Normal¶
-
class
tfsnippet.
Normal
(mean, std=None, logstd=None, is_reparameterized=True, check_numerics=None)¶ Bases:
tfsnippet.distributions.wrapper.ZhuSuanDistribution
Univariate Normal distribution.
See also
tfsnippet.distributions.Distribution
,zhusuan.distributions.Distribution
,zhusuan.distributions.Normal
Attributes Summary
base_distribution
Get the base distribution of this distribution. batch_shape
Get the batch shape of the samples. 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? logstd
Get the log standard deviation of the Normal distribution. mean
Get the mean of the Normal distribution. std
Get the standard deviation of the Normal distribution. 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, is_reparameterized, …])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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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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logstd
¶ Get the log standard deviation of the Normal distribution.
-
mean
¶ Get the mean of the Normal distribution.
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std
¶ Get the standard deviation of the Normal distribution.
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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, is_reparameterized=None, group_ndims=0, 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
-