Deep Leaning 学习笔记之改善神经网络的超参数（2.2）——优化算法的运行速度（实例）

最新推荐文章于 2022-04-16 13:05:11 发布

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最新推荐文章于 2022-04-16 13:05:11 发布 · 928 阅读

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1 mini-batch Gradient Descent

1.1 步骤概念

将样本随机打乱（确保X和Y一起打乱，保证X与lableY仍然相对应）
permutation = list(np.random.permutation(m))
shuffled_X = X[:, permutation]
shuffled_Y = Y[:, permutation].reshape((1,m))
划分小批量

    # Step 2: Partition (shuffled_X, shuffled_Y). Minus the end case.
    num_complete_minibatches = math.floor(m/mini_batch_size) # number of mini batches of size mini_batch_size in your partitionning
    for k in range(0, num_complete_minibatches):
        ### START CODE HERE ### (approx. 2 lines)
        mini_batch_X = shuffled_X[:,(k)*mini_batch_size:(k+1)*mini_batch_size]
        mini_batch_Y = shuffled_Y[:,(k)*mini_batch_size:(k+1)*mini_batch_size]
        ### END CODE HERE ###
        mini_batch = (mini_batch_X, mini_batch_Y)
        mini_batches.append(mini_batch)
    
    # Handling the end case (last mini-batch < mini_batch_size)
    if m % mini_batch_size != 0:
        ### START CODE HERE ### (approx. 2 lines)
        mini_batch_X = shuffled_X[:,mini_batch_size*(math.floor(m/mini_batch_size)):m]
        mini_batch_Y = shuffled_Y[:,mini_batch_size*(math.floor(m/mini_batch_size)):m]
        ### END CODE HERE ###
        mini_batch = (mini_batch_X, mini_batch_Y)
        mini_batches.append(mini_batch)

洗牌和分区是构建小型批所需的两个步骤
2的幂通常选择为小批大小，例如，16、32、64、128。

2. 动量Momentum

也就是V值等等的一系列操作

2.1 初始化initialize_velocity

# GRADED FUNCTION: initialize_velocity

def initialize_velocity(parameters):
    """
    Initializes the velocity as a python dictionary with:
                - keys: "dW1", "db1", ..., "dWL", "dbL" 
                - values: numpy arrays of zeros of the same shape as the corresponding gradients/parameters.
    Arguments:
    parameters -- python dictionary containing your parameters.
                    parameters['W' + str(l)] = Wl
                    parameters['b' + str(l)] = bl
    
    Returns:
    v -- python dictionary containing the current velocity.
                    v['dW' + str(l)] = velocity of dWl
                    v['db' + str(l)] = velocity of dbl
    """
    
    L = len(parameters) // 2 # number of layers in the neural networks
    v = {}
    
    # Initialize velocity
    for l in range(L):
        ### START CODE HERE ### (approx. 2 lines)
        v["dW" + str(l+1)] = np.zeros((parameters['W'+str(l+1)].shape[0],parameters['W'+str(l+1)].shape[1]))
        v["db" + str(l+1)] = np.zeros((parameters['b'+str(l+1)].shape[0],parameters['b'+str(l+1)].shape[1]))
        ### END CODE HERE ###
        
    return v

2.2 更新参数with动量

# GRADED FUNCTION: update_parameters_with_momentum

def update_parameters_with_momentum(parameters, grads, v, beta, learning_rate):
    """
    Update parameters using Momentum
    
    Arguments:
    parameters -- python dictionary containing your parameters:
                    parameters['W' + str(l)] = Wl
                    parameters['b' + str(l)] = bl
    grads -- python dictionary containing your gradients for each parameters:
                    grads['dW' + str(l)] = dWl
                    grads['db' + str(l)] = dbl
    v -- python dictionary containing the current velocity:
                    v['dW' + str(l)] = ...
                    v['db' + str(l)] = ...
    beta -- the momentum hyperparameter, scalar
    learning_rate -- the learning rate, scalar
    
    Returns:
    parameters -- python dictionary containing your updated parameters 
    v -- python dictionary containing your updated velocities
    """

    L = len(parameters) // 2 # number of layers in the neural networks
    
    # Momentum update for each parameter
    for l in range(L):
        
        ### START CODE HERE ### (approx. 4 lines)
        # compute velocities
        v["dW" + str(l+1)] = beta*v["dW" + str(l)]+(1-beta*v["dW" + str(l+1)])
        v["db" + str(l+1)] = beta*v["db" + str(l)]+(1-beta*v["db" + str(l+1)])
        # update parameters
        parameters["W" + str(l+1)] = parameters["W" + str(l+1)] - learning_rate*v["dW" + str(l+1)]
        parameters["b" + str(l+1)] = parameters["b" + str(l+1)] - learning_rate*v["db" + str(l+1)]
        ### END CODE HERE ###
        
    return parameters, v

3.Adam算法

Adam算法是训练神经网络最有效的优化算法之一。它结合了RMSProp(在讲座中描述)和Momentum的思想。

How does Adam work?

它计算过去梯度的指数加权平均值，并将其存储在变量 $v$ (偏差校正前)和 $v^{corrected}$ (偏差校正后)中。
它计算过去梯度的平方的指数加权平均值，并将其存储在变量 $s$ (偏差校正前)和 $s^{corrected}$ {纠正后)中。
它根据来自“1”和“2”的信息组合的方向更新参数。

The update rule is, for $l = 1, . . ., L$ :

$vdW[l]=β1vdW[l]+(1−β1)∂J∂W[l]vdW[l]corrected=vdW[l]1−(β1)tsdW[l]=β2sdW[l]+(1−β2)(∂J∂W[l])2sdW[l]corrected=sdW[l]1−(β2)tW[l]=W[l]−αvdW[l]correctedsdW[l]corrected+ε\begin{cases} v_{dW^{[l]}} = \beta_1 v_{dW^{[l]}} + (1 - \beta_1) \frac{\partial \mathcal{J} }{ \partial W^{[l]} } \\ v^{corrected}_{dW^{[l]}} = \frac{v_{dW^{[l]}}}{1 - (\beta_1)^t} \\ s_{dW^{[l]}} = \beta_2 s_{dW^{[l]}} + (1 - \beta_2) (\frac{\partial \mathcal{J} }{\partial W^{[l]} })^2 \\ s^{corrected}_{dW^{[l]}} = \frac{s_{dW^{[l]}}}{1 - (\beta_2)^t} \\ W^{[l]} = W^{[l]} - \alpha \frac{v^{corrected}_{dW^{[l]}}}{\sqrt{s^{corrected}_{dW^{[l]}}} + \varepsilon} \end{cases}$