# 信号的向前传播函数
def predict(network , X):
    \"\"\"模拟整个过程\"\"\"
    # 首先先获得权重矩阵
    W1,W2,W3 = network[\'W1\'],network[\'W2\'],network[\'W3\'],
    B1,B2,B3 = network[\'b1\'],network[\'b2\'],network[\'b3\'],
   
    # 传播过程
    # 神经网络信号的加权汇总
    A1 = np.dot(X,W1) + B1
    Z1 = sigmoid(A1)
    A2= np.dot(Z1 ,W2) + B2
    Z2 = sigmoid(A2)
    A3 = np.dot(Z2,W3) + B3
    y = softmax(A3)
    return y