深度学习优化方法总结

先敬大佬的一篇文章

《深度学习最全优化方法总结比较（SGD，Adagrad，Adadelta，Adam，Adamax，Nadam）》

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在assignment2 FullyConnectedNets作业中的optim.py里有以下几种优化机制（cs231n_2018_lecture07）：

• SGD
• SGD + Momentum
• RMSprop
• Adam

1. SGD

公式：   

缺点：1.来回振荡 2.会困在局部最优点或鞍点（鞍点在高维向量中很常见）

代码：

def sgd(w, dw, config=None):

    if config is None: config = {}
    config.setdefault('learning_rate', 1e-2)

    w -= config['learning_rate'] * dw
    return w, config

2. SGD + Momentum

公式： 

优缺点：帮助快速收敛，但会跳过某些点

代码：

def sgd_momentum(w, dw, config=None):

    if config is None: config = {}
    config.setdefault('learning_rate', 1e-2)
    config.setdefault('momentum', 0.9)
    v = config.get('velocity', np.zeros_like(w))

    next_w = None

    v = config['momentum'] * v - config['learning_rate'] * dw
    next_w = w + v

    config['velocity'] = v

    return next_w, config

3. RMSprop

公式：

优点：可以动态调节梯度，当dw较小时，cache较小，则较大，提高速度。

缺点：当开始速度慢，因为梯度小

代码：

def rmsprop(w, dw, config=None):
    """
    Uses the RMSProp update rule, which uses a moving average of squared
    gradient values to set adaptive per-parameter learning rates.

    config format:
    - learning_rate: Scalar learning rate.
    - decay_rate: Scalar between 0 and 1 giving the decay rate for the squared
      gradient cache.
    - epsilon: Small scalar used for smoothing to avoid dividing by zero.
    - cache: Moving average of second moments of gradients.
    """
    if config is None: config = {}
    config.setdefault('learning_rate', 1e-2)
    config.setdefault('decay_rate', 0.99)
    config.setdefault('epsilon', 1e-8)
    config.setdefault('cache', np.zeros_like(w))

    next_w = None
    
    cache = config['decay_rate'] * config['cache'] + (1 - config['decay_rate']) * dw ** 2
    next_w = w - config['learning_rate'] * dw / (np.sqrt(cache) + config['epsilon'])
    config['cache'] = cache

    return next_w, config

4. Adam（最常用）

公式：

优缺点：当迭代次数变多时，m变小，lr变小，逼近全局最优。

    """
    Uses the Adam update rule, which incorporates moving averages of both the
    gradient and its square and a bias correction term.

    config format:
    - learning_rate: Scalar learning rate.
    - beta1: Decay rate for moving average of first moment of gradient.
    - beta2: Decay rate for moving average of second moment of gradient.
    - epsilon: Small scalar used for smoothing to avoid dividing by zero.
    - m: Moving average of gradient.
    - v: Moving average of squared gradient.
    - t: Iteration number.
    """
    if config is None: config = {}
    config.setdefault('learning_rate', 1e-3)
    config.setdefault('beta1', 0.9)
    config.setdefault('beta2', 0.999)
    config.setdefault('epsilon', 1e-8)
    config.setdefault('m', np.zeros_like(w))
    config.setdefault('v', np.zeros_like(w))
    config.setdefault('t', 0)

    next_w = None

    config['t'] += 1
    m = config['beta1'] * config['m'] + (1 - config['beta1']) * dw
    mt = m / (1 - config['beta1'] ** config['t'])
    v = config['beta2'] * config['v'] + (1 - config['beta2']) * dw ** 2
    vt = v / (1 - config['beta2'] ** config['t'])
    next_w = w - config['learning_rate'] * mt / (np.sqrt(vt) + config['epsilon'])
    config['m'] = m
    config['v'] = v
    return next_w, config