#!/usr/bin/python
# -*- coding: utf-8 -*-

import tensorflow as tf

class TRNNConfig(object):
“””RNN配置参数”””

# 模型参数
embedding_dim = 64 # 词向量维度
seq_length = 600 # 序列长度
num_classes = 10 # 类别数
vocab_size = 5000 # 词汇表达小

num_layers= 2 # 隐藏层层数
hidden_dim = 128 # 隐藏层神经元
rnn = \’gru\’ # lstm 或 gru

dropout_keep_prob = 0.8 # dropout保留比例
learning_rate = 1e-3 # 学习率

batch_size = 128 # 每批训练大小
num_epochs = 10 # 总迭代轮次

print_per_batch = 100 # 每多少轮输出一次结果
save_per_batch = 10 # 每多少轮存入tensorboard

class TextRNN(object):
“””文本分类，RNN模型”””
def __init__(self, config):
self.config = config

# 三个待输入的数据
self.input_x = tf.placeholder(tf.int32, [None, self.config.seq_length], name=\’input_x\’)
self.input_y = tf.placeholder(tf.float32, [None, self.config.num_classes], name=\’input_y\’)
self.keep_prob = tf.placeholder(tf.float32, name=\’keep_prob\’)

self.rnn()

def rnn(self):
“””rnn模型”””

def lstm_cell(): # lstm核
return tf.contrib.rnn.BasicLSTMCell(self.config.hidden_dim, state_is_tuple=True)

def gru_cell(): # gru核
return tf.contrib.rnn.GRUCell(self.config.hidden_dim)

def dropout(): # 为每一个rnn核后面加一个dropout层
if (self.config.rnn == \’lstm\’):
cell = lstm_cell()
else:
cell = gru_cell()
return tf.contrib.rnn.DropoutWrapper(cell, output_keep_prob=self.keep_prob)

# 动作映射
with tf.device(\’/cpu:0\’):
embedding = tf.get_variable(\’embedding\’, [self.config.vocab_size, self.config.embedding_dim])
embedding_inputs = tf.nn.embedding_lookup(embedding, self.input_x)

with tf.name_scope(“rnn”):
# 多层rnn网络
cells = [dropout() for _ in range(self.config.num_layers)]
rnn_cell = tf.contrib.rnn.MultiRNNCell(cells, state_is_tuple=True)

_outputs, _ = tf.nn.dynamic_rnn(cell=rnn_cell, inputs=embedding_inputs, dtype=tf.float32)
last = _outputs[:, -1, :] # 取最后一个时序输出作为结果

with tf.name_scope(“score”):
# 全连接层，后面接dropout以及relu激活
fc = tf.layers.dense(last, self.config.hidden_dim, name=\’fc1\’)
fc = tf.contrib.layers.dropout(fc, self.keep_prob)
fc = tf.nn.relu(fc)

# 分类器
self.logits = tf.layers.dense(fc, self.config.num_classes, name=\’fc2\’)
# 预测类别
self.y_pred_cls = tf.argmax(tf.nn.softmax(self.logits), 1)

with tf.name_scope(“optimize”):
# 损失函数，交叉熵
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=self.logits, labels=self.input_y)
#求输入的所有行的预测值的均值
self.loss = tf.reduce_mean(cross_entropy)
# 优化器
self.optim = tf.train.AdamOptimizer(learning_rate=self.config.learning_rate).minimize(self.loss)

with tf.name_scope(“accuracy”):
# 准确率 其中 self.y_pred_cls为预测的类别
correct_pred = tf.equal(tf.argmax(self.input_y, 1), self.y_pred_cls)
#
self.acc = tf.reduce_mean(tf.cast(correct_pred, tf.float32))