New field distribution.graph represents graphical model of the distribution. #119
Supported drawing a grapical model by networkx.draw_networkx(distribution.graph.visible_graph()). #122
Added tutorial notebooks. #148
Enabled memoization for calcuration graph of pixyz to speed up. #149
changed api
Renamed DataDistribution to EmpiricalDistribution. #146
timestep_var is no longer specified by default (t is still the default in the display), which gives the time index when evaluating step loss or calling SliceStep. #142
Corrected the error of the time index in the display. #142
Reverted the TransformedDistribution arguments to their previous (=v0.1.4) format (Inference in TransformedDistribution internally uses the result of the sample / forward called immediately before). #139
Removed the input_var argument of the Loss API. #150
Added the ConstantVar class that sets the value of a variable before calling loss.eval. #150
The argument order of var and cond_var in Distribution API has been unified. (Var first) #147
Changed the output directory of example notebooks to make it easier to browse (and automatically installed sklearn with !pip). #141
bug fix
Eliminated of errors in pytorch 1.6 caused by flow's non-contiguous tensors. #140
Fixed bug related to documents. #138
Fixed a bug so that PoE does not cause an error when only one distribution is specified. #144
Fixed a bug when specifying the same random variable as args of init in DistributionBase. #143
Replaced forward call with __call__ call to take advantage of pytorch.nn.module hook options. #136
new feature
distribution.graph
represents graphical model of the distribution. #119networkx.draw_networkx(distribution.graph.visible_graph())
. #122changed api
timestep_var
is no longer specified by default (t is still the default in the display), which gives the time index when evaluating step loss or calling SliceStep. #142bug fix
forward
call with__call__
call to take advantage of pytorch.nn.module hook options. #136