人工智能-根据姓名判断性别

本帖训练一个可以根据姓名判断性别的CNN模型；我使用自己爬取的35万中文姓名进行训练。

使用同样的数据集还可以训练起名字模型，参看：

• TensorFlow练习7: 基于RNN生成古诗词

• https://github.com/tensorflow/models/tree/master/namignizer

• TensorFlow练习13: 制作一个简单的聊天机器人

准备姓名数据集

我上网找了一下，并没有找到现成的中文姓名数据集，额，看来只能自己动手了。

我写了一个简单的Python脚本，爬取了上万中文姓名，格式整理如下：

[python] view plain copy

姓名,性别
安镶怡,女
饶黎明,男
段焙曦,男
苗芯萌,男
覃慧藐,女
芦玥微,女
苏佳琬,女
王旎溪,女
彭琛朗,男
李昊,男
利欣怡,女
# 貌似有很多名字男女通用

数据集:https://pan.baidu.com/s/1hsHTEU4。

训练模型

[python] view plain copy

import tensorflow as tf
import numpy as np

name_dataset = 'name.csv'

train_x = []
train_y = []
with open(name_dataset, 'r') as f:
    first_line = True
    for line in f:
        if first_line is True:
            first_line = False
            continue
        sample = line.strip().split(',')
        if len(sample) == 2:
            train_x.append(sample[0])
            if sample[1] == '男':
                train_y.append([0, 1])  # 男
            else:
                train_y.append([1, 0])  # 女

max_name_length = max([len(name) for name in train_x])
print("最长名字的字符数: ", max_name_length)
max_name_length = 8

# 数据已shuffle
#shuffle_indices = np.random.permutation(np.arange(len(train_y)))
#train_x = train_x[shuffle_indices]
#train_y = train_y[shuffle_indices]

# 词汇表（参看聊天机器人练习）
counter = 0
vocabulary = {}
for name in train_x:
    counter += 1
    tokens = [word for word in name]
    for word in tokens:
        if word in vocabulary:
            vocabulary[word] += 1
        else:
            vocabulary[word] = 1

vocabulary_list = [' '] + sorted(vocabulary, key=vocabulary.get, reverse=True)
print(len(vocabulary_list))

# 字符串转为向量形式
vocab = dict([(x, y) for (y, x) in enumerate(vocabulary_list)])
train_x_vec = []
for name in train_x:
    name_vec = []
    for word in name:
        name_vec.append(vocab.get(word))
    while len(name_vec) < max_name_length:
        name_vec.append(0)
    train_x_vec.append(name_vec)

#######################################################

input_size = max_name_length
num_classes = 2

batch_size = 64
num_batch = len(train_x_vec) // batch_size

X = tf.placeholder(tf.int32, [None, input_size])
Y = tf.placeholder(tf.float32, [None, num_classes])

dropout_keep_prob = tf.placeholder(tf.float32)

def neural_network(vocabulary_size, embedding_size=128, num_filters=128):
    # embedding layer
    with tf.device('/cpu:0'), tf.name_scope("embedding"):
        W = tf.Variable(tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
        embedded_chars = tf.nn.embedding_lookup(W, X)
        embedded_chars_expanded = tf.expand_dims(embedded_chars, -1)
    # convolution + maxpool layer
    filter_sizes = [3,4,5]
    pooled_outputs = []
    for i, filter_size in enumerate(filter_sizes):
        with tf.name_scope("conv-maxpool-%s" % filter_size):
            filter_shape = [filter_size, embedding_size, 1, num_filters]
            W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1))
            b = tf.Variable(tf.constant(0.1, shape=[num_filters]))
            conv = tf.nn.conv2d(embedded_chars_expanded, W, strides=[1, 1, 1, 1], padding="VALID")
            h = tf.nn.relu(tf.nn.bias_add(conv, b))
            pooled = tf.nn.max_pool(h, ksize=[1, input_size - filter_size + 1, 1, 1], strides=[1, 1, 1, 1], padding='VALID')
            pooled_outputs.append(pooled)

    num_filters_total = num_filters * len(filter_sizes)
    h_pool = tf.concat(3, pooled_outputs)
    h_pool_flat = tf.reshape(h_pool, [-1, num_filters_total])
    # dropout
    with tf.name_scope("dropout"):
        h_drop = tf.nn.dropout(h_pool_flat, dropout_keep_prob)
    # output
    with tf.name_scope("output"):
        W = tf.get_variable("W", shape=[num_filters_total, num_classes], initializer=tf.contrib.layers.xavier_initializer())
        b = tf.Variable(tf.constant(0.1, shape=[num_classes]))
        output = tf.nn.xw_plus_b(h_drop, W, b)

    return output
# 训练
def train_neural_network():
    output = neural_network(len(vocabulary_list))

    optimizer = tf.train.AdamOptimizer(1e-3)
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(output, Y))
    grads_and_vars = optimizer.compute_gradients(loss)
    train_op = optimizer.apply_gradients(grads_and_vars)

    saver = tf.train.Saver(tf.global_variables())
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())

        for e in range(201):
            for i in range(num_batch):
                batch_x = train_x_vec[i*batch_size : (i+1)*batch_size]
                batch_y = train_y[i*batch_size : (i+1)*batch_size]
                _, loss_ = sess.run([train_op, loss], feed_dict={X:batch_x, Y:batch_y, dropout_keep_prob:0.5})
                print(e, i, loss_)
            # 保存模型
            if e % 50 == 0:
                saver.save(sess, "name2sex.model", global_step=e)

train_neural_network()

# 使用训练的模型
def detect_sex(name_list):
    x = []
    for name in name_list:
        name_vec = []
        for word in name:
            name_vec.append(vocab.get(word))
        while len(name_vec) < max_name_length:
            name_vec.append(0)
        x.append(name_vec)

    output = neural_network(len(vocabulary_list))

    saver = tf.train.Saver(tf.global_variables())
    with tf.Session() as sess:
        # 恢复前一次训练
        ckpt = tf.train.get_checkpoint_state('.')
        if ckpt != None:
            print(ckpt.model_checkpoint_path)
            saver.restore(sess, ckpt.model_checkpoint_path)
        else:
            print("没找到模型")

        predictions = tf.argmax(output, 1)
        res = sess.run(predictions, {X:x, dropout_keep_prob:1.0})

        i = 0
        for name in name_list:
            print(name, '女' if res[i] == 0 else '男')
            i += 1

detect_sex(["白富美", "高帅富", "王婷婷", "田野"])

执行结果：

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文章已获作者授权转载，原文链接如下：

http://blog.csdn.net/u014365862/article/details/53869732