利用神经网络对股票进行预测

神经网络对股票进行预测"/>

利用神经网络对股票进行预测

采用全连接神经网络，激活函数relu，损失函数为均方差。测试集的损失一直没有变化，出现过拟合。该怎么修改，求大神指导。

import numpy as np
import pandas as pd
from sklearn import preprocessing
import tensorflow as tf
import matplotlib.pyplot as plt
import os
dfdata = pd.read_csv('C:/Users/57888/Downloads/test1.csv')
'''slectedcols = [ 'pre_close', 'pre_settle', 'open', 'high','low', 'close', 'settle', 'change1', 'change2', 'vol', 'amount', 'oi','oi_chg']'''
slectedcols = ['open', 'high', 'low', 'close', 'pre_close','change', 'pct_chg', 'vol', 'amount']
sledata1 = dfdata[slectedcols]#筛选对应值
sledata = sledata1.copy()
logdir = './logs'
chevkpointpath = './ckpt/Titanic.{epoch:02d}-{val_loss:.2f}.ckpt'
callbacks = [tf.keras.callbacks.TensorBoard(log_dir = logdir,histogram_freq = 2),tf.keras.callbacks.ModelCheckpoint(filepath = chevkpointpath,save_weights_only=True,verbose=1,period=5)]
ckptdir = os.path.dirname(chevkpointpath)
latest = tf.train.latest_checkpoint(ckptdir)def predict(x,y):mod = tf.keras.models.Sequential()mod.add(tf.keras.layers.Dense(units=64,input_dim=9,use_bias=True,kernel_initializer='uniform',bias_initializer='zeros',activation='relu'))mod.add(tf.keras.layers.Dense(units=32, use_bias=True, kernel_initializer='uniform',bias_initializer='zeros', activation='relu'))mod.add(tf.keras.layers.Dropout(rate=0.3))#把一部分神经元在训练中舍弃防止过拟合mod.add(tf.keras.layers.Dense(units=1, use_bias=True, kernel_initializer='uniform',bias_initializer='zeros', activation='relu'))modpile(optimizer=tf.keras.optimizers.Adam(0.003),loss='mean_squared_error',metrics=['accuracy'])#模型设置优化器损失值#mod.load_weights(latest)#模型训练trainhistory = mod.fit(x=normfeatures,y=label,validation_split=0.2,epochs=100,batch_size=30,callbacks=callbacks,verbose=2)#verbose 训练过程显示模式# 0不显示1进度条2每epochs显示一行return trainhistory
def prepare_data(sledata):npdata = sledata.values  # 装换为np数组# 特征值features = npdata[:2371, :]# 第2列是标签label = npdata[1:, 0]minmax = preprocessing.MinMaxScaler(feature_range=(0, 1))normfeatures = minmax.fit_transform(features)return normfeatures, label
def visu(trainhistory,tm,vm ):plt.plot(trainhistory.history[tm] )plt.plot(trainhistory.history[vm])plt.title('训练历史')plt.ylabel(tm)plt.xlabel('epoch')plt.legend(['train','validation'],loc='upper left')plt.show()
if __name__ == '__main__':normfeatures,label = prepare_data(sledata)trainhistory  = predict(normfeatures,label)visu(trainhistory, 'loss', 'val_loss')``