用julia写一个rnn

以下是一个简单的RNN实现，使用Julia语言和Flux深度学习库：

using Flux

# 创建一个简单的RNN模型
function create_rnn(input_size, hidden_size, output_size)
    # 定义模型的参数
    Wxh = param(randn(hidden_size, input_size))
    Whh = param(randn(hidden_size, hidden_size))
    Why = param(randn(output_size, hidden_size))
    bh = param(zeros(hidden_size))
    by = param(zeros(output_size))

    # 定义RNN模型的前向传播函数
    function rnn(x, h)
        h = tanh.(Wxh * x .+ Whh * h .+ bh)
        y = Why * h .+ by
        return y, h
    end

    # 定义损失函数和优化器
    loss(x, y) = logitbinarycrossentropy(loss_model(x), y)
    opt = ADAM()

    # 定义模型
    model = Chain(Dense(input_size, hidden_size, tanh), Recur(rnn, hidden_size, hidden_size), Dense(hidden_size, output_size))
    return model, loss, opt
end

# 定义训练函数
function train_rnn(model, lossfun, opt, input, target, epochs)
    for epoch in 1:epochs
        total_loss = 0
        for i in 1:size(input, 2)
            x = input[:, i]
            y = target[:, i]
            loss_val = lossfun(x, y)
            total_loss += loss_val
            Flux.back!(loss_val)
            opt.update!(params(model))
        end
        println("Epoch $epoch, loss: $(total_loss/size(input,2))")
    end
end

# 生成数据
input_size = 4
output_size = 1
sequence_length = 10
batch_size = 16
input = rand(input_size, sequence_length, batch_size)
target = rand(output_size, sequence_length, batch_size)

# 创建模型并训练
model, loss, opt = create_rnn(input_size, 128, output_size)
train_rnn(model, loss, opt, input, target, 100)

该代码生成一个随机输入和目标数据的序列，然后使用训练数据训练一个简单的RNN模型。在训练过程中，损失值将被输出到控制台，以便您了解模型的训练进度。