.util.all_util import remove_undocumented
|
||||
|
||||
@ -18,8 +18,8 @@ from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import distribution
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
from tensorflow.python.ops.distributions import distribution
|
||||
|
||||
|
||||
class ConditionalDistribution(distribution.Distribution):
|
||||
@ -25,13 +25,13 @@ import types
|
||||
import numpy as np
|
||||
import six
|
||||
|
||||
from tensorflow.contrib.distributions.python.ops import distribution_util
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
from tensorflow.python.framework import tensor_shape
|
||||
from tensorflow.python.framework import tensor_util
|
||||
from tensorflow.python.ops import array_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops.distributions import util
|
||||
from tensorflow.python.util import tf_inspect
|
||||
|
||||
|
||||
@ -241,7 +241,7 @@ class Distribution(_BaseDistribution):
|
||||
docstrings for their method specializations. For example:
|
||||
|
||||
```python
|
||||
@distribution_util.AppendDocstring("Some other details.")
|
||||
@util.AppendDocstring("Some other details.")
|
||||
def _log_prob(self, value):
|
||||
...
|
||||
```
|
||||
@ -1033,10 +1033,9 @@ class Distribution(_BaseDistribution):
|
||||
if ndims is None:
|
||||
# Maybe expand_dims.
|
||||
ndims = array_ops.rank(x)
|
||||
expanded_shape = distribution_util.pick_vector(
|
||||
expanded_shape = util.pick_vector(
|
||||
math_ops.equal(ndims, 0),
|
||||
np.array([1], dtype=np.int32),
|
||||
array_ops.shape(x))
|
||||
np.array([1], dtype=np.int32), array_ops.shape(x))
|
||||
x = array_ops.reshape(x, expanded_shape)
|
||||
elif ndims == 0:
|
||||
# Definitely expand_dims.
|
||||
693
tensorflow/python/ops/distributions/util.py
Normal file
693
tensorflow/python/ops/distributions/util.py
Normal file
@ -0,0 +1,693 @@
|
||||
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
|
||||
#
|
||||
# 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.
|
||||
# ==============================================================================
|
||||
"""Utilities for probability distributions."""
|
||||
|
||||
from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import functools
|
||||
import hashlib
|
||||
import math
|
||||
import numpy as np
|
||||
|
||||
from tensorflow.python.framework import constant_op
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
from tensorflow.python.framework import tensor_shape
|
||||
from tensorflow.python.framework import tensor_util
|
||||
from tensorflow.python.ops import array_ops
|
||||
from tensorflow.python.ops import check_ops
|
||||
from tensorflow.python.ops import control_flow_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import nn
|
||||
|
||||
|
||||
def assert_close(
|
||||
x, y, data=None, summarize=None, message=None, name="assert_close"):
|
||||
"""Assert that that x and y are within machine epsilon of each other.
|
||||
|
||||
Args:
|
||||
x: Floating-point `Tensor`
|
||||
y: Floating-point `Tensor`
|
||||
data: The tensors to print out if the condition is `False`. Defaults to
|
||||
error message and first few entries of `x` and `y`.
|
||||
summarize: Print this many entries of each tensor.
|
||||
message: A string to prefix to the default message.
|
||||
name: A name for this operation (optional).
|
||||
|
||||
Returns:
|
||||
Op raising `InvalidArgumentError` if |x - y| > machine epsilon.
|
||||
"""
|
||||
message = message or ""
|
||||
x = ops.convert_to_tensor(x, name="x")
|
||||
y = ops.convert_to_tensor(y, name="y")
|
||||
|
||||
if data is None:
|
||||
data = [
|
||||
message,
|
||||
"Condition x ~= y did not hold element-wise: x = ", x.name, x, "y = ",
|
||||
y.name, y
|
||||
]
|
||||
|
||||
if x.dtype.is_integer:
|
||||
return check_ops.assert_equal(
|
||||
x, y, data=data, summarize=summarize, message=message, name=name)
|
||||
|
||||
with ops.name_scope(name, "assert_close", [x, y, data]):
|
||||
tol = np.finfo(x.dtype.as_numpy_dtype).eps
|
||||
condition = math_ops.reduce_all(math_ops.less_equal(math_ops.abs(x-y), tol))
|
||||
return control_flow_ops.Assert(
|
||||
condition, data, summarize=summarize)
|
||||
|
||||
|
||||
def assert_integer_form(
|
||||
x, data=None, summarize=None, message=None, name="assert_integer_form"):
|
||||
"""Assert that x has integer components (or floats equal to integers).
