# 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