def __init__(self,
vc: VarCollection,
momentum: float = 0.999,
debias: bool = False,
eps: float = 1e-6):
"""Creates ExponentialMovingAverage instance with given hyperparameters.
Args:
momentum: the decay factor for the moving average.
debias: bool indicating whether to use initialization bias correction.
eps: small adjustment to prevent division by zero.
"""
self.momentum = momentum
self.debias = debias
self.eps = eps
self.step = StateVar(jn.array(0, jn.uint32), reduce=lambda x: x[0])
# Deduplicate variables and skip RandomState vars since they cannot be averaged.
trainable, non_trainable = {}, {
} # Use dicts since they are ordered since python >= 3.6
for v in vc:
if isinstance(v, RandomState):
continue
if isinstance(v, TrainRef):
v = v.ref
if isinstance(v, TrainVar):
trainable[v] = True
else:
non_trainable[v] = True
self.refs = ModuleList(
list(non_trainable.keys()) +
[TrainRef(v) for v in trainable.keys()])
self.m = ModuleList(
StateVar(jn.zeros_like(x.value)) for x in self.refs)
def __init__(self, vc: VarCollection, beta1: float = 0.9, beta2: float = 0.999, eps: float = 1e-8):
"""Constructor for Adam optimizer class.
Args:
vc: collection of variables to optimize.
beta1: value of Adam's beta1 hyperparameter. Defaults to 0.9.
beta2: value of Adam's beta2 hyperparameter. Defaults to 0.999.
eps: value of Adam's epsilon hyperparameter. Defaults to 1e-8.
"""
self.beta1 = beta1
self.beta2 = beta2
self.eps = eps
self.step = StateVar(jn.array(0, jn.uint32), reduce=lambda x: x[0])
self.train_vars = ModuleList(TrainRef(x) for x in vc.subset(TrainVar))
self.m = ModuleList(StateVar(jn.zeros_like(x.value)) for x in self.train_vars)
self.v = ModuleList(StateVar(jn.zeros_like(x.value)) for x in self.train_vars)
def __init__(self, dims: Iterable[int], redux: Iterable[int], momentum: float = 0.999, eps: float = 1e-6):
"""Creates a BatchNorm module instance.
Args:
dims: shape of the batch normalization state variables.
redux: list of indices of reduction axes. Batch norm statistics are computed by averaging over these axes.
momentum: value used to compute exponential moving average of batch statistics.
eps: small value which is used for numerical stability.
"""
super().__init__()
dims = tuple(dims)
self.momentum = momentum
self.eps = eps
self.redux = tuple(redux)
self.running_mean = StateVar(jn.zeros(dims))
self.running_var = StateVar(jn.ones(dims))
self.beta = TrainVar(jn.zeros(dims))
self.gamma = TrainVar(jn.ones(dims))
def __init__(self, shape: Tuple[int, ...], buffer_size: int, init_value: float = 0):
"""Creates a MovingAverage module instance.
Args:
shape: shape of the input tensor.
buffer_size: buffer size for moving average.
init_value: initial value for moving average buffer.
"""
self.buffer = StateVar(jn.zeros((buffer_size,) + shape) + init_value)
def __init__(self, shape: Tuple[int, ...], momentum: float = 0.999, init_value: float = 0):
"""Creates a ExponentialMovingAverage module instance.
Args:
shape: shape of the input tensor.
momentum: momentum for exponential decrease of accumulated value.
init_value: initial value for exponential moving average.
"""
self.momentum = momentum
self.avg = StateVar(jn.zeros(shape) + init_value)
def __init__(self,
vc: VarCollection,
momentum: float = 0.9,
nesterov: bool = False):
"""Constructor for momentum optimizer class.
Args:
vc: collection of variables to optimize.
momentum: the momentum hyperparameter.
nesterov: bool indicating whether to use the Nesterov method.
"""
self.momentum = momentum
self.nesterov = nesterov
self.train_vars = ModuleList(TrainRef(x) for x in vc.subset(TrainVar))
self.m = ModuleList(
StateVar(jn.zeros_like(x.value)) for x in self.train_vars)
def __init__(self,
vc: VarCollection,
momentum: float = 0.9,
weight_decay: float = 1e-4,
tc: float = 1e-3,
eps: float = 1e-5):
"""Constructor for LARS optimizer.
Args:
vc: collection of variables to optimize.
momentum: coefficient used for the moving average of the gradient.
weight_decay: weight decay coefficient.
tc: trust coefficient eta ( < 1) for trust ratio computation.
eps: epsilon used for trust ratio computation.
"""
self.momentum = momentum
self.weight_decay = weight_decay
self.tc = tc
self.eps = eps
self.train_vars = ModuleList(TrainRef(x) for x in vc.subset(TrainVar))
self.m = ModuleList(
StateVar(jn.zeros_like(x.value)) for x in self.train_vars)
def init_state(self, batch_size):
"""Initialize hidden state for input batch of size ``batch_size``."""
self.state = StateVar(jn.zeros((batch_size, self.nstate)))