|
||||
|
||||
Args:
|
||||
x: Floating-point `Tensor`
|
||||
data: The tensors to print out if the condition is `False`. Defaults to
|
||||
error message and first few entries of `x` and `y`.
|
||||
summarize: Print this many entries of each tensor.
|
||||
message: A string to prefix to the default message.
|
||||
name: A name for this operation (optional).
|
||||
|
||||
Returns:
|
||||
Op raising `InvalidArgumentError` if round(x) != x.
|
||||
"""
|
||||
|
||||
message = message or "x has non-integer components"
|
||||
x = ops.convert_to_tensor(x, name="x")
|
||||
casted_x = math_ops.to_int64(x)
|
||||
return check_ops.assert_equal(
|
||||
x, math_ops.cast(math_ops.round(casted_x), x.dtype),
|
||||
data=data, summarize=summarize, message=message, name=name)
|
||||
|
||||
|
||||
def assert_symmetric(matrix):
|
||||
matrix_t = array_ops.matrix_transpose(matrix)
|
||||
return control_flow_ops.with_dependencies(
|
||||
[check_ops.assert_equal(matrix, matrix_t)], matrix)
|
||||
|
||||
|
||||
def embed_check_nonnegative_discrete(x, check_integer=True):
|
||||
"""Assert x is a non-negative tensor, and optionally of integers."""
|
||||
assertions = [check_ops.assert_non_negative(
|
||||
x, message="x must be non-negative.")]
|
||||
if check_integer:
|
||||
assertions += [assert_integer_form(
|
||||
x, message="x cannot contain fractional components.")]
|
||||
return control_flow_ops.with_dependencies(assertions, x)
|
||||
|
||||
|
||||
def same_dynamic_shape(a, b):
|
||||
"""Returns whether a and b have the same dynamic shape.
|
||||
|
||||
Args:
|
||||
a: `Tensor`
|
||||
b: `Tensor`
|
||||
|
||||
Returns:
|
||||
`bool` `Tensor` representing if both tensors have the same shape.
|
||||
"""
|
||||
a = ops.convert_to_tensor(a, name="a")
|
||||
b = ops.convert_to_tensor(b, name="b")
|
||||
|
||||
# Here we can't just do math_ops.equal(a.shape, b.shape), since
|
||||
# static shape inference may break the equality comparison between
|
||||
# shape(a) and shape(b) in math_ops.equal.
|
||||
def all_shapes_equal():
|
||||
return math_ops.reduce_all(math_ops.equal(
|
||||
array_ops.concat([array_ops.shape(a), array_ops.shape(b)], 0),
|
||||
array_ops.concat([array_ops.shape(b), array_ops.shape(a)], 0)))
|
||||
|
||||
# One of the shapes isn't fully defined, so we need to use the dynamic
|
||||
# shape.
|
||||
return control_flow_ops.cond(
|
||||
math_ops.equal(array_ops.rank(a), array_ops.rank(b)),
|
||||
all_shapes_equal,
|
||||
lambda: constant_op.constant(False))
|
||||
|
||||
|
||||
def get_logits_and_probs(logits=None,
|
||||
probs=None,
|
||||
multidimensional=False,
|
||||
validate_args=False,
|
||||
name="get_logits_and_probs"):
|
||||
"""Converts logit to probabilities (or vice-versa), and returns both.
|
||||
|
||||
Args:
|
||||
logits: Floating-point `Tensor` representing log-odds.
|
||||
probs: Floating-point `Tensor` representing probabilities.
|
||||
multidimensional: Python `bool`, default `False`.
|
||||
If `True`, represents whether the last dimension of `logits` or `probs`,
|
||||
a `[N1, N2, ... k]` dimensional tensor, representing the
|
||||
logit or probability of `shape[-1]` classes.
|
||||
validate_args: Python `bool`, default `False`. When `True`, either assert
|
||||
`0 <= probs <= 1` (if not `multidimensional`) or that the last dimension
|
||||
of `probs` sums to one.
|
||||
name: A name for this operation (optional).
|
||||
|
||||
Returns:
|
||||
logits, probs: Tuple of `Tensor`s. If `probs` has an entry that is `0` or
|
||||
`1`, then the corresponding entry in the returned logit will be `-Inf` and
|
||||
`Inf` respectively.
|
||||
|
||||
Raises:
|
||||
ValueError: if neither `probs` nor `logits` were passed in, or both were.
|
||||
"""
|
||||
with ops.name_scope(name, values=[probs, logits]):
|
||||
if (probs is None) == (logits is None):
|
||||
raise ValueError("Must pass probs or logits, but not both.")
