Note

This documentation is for a development version. Click here for the latest stable release (v0.2.0).

# Release history¶

## Changelog¶

### 0.2.1 (unreleased)¶

### 0.2.0 (February 18, 2021)¶

*Compatible with TensorFlow 2.1.0 - 2.4.0*

**Added**

Added the

`keras_spiking.Alpha`

filter, which provides second-order lowpass filtering for better noise removal for spiking layers. (#4)Added

`keras_spiking.callbacks.DtScheduler`

, which can be used to update layer`dt`

parameters during training. (#5)Added

`keras_spiking.default.dt`

, which can be used to set the default`dt`

for all layers that don’t directly specify`dt`

. (#5)Added

`keras_spiking.regularizers.RangedRegularizer`

, which can be used to apply some other regularizer (e.g.`tf.keras.regularizers.L2`

) with respect to some non-zero target point, or a range of acceptable values. This functionality has also been added to`keras_spiking.regularizers.L1L2/L1/L2`

(so they can now be applied with respect to a single reference point or a range). (#6)Added

`keras_spiking.regularizers.Percentile`

which computes a percentile across a number of examples, and regularize that statistic. (#6)Added

`keras_spiking.ModelEnergy`

to estimate energy usage for Keras Models. (#7)

**Changed**

`keras_spiking.SpikingActivation`

and`keras_spiking.Lowpass`

now return sequences by default. This means that these layers will now have outputs that have the same number of timesteps as their inputs. This makes it easier to process create multi-layer spiking networks, where time is preserved throughout the network. The spiking fashion-MNIST example has been updated accordingly. (#3)Layers now support multi-dimensional inputs (e.g., output of

`Conv2D`

layers). (#5)

**Fixed**