我试图在张量流中使用LSTM来进行空气质量预测 . 数据来自https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data .
它是一个8输入和8输出型号 .
在我训练模型并进行预测之后，答案总是相同的 .
[ 1.07586029e+02 1.60341334e+00 1.03546591e+01 1.01711316e+03
1.08991003e+00 2.57353954e+01 -6.55468106e-02 1.81939721e-01]
[ 1.07586029e+02 1.60341334e+00 1.03546591e+01 1.01711316e+03
1.08991003e+00 2.57353954e+01 -6.55468106e-02 1.81939721e-01]
[ 1.07586029e+02 1.60341334e+00 1.03546591e+01 1.01711316e+03
1.08991003e+00 2.57353954e+01 -6.55468106e-02 1.81939721e-01]
[ 1.07586029e+02 1.60341334e+00 1.03546591e+01 1.01711316e+03
1.08991003e+00 2.57353954e+01 -6.55468106e-02 1.81939721e-01]
在每个[]是每天的空气质量，它应该是不同的 . 即使我更改输入数据也没有什么不同 . 再训练剂量不起作用 . 我感到很困惑 . 请告诉我它为什么会发生以及如何解决它 . 非常感谢！
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 6 10:56:52 2018
@author: Administrator
"""
import tensorflow as tf
import pandas as pd
import numpy as np
import csv
tf.reset_default_graph()
rnn_unit = 50 #the amount of hidden lstm units
batch_size = 72 #the amount of data trained every time
input_size = 8 #size of input
output_size = 8 #size of output
lr = 0.006 #learn rate
train_x, train_y = [], [] #
f = open('C:\\Users\\Administrator\\Desktop\\BJair\\BjAirDat3.csv',encoding='UTF-8')
df = pd.read_csv(f) #read the csv file
#get data, use the data between 2010 ans 1013 for train，the data of 2014 as exam
weatherdata = df.iloc[0:39312 ,5:13] #weather with 7 items, not including PM2.5, for train
#pm25data = df.iloc[0+batch_size:39312+batch_size ,5:6] #pm2.5 data, for train
weathertest = df.iloc[39312:, 5:13] #weatherdata with 7 items, not including PM2.5, for exam
#pm25test = df.iloc[39312: 5:6] #pm2.5 data, for exam
#train_x is a tensor which [?,batch_size,input_size]
#train_y is a tensor which [?,batch_size,output_size]
i = 0
while i < len(weatherdata):
x = weatherdata[i:i+batch_size].values #conver weatherdata to a tensor
#y = pm25data[i:i+batch_size].values #the same with the pm25data
train_x.append(x.tolist()) #push them into train_x ans train_y
#train_y.append(y.tolist())
i += batch_size
#placeholder
X = tf.placeholder(tf.float32, [None, batch_size, input_size]) #a placeholder as the input tensor
Y = tf.placeholder(tf.float32, [None, batch_size, output_size]) #the lable
#initialize the weights and biases [7,50] [50,1]
weights = {
'in':tf.Variable(tf.random_normal([input_size, rnn_unit], seed = 1)),
'out':tf.Variable(tf.random_normal([rnn_unit, output_size], seed = 2))
}
biases = {
'in':tf.Variable(tf.random_normal([batch_size, rnn_unit], seed = 5)),
'out':tf.Variable(tf.random_normal([batch_size, output_size], seed = 8))
}
def lstm(batch):
w_in = weights['in']
b_in = biases['in']
w_out = weights['out']
b_out = biases['out']
#convert the tensor(X Accepts value from outside function) to a 2-dimensional tensor, [?*7]
input_ = tf.reshape(X, [-1, input_size])
#make matrix multiplication between input_ and w_in, then add b_in
input_rnn = tf.matmul(input_, w_in) + b_in
#convert input_rnn to a 3-dimensional tensor as the input of BaicSTMCell
input_rnn = tf.reshape(input_rnn, [-1, batch, rnn_unit])
#BasicLSTMCell cell，the amount of rnn_unit
cell = tf.nn.rnn_cell.BasicLSTMCell(rnn_unit)
#initial cell，batch_size is equal with the input parameter BATCH
init_state = cell.zero_state(batch, dtype = tf.float32)
#outputs is a tensor of shape [batch_size, max_time, cell_state_size]
#final_state is a tensor of shape [batch_size, cell_state_size]
#Create a Cell
#time_major = True ==> Tensorshape [max_time, batch_size, ...] something goes wrong when FALSE
output_rnn, final_state = tf.nn.dynamic_rnn(cell, input_rnn, initial_state = init_state, dtype = tf.float32, time_major = True)
#convert the output tensor to a 2-dimensional tensor, then calculate the output
output = tf.reshape(output_rnn, [-1, rnn_unit])
#make matrix multiplication between output and w_out, then add b_out
pred = tf.matmul(output, w_out) + b_out
return pred,final_state
def train_lstm():
print('start train lstm')
print(len(train_x))
global batch_size
pred,_ = lstm(batch_size)
#calculate the loss, use the sum of variance between PRED and Y
##
##loss need to be improved
##
loss = tf.reduce_sum(tf.square(tf.reshape(pred, [-1]) - tf.reshape(Y, [-1])))
#use lr as the learn rate, to make the loss minimize
train_op = tf.train.AdamOptimizer(lr).minimize(loss)
#save the model
saver = tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(1000):
step = 0
start = 0
end = start + 1
while(end < len(train_x)-1):
loss_ = sess.run([train_op, loss], feed_dict = {X:train_x[start:end], Y:train_x[start+1:end+1]})
start += 1
end += 1
if step%100 == 0:
print('round: ' , i , ' step: ' , step, ' loss : ' , loss_)
if step % 1000 == 0:
saver.save(sess, "C:\\Users\\Administrator\\Desktop\\modell\\model.ckpt")
print('save model')`enter code here`
step += 1
#train_lstm()
def predection():
print('start predection')
#test_x, test_y = [], []
x_t = weathertest[100:172].values
#y_t = pm25test[i:i+72].values
#test_x.append(x_t.tolist())
#test_y.append(y_t.tolist())
prev_seq = x_t
pred,_ = lstm(72)
saver = tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
saver.restore(sess, "C:\\Users\\Administrator\\Desktop\\modell\\model.ckpt")
#print(test_x)
predict = []
for i in range(30):
next_seq = sess.run(pred, feed_dict = {X:[prev_seq]})
#print(prev_seq[0])
#if i == 16:
#print(next_seq[-1])
predict.append(next_seq[-1])
prev_seq = np.vstack((prev_seq[1:], next_seq[-1]))
print(predict[-1])
predection()