|
||||
|
||||
if probs is None:
|
||||
logits = ops.convert_to_tensor(logits, name="logits")
|
||||
if multidimensional:
|
||||
return logits, nn.softmax(logits, name="probs")
|
||||
return logits, math_ops.sigmoid(logits, name="probs")
|
||||
|
||||
probs = ops.convert_to_tensor(probs, name="probs")
|
||||
if validate_args:
|
||||
with ops.name_scope("validate_probs"):
|
||||
one = constant_op.constant(1., probs.dtype)
|
||||
dependencies = [check_ops.assert_non_negative(probs)]
|
||||
if multidimensional:
|
||||
dependencies += [assert_close(math_ops.reduce_sum(probs, -1), one,
|
||||
message="probs does not sum to 1.")]
|
||||
else:
|
||||
dependencies += [check_ops.assert_less_equal(
|
||||
probs, one, message="probs has components greater than 1.")]
|
||||
probs = control_flow_ops.with_dependencies(dependencies, probs)
|
||||
|
||||
with ops.name_scope("logits"):
|
||||
if multidimensional:
|
||||
# Here we don't compute the multidimensional case, in a manner
|
||||
# consistent with respect to the unidimensional case. We do so
|
||||
# following the TF convention. Typically, you might expect to see
|
||||
# logits = log(probs) - log(probs[pivot]). A side-effect of
|
||||
# being consistent with the TF approach is that the unidimensional case
|
||||
# implicitly handles the second dimension but the multidimensional case
|
||||
# explicitly keeps the pivot dimension.
|
||||
return math_ops.log(probs), probs
|
||||
return math_ops.log(probs) - math_ops.log1p(-1. * probs), probs
|
||||
|
||||
|
||||
def log_combinations(n, counts, name="log_combinations"):
|
||||
"""Multinomial coefficient.
|
||||
|
||||
Given `n` and `counts`, where `counts` has last dimension `k`, we compute
|
||||
the multinomial coefficient as:
|
||||
|
||||
```n! / sum_i n_i!```
|
||||
|
||||
where `i` runs over all `k` classes.
|
||||
|
||||
Args:
|
||||
n: Floating-point `Tensor` broadcastable with `counts`. This represents `n`
|
||||
outcomes.
|
||||
counts: Floating-point `Tensor` broadcastable with `n`. This represents
|
||||
counts in `k` classes, where `k` is the last dimension of the tensor.
|
||||
name: A name for this operation (optional).
|
||||
|
||||
Returns:
|
||||
`Tensor` representing the multinomial coefficient between `n` and `counts`.
|
||||
"""
|
||||
# First a bit about the number of ways counts could have come in:
|
||||
# E.g. if counts = [1, 2], then this is 3 choose 2.
|
||||
# In general, this is (sum counts)! / sum(counts!)
|
||||
# The sum should be along the last dimension of counts. This is the
|
||||
# "distribution" dimension. Here n a priori represents the sum of counts.
|
||||
with ops.name_scope(name, values=[n, counts]):
|
||||
n = ops.convert_to_tensor(n, name="n")
|
||||
counts = ops.convert_to_tensor(counts, name="counts")
|
||||
total_permutations = math_ops.lgamma(n + 1)
|
||||
counts_factorial = math_ops.lgamma(counts + 1)
|
||||
redundant_permutations = math_ops.reduce_sum(counts_factorial, axis=[-1])
|
||||
return total_permutations - redundant_permutations
|
||||
|
||||
|
||||
def matrix_diag_transform(matrix, transform=None, name=None):
|
||||
"""Transform diagonal of [batch-]matrix, leave rest of matrix unchanged.
|
||||
|
||||
Create a trainable covariance defined by a Cholesky factor:
|
||||
|
||||
```python
|
||||
# Transform network layer into 2 x 2 array.
|
||||
matrix_values = tf.contrib.layers.fully_connected(activations, 4)
|
||||
matrix = tf.reshape(matrix_values, (batch_size, 2, 2))
|
||||
|
||||
# Make the diagonal positive. If the upper triangle was zero, this would be a
|
||||
# valid Cholesky factor.
|
||||
chol = matrix_diag_transform(matrix, transform=tf.nn.softplus)
|
||||
|
||||
# OperatorPDCholesky ignores the upper triangle.
|
||||
operator = OperatorPDCholesky(chol)
|
||||
```
|
||||
|
||||
Example of heteroskedastic 2-D linear regression.
