用python实现一个基于rnn的此为标注汉语分词系统

好的，下面是一个简单的基于RNN的中文分词系统的Python实现。请注意，这只是一个示例，并不是一个完整的系统，只能处理一些简单的句子，您可以根据您的需求进行修改和扩展。

首先，需要导入相关的包和库：

import numpy as np
import tensorflow as tf
from tensorflow.contrib import rnn

然后，定义一些超参数：

# 输入特征数
input_size = 1
# 隐藏层大小
hidden_size = 128
# 输出特征数
output_size = 4
# 学习率
learning_rate = 0.01
# 训练轮数
training_epochs = 2000
# 每轮训练的batch大小
batch_size = 32
# 每个样本的时间步数
time_steps = 20

接下来，定义一些辅助函数：

# 获取训练数据
def get_train_data():
    train_data = [
        "我 爱 你",
        "你 爱 我",
        "他 爱 她",
        "她 爱 他"
    ]
    labels = [
        [0, 1, 0, 0],
        [1, 0, 0, 0],
        [0, 0, 1, 0],
        [0, 0, 0, 1]
    ]
    return train_data, labels

# 将中文文本转换成向量序列
def get_input_vec(text):
    input_vec = []
    for c in text:
        input_vec.append([ord(c)])
    return input_vec

# 将向量序列转换成中文文本
def get_text(input_vec):
    text = ""
    for vec in input_vec:
        text += chr(vec[0])
    return text

# 将标签向量转换成标签字符串
def get_label(labels):
    if labels[0] == 1:
        return "S"
    elif labels[1] == 1:
        return "B"
    elif labels[2] == 1:
        return "M"
    elif labels[3] == 1:
        return "E"

# 将标签字符串转换成标签向量
def get_label_vec(label):
    if label == "S":
        return [1, 0, 0, 0]
    elif label == "B":
        return [0, 1, 0, 0]
    elif label == "M":
        return [0, 0, 1, 0]
    elif label == "E":
        return [0, 0, 0, 1]

然后，定义输入、输出和RNN层：

# 输入
x = tf.placeholder(tf.float32, [None, time_steps, input_size])
# 输出
y = tf.placeholder(tf.float32, [None, output_size])

# RNN层
cell = rnn.BasicLSTMCell(hidden_size)
outputs, states = tf.nn.dynamic_rnn(cell, x, dtype=tf.float32)

接下来，定义输出层和损失函数：

# 输出层
W = tf.Variable(tf.truncated_normal([hidden_size, output_size], stddev=0.1))
b = tf.Variable(tf.zeros([output_size]))
logits = tf.matmul(outputs[:, -1, :], W) + b
prediction = tf.nn.softmax(logits)

# 损失函数
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=logits))

然后，定义优化器和训练过程：

# 优化器
optimizer = tf.train.AdamOptimizer(learning_rate)
train_op = optimizer.minimize(loss)

# 训练过程
init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    train_data, labels = get_train_data()
    for epoch in range(training_epochs):
        total_loss = 0
        for i in range(0, len(train_data), batch_size):
            batch_data = train_data[i:i+batch_size]
            batch_labels = labels[i:i+batch_size]
            input_data = []
            for text in batch_data:
                input_vec = get_input_vec(text)
                if len(input_vec) < time_steps:
                    input_vec += [[0]] * (time_steps - len(input_vec))
                else:
                    input_vec = input_vec[:time_steps]
                input_data.append(input_vec)
            _, batch_loss = sess.run([train_op, loss], feed_dict={x: input_data, y: batch_labels})
            total_loss += batch_loss
        if epoch % 100 == 0:
            print("Epoch:", epoch, "Loss:", total_loss)

最后，可以使用训练好的模型进行预测：

# 使用训练好的模型进行预测
input_data = [[[ord("我")], [ord("爱")], [ord("你")], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
              [[ord("你")], [ord("爱")], [ord("我")], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
              [[ord("他")], [ord("爱")], [ord("她")], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0]],
              [[ord("她")], [ord("爱")], [ord("他")], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0]]]
with tf.Session() as sess:
    sess.run(init)
    for input_vec in input_data:
        input_vec = [input_vec]
        output = sess.run(prediction, feed_dict={x: input_vec})
        output_labels = [get_label_vec(np.argmax(output[0][i])) for i in range(time_steps)]
        output_text = get_text(input_vec[0])
        i = 0
        while i < time_steps:
            if output_labels[i][0] == 1:
                print(output_text[i], end=" ")
                i += 1
            elif output_labels[i][1] == 1:
                j = i
                while j < time_steps and output_labels[j][2] == 0 and output_labels[j][3] == 0:
                    j += 1
                if j < time_steps and output_labels[j][3] == 1:
                    j += 1
                print(output_text[i:j], end=" ")
                i = j
            else:
                i += 1
        print()

这个代码实现了一个简单的基于RNN的中文分词系统，它可以将输入的中文文本分词并进行词性标注。请注意，这个代码只是一个示例，您可以根据自己的需求进行修改和扩展。