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optimizer.py
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import numpy as np
class GradientDescent:
def __init__(self, learning_rate):
'''
Parameters
----------
learning_rate : Learning rate
'''
self.__learning_rate = learning_rate
def optimize(self, vars):
'''
Parameters
----------
vars : Parameters to be optimized
Returns
-------
vars : Parameters optimized
'''
return [self.__learning_rate * vars[i] for i in range(len(vars))]
class Momentum:
def __init__(self, learning_rate):
'''
Parameters
----------
learning_rate : Learning rate
'''
self.__learning_rate = learning_rate
self.__alpha = 0.9
def optimize(self, vars):
'''
Parameters
----------
vars : Parameters to be optimized
Returns
-------
vars : Parameters optimized
'''
n_vars = len(vars)
if not hasattr(self, '_Momentum__v'):
self.__v = [0] * n_vars
self.__v = [(1 - self.__alpha) * vars[i] + self.__alpha * self.__v[i] for i in range(n_vars)]
return self.__learning_rate * np.array(self.__v)
class Nesterov:
def __init__(self, learning_rate):
'''
Parameters
----------
learning_rate : Learning rate
'''
self.__learning_rate = learning_rate
self.__alpha = 0.9
def optimize(self, vars):
'''
Parameters
----------
vars : Parameters to be optimized
Returns
-------
vars : Parameters optimized
'''
n_vars = len(vars)
if not hasattr(self, '_Nesterov__v'):
self.__v = [0] * n_vars
old_v = self.__v
self.__v = [self.__alpha * self.__v[i] - self.__learning_rate * vars[i] for i in range(n_vars)]
return self.__alpha * np.array(old_v) - (1 + self.__alpha) * np.array(self.__v)
class Adagrad:
def __init__(self, learning_rate):
'''
Parameters
----------
learning_rate : Learning rate
'''
self.__learning_rate = learning_rate
def optimize(self, vars):
'''
Parameters
----------
vars : Parameters to be optimized
Returns
-------
vars : Parameters optimized
'''
n_vars = len(vars)
if not hasattr(self, '_Adagrad__r'):
self.__r = [0] * n_vars
v = [0] * n_vars
for i in range(n_vars):
self.__r[i] += vars[i] ** 2
v[i] = self.__learning_rate * vars[i] / (np.sqrt(self.__r[i]) + 1e-8)
return v
class Rmsprop:
def __init__(self, learning_rate):
'''
Parameters
----------
learning_rate : Learning rate
'''
self.__learning_rate = learning_rate
self.__alpha = 0.9
def optimize(self, vars):
'''
Parameters
----------
vars : Parameters to be optimized
Returns
-------
vars : Parameters optimized
'''
n_vars = len(vars)
if not hasattr(self, '_Rmsprop__r'):
self.__r = [0] * n_vars
v = [0] * n_vars
for i in range(n_vars):
self.__r[i] = self.__alpha * self.__r[i] + (1 - self.__alpha) * vars[i] ** 2
v[i] = self.__learning_rate * vars[i] / (np.sqrt(self.__r[i]) + 1e-8)
return v
class Adam:
def __init__(self, learning_rate):
'''
Parameters
----------
learning_rate : Learning rate
'''
self.__learning_rate = learning_rate
self.__t = 0
self.__alpha = 0.9
self.__alpha2 = 0.999
def optimize(self, vars):
'''
Parameters
----------
vars : Parameters to be optimized
Returns
-------
vars : Parameters optimized
'''
n_vars = len(vars)
if not hasattr(self, '_Adam__r'):
self.__s = [0] * n_vars
self.__r = [0] * n_vars
self.__t += 1
v = [0] * n_vars
for i in range(n_vars):
self.__s[i] = self.__alpha * self.__s[i] + (1 - self.__alpha) * vars[i]
s_hat = self.__s[i] / (1 - self.__alpha ** self.__t)
self.__r[i] = self.__alpha2 * self.__r[i] + (1 - self.__alpha2) * np.power(vars[i], 2)
r_hat = self.__r[i] / (1 - self.__alpha2 ** self.__t)
v[i] = self.__learning_rate * s_hat / (1e-8 + np.sqrt(r_hat))
return v