|
||||
|
||||
```python
|
||||
# Get a trainable Cholesky factor.
|
||||
matrix_values = tf.contrib.layers.fully_connected(activations, 4)
|
||||
matrix = tf.reshape(matrix_values, (batch_size, 2, 2))
|
||||
chol = matrix_diag_transform(matrix, transform=tf.nn.softplus)
|
||||
|
||||
# Get a trainable mean.
|
||||
mu = tf.contrib.layers.fully_connected(activations, 2)
|
||||
|
||||
# This is a fully trainable multivariate normal!
|
||||
dist = tf.contrib.distributions.MVNCholesky(mu, chol)
|
||||
|
||||
# Standard log loss. Minimizing this will "train" mu and chol, and then dist
|
||||
# will be a distribution predicting labels as multivariate Gaussians.
|
||||
loss = -1 * tf.reduce_mean(dist.log_prob(labels))
|
||||
```
|
||||
|
||||
Args:
|
||||
matrix: Rank `R` `Tensor`, `R >= 2`, where the last two dimensions are
|
||||
equal.
|
||||
transform: Element-wise function mapping `Tensors` to `Tensors`. To
|
||||
be applied to the diagonal of `matrix`. If `None`, `matrix` is returned
|
||||
unchanged. Defaults to `None`.
|
||||
name: A name to give created ops.
|
||||
Defaults to "matrix_diag_transform".
|
||||
|
||||
Returns:
|
||||
A `Tensor` with same shape and `dtype` as `matrix`.
|
||||
"""
|
||||
with ops.name_scope(name, "matrix_diag_transform", [matrix]):
|
||||
matrix = ops.convert_to_tensor(matrix, name="matrix")
|
||||
if transform is None:
|
||||
return matrix
|
||||
# Replace the diag with transformed diag.
|
||||
diag = array_ops.matrix_diag_part(matrix)
|
||||
transformed_diag = transform(diag)
|
||||
transformed_mat = array_ops.matrix_set_diag(matrix, transformed_diag)
|
||||
|
||||
return transformed_mat
|
||||
|
||||
|
||||
def rotate_transpose(x, shift, name="rotate_transpose"):
|
||||
"""Circularly moves dims left or right.
|
||||
|
||||
Effectively identical to:
|
||||
|
||||
```python
|
||||
numpy.transpose(x, numpy.roll(numpy.arange(len(x.shape)), shift))
|
||||
```
|
||||
|
||||
When `validate_args=False` additional graph-runtime checks are
|
||||
performed. These checks entail moving data from to GPU to CPU.
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
x = ... # Tensor of shape [1, 2, 3, 4].
|
||||
rotate_transpose(x, -1) # result shape: [2, 3, 4, 1]
|
||||
rotate_transpose(x, -2) # result shape: [3, 4, 1, 2]
|
||||
rotate_transpose(x, 1) # result shape: [4, 1, 2, 3]
|
||||
rotate_transpose(x, 2) # result shape: [3, 4, 1, 2]
|
||||
rotate_transpose(x, 7) == rotate_transpose(x, 3)
|
||||
rotate_transpose(x, -7) == rotate_transpose(x, -3)
|
||||
```
|
||||
|
||||
Args:
|
||||
x: `Tensor`.
|
||||
shift: `Tensor`. Number of dimensions to transpose left (shift<0) or
|
||||
transpose right (shift>0).
|
||||
name: Python `str`. The name to give this op.
|
||||
|
||||
Returns:
|
||||
rotated_x: Input `Tensor` with dimensions circularly rotated by shift.
|
||||
|
||||
Raises:
|
||||
TypeError: if shift is not integer type.
|
||||
"""
|
||||
with ops.name_scope(name, values=[x, shift]):
|
||||
x = ops.convert_to_tensor(x, name="x")
|
||||
shift = ops.convert_to_tensor(shift, name="shift")
|
||||
# We do not assign back to preserve constant-ness.
|
||||
check_ops.assert_integer(shift)
|
||||
shift_value_static = tensor_util.constant_value(shift)
|
||||
ndims = x.get_shape().ndims
|
||||
if ndims is not None and shift_value_static is not None:
|
||||
if ndims < 2: return x
|
||||
shift_value_static = np.sign(shift_value_static) * (
|
||||
abs(shift_value_static) % ndims)
|
||||
if shift_value_static == 0: return x
|
||||
perm = np.roll(np.arange(ndims), shift_value_static)
|
||||
return array_ops.transpose(x, perm=perm)
|
||||
else:
