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Adagrad.py
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import torch
from torch.optim.optimizer import Optimizer
class Adagrad_GCC(Optimizer):
"""Implements Adagrad algorithm.
It has been proposed in `Adaptive Subgradient Methods for Online Learning
and Stochastic Optimization`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-2)
lr_decay (float, optional): learning rate decay (default: 0)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-10)
.. _Adaptive Subgradient Methods for Online Learning and Stochastic
Optimization: http://jmlr.org/papers/v12/duchi11a.html
"""
def __init__(self, params, lr=1e-2, lr_decay=0, weight_decay=0, initial_accumulator_value=0, eps=1e-10):
if not 0.0 <= lr:
raise ValueError("Invalid learning rate: {}".format(lr))
if not 0.0 <= lr_decay:
raise ValueError("Invalid lr_decay value: {}".format(lr_decay))
if not 0.0 <= weight_decay:
raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
if not 0.0 <= initial_accumulator_value:
raise ValueError("Invalid initial_accumulator_value value: {}".format(initial_accumulator_value))
if not 0.0 <= eps:
raise ValueError("Invalid epsilon value: {}".format(eps))
defaults = dict(lr=lr, lr_decay=lr_decay, eps=eps, weight_decay=weight_decay,
initial_accumulator_value=initial_accumulator_value)
super(Adagrad_GCC, self).__init__(params, defaults)
for group in self.param_groups:
for p in group['params']:
state = self.state[p]
state['step'] = 0
state['sum'] = torch.full_like(p.data, initial_accumulator_value)
def share_memory(self):
for group in self.param_groups:
for p in group['params']:
state = self.state[p]
state['sum'].share_memory_()
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data
state = self.state[p]
state['step'] += 1
if group['weight_decay'] != 0:
if p.grad.data.is_sparse:
raise RuntimeError("weight_decay option is not compatible with sparse gradients")
grad = grad.add(group['weight_decay'], p.data)
#GC operation for Conv layers
if len(list(grad.size()))>3:
grad.add_(-grad.mean(dim = tuple(range(1,len(list(grad.size())))), keepdim = True))
clr = group['lr'] / (1 + (state['step'] - 1) * group['lr_decay'])
if grad.is_sparse:
grad = grad.coalesce() # the update is non-linear so indices must be unique
grad_indices = grad._indices()
grad_values = grad._values()
size = grad.size()
def make_sparse(values):
constructor = grad.new
if grad_indices.dim() == 0 or values.dim() == 0:
return constructor().resize_as_(grad)
return constructor(grad_indices, values, size)
state['sum'].add_(make_sparse(grad_values.pow(2)))
std = state['sum'].sparse_mask(grad)
std_values = std._values().sqrt_().add_(group['eps'])
p.data.add_(-clr, make_sparse(grad_values / std_values))
else:
state['sum'].addcmul_(1, grad, grad)
std = state['sum'].sqrt().add_(group['eps'])
p.data.addcdiv_(-clr, grad, std)
return loss
class Adagrad_GC(Optimizer):
"""Implements Adagrad algorithm.
It has been proposed in `Adaptive Subgradient Methods for Online Learning
and Stochastic Optimization`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-2)
lr_decay (float, optional): learning rate decay (default: 0)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-10)
.. _Adaptive Subgradient Methods for Online Learning and Stochastic
Optimization: http://jmlr.org/papers/v12/duchi11a.html
"""
def __init__(self, params, lr=1e-2, lr_decay=0, weight_decay=0, initial_accumulator_value=0, eps=1e-10):
if not 0.0 <= lr:
raise ValueError("Invalid learning rate: {}".format(lr))
if not 0.0 <= lr_decay:
raise ValueError("Invalid lr_decay value: {}".format(lr_decay))
if not 0.0 <= weight_decay:
raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
if not 0.0 <= initial_accumulator_value:
raise ValueError("Invalid initial_accumulator_value value: {}".format(initial_accumulator_value))
if not 0.0 <= eps:
raise ValueError("Invalid epsilon value: {}".format(eps))
defaults = dict(lr=lr, lr_decay=lr_decay, eps=eps, weight_decay=weight_decay,
initial_accumulator_value=initial_accumulator_value)
super(Adagrad_GC, self).__init__(params, defaults)
for group in self.param_groups:
for p in group['params']:
state = self.state[p]
state['step'] = 0
state['sum'] = torch.full_like(p.data, initial_accumulator_value)
def share_memory(self):
for group in self.param_groups:
for p in group['params']:
state = self.state[p]
state['sum'].share_memory_()
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data
state = self.state[p]
state['step'] += 1
if group['weight_decay'] != 0:
if p.grad.data.is_sparse:
raise RuntimeError("weight_decay option is not compatible with sparse gradients")
grad = grad.add(group['weight_decay'], p.data)
#GC operation for Conv layers
if len(list(grad.size()))>1:
grad.add_(-grad.mean(dim = tuple(range(1,len(list(grad.size())))), keepdim = True))
clr = group['lr'] / (1 + (state['step'] - 1) * group['lr_decay'])
if grad.is_sparse:
grad = grad.coalesce() # the update is non-linear so indices must be unique
grad_indices = grad._indices()
grad_values = grad._values()
size = grad.size()
def make_sparse(values):
constructor = grad.new
if grad_indices.dim() == 0 or values.dim() == 0:
return constructor().resize_as_(grad)
return constructor(grad_indices, values, size)
state['sum'].add_(make_sparse(grad_values.pow(2)))
std = state['sum'].sparse_mask(grad)
std_values = std._values().sqrt_().add_(group['eps'])
p.data.add_(-clr, make_sparse(grad_values / std_values))
else:
state['sum'].addcmul_(1, grad, grad)
std = state['sum'].sqrt().add_(group['eps'])
p.data.addcdiv_(-clr, grad, std)
return loss