def optimize(self, engine: Engine, calculate_function):
variables = engine.variables
for variable in variables:
value_cache = self.__gradient_engine.value_cache
self.__gradient_engine.symbol = engine.gradient(variable)
self.__gradient_engine.bind = engine.bind
if self.__consistent:
self.__gradient_engine.value_cache = value_cache
current_gradient = self.__gradient_engine.value()
self.__estimation_map.setdefault(variable, 0)
self.__square_estimation_map.setdefault(variable, 0)
self.__estimation_map[
variable] = self.__decay * self.__estimation_map[variable] + (
1 - self.__decay) * current_gradient
self.__square_estimation_map[
variable] = self.__square_decay * self.__square_estimation_map[
variable] + (1 - self.__square_decay) * current_gradient**2
estimation = self.__estimation_map[variable] / (
1 - self.__decay**self.__step)
square_estimation = self.__square_estimation_map[variable] / (
1 - self.__square_decay**self.__step)
self.__step += 1
regularization_value = estimation / (square_estimation + 1e-8)**0.5
variable.value = calculate_function(
variable.value, self.__rate * regularization_value)
engine.modified()
self.__gradient_engine.modified()
def maximize(self, engine: Engine):
engine.differentiate()
variables = engine.variables
for variable in variables:
self.__gradient_engine.symbol(engine.gradient(variable))
self.__gradient_engine.bind = engine.bind
variable.value += self.__rate * self.__gradient_engine.value()
engine.modified()
def optimize(self, engine: Engine, calculate_function):
variables = engine.variables
for variable in variables:
value_cache = self.__gradient_engine.value_cache
self.__gradient_engine.symbol = engine.gradient(variable)
self.__gradient_engine.bind = engine.bind
if self.__consistent:
self.__gradient_engine.value_cache = value_cache
variable.value = calculate_function(variable.value, self.__rate * self.__gradient_engine.value())
engine.modified()
self.__gradient_engine.modified()
def optimize(self, engine: Engine, calculate_function):
variables = engine.variables
for variable in variables:
value_cache = self.__gradient_engine.value_cache
self.__gradient_engine.symbol = engine.gradient(variable)
self.__gradient_engine.bind = engine.bind
if self.__consistent:
self.__gradient_engine.value_cache = value_cache
momentum = self.__gradient_engine.value(
) + self.__factor * self.__old_gradient_map.get(variable, 0)
self.__old_gradient_map[variable] = momentum
variable.value = calculate_function(variable.value,
self.__rate * momentum)
engine.modified()
self.__gradient_engine.modified()
def optimize(self, engine: Engine, calculate_function):
variables = engine.variables
for variable in variables:
value_cache = self.__gradient_engine.value_cache
self.__gradient_engine.symbol = engine.gradient(variable)
self.__gradient_engine.bind = engine.bind
if self.__consistent:
self.__gradient_engine.value_cache = value_cache
current_gradient = self.__gradient_engine.value()
self.__accumulate_gradient_map.setdefault(variable, 0)
self.__accumulate_gradient_map[variable] += current_gradient**2
regularization_value = current_gradient / (
self.__accumulate_gradient_map[variable] + 1e-8)**0.5
variable.value = calculate_function(
variable.value, self.__rate * regularization_value)
engine.modified()
self.__gradient_engine.modified()
def optimize(self, engine: Engine, calculate_function):
variables = engine.variables
for variable in variables:
value_cache = self.__gradient_engine.value_cache
self.__gradient_engine.symbol = engine.gradient(variable)
self.__gradient_engine.bind = engine.bind
if self.__consistent:
self.__gradient_engine.value_cache = value_cache
current_gradient = self.__gradient_engine.value()
self.__accumulate_gradient_map.setdefault(variable, 0)
self.__expectation_map.setdefault(variable, 0)
self.__accumulate_gradient_map[
variable] = self.__decay * self.__accumulate_gradient_map[
variable] + (1 - self.__decay) * current_gradient**2
delta = (self.__expectation_map[variable] +
1e-8)**0.5 / (self.__accumulate_gradient_map[variable] +
1e-8)**0.5 * current_gradient
self.__expectation_map[
variable] = self.__decay * self.__expectation_map[variable] + (
1 - self.__decay) * delta**2
variable.value = calculate_function(variable.value, delta)
engine.modified()
self.__gradient_engine.modified()
def maximize(self, engine: Engine):
engine.differentiate()
variables = engine.variables
for variable in variables:
variable.value += self.__rate * Engine(
engine.gradient(variable)).value()