|
||||
# Consider if we always had a positive shift, and some specified
|
||||
# direction.
|
||||
# When shifting left we want the new array:
|
||||
# last(x, n-shift) + first(x, shift)
|
||||
# and if shifting right then we want:
|
||||
# last(x, shift) + first(x, n-shift)
|
||||
# Observe that last(a) == slice(a, n) and first(a) == slice(0, a).
|
||||
# Also, we can encode direction and shift as one: direction * shift.
|
||||
# Combining these facts, we have:
|
||||
# a = cond(shift<0, -shift, n-shift)
|
||||
# last(x, n-a) + first(x, a) == x[a:n] + x[0:a]
|
||||
# Finally, we transform shift by modulo length so it can be specified
|
||||
# independently from the array upon which it operates (like python).
|
||||
ndims = array_ops.rank(x)
|
||||
shift = array_ops.where(math_ops.less(shift, 0),
|
||||
math_ops.mod(-shift, ndims),
|
||||
ndims - math_ops.mod(shift, ndims))
|
||||
first = math_ops.range(0, shift)
|
||||
last = math_ops.range(shift, ndims)
|
||||
perm = array_ops.concat([last, first], 0)
|
||||
return array_ops.transpose(x, perm=perm)
|
||||
|
||||
|
||||
def pick_vector(cond,
|
||||
true_vector,
|
||||
false_vector,
|
||||
name="pick_vector"):
|
||||
"""Picks possibly different length row `Tensor`s based on condition.
|
||||
|
||||
Value `Tensor`s should have exactly one dimension.
|
||||
|
||||
If `cond` is a python Boolean or `tf.constant` then either `true_vector` or
|
||||
`false_vector` is immediately returned. I.e., no graph nodes are created and
|
||||
no validation happens.
|
||||
|
||||
Args:
|
||||
cond: `Tensor`. Must have `dtype=tf.bool` and be scalar.
|
||||
true_vector: `Tensor` of one dimension. Returned when cond is `True`.
|
||||
false_vector: `Tensor` of one dimension. Returned when cond is `False`.
|
||||
name: Python `str`. The name to give this op.
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
pick_vector(tf.less(0, 5), tf.range(10, 12), tf.range(15, 18))
|
||||
# result is tensor: [10, 11].
|
||||
pick_vector(tf.less(5, 0), tf.range(10, 12), tf.range(15, 18))
|
||||
# result is tensor: [15, 16, 17].
|
||||
```
|
||||
|
||||
Returns:
|
||||
true_or_false_vector: `Tensor`.
|
||||
|
||||
Raises:
|
||||
TypeError: if `cond.dtype != tf.bool`
|
||||
TypeError: if `cond` is not a constant and
|
||||
`true_vector.dtype != false_vector.dtype`
|
||||
"""
|
||||
with ops.name_scope(name, values=(cond, true_vector, false_vector)):
|
||||
cond = ops.convert_to_tensor(cond, name="cond")
|
||||
if cond.dtype != dtypes.bool:
|
||||
raise TypeError("%s.dtype=%s which is not %s" %
|
||||
(cond.name, cond.dtype, dtypes.bool))
|
||||
cond_value_static = tensor_util.constant_value(cond)
|
||||
if cond_value_static is not None:
|
||||
return true_vector if cond_value_static else false_vector
|
||||
true_vector = ops.convert_to_tensor(true_vector, name="true_vector")
|
||||
false_vector = ops.convert_to_tensor(false_vector, name="false_vector")
|
||||
if true_vector.dtype != false_vector.dtype:
|
||||
raise TypeError(
|
||||
"%s.dtype=%s does not match %s.dtype=%s"
|
||||
% (true_vector.name, true_vector.dtype,
|
||||
false_vector.name, false_vector.dtype))
|
||||
n = array_ops.shape(true_vector)[0]
|
||||
return array_ops.slice(
|
||||
array_ops.concat([true_vector, false_vector], 0),
|
||||
[array_ops.where(cond, 0, n)], [array_ops.where(cond, n, -1)])
|
||||
|
||||
|
||||
def gen_new_seed(seed, salt):
|
||||
"""Generate a new seed, from the given seed and salt."""
|
||||
if seed is None:
|
||||
return None
|
||||
string = (str(seed) + salt).encode("utf-8")
|
||||
return int(hashlib.md5(string).hexdigest()[:8], 16) & 0x7FFFFFFF
|
||||
|
||||
|
||||
def fill_lower_triangular(x, validate_args=False, name="fill_lower_triangular"):
|
||||
"""Creates a (batch of) lower triangular matrix from a vector of inputs.
