RNN的python实现

RNN的Python实现可以参考以下代码：

# 引用\[2\]
# 定义RNN类
class RNN:
    def __init__(self):
        self.input_n = 0
        self.hidden_n = 0
        self.output_n = 0
        self.input_weights = \[\]  # (input, hidden)
        self.output_weights = \[\]  # (hidden, output)
        self.hidden_weights = \[\]  # (hidden, hidden)
    
    def setup(self, ni, nh, no):
        self.input_n = ni
        self.hidden_n = nh
        self.output_n = no
        self.input_weights = make_rand_mat(self.input_n, self.hidden_n)
        self.output_weights = make_rand_mat(self.hidden_n, self.output_n)
        self.hidden_weights = make_rand_mat(self.hidden_n, self.hidden_n)

# 引用\[1\]
# 定义test()方法作为示例代码的入口
def test(self):
    self.setup(2, 16, 1)
    for i in range(20000):
        a_int = int(rand(0, 127))
        a = int_to_bin(a_int, dim=8)
        a = np.array(\[int(t) for t in a\])
        b_int = int(rand(0, 127))
        b = int_to_bin(b_int, dim=8)
        b = np.array(\[int(t) for t in b\])
        c_int = a_int + b_int
        c = int_to_bin(c_int, dim=8)
        c = np.array(\[int(t) for t in c\])
        guess, error = self.do_train(\[a, b\], c, dim=8)
        if i % 1000 == 0:
            print("Predict:" + str(guess))
            print("True:" + str(c))
            print("Error:" + str(error))
        out = 0
        for index, x in enumerate(reversed(guess)):
            out += x * pow(2, index)
        print(str(a_int) + " + " + str(b_int) + " = " + str(out))
    result = str(self.predict(\[a, b\], dim=8))
    print(result)
    print("===============")

以上是一个简单的RNN类的Python实现示例，其中包括了初始化方法和设置方法。在示例代码的`test()`方法中，展示了如何使用RNN进行训练和预测。具体的实现细节可能需要根据具体的需求进行调整和修改。
#### 引用[.reference_title]
- *1* *3* [Python实现RNN](https://blog.csdn.net/weixin_33853827/article/details/85889528)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^koosearch_v1,239^v3^insert_chatgpt"}} ] [.reference_item]
- *2* [【深度学习系列（六）】：RNN系列（1）：基于python的RNN实现](https://blog.csdn.net/wxplol/article/details/103882564)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^koosearch_v1,239^v3^insert_chatgpt"}} ] [.reference_item]
[ .reference_list ]