首先从阅读论文开始。

先后阅读了如下文章

关于《A Critical Review of Recurrent Neural Networks for Sequence Learning》的阅读理解

《Understanding LSTM Networks》——文章对 LSTM 结构为什么这样设计，做了一步步的推理解释

关于《Supervised Sequence Labelling with Recurrent Neural Networks》的阅读理解

……一些文章

然后是一些tensorflow实现RNN或LSTM的例子。

目前，把普通的神经网络改造成RNN的成果如下。对RNN用tensorflow实现的逻辑可以理顺，但是实现起来有错误，提示维度不匹配。正在检查原因。

import numpyas np

import pandasas pd

import tensorflowas tf

# 转为onehot编码

def turn_onehot(df):

for keyin df.columns:

oneHot = pd.get_dummies(df[key])

for oneHotKeyin oneHot.columns:# 防止重名

            oneHot = oneHot.rename(columns={oneHotKey: key +'_' +str(oneHotKey)})

df = df.drop(key,axis=1)

df = df.join(oneHot)

return df

# 获取一批次的数据

def get_batch(x_date, y_date, batch):

global pointer

x_date_batch = x_date[pointer:pointer + batch]

y_date_batch = y_date[pointer:pointer + batch]

pointer = pointer + batch

return x_date_batch, y_date_batch

# 生成layer

def add_layer(input_num, output_num, x, layer, active=None):

# 生成权重

    with tf.name_scope('layer' + layer +'/W' + layer):

W = tf.Variable(tf.random_normal([2*input_num, output_num],dtype=tf.float32),name='W' + layer)

tf.summary.histogram('layer' + layer +'/W' + layer, W)

# 加入L2正则化

        if isregularization:

tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(lambda1)(W))

# 生成偏移量

    with tf.name_scope('layer' + layer +'/b' + layer):

b = tf.Variable(tf.zeros([output_num]) +0.1,dtype=tf.float32,name='b' + layer)

tf.summary.histogram('layer' + layer +'/b' + layer, b)

# 激活

    with tf.name_scope('layer' + layer +'/l' + layer):

l = active(tf.matmul(x, W) + b)# 使用sigmoid激活函数，备用函数还有relu

        tf.summary.histogram('layer' + layer +'/l' + layer, l)

return l

hiddenDim =1000  # 隐藏层神经元数

lambda1 =0.5  # 正则化超参数

save_file ='./train_model.ckpt'

pointer =0

time_step =1

istrain =True  # 启用训练模式

istensorborad =False  # 启用tensorboard

isregularization =False  # 启用正则化

if istrain:

samples =2000

    batch =1  # 每批次的数据输入数量

else:

samples =550

    batch =1  # 每批次的数据输入数量

with tf.name_scope('inputdate-x-y'):

# 导入

    df = pd.DataFrame(pd.read_csv('GHMX.CSV',header=0))

# 产生 y_data 值 (n, 1)

    y_date = df['number'].values

y_date = y_date.reshape((-1,1))

# 产生 x_data 值 (n, 4+12+31+24)

    df = df.drop('number',axis=1)

df = turn_onehot(df)

x_data = df.values

###生成神经网络模型

# 占位符

with tf.name_scope('inputs'):

x = tf.placeholder(tf.float32,shape=[None, time_step,71],name='x_input')

y_ = tf.placeholder(tf.float32,shape=[None,1],name='y_input')

keep_prob = tf.placeholder(tf.float32,name='keep_prob')

# 生成神经网络

lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(num_units=71,forget_bias=1.0,state_is_tuple=True)

lstm_cell = tf.nn.rnn_cell.DropoutWrapper(cell=lstm_cell,input_keep_prob=1.0,output_keep_prob=keep_prob)

mlstm_cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cellfor _in range(3)])

init_state = mlstm_cell.zero_state(batch,dtype=tf.float32)

outputs, date = tf.nn.dynamic_rnn(mlstm_cell,inputs=x,initial_state=init_state,time_major=False)

h_date= outputs[:, -1, :]

y = add_layer(71,1, h_date,'1', tf.nn.relu)

# 计算loss

with tf.name_scope('loss'):

# loss = tf.reduce_mean(tf.reduce_sum(tf.square(y - y_), name='square'), name='loss')  #损失函数，损失不下降，换用别的函数

# loss = -tf.reduce_sum(y_*tf.log(y))  #损失仍然不下降

# loss = -tf.reduce_sum(y_*tf.log(tf.clip_by_value(y,1e-10,1.0)) , name='loss')

    loss = tf.losses.mean_squared_error(labels=y_,predictions=y)

#tf.add_to_collection('losses', mse_loss)  # 损失集合

#loss = tf.add_n(tf.get_collection('losses'))

    tf.summary.scalar('loss', loss)

# 梯度下降

with tf.name_scope('train_step'):

train_step = tf.train.GradientDescentOptimizer(0.0005).minimize(loss)# 有效的学习率0.000005

# 初始化

init = tf.global_variables_initializer()

sess = tf.Session()

if istensorborad:

merged = tf.summary.merge_all()

writer = tf.summary.FileWriter('logs/', sess.graph)

sess.run(init)

# 保存/读取模型

saver = tf.train.Saver()

if not istrain:

saver.restore(sess, save_file)

for iin range(samples):

x_date_batch, y_date_batch = get_batch(x_data, y_date, batch)

feed_dict = {x: x_date_batch, y_: y_date_batch, keep_prob:1.0}

if istrain:

_, loss_value, y_value, y__value = sess.run((train_step, loss, y, y_),feed_dict=feed_dict)

print('y=', y_value,'----ture=', y__value)

print(loss_value)

else:

loss_value, y_value, y__value = sess.run((loss, y, y_),feed_dict=feed_dict)

print('y=', y_value,'----ture=', y__value)

print(loss_value)

if istensorborad:

result = sess.run(merged,feed_dict=feed_dict)

writer.add_summary(result, i)

# 保存模型

if istrain:

saver.save(sess, save_file)