def __init__(self, params: ExpertParams, creator: TensorCreator = torch):
"""Initialises the flock.
Args:
params: parameters of the flock, see ExpertParams form default values.
creator:
"""
super_params = params.clone()
super_params.spatial.buffer_size = 1 # these are just internal buffers local to each expert,
# they do not learn from them.
super().__init__(super_params, creator)
super()._validate_universal_params(params)
self._validate_conv_learning_params(params)
float_dtype = get_float(self._device)
# Common buffer where each flock stores data and from which they learn.
self.common_buffer = SPFlockBuffer(
creator=creator,
flock_size=1,
buffer_size=params.spatial.buffer_size,
input_size=self.input_size,
n_cluster_centers=self.n_cluster_centers)
# The initial clusters are randomised
self.common_cluster_centers = creator.randn(
(1, self.n_cluster_centers, self.input_size),
device=self._device,
dtype=float_dtype)
# Virtual replications of the common cluster centers, mainly for observation purposes.
self.cluster_centers = self.common_cluster_centers.expand(
self.flock_size, self.n_cluster_centers, self.input_size)
# For keeping track of which clusters are being boosted and for how long
self.cluster_boosting_durations = creator.full(
(1, self.n_cluster_centers),
fill_value=self.cluster_boost_threshold,
device=self._device,
dtype=creator.int64)
self.prev_boosted_clusters = creator.zeros((1, self.n_cluster_centers),
device=self._device,
dtype=creator.uint8)
# For holding the targets and deltas of the cluster centers
self.cluster_center_targets = creator.zeros(
(1, self.n_cluster_centers, self.input_size),
device=self._device,
dtype=float_dtype)
self.cluster_center_deltas = creator.zeros(
(1, self.n_cluster_centers, self.input_size),
device=self._device,
dtype=float_dtype)
self.boosting_targets = creator.zeros((1, self.n_cluster_centers),
device=self._device,
dtype=creator.int64)
self.tmp_boosting_targets = creator.zeros((1, self.n_cluster_centers),
device=self._device,
dtype=creator.int64)
# There only needs to be one learning counter as only one expert learns in the convolutional case,
# but we need the expanded version for cluster observer.
self.common_execution_counter_learning = creator.zeros(
(1, 1), device=self._device, dtype=creator.int64)
self.execution_counter_learning = self.common_execution_counter_learning.expand(
self.flock_size, 1)
def __init__(self, creator: TensorCreator):
super().__init__('cpu')
self.output = creator.randn((1, ))