|
||||
|
||||
If `x.get_shape()` is `[b1, b2, ..., bK, d]` then the output shape is `[b1,
|
||||
b2, ..., bK, n, n]` where `n` is such that `d = n(n+1)/2`, i.e.,
|
||||
`n = int(0.5 * (math.sqrt(1. + 8. * d) - 1.))`.
|
||||
|
||||
Although the non-batch complexity is O(n**2), large constants and sub-optimal
|
||||
vectorization means the complexity of this function is 5x slower than zeroing
|
||||
out the upper triangular, i.e., `tf.matrix_band_part(X, -1, 0)`. This
|
||||
function becomes competitive only when several matmul/cholesky/etc ops can be
|
||||
ellided in constructing the input. Example: wiring a fully connected layer as
|
||||
a covariance matrix; this function reduces the final layer by 2x and possibly
|
||||
reduces the network arch complexity considerably. In most cases it is better
|
||||
to simply build a full matrix and zero out the upper triangular elements,
|
||||
e.g., `tril = tf.matrix_band_part(full, -1, 0)`, rather than directly
|
||||
construct a lower triangular.
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
fill_lower_triangular([1, 2, 3, 4, 5, 6])
|
||||
# Returns: [[1, 0, 0],
|
||||
# [2, 3, 0],
|
||||
# [4, 5, 6]]
|
||||
```
|
||||
|
||||
For comparison, a pure numpy version of this function can be found in
|
||||
`distribution_util_test.py`, function `_fill_lower_triangular`.
|
||||
|
||||
Args:
|
||||
x: `Tensor` representing lower triangular elements.
|
||||
validate_args: Python `bool`, default `False`. Whether to ensure the shape
|
||||
of `x` can be mapped to a lower triangular matrix (controls non-static
|
||||
checks only).
|
||||
name: Python `str`. The name to give this op.
|
||||
|
||||
Returns:
|
||||
tril: `Tensor` with lower triangular elements filled from `x`.
|
||||
|
||||
Raises:
|
||||
ValueError: if shape if `x` has static shape which cannot be mapped to a
|
||||
lower triangular matrix.
|
||||
"""
|
||||
# TODO(jvdillon): Replace this code with dedicated op when it exists.
|
||||
with ops.name_scope(name, values=[x]):
|
||||
x = ops.convert_to_tensor(x, name="x")
|
||||
if (x.get_shape().ndims is not None and
|
||||
x.get_shape()[-1].value is not None):
|
||||
d = x.get_shape()[-1].value
|
||||
# d = n(n+1)/2 implies n is:
|
||||
n = int(0.5 * (math.sqrt(1. + 8. * d) - 1.))
|
||||
d_inferred = n * (n + 1) /2
|
||||
if d != d_inferred:
|
||||
raise ValueError("Input cannot be mapped to a lower triangular; "
|
||||
"n*(n+1)/2 = %d != %d" % (d_inferred, d))
|
||||
final_shape = x.get_shape()[:-1].concatenate(
|
||||
tensor_shape.TensorShape([n, n]))
|
||||
else:
|
||||
d = math_ops.cast(array_ops.shape(x)[-1], dtype=dtypes.float32)
|
||||
# d = n(n+1)/2 implies n is:
|
||||
n = math_ops.cast(0.5 * (dtypes.sqrt(1. + 8. * d) - 1.),
|
||||
dtype=dtypes.int32)
|
||||
if validate_args:
|
||||
is_valid_input_shape = check_ops.assert_equal(
|
||||
n * (n + 1) / 2, d,
|
||||
message="Input cannot be mapped to a lower triangular.")
|
||||
n = control_flow_ops.with_dependencies([is_valid_input_shape], n)
|
||||
final_shape = x.get_shape()[:-1].concatenate(
|
||||
tensor_shape.TensorShape([None, None]))
|
||||
|
||||
def tril_ids(n):
|
||||
"""Internal helper to create vector of linear indices into y."""
|
||||
# Build the ids statically; chose 512 because it implies 1MiB.
|
||||
if not tensor_util.is_tensor(n) and n <= 512:
|
||||
ids = np.arange(n**2, dtype=np.int32)
|
||||
rows = (ids / n).astype(np.int32) # Implicit floor.
