当前位置： 首页 > news >正文

购物网站建设机构wordpress文艺主题

news 2025/9/17 18:04:34
购物网站建设机构,wordpress文艺主题,二级域名怎么解析,怎么样在百度搜到自己的网站关于 本实验采用DEAP情绪数据集进行数据分类任务。使用了三种典型的深度学习网络：2D 卷积神经网络；1D卷积神经网络GRU； LSTM网络。 工具 数据集 DEAP数据 图片来源： DEAP: A Dataset for Emotion Analysis using Physiological…

关于

本实验采用DEAP情绪数据集进行数据分类任务。使用了三种典型的深度学习网络：2D 卷积神经网络；1D卷积神经网络+GRU； LSTM网络。

工具

数据集

DEAP数据

图片来源： DEAP: A Dataset for Emotion Analysis using Physiological and Audiovisual Signals

方法实现

2D-CNN网络
加载必要库函数
import pandas as pd
import keras.backend as K
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
from keras.models import Sequential
from keras.layers.convolutional import Conv1D
from keras.layers.convolutional import MaxPooling1D
from tensorflow.keras.utils import to_categorical 
from keras.layers import Flatten
from keras.layers import Dense
import numpy as np
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from keras import backend as K
from keras.models import Model
import timeit
from keras.models import Sequential
from keras.layers.core import Flatten, Dense, Dropout
from keras.layers.convolutional import Convolution1D, MaxPooling1D, ZeroPadding1D
from tensorflow.keras.optimizers import SGD
#import cv2, numpy as np
import warnings
warnings.filterwarnings('ignore')
加载DEAP数据集

data_training = []
label_training = []
data_testing = []
label_testing = []for subjects in subjectList:with open('/content/drive/My Drive/leading_ai/try/s' + subjects + '.npy', 'rb') as file:sub = np.load(file,allow_pickle=True)for i in range (0,sub.shape[0]):if i % 5 == 0:data_testing.append(sub[i][0])label_testing.append(sub[i][1])else:data_training.append(sub[i][0])label_training.append(sub[i][1])np.save('/content/drive/My Drive/leading_ai/data_training', np.array(data_training), allow_pickle=True, fix_imports=True)
np.save('/content/drive/My Drive/leading_ai/label_training', np.array(label_training), allow_pickle=True, fix_imports=True)
print("training dataset:", np.array(data_training).shape, np.array(label_training).shape)np.save('/content/drive/My Drive/leading_ai/data_testing', np.array(data_testing), allow_pickle=True, fix_imports=True)
np.save('/content/drive/My Drive/leading_ai/label_testing', np.array(label_testing), allow_pickle=True, fix_imports=True)
print("testing dataset:", np.array(data_testing).shape, np.array(label_testing).shape)
 数据标准化
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.fit_transform(x_test)
定义训练超参数
batch_size = 256
num_classes = 10
epochs = 200
input_shape=(x_train.shape[1], 1)
 定义模型
from keras.layers import Convolution1D, ZeroPadding1D, MaxPooling1D, BatchNormalization, Activation, Dropout, Flatten, Dense
from keras.regularizers import l2model = Sequential()
intput_shape=(x_train.shape[1], 1)
model.add(Conv1D(164, kernel_size=3,padding = 'same',activation='relu', input_shape=input_shape))
model.add(BatchNormalization())
model.add(MaxPooling1D(pool_size=(2)))
model.add(Conv1D(164,kernel_size=3,padding = 'same', activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling1D(pool_size=(2)))
model.add(Conv1D(82,kernel_size=3,padding = 'same', activation='relu'))
model.add(MaxPooling1D(pool_size=(2)))
model.add(Flatten())
model.add(Dense(82, activation='tanh'))
model.add(Dropout(0.2))
model.add(Dense(42, activation='tanh'))
model.add(Dropout(0.2))
model.add(Dense(21, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
model.summary()
模型配置和训练
model.compile(loss=keras.losses.categorical_crossentropy,optimizer='adam',metrics=['accuracy'])history=model.fit(x_train, y_train,batch_size=batch_size,epochs=epochs,  verbose=1,validation_data=(x_test,y_test))

 

模型测试集验证
score = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

 

模型训练过程可视化
# summarize history for accuracy
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()

 

 

模型测试集分类混沌矩阵
cmatrix=confusion_matrix(y_test1, y_pred)import seaborn as sns
figure = plt.figure(figsize=(8, 8))
sns.heatmap(cmatrix, annot=True,cmap=plt.cm.Blues)
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()

