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正向传播与反向传播（神经网络思维的逻辑回归）

news 2025/8/13 5:09:38

神经网络的计算过程都是按照前向或者反向传播i过程来实现的，首先计算出神经网络的输出，紧接着进行一个反向传输操作，后者我们用来计算对应的梯度或者导数。

通俗来讲，神经网络的训练过程，就是经过很多次前向传播与反向传播的轮回，最终不断调整其内部参数（权重 ω 与偏置 b），以拟合任意复杂函数的过程。前向传播指的是：从输入到输入的一个非线性函数的推理过程，获得拟合的参数，得到一个拟合参数后的激活函数。反向传播指的是：根据前向传播拟合的参数计算预测值，并计算损失函数，通过梯度下降算法不断的优化这个参数。

一、计算图

以一个简单的J函数（成本函数）为例，它的计算过程如下：

从上图可以看出，从左向右的过程，可以计算出J的值；那么从右向左的过程如下图所示：

从右向左的计算过程，正对应着我们不断优化参数值，寻找最小J函数值的过程，我们知道计算一个函数的最小值，就是当这个函数的导数为0 的时候，因为我们的成本函数近似于一个凸函数，当导数为0时就对应着J函数的最小值。所以，从右向左的过程也就对应着计算导数的过程。

在从右向左的计算中，我们可以逐步计算出J函数对每一个变量的偏导数，从而便于我们对每一个变量进行梯度下降算法，找到最小的J函数值。下面是以逻辑回归的单个样本的梯度下降为实例讲解反向传播过程：

m的个样本的梯度下降，就是将所有样本关于参数的导数加起来求平均。

上述所有的示例，是为了让我们更好地理解正向传播和反向传播。

二、构建一个逻辑回归分类器来识别猫（使用神经网络思维）

（一）数据集

数据集（“data.h5”），其中包含： - 标记为 cat （y=1）或非 cat （y=0）的 m_train 张图像的训练集 - 标记为 cat 或非 cat 的 m_test 张图像的测试集 - 每个图像的形状（num_px、num_px、3），其中 3 表示 3 个通道（RGB）。因此，每个图像都是正方形（高度 = num_px）和（宽度 = num_px）。训练集、测试集、每一个样本图像的像素长度或者宽度。

对训练集和测试集特征进行矩阵形状的重塑：在此之后，我们的训练（和测试）数据集是一个 numpy 数组，其中每列代表一个扁平化的图像。应该有 m_train 列（分别 m_test 列）。

标准化数据：机器学习中一个常见的预处理步骤是将数据集居中并标准化，这意味着您从每个示例中减去整个 numpy 数组的平均值，然后将每个示例除以整个 numpy 数组的标准差。但对于图片数据集，它更简单、更方便，并且只需将数据集的每一行除以 255（像素通道的最大值）几乎一样有效。

train_set_x = train_set_x_flatten/255.

test_set_x = test_set_x_flatten/255.

（二）使用神经网络思维方式构建逻辑回归

使用逻辑回归构建识别猫的模型的基本流程如下：

辅助函数sigmoid:

def sigmoid(z):"""Compute the sigmoid of zArguments:z -- A scalar or numpy array of any size.Return:s -- sigmoid(z)"""### START CODE HERE ### (≈ 1 line of code)s = 1/(1+np.exp(-z))### END CODE HERE ###return s

初始化参数：

def initialize_with_zeros(dim):"""This function creates a vector of zeros of shape (dim, 1) for w and initializes b to 0.Argument:dim -- size of the w vector we want (or number of parameters in this case)Returns:w -- initialized vector of shape (dim, 1)b -- initialized scalar (corresponds to the bias)"""### START CODE HERE ### (≈ 1 line of code)w = np.zeros((dim,1))b = 0### END CODE HERE ###assert(w.shape == (dim, 1))assert(isinstance(b, float) or isinstance(b, int))return w, b

向前传播+向后传播：计算成本函数和梯度

# GRADED FUNCTION: propagatedef propagate(w, b, X, Y):"""Implement the cost function and its gradient for the propagation explained aboveArguments:w -- weights, a numpy array of size (num_px * num_px * 3, 1)b -- bias, a scalarX -- data of size (num_px * num_px * 3, number of examples)Y -- true "label" vector (containing 0 if non-cat, 1 if cat) of size (1, number of examples)Return:cost -- negative log-likelihood cost for logistic regressiondw -- gradient of the loss with respect to w, thus same shape as wdb -- gradient of the loss with respect to b, thus same shape as bTips:- Write your code step by step for the propagation. np.log(), np.dot()"""m = X.shape[1]# FORWARD PROPAGATION (FROM X TO COST)### START CODE HERE ### (≈ 2 lines of code)y_pre = sigmoid(np.dot(w.T,X)+b)cost = np.sum(Y*np.log(y_pre)+(1-Y)*np.log(1-y_pre))/(-m)### END CODE HERE #### BACKWARD PROPAGATION (TO FIND GRAD)### START CODE HERE ### (≈ 2 lines of code)dw = np.dot(X,(y_pre-Y).T)/mdb = np.mean(y_pre-Y)### END CODE HERE ###assert(dw.shape == w.shape)assert(db.dtype == float)cost = np.squeeze(cost)assert(cost.shape == ())grads = {"dw": dw,"db": db}return grads, cost