|
||||
# We need to stop incrementing the index when we encounter
|
||||
# upper-triangular elements. The idea here is to compute the
|
||||
# lower-right number of zeros then by "symmetry" subtract this from the
|
||||
# total number of zeros, n(n-1)/2.
|
||||
# Then we note that: n(n-1)/2 - (n-r)*(n-r-1)/2 = r(2n-r-1)/2
|
||||
offset = (rows * (2 * n - rows - 1) / 2).astype(np.int32)
|
||||
# We could also zero out when (rows < cols) == (rows < ids-n*rows).
|
||||
# mask = (ids <= (n + 1) * rows).astype(np.int32)
|
||||
else:
|
||||
ids = math_ops.range(n**2)
|
||||
rows = math_ops.cast(ids / n, dtype=dtypes.int32)
|
||||
offset = math_ops.cast(rows * (2 * n - rows - 1) / 2,
|
||||
dtype=dtypes.int32)
|
||||
return ids - offset
|
||||
|
||||
# Special-case non-batch case.
|
||||
if x.get_shape().ndims == 1:
|
||||
y = array_ops.gather(x, array_ops.reshape(tril_ids(n), [n, n]))
|
||||
y = array_ops.matrix_band_part(y, -1, 0)
|
||||
y.set_shape(y.get_shape().merge_with(final_shape))
|
||||
return y
|
||||
|
||||
# Make ids for each batch dim.
|
||||
if (x.get_shape().ndims is not None and
|
||||
x.get_shape()[:-1].is_fully_defined()):
|
||||
batch_shape = np.asarray(x.get_shape()[:-1].as_list(), dtype=np.int32)
|
||||
m = np.prod(batch_shape).astype(np.int32)
|
||||
else:
|
||||
batch_shape = array_ops.shape(x)[:-1]
|
||||
m = array_ops.reduce_prod(array_ops.shape(x)[:-1])
|
||||
batch_ids = math_ops.range(m)
|
||||
|
||||
# Assemble the tril_ids into batch,tril_id pairs.
|
||||
idx = array_ops.stack([
|
||||
array_ops.tile(array_ops.expand_dims(batch_ids, 1), [1, n * n]),
|
||||
array_ops.tile(array_ops.expand_dims(tril_ids(n), 0), [m, 1])
|
||||
])
|
||||
idx = array_ops.transpose(idx, [1, 2, 0])
|
||||
|
||||
# Gather up, reshape, and return.
|
||||
y = array_ops.reshape(x, [-1, d])
|
||||
y = array_ops.gather_nd(y, idx)
|
||||
y = array_ops.reshape(y, array_ops.concat([batch_shape, [n, n]], 0))
|
||||
y = array_ops.matrix_band_part(y, -1, 0)
|
||||
y.set_shape(y.get_shape().merge_with(final_shape))
|
||||
return y
|
||||
|
||||
|
||||
# TODO(jvdillon): Merge this test back into:
|
||||
# tensorflow/python/ops/softplus_op_test.py
|
||||
# once TF core is accepting new ops.
|
||||
def softplus_inverse(x, name=None):
|
||||
"""Computes the inverse softplus, i.e., x = softplus_inverse(softplus(x)).
|
||||
|
||||
Mathematically this op is equivalent to:
|
||||
|
||||
```none
|
||||
softplus_inverse = log(exp(x) - 1.)
|
||||
```
|
||||
|
||||
Args:
|
||||
x: `Tensor`. Non-negative (not enforced), floating-point.
|
||||
name: A name for the operation (optional).
|
||||
|
||||
Returns:
|
||||
`Tensor`. Has the same type/shape as input `x`.
|
||||
"""
|
||||
with ops.name_scope(name, "softplus_inverse", values=[x]):
|
||||
x = ops.convert_to_tensor(x, name="x")
|
||||
# We begin by deriving a more numerically stable softplus_inverse:
|
||||
# x = softplus(y) = Log[1 + exp{y}], (which means x > 0).
|
||||
# ==> exp{x} = 1 + exp{y} (1)
|
||||
# ==> y = Log[exp{x} - 1] (2)
|
||||
# = Log[(exp{x} - 1) / exp{x}] + Log[exp{x}]
|
||||
# = Log[(1 - exp{-x}) / 1] + Log[exp{x}]
|
||||
# = Log[1 - exp{-x}] + x (3)
|
||||
# (2) is the "obvious" inverse, but (3) is more stable than (2) for large x.
|
||||
# For small x (e.g. x = 1e-10), (3) will become -inf since 1 - exp{-x} will
|
||||
# be zero. To fix this, we use 1 - exp{-x} approx x for small x > 0.
|
||||
#
|
||||
# In addition to the numerically stable derivation above, we clamp
|
||||
# small/large values to be congruent with the logic in:
|
||||
# tensorflow/core/kernels/softplus_op.h
|
||||
#
|
||||
# Finally, we set the input to one whenever the input is too large or too
|
||||
# small. This ensures that no unchosen codepath is +/- inf. This is
|
||||
# necessary to ensure the gradient doesn't get NaNs. Recall that the
|
||||
# gradient of `where` behaves like `pred*pred_true + (1-pred)*pred_false`
|
||||
# thus an `inf` in an unselected path results in `0*inf=nan`. We are careful
|
||||
# to overwrite `x` with ones only when we will never actually use this
|
||||
# value. Note that we use ones and not zeros since `log(expm1(0.)) = -inf`.
|
||||
threshold = np.log(np.finfo(x.dtype.as_numpy_dtype).eps) + 2.
