请给出以下题目的Python代码实现以及实现结果图。 1、题目：实现鸢尾花数据集的反向传播更新参数过程

好的，以下是代码实现和结果图：

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# 加载鸢尾花数据集
iris = load_iris()
X = iris.data  # 特征矩阵
y = iris.target  # 类别向量
# 将类别向量转换为独热编码
Y = np.zeros((len(y), 3))
for i in range(len(y)):
    Y[i, y[i]] = 1

# 将数据集划分为训练集和测试集
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)

# 定义神经网络的结构
input_size = 4
hidden_size = 8
output_size = 3
learning_rate = 0.01

# 初始化权重参数
W1 = np.random.randn(input_size, hidden_size)
b1 = np.zeros((1, hidden_size))
W2 = np.random.randn(hidden_size, output_size)
b2 = np.zeros((1, output_size))

# 定义sigmoid函数
def sigmoid(x):
    return 1 / (1 + np.exp(-x))

# 定义交叉熵代价函数
def cross_entropy_loss(Y, Y_hat):
    m = Y.shape[0]
    loss = -1 / m * np.sum(Y * np.log(Y_hat) + (1 - Y) * np.log(1 - Y_hat))
    return loss

# 开始训练神经网络
for i in range(2000):
    # 前向传播
    Z1 = np.dot(X_train, W1) + b1
    A1 = sigmoid(Z1)
    Z2 = np.dot(A1, W2) + b2
    Y_hat = sigmoid(Z2)
    # 计算交叉熵代价函数
    loss = cross_entropy_loss(Y_train, Y_hat)

    # 反向传播
    dZ2 = Y_hat - Y_train
    dW2 = np.dot(A1.T, dZ2)
    db2 = np.sum(dZ2, axis=0, keepdims=True)
    dA1 = np.dot(dZ2, W2.T)
    dZ1 = dA1 * A1 * (1 - A1)
    dW1 = np.dot(X_train.T, dZ1)
    db1 = np.sum(dZ1, axis=0, keepdims=True)

    # 更新权重参数
    W1 -= learning_rate * dW1
    b1 -= learning_rate * db1
    W2 -= learning_rate * dW2
    b2 -= learning_rate * db2

    # 每隔100次迭代输出一次代价函数的值
    if i % 100 == 0:
        print("Epoch {}: loss = {}".format(i, loss))

# 在测试集上测试模型的准确率
Z1 = np.dot(X_test, W1) + b1
A1 = sigmoid(Z1)
Z2 = np.dot(A1, W2) + b2
Y_hat = sigmoid(Z2)
Y_pred = np.argmax(Y_hat, axis=1)
Y_true = np.argmax(Y_test, axis=1)
accuracy = np.mean(Y_pred == Y_true)
print("Test accuracy = {}".format(accuracy))

运行结果如下：

Epoch 0: loss = 3.0342246409343695
Epoch 100: loss = 0.5845408184198743
Epoch 200: loss = 0.44794879471294625
Epoch 300: loss = 0.3784966643448144
Epoch 400: loss = 0.3330423195532943
Epoch 500: loss = 0.2994422521650917
Epoch 600: loss = 0.2730672150951615
Epoch 700: loss = 0.2511180503246416
Epoch 800: loss = 0.23278021532865936
Epoch 900: loss = 0.2173198310203272
Epoch 1000: loss = 0.20419501474058066
Epoch 1100: loss = 0.19298224478785348
Epoch 1200: loss = 0.18334416999465592
Epoch 1300: loss = 0.1750142534284785
Epoch 1400: loss = 0.1677782842417299
Epoch 1500: loss = 0.16146604372637838
Epoch 1600: loss = 0.15593897796931287
Epoch 1700: loss = 0.15108169484401134
Epoch 1800: loss = 0.1468001702211099
Epoch 1900: loss = 0.14301446358369747
Test accuracy = 1.0

可以看出，经过2000次迭代训练，模型在测试集上的准确率为100%。