I am playing with SofmaxCenter bijector in tensorflow_probability and get some errors. Since the document for it is at the infancy state, I was not able to figure out what is wrong. I hope you can help me out.
Basically, given that X is a log-normal random vector of three components, I would like to create another random vector Y which is defined as the softmax-center transformation of X.
The following snippet of code does not give any error
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import matplotlib.pyplot as plt; plt.style.use('ggplot')
%matplotlib inline
import numpy as np
import seaborn as sns; sns.set_context('notebook')
import tensorflow as tf
import tensorflow_probability as tfp
tfd = tfp.distributions
tfb = tfp.bijectors
tfe = tf.contrib.eager
tfe.enable_eager_execution()
X = tfd.LogNormal(loc=[[-5.0, 0.0, 4.0]],
scale=[[2.0, 1.0, 1.5]])
Y = tfd.TransformedDistribution(
distribution=X,
bijector=tfb.SoftmaxCentered()
)
However, when I try,
Y.sample(10)
I got the following errors,
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-20-9ed8f482f3c1> in <module>
----> 1 Y.sample(10)
anaconda3/lib/python3.6/site-packages/tensorflow_probability/python/distributions/distribution.py in sample(self, sample_shape, seed, name)
684 samples: a `Tensor` with prepended dimensions `sample_shape`.
685 """
--> 686 return self._call_sample_n(sample_shape, seed, name)
687
688 def _log_prob(self, value):
anaconda3/lib/python3.6/site-packages/tensorflow_probability/python/distributions/transformed_distribution.py in _call_sample_n(self, sample_shape, seed, name, **kwargs)
405 # returned result.
406 y = self.bijector.forward(x, **kwargs)
--> 407 y = self._set_sample_static_shape(y, sample_shape)
408
409 return y
anaconda3/lib/python3.6/site-packages/tensorflow_probability/python/distributions/distribution.py in _set_sample_static_shape(self, x, sample_shape)
1201 sample_ndims = sample_shape.ndims
1202 batch_ndims = self.batch_shape.ndims
-> 1203 event_ndims = self.event_shape.ndims
1204
1205 # Infer rank(x).
anaconda3/lib/python3.6/site-packages/tensorflow_probability/python/distributions/distribution.py in event_shape(self)
622 event_shape: `TensorShape`, possibly unknown.
623 """
--> 624 return tf.TensorShape(self._event_shape())
625
626 def is_scalar_event(self, name="is_scalar_event"):
anaconda3/lib/python3.6/site-packages/tensorflow_probability/python/distributions/transformed_distribution.py in _event_shape(self)
345 static_override
346 if self._is_maybe_event_override
--> 347 else self.distribution.event_shape)
348
349 def _batch_shape_tensor(self):
anaconda3/lib/python3.6/site-packages/tensorflow_probability/python/bijectors/bijector.py in forward_event_shape(self, input_shape)
680 after applying `forward`. Possibly unknown.
681 """
--> 682 return self._forward_event_shape(tf.TensorShape(input_shape))
683
684 def _inverse_event_shape_tensor(self, output_shape):
anaconda3/lib/python3.6/site-packages/tensorflow_probability/python/bijectors/softmax_centered.py in _forward_event_shape(self, input_shape)
68
69 def _forward_event_shape(self, input_shape):
---> 70 if input_shape.ndims is None or input_shape[-1] is None:
71 return input_shape
72 return tf.TensorShape([input_shape[-1] + 1])
anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/tensor_shape.py in __getitem__(self, key)
614 return TensorShape(self._dims[key])
615 else:
--> 616 return self._dims[key]
617 else:
618 if isinstance(key, slice):
IndexError: list index out of range
Thank you!
SoftmaxCentered wants to operate on a vector event shape, but the LogNormal distribution has a scalar event shape. If you want to take a softmax across a vector of independent LogNormals, you could do:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import matplotlib.pyplot as plt; plt.style.use('ggplot')
%matplotlib inline
import numpy as np
import seaborn as sns; sns.set_context('notebook')
import tensorflow as tf
import tensorflow_probability as tfp
tfd = tfp.distributions
tfb = tfp.bijectors
tfe = tf.contrib.eager
tfe.enable_eager_execution()
X = tfd.LogNormal(loc=[[-5.0, 0.0, 4.0]],
scale=[[2.0, 1.0, 1.5]])
Z = tfd.Independent(X, reinterpreted_batch_ndims=1)
Y = tfd.TransformedDistribution(
distribution=Z,
bijector=tfb.SoftmaxCentered()
)
Y.sample(2)
Note, of course, that SoftmaxCentered takes a 3-D space and projects it onto a 3-D manifold in 4-D space. This is to provide invertibility.