nupic.tensorflow.layers¶

class
KWinners
(percent_on, k_inference_factor=1.0, boost_strength=1.0, boost_strength_factor=1.0, duty_cycle_period=1000, name=None, **kwargs)[source]¶ Bases:
nupic.tensorflow.layers.k_winners.KWinnersBase
Applies KWinner function to the input tensor.
 Parameters
percent_on (float) – The activity of the top k = percent_on * n will be allowed to remain, the rest are set to zero.
k_inference_factor (float) – During inference (training=False) we increase percent_on by this factor. percent_on * k_inference_factor must be strictly less than 1.0, ideally much lower than 1.0
boost_strength (float) – boost strength (0.0 implies no boosting).
boost_strength_factor (float) – Boost strength factor to use [0..1]
duty_cycle_period (int) – The period used to calculate duty cycles
kwargs (dict) – Additional args passed to
keras.layers.Layer

class
KWinners2d
(percent_on=0.1, k_inference_factor=1.5, boost_strength=1.0, boost_strength_factor=0.9, duty_cycle_period=1000, data_format=tensorflow.python.keras.backend.image_data_format, name=None, **kwargs)[source]¶ Bases:
nupic.tensorflow.layers.k_winners.KWinnersBase
Applies KWinner function to Conv2D input tensor.
 Parameters
percent_on (float) – The activity of the top k = percent_on * number of input units will be allowed to remain, the rest are set to zero.
k_inference_factor (float) – During inference (training=False) we increase percent_on by this factor. percent_on * k_inference_factor must be strictly less than 1.0, ideally much lower than 1.0
boost_strength (float) – boost strength (0.0 implies no boosting).
boost_strength_factor (float) – Boost strength factor to use [0..1]
duty_cycle_period (int) – The period used to calculate duty cycles
data_format (str) – one of channels_first or channels_last. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width). Similar to data_format argument in
keras.layers.Conv2D
.kwargs (dict) – Additional args passed to
keras.layers.Layer

class
KWinnersBase
(percent_on, k_inference_factor, boost_strength, boost_strength_factor, duty_cycle_period, name=None, **kwargs)[source]¶ Bases:
tensorflow.keras.layers.Layer
Base KWinners class.
 Parameters
percent_on (float) – The activity of the top k = percent_on * number of input units will be allowed to remain, the rest are set to zero.
k_inference_factor (float) – During inference (training=False) we increase percent_on by this factor. percent_on * k_inference_factor must be strictly less than 1.0, ideally much lower than 1.0
boost_strength (float) – boost strength (0.0 implies no boosting). Must be >= 0.0
boost_strength_factor (float) – Boost strength factor to use [0..1]
duty_cycle_period (int) – The period used to calculate duty cycles
kwargs (dict) – Additional args passed to
keras.layers.Layer

compute_kwinners
(x, k, duty_cycles, boost_strength)[source]¶ Use the boost strength to compute a boost factor for each unit represented in x. These factors are used to increase the impact of each unit to improve their chances of being chosen. This encourages participation of more columns in the learning process.
The boosting function is a curve defined as: boost_factors = exp[  boost_strength * (dutyCycle  target_density)] Intuitively this means that units that have been active (i.e. in the topk) at the target activation level have a boost factor of 1, meaning their activity is not boosted. Columns whose duty cycle drops too much below that of their neighbors are boosted depending on how infrequently they have been active. Unit that has been active more than the target activation level have a boost factor below 1, meaning their activity is suppressed and they are less likely to be in the topk.
Note that we do not transmit the boosted values. We only use boosting to determine the winning units.
The target activation density for each unit is k / number of units. The boostFactor depends on the dutyCycle via an exponential function:
boostFactor ^  \  \ 1 _  \  _  _ _  _ _ _ _ +> dutyCycle  target_density
 Parameters
x – Current activity of each unit.
k – The activity of the top k units will be allowed to remain, the rest are set to zero.
duty_cycles – The averaged duty cycle of each unit.
boost_strength – A boost strength of 0.0 has no effect on x.
 Returns
A tensor representing the activity of x after kwinner take all.