在julia中不使用Flux构建并测试只有一层隐含层的神经网络的完整代码

好的，以下是在Julia中不使用Flux构建并测试只有一层隐含层的神经网络的完整代码：

using Random

# 定义sigmoid函数
sigmoid(x) = 1 / (1 + exp(-x))

# 定义神经网络结构
struct NeuralNetwork
    W1::Array{Float64, 2}
    W2::Array{Float64, 2}
    b1::Array{Float64, 1}
    b2::Array{Float64, 1}
end

# 初始化神经网络参数
function initialize_parameters(input_size, hidden_size, output_size)
    W1 = randn(hidden_size, input_size)
    b1 = randn(hidden_size)
    W2 = randn(output_size, hidden_size)
    b2 = randn(output_size)
    NeuralNetwork(W1, W2, b1, b2)
end

# 前向传播函数
function forward(nn::NeuralNetwork, x)
    z1 = nn.W1 * x .+ nn.b1
    a1 = sigmoid.(z1)
    z2 = nn.W2 * a1 .+ nn.b2
    a2 = sigmoid.(z2)
    (a1, a2)
end

# 训练函数
function train(nn::NeuralNetwork, x_train, y_train, learning_rate, num_iterations)
    for i in 1:num_iterations
        # 随机选择一个样本
        idx = rand(1:size(x_train, 2))
        x = x_train[:, idx]
        y = y_train[:, idx]

        # 前向传播
        a1, y_pred = forward(nn, x)

        # 计算损失
        loss = sum((y - y_pred).^2)

        # 反向传播
        delta2 = (y_pred - y) .* y_pred .* (1 - y_pred)
        delta1 = nn.W2' * delta2 .* a1 .* (1 - a1)

        # 更新参数
        nn.W2 -= learning_rate * delta2 * a1'
        nn.b2 -= learning_rate * delta2
        nn.W1 -= learning_rate * delta1 * x'
        nn.b1 -= learning_rate * delta1
    end
    nn
end

# 测试函数
function test(nn::NeuralNetwork, x_test, y_test)
    correct = 0
    for i in 1:size(x_test, 2)
        x = x_test[:, i]
        y = y_test[:, i]
        _, y_pred = forward(nn, x)
        correct += argmax(y_pred) == argmax(y)
    end
    correct / size(x_test, 2)
end

# 生成训练数据和测试数据
Random.seed!(123)
num_samples = 1000
input_size = 4
hidden_size = 5
output_size = 3
x_train = randn(input_size, num_samples)
y_train = zeros(output_size, num_samples)
for i in 1:num_samples
    y_train[rand(1:output_size), i] = 1
end
x_test = randn(input_size, num_samples)
y_test = zeros(output_size, num_samples)
for i in 1:num_samples
    y_test[rand(1:output_size), i] = 1
end

# 初始化神经网络
nn = initialize_parameters(input_size, hidden_size, output_size)

# 训练神经网络
nn_trained = train(nn, x_train, y_train, 0.1, 10000)

# 测试神经网络
accuracy = test(nn_trained, x_test, y_test)
println("Accuracy: $accuracy")

这段代码中，我们使用了随机梯度下降算法进行模型的训练，并使用了均方误差作为损失函数。在测试过程中，我们计算了模型的准确率。