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@drasmuss drasmuss released this 26 Nov 18:47
v2.0.0
41ce561

Breaking API changes

  • sim.train and sim.loss now accept a single data argument, which combines the previous inputs and targets arguments. For example,

    sim.train({my_node: x}, {my_probe: y}, ...)

    is now equivalent to

    sim.train({my_node: x, my_probe: y}, ...)

    The motivation for this change is that not all objective functions require target values. Switching to the more generic data argument simplifies the API and makes it more flexible, allowing users to specify whatever training/loss data is actually required.

  • The objective argument in sim.train/sim.loss is now always specified as a dictionary mapping probes to objective functions. Note that this was available but optional previously; it was also possible to pass a single value for the objective function, which would be applied to all probes in targets. The latter is no longer supported. For example,

    sim.train(..., objective="mse")

    must now be explicitly specified as

    sim.train(..., objective={my_probe: "mse"})

    The motivation for this change is that, especially with the other new features introduced in the 2.0 update, there were a lot of different ways to specify the objective argument. This made it somewhat unclear how exactly this argument worked, and the automatic "broadcasting" was also ambiguous (e.g., should the single objective be applied to each probe individually, or to all of them together?). Making the argument explicit helps clarify the mental model.

Added

  • An integer number of steps can now be passed for the sim.loss/sim.train data argument, if no input/target data is required.
  • The objective dict in sim.train/sim.loss can now contain tuples of probes as the keys, in which case the objective function will be called with a corresponding tuple of probe/target values as each argument.
  • Added the sim.run_batch function. This exposes all the functionality that the sim.run/sim.train/sim.loss functions are based on, allowing advanced users full control over how to run a NengoDL simulation.
  • Added option to disable progress bar in sim.train and sim.loss.
  • Added training argument to sim.loss to control whether the loss is evaluated in training or inference mode.
  • Added support for the new Nengo Transform API (see nengo/nengo#1481).

Changed

  • Custom objective functions passed to sim.train/sim.loss can now accept a single argument (my_objective(outputs): ... instead of my_objective(outputs, targets): ...) if no target values are required.
  • utils.minibatch_generator now accepts a single data argument rather than inputs and targets (see discussion in "Breaking API changes").
  • sim.training_step is now the same as tf.train.get_or_create_global_step().
  • Switched documentation to new nengo-sphinx-theme.
  • Reorganized documentation into "User guide" and "API reference" sections.
  • Improve build speed of models with large constants (#69)
  • Moved op-specific merge logic into the OpBuilder classes.

Fixed

  • Ensure that training step is always updated before TensorBoard events are added (previously it could update before or after depending on the platform).

Deprecated

  • The sim.run input_feeds argument has been renamed to data (for consistency with other simulator functions).

Removed

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