|
||||
is_too_small = math_ops.less(x, np.exp(threshold))
|
||||
is_too_large = math_ops.greater(x, -threshold)
|
||||
too_small_value = math_ops.log(x)
|
||||
too_large_value = x
|
||||
# This `where` will ultimately be a NOP because we won't select this
|
||||
# codepath whenever we used the surrogate `ones_like`.
|
||||
x = array_ops.where(math_ops.logical_or(is_too_small, is_too_large),
|
||||
array_ops.ones_like(x), x)
|
||||
y = x + math_ops.log(-math_ops.expm1(-x)) # == log(expm1(x))
|
||||
return array_ops.where(is_too_small, too_small_value,
|
||||
array_ops.where(is_too_large, too_large_value, y))
|
||||
|
||||
|
||||
# TODO(b/35290280): Add unit-tests.
|
||||
def dimension_size(x, axis):
|
||||
"""Returns the size of a specific dimension."""
|
||||
# Since tf.gather isn't "constant-in, constant-out", we must first check the
|
||||
# static shape or fallback to dynamic shape.
|
||||
num_rows = (None if x.get_shape().ndims is None
|
||||
else x.get_shape()[axis].value)
|
||||
if num_rows is not None:
|
||||
return num_rows
|
||||
return array_ops.shape(x)[axis]
|
||||
|
||||
|
||||
class AppendDocstring(object):
|
||||
"""Helper class to promote private subclass docstring to public counterpart.
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
class TransformedDistribution(Distribution):
|
||||
@distribution_util.AppendDocstring(
|
||||
additional_note="A special note!",
|
||||
kwargs_dict={"foo": "An extra arg."})
|
||||
def _prob(self, y, foo=None):
|
||||
pass
|
||||
```
|
||||
|
||||
In this case, the `AppendDocstring` decorator appends the `additional_note` to
|
||||
the docstring of `prob` (not `_prob`) and adds a new `kwargs`
|
||||
section with each dictionary item as a bullet-point.
|
||||
|
||||
For a more detailed example, see `TransformedDistribution`.
|
||||
"""
|
||||
|
||||
def __init__(self, additional_note="", kwargs_dict=None):
|
||||
"""Initializes the AppendDocstring object.
|
||||
|
||||
Args:
|
||||
additional_note: Python string added as additional docstring to public
|
||||
version of function.
|
||||
kwargs_dict: Python string/string dictionary representing
|
||||
specific kwargs expanded from the **kwargs input.
|
||||
|
||||
Raises:
|
||||
ValueError: if kwargs_dict.key contains whitespace.
|
||||
ValueError: if kwargs_dict.value contains newlines.
|
||||
"""
|
||||
self._additional_note = additional_note
|
||||
if kwargs_dict:
|
||||
bullets = []
|
||||
for key in sorted(kwargs_dict.keys()):
|
||||
value = kwargs_dict[key]
|
||||
if any(x.isspace() for x in key):
|
||||
raise ValueError(
|
||||
"Parameter name \"%s\" contains whitespace." % key)
|
||||
value = value.lstrip()
|
||||
if "\n" in value:
|
||||
raise ValueError(
|
||||
"Parameter description for \"%s\" contains newlines." % key)
|
||||
bullets.append("* `%s`: %s" % (key, value))
|
||||
self._additional_note += ("\n\n##### `kwargs`:\n\n" +
|
||||
"\n".join(bullets))
|
||||
|
||||
def __call__(self, fn):
|
||||
@functools.wraps(fn)
|
||||
def _fn(*args, **kwargs):
|
||||
return fn(*args, **kwargs)
|
||||
if _fn.__doc__ is None:
|
||||
_fn.__doc__ = self._additional_note
|
||||
else:
|
||||
_fn.__doc__ += "\n%s" % self._additional_note
|
||||
return _fn
|
||||
Loading…
Reference in New Issue
Block a user