[英]Tensorflow probability incompatible shapes error on log prob of JointDistributionSequential samples
Hi I'm trying to figure out whether I made a mistake with TFP shapes or if this is a TFP bug.嗨,我想弄清楚我是否在 TFP 形状上犯了错误,或者这是否是 TFP 错误。 I can sample from this simple joint distribution which uses a Normal prior on the mean of a 3-dim multivariate normal and 3 draws from a Half Cauchy as a prior on the diagonal of the MVN covariance.
我可以从这个简单的联合分布中采样,它在 3-dim 多元正态的平均值上使用正态先验,在 MVN 协方差的对角线上使用半柯西的 3 个绘制作为先验。
I'm using tensorflow 2.2.0, and this bug occurs on both tensorflow-probability 0.10.1 and the nighly built tensorflow-probability 0.12.0-dev20200719.我正在使用 tensorflow 2.2.0,这个错误发生在 tensorflow-probability 0.10.1 和几乎构建的 tensorflow-probability 0.12.0-dev20200719 上。
This code should work standalone:此代码应独立工作:
import tensorflow as tf
import tensorflow_probability as tfp
tfd = tfp.distributions
joint_model = tfd.JointDistributionSequential([
tfd.Normal(loc=0., scale=1., name='z_0'),
tfd.HalfCauchy(loc=tf.zeros([3]), scale=2., name='lambda_k'),
lambda lambda_k, z_0: tfd.MultivariateNormalDiag( # z_k ~ MVN(z_0, lambda_k)
loc=z_0[...,tf.newaxis],
scale_diag=lambda_k,
name='z_k'),
])
# These work
joint_model.sample()
joint_model.sample(4)
joint_model.log_prob(joint_model.sample())
# This breaks
joint_model.log_prob(joint_model.sample(4))
Here's the error message:这是错误消息:
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-41-61bae7ab690a> in <module>
1 joint_model.log_prob(joint_model.sample())
2 # ERROR
----> 3 joint_model.log_prob(joint_model.sample(4))
~/miniconda3/envs/latent2/lib/python3.7/site-packages/tensorflow_probability/python/distributions/joint_distribution.py in log_prob(self, *args, **kwargs)
479 model_flatten_fn=self._model_flatten,
480 model_unflatten_fn=self._model_unflatten)
--> 481 return self._call_log_prob(value, **unmatched_kwargs)
482
483 # Override the base method to capture *args and **kwargs, so we can
~/miniconda3/envs/latent2/lib/python3.7/site-packages/tensorflow_probability/python/distributions/distribution.py in _call_log_prob(self, value, name, **kwargs)
944 with self._name_and_control_scope(name, value, kwargs):
945 if hasattr(self, '_log_prob'):
--> 946 return self._log_prob(value, **kwargs)
947 if hasattr(self, '_prob'):
948 return tf.math.log(self._prob(value, **kwargs))
~/miniconda3/envs/latent2/lib/python3.7/site-packages/tensorflow_probability/python/distributions/joint_distribution.py in _log_prob(self, value)
391 def _log_prob(self, value):
392 xs = self._map_measure_over_dists('log_prob', value)
--> 393 return sum(maybe_check_wont_broadcast(xs, self.validate_args))
394
395 @distribution_util.AppendDocstring(kwargs_dict={
~/miniconda3/envs/latent2/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py in binary_op_wrapper(x, y)
982 with ops.name_scope(None, op_name, [x, y]) as name:
983 if isinstance(x, ops.Tensor) and isinstance(y, ops.Tensor):
--> 984 return func(x, y, name=name)
985 elif not isinstance(y, sparse_tensor.SparseTensor):
986 try:
~/miniconda3/envs/latent2/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py in _add_dispatch(x, y, name)
1274 return gen_math_ops.add(x, y, name=name)
1275 else:
-> 1276 return gen_math_ops.add_v2(x, y, name=name)
1277
1278
~/miniconda3/envs/latent2/lib/python3.7/site-packages/tensorflow/python/ops/gen_math_ops.py in add_v2(x, y, name)
478 pass # Add nodes to the TensorFlow graph.
479 except _core._NotOkStatusException as e:
--> 480 _ops.raise_from_not_ok_status(e, name)
481 # Add nodes to the TensorFlow graph.
482 _, _, _op, _outputs = _op_def_library._apply_op_helper(
~/miniconda3/envs/latent2/lib/python3.7/site-packages/tensorflow/python/framework/ops.py in raise_from_not_ok_status(e, name)
6651 message = e.message + (" name: " + name if name is not None else "")
6652 # pylint: disable=protected-access
-> 6653 six.raise_from(core._status_to_exception(e.code, message), None)
6654 # pylint: enable=protected-access
6655
~/miniconda3/envs/latent2/lib/python3.7/site-packages/six.py in raise_from(value, from_value)
InvalidArgumentError: Incompatible shapes: [4] vs. [4,3] [Op:AddV2]
This has been driving me absolutely insane so any help is appreciated这一直让我发疯所以任何帮助表示赞赏
Thanks @Miles Turpin, for sharing the solution reference.感谢@Miles Turpin,分享解决方案参考。 For the benefit of community I am providing solution here (answer section) given by jeffpollock9 in github .
为了社区的利益,我在 github 中提供了jeffpollock9给出的解决方案(答案部分)。
There is a mix up between the batch_shape
and event_shape
in joint distribution, and it can be fixed by wrapping the half Cauchy distribution with tfd.Independent
联合分布中的
batch_shape
和event_shape
之间存在混淆,可以通过用tfd.Independent
包裹半 Cauchy 分布来修复
Please refer working code in below请参考下面的工作代码
import tensorflow as tf
import tensorflow_probability as tfp
tfd = tfp.distributions
joint_model = tfd.JointDistributionSequential([
tfd.Normal(loc=0., scale=1., name='z_0'),
tfd.Independent(tfd.HalfCauchy(loc=tf.zeros([3]), scale=2., name='lambda_k'), reinterpreted_batch_ndims=1),
lambda lambda_k, z_0: tfd.MultivariateNormalDiag( # z_k ~ MVN(z_0, lambda_k)
loc=z_0[...,tf.newaxis],
scale_diag=lambda_k,
name='z_k'),
])
print(joint_model)
print(joint_model.log_prob(joint_model.sample(4)))
Output: Output:
tfp.distributions.JointDistributionSequential("JointDistributionSequential", batch_shape=[[], [], []], event_shape=[[], [3], [3]], dtype=[float32, float32, float32])
tf.Tensor([-14.330933 -16.854149 -15.07704 -6.9233823], shape=(4,), dtype=float32)
Please refer this reasoning about Shapes and Probability Distributions.请参考这个关于形状和概率分布的推理。
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