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optim.py
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class Adam:
def __init__(self, params, lr: float=0.01, betas: tuple[float, float]=(0.9, 0.999), eps: float=1e-8):
self.params = params
self.lr = lr
self.betas = betas
self.eps = eps
self.m = [param.xp.zeros_like(param.data) for param in self.params]
self.v = [param.xp.zeros_like(param.data) for param in self.params]
self.t = 0
def step(self):
self.t += 1
for i, param in enumerate(self.params):
if param.grad is None:
continue
self.m[i] = self.betas[0] * self.m[i] + (1 - self.betas[0]) * param.grad
self.v[i] = self.betas[1] * self.v[i] + (1 - self.betas[1]) * param.grad**2
m_hat = self.m[i] / (1 - self.betas[0] ** self.t)
v_hat = self.v[i] / (1 - self.betas[1] ** self.t)
param.data -= self.lr * m_hat / (param.xp.sqrt(v_hat) + self.eps)
def zero_grad(self):
for param in self.params:
param.grad = None
class SGD:
def __init__(self, params, lr: float=0.01):
self.params = params
self.lr = lr
def step(self):
for param in self.params:
if param.grad is None:
continue
param.data -= self.lr * param.grad
def zero_grad(self):
for param in self.params:
param.grad = None
class Momentum:
def __init__(self, params, lr=0.01, momentum: float=0.9):
self.params = params
self.lr = lr
self.momentum = momentum
self.m = [param.xp.zeros_like(param.data) for param in self.params]
def step(self):
for i, param in enumerate(self.params):
if param.grad is None:
continue
self.m[i] = self.momentum * self.m[i] + (1 - self.momentum) * param.grad
param.data -= self.m[i] * self.lr
def zero_grad(self):
for param in self.params:
param.grad = None if param.grad is None else param.xp.zeros_like(param.grad)
class RMSprop:
def __init__(self, params, lr: float=0.01, alpha: float=0.99, eps: float=1e-8):
self.params = params
self.lr = lr
self.alpha = alpha
self.eps = eps
self.m = [param.xp.zeros_like(param.data) for param in self.params]
def step(self):
for i, param in enumerate(self.params):
if param.grad is None:
continue
self.m[i] = self.alpha * self.m[i] + (1 - self.alpha) * param.grad**2
param.data -= self.lr * param.grad / (param.xp.sqrt(self.m[i]) + self.eps)
def zero_grad(self):
for param in self.params:
param.grad = None
class Adagrad:
def __init__(self, params, lr: float=0.01, eps: float=1e-8):
self.params = params
self.lr = lr
self.eps = eps
self.m = [param.xp.zeros_like(param.data) for param in self.params]
def step(self):
for i, param in enumerate(self.params):
if param.grad is None:
continue
self.m[i] += param.grad**2
param.data -= self.lr * param.grad / (param.xp.sqrt(self.m[i]) + self.eps)
def zero_grad(self):
for param in self.params:
param.grad = None
class Adadelta:
def __init__(self, params, lr: float=1.0, rho: float=0.9, eps: float=1e-6):
self.params = params
self.lr = lr
self.rho = rho
self.eps = eps
self.m = [param.xp.zeros_like(param.data) for param in self.params]
self.v = [param.xp.zeros_like(param.data) for param in self.params]
def step(self):
for i, param in enumerate(self.params):
if param.grad is None:
continue
self.m[i] = self.rho * self.m[i] + (1 - self.rho) * param.grad**2
delta_x = (
-(param.xp.sqrt(self.v[i] + self.eps) / param.xp.sqrt(self.m[i] + self.eps))
* param.grad
)
self.v[i] = self.rho * self.v[i] + (1 - self.rho) * delta_x**2
param.data += delta_x
def zero_grad(self):
for param in self.params:
param.grad = None
class Adamax:
def __init__(self, params, lr: float=0.002, betas: tuple[float, float]=(0.9, 0.999), eps: float=1e-8):
self.params = params
self.lr = lr
self.betas = betas
self.eps = eps
self.m = [param.xp.zeros_like(param.data) for param in self.params]
self.v = [param.xp.zeros_like(param.data) for param in self.params]
self.t = 0
def step(self):
self.t += 1
for i, param in enumerate(self.params):
if param.grad is None:
continue
self.m[i] = self.betas[0] * self.m[i] + (1 - self.betas[0]) * param.grad
self.v[i] = param.xp.maximum(self.betas[1] * self.v[i], param.xp.abs(param.grad))
m_hat = self.m[i] / (1 - self.betas[0] ** self.t)
param.data -= self.lr * m_hat / (self.v[i] + self.eps)
def zero_grad(self):
for param in self.params:
param.grad = None
class NAdam:
def __init__(self, params, lr: float=0.002, betas: tuple[float, float]=(0.9, 0.999), eps: float=1e-8):
self.params = params
self.lr = lr
self.betas = betas
self.eps = eps
self.m = [param.xp.zeros_like(param.data) for param in self.params]
self.v = [param.xp.zeros_like(param.data) for param in self.params]
self.t = 0
def step(self):
self.t += 1
for i, param in enumerate(self.params):
if param.grad is None:
continue
self.m[i] = self.betas[0] * self.m[i] + (1 - self.betas[0]) * param.grad
self.v[i] = self.betas[1] * self.v[i] + (1 - self.betas[1]) * param.grad**2
m_hat = self.m[i] / (1 - self.betas[0] ** self.t) + (1 - self.betas[0]) * param.grad / (
1 - self.betas[0] ** self.t
)
v_hat = self.v[i] / (1 - self.betas[1] ** self.t)
param.data -= self.lr * m_hat / (param.xp.sqrt(v_hat) + self.eps)
def zero_grad(self):
for param in self.params:
param.grad = None