Function checkGradient

NeuralNetwok/NeuralNetwork.py:199–232

构造一个小型的神经网络验证，因为数值法计算梯度很浪费时间，而且验证正确后之后就不再需要验证了

(Lambda = 0)

# 检验梯度是否计算正确
def checkGradient(Lambda = 0):
    '''构造一个小型的神经网络验证，因为数值法计算梯度很浪费时间，而且验证正确后之后就不再需要验证了'''
    input_layer_size = 3
    hidden_layer_size = 5
    num_labels = 3
    m = 5
    initial_Theta1 = debugInitializeWeights(input_layer_size,hidden_layer_size); 
    initial_Theta2 = debugInitializeWeights(hidden_layer_size,num_labels)
    X = debugInitializeWeights(input_layer_size-1,m)
    y = np.transpose(np.mod(np.arange(1,m+1), num_labels))# 初始化y
    
    y = y.reshape(-1,1)
    nn_params = np.vstack((initial_Theta1.reshape(-1,1),initial_Theta2.reshape(-1,1)))  #展开theta 
    '''BP求出梯度'''
    grad = nnGradient(nn_params, input_layer_size, hidden_layer_size, 
                     num_labels, X, y, Lambda)  
    '''使用数值法计算梯度'''
    num_grad = np.zeros((nn_params.shape[0]))
    step = np.zeros((nn_params.shape[0]))
    e = 1e-4
    for i in range(nn_params.shape[0]):
        step[i] = e
        loss1 = nnCostFunction(nn_params-step.reshape(-1,1), input_layer_size, hidden_layer_size, 
                              num_labels, X, y, 
                              Lambda)
        loss2 = nnCostFunction(nn_params+step.reshape(-1,1), input_layer_size, hidden_layer_size, 
                              num_labels, X, y, 
                              Lambda)
        num_grad[i] = (loss2-loss1)/(2*e)
        step[i]=0
    # 显示两列比较
    res = np.hstack((num_grad.reshape(-1,1),grad.reshape(-1,1)))
    print("检查梯度的结果，第一列为数值法计算得到的，第二列为BP得到的:")
    print (res)

# 初始化调试的theta权重
def debugInitializeWeights(fan_in,fan_out):