文章目录
- Keras Adam
- 初始化
- 更新函数
- 带EMA的Adam
Adam理论可以参考下这里
优化算法的选择
Keras Adam
class Adam(Optimizer):
"""Adam optimizer.
Default parameters follow those provided in the original paper.
# Arguments
learning_rate: float >= 0. Learning rate.
beta_1: float, 0 < beta < 1. Generally close to 1.
beta_2: float, 0 < beta < 1. Generally close to 1.
amsgrad: boolean. Whether to apply the AMSGrad variant of this
algorithm from the paper "On the Convergence of Adam and
Beyond".
# References
- [Adam - A Method for Stochastic Optimization](
https://arxiv.org/abs/1412.6980v8)
- [On the Convergence of Adam and Beyond](
https://openreview.net/forum?id=ryQu7f-RZ)
"""
def __init__(self, learning_rate=0.001, beta_1=0.9, beta_2=0.999,
amsgrad=False, **kwargs):
self.initial_decay = kwargs.pop('decay', 0.0)
self.epsilon = kwargs.pop('epsilon', K.epsilon())
learning_rate = kwargs.pop('lr', learning_rate)
super(Adam, self).__init__(**kwargs)
with K.name_scope(self.__class__.__name__):
self.iterations = K.variable(0, dtype='int64', name='iterations')
self.learning_rate = K.variable(learning_rate, name='learning_rate')
self.beta_1 = K.variable(beta_1, name='beta_1')
self.beta_2 = K.variable(beta_2, name='beta_2')
self.decay = K.variable(self.initial_decay, name='decay')
self.amsgrad = amsgrad
@interfaces.legacy_get_updates_support
@K.symbolic
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params) # 获取梯度
self.updates = [K.update_add(self.iterations, 1)]
lr = self.learning_rate
# 如果初始学习速率衰减因子不为0，则随着迭代次数增加，学习速率将不断减小
if self.initial_decay > 0:
lr = lr * (1. / (1. + self.decay * K.cast(self.iterations,
K.dtype(self.decay))))
t = K.cast(self.iterations, K.floatx()) + 1
# 有偏估计到无偏估计的校正值
# 这里将循环内的公共计算提到循环外面，提高速度
lr_t = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) /
(1. - K.pow(self.beta_1, t)))
# 一阶矩估计初始值
ms = [K.zeros(K.int_shape(p),
dtype=K.dtype(p),
name='m_' + str(i))
for (i, p) in enumerate(params)]
# 二阶矩估计初始值
vs = [K.zeros(K.int_shape(p),
dtype=K.dtype(p),
name='v_' + str(i))
for (i, p) in enumerate(params)]
if self.amsgrad:
vhats = [K.zeros(K.int_shape(p),
dtype=K.dtype(p),
name='vhat_' + str(i))
for (i, p) in enumerate(params)]
else:
vhats = [K.zeros(1, name='vhat_' + str(i))
for i in range(len(params))]
self.weights = [self.iterations] + ms + vs + vhats
for p, g, m, v, vhat in zip(params, grads, ms, vs, vhats):
m_t = (self.beta_1 * m) + (1. - self.beta_1) * g # 一阶矩估计
v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g) # 二阶矩估计
if self.amsgrad:
vhat_t = K.maximum(vhat, v_t)
p_t = p - lr_t * m_t / (K.sqrt(vhat_t) + self.epsilon)
self.updates.append(K.update(vhat, vhat_t))
else:
p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon) # 权值更新
self.updates.append(K.update(m, m_t))
self.updates.append(K.update(v, v_t))
new_p = p_t
# 如果参数有约束，对权值添加约束
if getattr(p, 'constraint', None) is not None:
new_p = p.constraint(new_p)
self.updates.append(K.update(p, new_p))
return self.updates
# 获取当前超参数
def get_config(self):
config = {'learning_rate': float(K.get_value(self.learning_rate)),
'beta_1': float(K.get_value(self.beta_1)),
'beta_2': float(K.get_value(self.beta_2)),
'decay': float(K.get_value(self.decay)),
'epsilon': self.epsilon,
'amsgrad': self.amsgrad}
base_config = super(Adam, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
初始化
继承父类optimizer初始化了self.updates = []和self.weights = []，allowed_kwargs用于初始化裁剪梯度的函数l1或者l2，这个参数貌似很少输入
def __init__(self, **kwargs):
allowed_kwargs = {'clipnorm', 'clipvalue'}
for k in kwargs:
if k not in allowed_kwargs:
raise TypeError('Unexpected keyword argument '
'passed to optimizer: ' + str(k))
self.__dict__.update(kwargs)
self.updates = []
self.weights = []
Adam初始化了
initial_decayepsilon接近0的数，避免除0
learning_rate 生成变量空间存放了以下常量
iterations 迭代次数
learning_rate 学习率
beta_1 一阶矩估计的指数衰减因子
beta_2二阶矩估计的指数衰减因子
decay 学习速率衰减因子
amsgrad adam的一种优化方式
更新函数
见注释
带EMA的Adam
@export_to_custom_objects装饰器主要是对新建的优化器类命名并添加到keras的custom_object中
keras.utils.get_custom_objects()[name] = NewOptimizer 其他的请看注释，执行流程有个问题就是keras训练过程中如何控制ema权重的初始化代码不再执行的，也就是下面的代码：
K.batch_set_value(zip(self.ema_weights, self.old_weights))
@export_to_custom_objects
def extend_with_exponential_moving_average(BaseOptimizer):
"""返回新的优化器类，加入EMA（权重滑动平均）
"""
class NewOptimizer(BaseOptimizer):
"""带EMA（权重滑动平均）的优化器，EMA实际上就是权重，只不过我们最后用
"""
@insert_arguments(ema_momentum=0.999)
def __init__(self, *args, **kwargs):
super(NewOptimizer, self).__init__(*args, **kwargs)
def get_updates(self, loss, params):
# 调用父类 get_updates 就更新了权重 m v
updates = super(NewOptimizer, self).get_updates(loss, params)
self.model_weights = params # 用于更新和reset
self.ema_weights = [K.zeros(K.shape(w)) for w in params] # ema 初始化
self.old_weights = K.batch_get_value(params)
# 滑动平均不是这样的，是否权重初始化后后续只能K.update
K.batch_set_value(zip(self.ema_weights, self.old_weights))
ema_updates, ema_momentum = [], self.ema_momentum
# 控制依赖，后续执行需要在updates执行后，执行后params就做了更新
with tf.control_dependencies(updates):
for w1, w2 in zip(self.ema_weights, params):
new_w = ema_momentum * w1 + (1 - ema_momentum) * w2
ema_updates.append(K.update(w1, new_w))
return ema_updates
def get_config(self):
config = {'ema_momentum': self.ema_momentum,
}
base_config = super(NewOptimizer, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def apply_ema_weights(self):
"""备份原模型权重，然后将平均权重应用到模型上去。
"""
self.old_weights = K.batch_get_value(self.model_weights)
ema_weights = K.batch_get_value(self.ema_weights)
K.batch_set_value(zip(self.model_weights, ema_weights))
def reset_old_weights(self):
"""恢复模型到旧权重。
"""
K.batch_set_value(zip(self.model_weights, self.old_weights))
return NewOptimizer
本文参考
Keras 中的 Adam 优化器（Optimizer）算法+源码研究让Keras更酷一些：中间变量、权重滑动和安全生成器
如果想要改写自己的优化器可以参考
玩转Keras之小众需求：自定义优化器
