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