Mixture¶
-
class
tfsnippet.
Mixture
(categorical, components, is_reparameterized=False)¶ Bases:
tfsnippet.distributions.base.Distribution
Mixture distribution.
Given a categorical distribution, and corresponding component distributions, this class derives a mixture distribution, formulated as follows:
\[p(x) = \sum_{k=1}^{K} \pi(k) p_k(x)\]where \(\pi(k)\) is the probability of taking the k-th component, derived by the categorical distribution, and \(p_k(x)\) is the density of the k-th component distribution.
Attributes Summary
base_distribution
Get the base distribution of this distribution. batch_shape
Get the batch shape of the samples. categorical
Get the categorical distribution of this mixture. components
Get the mixture components of this distribution. dtype
Get the data type of samples. is_continuous
Whether or not the distribution is continuous? is_reparameterized
Whether or not the distribution is re-parameterized? n_components
Get the number of mixture components. value_ndims
Get the number of value dimensions in samples. Methods Summary
batch_ndims_to_value
(ndims)Convert the last few batch_ndims into value_ndims. expand_value_ndims
(ndims)Convert the last few batch_ndims into value_ndims. get_batch_shape
()Get the static batch shape of the samples. log_prob
(given[, group_ndims, name])Compute the log-densities of x against the distribution. prob
(given[, group_ndims, name])Compute the densities of x against the distribution. sample
([n_samples, group_ndims, …])Generate samples from the distribution. Attributes Documentation
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base_distribution
¶ Get the base distribution of this distribution.
For distribution other than
tfsnippet.BatchToValueDistribution
, this property should return this distribution itself.Returns: The base distribution. Return type: Distribution
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batch_shape
¶ Get the batch shape of the samples.
Returns: The batch shape as tensor. Return type: tf.Tensor
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categorical
¶ Get the categorical distribution of this mixture.
Returns: The categorical distribution. Return type: Categorical
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components
¶ Get the mixture components of this distribution.
Returns: The mixture components. Return type: tuple[Distribution]
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dtype
¶ Get the data type of samples.
Returns: Data type of the samples. Return type: tf.DType
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is_continuous
¶ Whether or not the distribution is continuous?
Returns: A boolean indicating whether it is continuous. Return type: bool
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is_reparameterized
¶ Whether or not the distribution is re-parameterized?
The re-parameterization trick is proposed in “Auto-Encoding Variational Bayes” (Kingma, D.P. and Welling), allowing the gradients to be propagated back along the samples. Note that the re-parameterization can be disabled by specifying
is_reparameterized = False
as an argument ofsample()
.Returns: A boolean indicating whether it is re-parameterized. Return type: bool
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n_components
¶ Get the number of mixture components.
Returns: The number of mixture components. Return type: int
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value_ndims
¶ Get the number of value dimensions in samples.
Returns: The number of value dimensions in samples. Return type: int
Methods Documentation
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batch_ndims_to_value
(ndims)¶ Convert the last few batch_ndims into value_ndims.
For a particular
Distribution
, the number of dimensions between the samples and the log-probability of the samples should satisfy:samples.ndims - distribution.value_ndims == log_det.ndims
We denote samples.ndims - distribution.value_ndims by batch_ndims. This method thus wraps the current distribution, converts the last few batch_ndims into value_ndims.
Parameters: ndims (int) – The last few batch_ndims to be converted into value_ndims. Must be non-negative. Returns: The converted distribution. Return type: Distribution
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expand_value_ndims
(ndims)¶ Convert the last few batch_ndims into value_ndims.
For a particular
Distribution
, the number of dimensions between the samples and the log-probability of the samples should satisfy:samples.ndims - distribution.value_ndims == log_det.ndims
We denote samples.ndims - distribution.value_ndims by batch_ndims. This method thus wraps the current distribution, converts the last few batch_ndims into value_ndims.
Parameters: ndims (int) – The last few batch_ndims to be converted into value_ndims. Must be non-negative. Returns: The converted distribution. Return type: Distribution
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get_batch_shape
()¶ Get the static batch shape of the samples.
Returns: The batch shape. Return type: tf.TensorShape
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log_prob
(given, group_ndims=0, name=None)¶ Compute the log-densities of x against the distribution.
Parameters: - given (Tensor) – The samples to be tested.
- group_ndims (int or tf.Tensor) – If specified, the last group_ndims dimensions of the log-densities will be summed up. (default 0)
- name – TensorFlow name scope of the graph nodes. (default “log_prob”).
Returns: The log-densities of given.
Return type: tf.Tensor
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prob
(given, group_ndims=0, name=None)¶ Compute the densities of x against the distribution.
Parameters: - given (Tensor) – The samples to be tested.
- group_ndims (int or tf.Tensor) – If specified, the last group_ndims dimensions of the log-densities will be summed up. (default 0)
- name – TensorFlow name scope of the graph nodes. (default “prob”).
Returns: The densities of given.
Return type: tf.Tensor
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sample
(n_samples=None, group_ndims=0, is_reparameterized=None, compute_density=None, name=None)¶ Generate samples from the distribution.
Parameters: - n_samples (int or tf.Tensor or None) – A 0-D int32 Tensor or None.
How many independent samples to draw from the distribution.
The samples will have shape
[n_samples] + batch_shape + value_shape
, orbatch_shape + value_shape
if n_samples isNone
. - group_ndims (int or tf.Tensor) – Number of dimensions at the end of
[n_samples] + batch_shape
to be considered as events group. This will effect the behavior oflog_prob()
andprob()
. (default 0) - is_reparameterized (bool) – If
True
, raisesRuntimeError
if the distribution is not re-parameterized. IfFalse
, disable re-parameterization even if the distribution is re-parameterized. (defaultNone
, following the setting of distribution) - compute_density (bool) – Whether or not to immediately compute the
log-density for the samples? (default
None
, determine by the distribution class itself) - name – TensorFlow name scope of the graph nodes. (default “sample”).
Returns: - The samples as
StochasticTensor
.
Return type: tfsnippet.stochastic.StochasticTensor
- n_samples (int or tf.Tensor or None) – A 0-D int32 Tensor or None.
How many independent samples to draw from the distribution.
The samples will have shape
-