原来我们文本分类采用的贝叶斯模型，也有很多人使用TEXTCNN方法做文本分类，感觉效果应该优于贝叶斯原理，个人觉得因为贝叶斯有一个强的前置条件是各个特征之间是独立的，可能实际情况不一定如此。
cnn原理可以参考：https://www.cnblogs.com/charlotte77/p/7759802.html
感觉写的十分清晰。
Mac 安装tensorflow可以参考网上资料。我的电脑安装的是python2.7.但在python代码中调用tensorflow包时报错，需要将tensorflow下的包拷贝到/usr/lib/python2.7目录下。mac不允许拷贝，需要关闭SIP
关闭SIP步骤：
重启电脑，按住Command+R(直到出现苹果标志)进入Recovery Mode(恢复模式)
左上角菜单里找到实用工具 -> 终端
输入 csrutil enable即可
原文：https://blog.csdn.net/cocos2der/article/details/53898206
TEXTCNN实现摘自别人一段完整的python文件代码：
链接：https://www.jianshu.com/p/b1000d5345bb
为了适合我机器的python版本，略做了一点修改：
Mac单机运行1小时左右。
import io
with io.open('cnews.train.txt', encoding='utf8') as file:
line_list = [k.strip() for k in file.readlines()]
train_label_list = [k.split()[0] for k in line_list]
train_content_list = [k.split(None,1)[1] for k in line_list]
with io.open('cnews.vocab.txt', encoding='utf8') as file:
vocabulary_list = [k.strip() for k in file.readlines()]
word2id_dict = dict([(b, a) for a, b in enumerate(vocabulary_list)])
content2idList = lambda content : [word2id_dict[word] for word in content if word in word2id_dict]
train_idlist_list = [content2idList(content) for content in train_content_list]
vocab_size = 5000
seq_length = 600
embedding_dim = 64
num_classes = 10
num_filters = 256
kernel_size = 5
hidden_dim = 128
dropout_keep_prob = 0.5
learning_rate = 1e-3
batch_size = 64
import tensorflow.contrib.keras as kr
train_X = kr.preprocessing.sequence.pad_sequences(train_idlist_list, seq_length)
from sklearn.preprocessing import LabelEncoder
labelEncoder = LabelEncoder()
train_y = labelEncoder.fit_transform(train_label_list)
train_Y = kr.utils.to_categorical(train_y, num_classes)
import tensorflow as tf
tf.reset_default_graph()
X_holder = tf.placeholder(tf.int32, [None, seq_length])
Y_holder = tf.placeholder(tf.float32, [None, num_classes])
embedding = tf.get_variable('embedding', [vocab_size, embedding_dim])
embedding_inputs = tf.nn.embedding_lookup(embedding, X_holder)
conv = tf.layers.conv1d(embedding_inputs, num_filters, kernel_size)
max_pooling = tf.reduce_max(conv, reduction_indices=[1])
full_connect = tf.layers.dense(max_pooling, hidden_dim)
full_connect_dropout = tf.contrib.layers.dropout(full_connect, keep_prob=0.75)
full_connect_activate = tf.nn.relu(full_connect_dropout)
softmax_before = tf.layers.dense(full_connect_activate, num_classes)
predict_Y = tf.nn.softmax(softmax_before)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels=Y_holder, logits=softmax_before)
loss = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate)
train = optimizer.minimize(loss)
isCorrect = tf.equal(tf.argmax(Y_holder, 1), tf.argmax(predict_Y, 1))
accuracy = tf.reduce_mean(tf.cast(isCorrect, tf.float32))
init = tf.global_variables_initializer()
session = tf.Session()
session.run(init)
with io.open('cnews.test.txt', encoding='utf8') as file:
line_list = [k.strip() for k in file.readlines()]
test_label_list = [k.split()[0] for k in line_list]
test_content_list = [k.split(None,1)[1] for k in line_list]
test_idlist_list = [content2idList(content) for content in test_content_list]
test_X = kr.preprocessing.sequence.pad_sequences(test_idlist_list, seq_length)
test_y = labelEncoder.transform(test_label_list)
test_Y = kr.utils.to_categorical(test_y, num_classes)
import random
for i in range(3000):
selected_index = random.sample(list(range(len(train_y))), k=batch_size)
batch_X = train_X[selected_index]
batch_Y = train_Y[selected_index]
session.run(train, {X_holder:batch_X, Y_holder:batch_Y})
step = i + 1
if step % 100 == 0:
selected_index = random.sample(list(range(len(test_y))), k=200)
batch_X = test_X[selected_index]
batch_Y = test_Y[selected_index]
loss_value, accuracy_value = session.run([loss, accuracy], {X_holder:batch_X, Y_holder:batch_Y})
print('step:%d loss:%.4f accuracy:%.4f' %(step, loss_value, accuracy_value))