深度卷积网络对中文文本传抄对比的python算法

对于中文文本传抄的对比，可以使用深度卷积网络进行处理。以下是一个简单的Python算法：

导入必要的库，包括tensorflow、numpy和pandas：

import tensorflow as tf
import numpy as np
import pandas as pd

定义数据集，包括原始文本和传抄文本：

original_text = ['这是一段原始文本。', '这是另一段原始文本。', '这是第三段原始文本。']
copied_text = ['这是一段抄袭文本。', '这是另一段抄袭文本。', '这是第三段抄袭文本。']

将原始文本和传抄文本转换成数字向量：

vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor(50)
original_text_vectors = np.array(list(vocab_processor.fit_transform(original_text)))
copied_text_vectors = np.array(list(vocab_processor.transform(copied_text)))

定义深度卷积网络模型：

input_x = tf.placeholder(tf.int32, [None, 50], name='input_x')
input_y = tf.placeholder(tf.float32, [None, 2], name='input_y')
dropout_keep_prob = tf.placeholder(tf.float32, name='dropout_keep_prob')

embedding_size = 50
filter_sizes = [3, 4, 5]
num_filters = 128

embedded_chars = tf.contrib.layers.embed_sequence(input_x, vocab_size=len(vocab_processor.vocabulary_), embed_dim=embedding_size)
embedded_chars_expanded = tf.expand_dims(embedded_chars, -1)

pooled_outputs = []
for i, filter_size in enumerate(filter_sizes):
    filter_shape = [filter_size, embedding_size, 1, num_filters]
    W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name='W')
    b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name='b')
    conv = tf.nn.conv2d(embedded_chars_expanded, W, strides=[1, 1, 1, 1], padding='VALID', name='conv')
    h = tf.nn.relu(tf.nn.bias_add(conv, b), name='relu')
    pooled = tf.nn.max_pool(h, ksize=[1, 50 - filter_size + 1, 1, 1], strides=[1, 1, 1, 1], padding='VALID', name='pool')
    pooled_outputs.append(pooled)

num_filters_total = num_filters * len(filter_sizes)
h_pool = tf.concat(pooled_outputs, 3)
h_pool_flat = tf.reshape(h_pool, [-1, num_filters_total])

W = tf.Variable(tf.truncated_normal([num_filters_total, 2], stddev=0.1), name='W')
b = tf.Variable(tf.constant(0.1, shape=[2]), name='b')
scores = tf.nn.xw_plus_b(h_pool_flat, W, b, name='scores')
predictions = tf.argmax(scores, 1, name='predictions')

训练模型：

batch_size = 64
num_epochs = 10
learning_rate = 1e-3

loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=scores, labels=input_y))
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    for epoch in range(num_epochs):
        for i in range(0, len(original_text_vectors), batch_size):
            x_batch = original_text_vectors[i:i+batch_size]
            y_batch = [[1, 0] for _ in range(len(x_batch))]
            x_batch_copied = copied_text_vectors[i:i+batch_size]
            y_batch_copied = [[0, 1] for _ in range(len(x_batch_copied))]
            x_batch = np.concatenate([x_batch, x_batch_copied], 0)
            y_batch = np.concatenate([y_batch, y_batch_copied], 0)
            feed_dict = {input_x: x_batch, input_y: y_batch, dropout_keep_prob: 0.5}
            _, loss_value = sess.run([optimizer, loss], feed_dict=feed_dict)
        print('Epoch %d: loss = %.4f' % (epoch, loss_value))

使用模型进行预测：

test_text = ['这是一段测试文本。', '这是另一段测试文本。']
test_text_vectors = np.array(list(vocab_processor.transform(test_text)))

with tf.Session() as sess:
    saver = tf.train.Saver()
    saver.restore(sess, 'model.ckpt')
    predictions = sess.run(predictions, feed_dict={input_x: test_text_vectors, dropout_keep_prob: 1.0})
    print(predictions)

注意：这只是一个简单的示例，实际应用中需要根据具体情况进行调整和优化。