使用梯度下降来更新参数：

# GRADED FUNCTION: optimizedef optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False):"""This function optimizes w and b by running a gradient descent algorithmArguments:w -- weights, a numpy array of size (num_px * num_px * 3, 1)b -- bias, a scalarX -- data of shape (num_px * num_px * 3, number of examples)Y -- true "label" vector (containing 0 if non-cat, 1 if cat), of shape (1, number of examples)num_iterations -- number of iterations of the optimization looplearning_rate -- learning rate of the gradient descent update ruleprint_cost -- True to print the loss every 100 stepsReturns:params -- dictionary containing the weights w and bias bgrads -- dictionary containing the gradients of the weights and bias with respect to the cost functioncosts -- list of all the costs computed during the optimization, this will be used to plot the learning curve.Tips:You basically need to write down two steps and iterate through them:1) Calculate the cost and the gradient for the current parameters. Use propagate().2) Update the parameters using gradient descent rule for w and b."""costs = []for i in range(num_iterations):# Cost and gradient calculation (≈ 1-4 lines of code)### START CODE HERE ### grads, cost = propagate(w, b, X, Y)### END CODE HERE #### Retrieve derivatives from gradsdw = grads["dw"]db = grads["db"]# update rule (≈ 2 lines of code)### START CODE HERE ###w = w - dw*learning_rateb = b - db*learning_rate### END CODE HERE #### Record the costsif i % 100 == 0:costs.append(cost)# Print the cost every 100 training examplesif print_cost and i % 100 == 0:print ("Cost after iteration %i: %f" %(i, cost))params = {"w": w,"b": b}grads = {"dw": dw,"db": db}return params, grads, costs

使用优化之后的参数进行预测：

# GRADED FUNCTION: predictdef predict(w, b, X):'''Predict whether the label is 0 or 1 using learned logistic regression parameters (w, b)Arguments:w -- weights, a numpy array of size (num_px * num_px * 3, 1)b -- bias, a scalarX -- data of size (num_px * num_px * 3, number of examples)Returns:Y_prediction -- a numpy array (vector) containing all predictions (0/1) for the examples in X'''m = X.shape[1]Y_prediction = np.zeros((1,m))w = w.reshape(X.shape[0], 1)# Compute vector "A" predicting the probabilities of a cat being present in the picture### START CODE HERE ### (≈ 1 line of code)A = np.dot(w.T,X)+b### END CODE HERE ###for i in range(A.shape[1]):# Convert probabilities A[0,i] to actual predictions p[0,i]### START CODE HERE ### (≈ 4 lines of code)if(A[0][i]<=0.5):Y_prediction[0][i] = 0else:Y_prediction[0][i] = 1### END CODE HERE ###assert(Y_prediction.shape == (1, m))return Y_prediction

将所有的函数合并到一个模型中：

def model(X_train, Y_train, X_test, Y_test, num_iterations = 2000, learning_rate = 0.5, print_cost = False):"""Builds the logistic regression model by calling the function you've implemented previouslyArguments:X_train -- training set represented by a numpy array of shape (num_px * num_px * 3, m_train)Y_train -- training labels represented by a numpy array (vector) of shape (1, m_train)X_test -- test set represented by a numpy array of shape (num_px * num_px * 3, m_test)Y_test -- test labels represented by a numpy array (vector) of shape (1, m_test)num_iterations -- hyperparameter representing the number of iterations to optimize the parameterslearning_rate -- hyperparameter representing the learning rate used in the update rule of optimize()print_cost -- Set to true to print the cost every 100 iterationsReturns:d -- dictionary containing information about the model."""### START CODE HERE #### initialize parameters with zeros (≈ 1 line of code)w,b = initialize_with_zeros(X_train.shape[0])# Gradient descent (≈ 1 line of code)grads,cost = propagate(w, b, X_train, Y_train)# Retrieve parameters w and b from dictionary "parameters"params, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)w = params["w"]b = params["b"]# Predict test/train set examples (≈ 2 lines of code)Y_prediction_test = predict(w, b, X_test)Y_prediction_train = predict(w, b, X_train)### END CODE HERE #### Print train/test Errorsprint("train accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100))print("test accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100))d = {"costs": costs,"Y_prediction_test": Y_prediction_test, "Y_prediction_train" : Y_prediction_train, "w" : w, "b" : b,"learning_rate" : learning_rate,"num_iterations": num_iterations}return d

总结;1.预处理数据集很重要。2. 分别实现了每个函数：initialize（）、propagate（）、optimize（）。然后构建了一个 model（）。3. 调整学习率（这是“超参数”的一个例子）可以对算法产生很大的影响。

最后，防止过拟合可以加入正则化项，学习率的选择可以使用动态优化学习率函数。机器学习课程中都有讲过。

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