 

模型测试集分类report
from sklearn import metrics
y_pred = np.around(model.predict(x_test))
print(metrics.classification_report(y_test,y_pred))

 

1D-CNN+GRU网络
数据预处理

必要库函数加载,数据加载预处理，同2D CNN一样，不在赘述。

!pip install git+https://github.com/forrestbao/pyeeg.git
import numpy as np
import pyeeg as pe
import pickle as pickle
import pandas as pd
import matplotlib.pyplot as plt
import mathimport os
import time
import timeit
import keras
import keras.backend as K
from keras.models import Model
from keras.layers import Flatten
from keras.datasets import mnist
from keras.models import Sequential
from sklearn.preprocessing import normalize
from tensorflow.keras.optimizers import SGD
from keras.layers.convolutional import Conv1D
from keras.layers.convolutional import MaxPooling1D
from keras.layers.convolutional import ZeroPadding1D
from tensorflow.keras.utils import to_categorical
from keras.layers import Dense, Dropout, Flatten,GRUimport warnings
warnings.filterwarnings('ignore')
模型搭建
from keras.layers import Convolution1D, ZeroPadding1D, MaxPooling1D, BatchNormalization, Activation, Dropout, Flatten, Dense,GRU,LSTM
from keras.regularizers import l2from keras.models import load_model
from keras.layers import Lambda
import tensorflow as tfmodel_2 = Sequential()model_2.add(Conv1D(128, 3, activation='relu', input_shape=input_shape))
model_2.add(MaxPooling1D(pool_size=2))
model_2.add(Dropout(0.2))model_2.add(Conv1D(128, 3,  activation='relu'))
model_2.add(MaxPooling1D(pool_size=2))
model_2.add(Dropout(0.2))model_2.add(GRU(units = 256, return_sequences=True))  
model_2.add(Dropout(0.2))model_2.add(GRU(units = 32))
model_2.add(Dropout(0.2))model_2.add(Flatten())model_2.add(Dense(units = 128, activation='relu'))
model_2.add(Dropout(0.2))model_2.add(Dense(units = num_classes))
model_2.add(Activation('softmax'))model_2.summary()

 

模型编译和训练
model_2.compile(optimizer ="adam",loss = 'categorical_crossentropy',metrics=["accuracy"]
)history_2 = model_2.fit(x_train, y_train,epochs=epochs,batch_size=batch_size,verbose=1,validation_data=(x_test, y_test),callbacks=[keras.callbacks.EarlyStopping(monitor='val_loss',patience=20,restore_best_weights=True)]
)

 模型训练过程可视化
# summarize history for accuracy
plt.plot(history_2.history['accuracy'],color='green',linewidth=3.0)
plt.plot(history_2.history['val_accuracy'],color='red',linewidth=3.0)
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')plt.savefig("/content/drive/My Drive/GRU/model accuracy.png")
plt.show()# summarize history for loss
plt.plot(history_2.history['loss'],color='green',linewidth=2.0)
plt.plot(history_2.history['val_loss'],color='red',linewidth=2.0)
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')plt.savefig("/content/drive/My Drive/GRU/model loss.png")
plt.show()

 模型测试集分类混沌矩阵和分类report

LSTM网络
数据加载/预处理

同上

模型搭建和训练
  from keras.regularizers import l2from keras.layers import Bidirectionalfrom keras.layers import LSTMmodel = Sequential()model.add(Bidirectional(LSTM(164, return_sequences=True), input_shape=input_shape))model.add(Dropout(0.6))model.add(LSTM(units = 256, return_sequences = True))  model.add(Dropout(0.6))model.add(LSTM(units = 82, return_sequences = True))  model.add(Dropout(0.6))model.add(LSTM(units = 82, return_sequences = True))  model.add(Dropout(0.4))model.add(LSTM(units = 42))model.add(Dropout(0.4))model.add(Dense(units = 21))model.add(Activation('relu'))model.add(Dense(units = num_classes))model.add(Activation('softmax'))model.compile(optimizer ="adam", loss =keras.losses.categorical_crossentropy,metrics=["accuracy"])model.summary()m=model.fit(x_train, y_train,epochs=200,batch_size=256,verbose=1,validation_data=(x_test, y_test))

模型训练过程可视化
import matplotlib.pyplot as plt
print(m.history.keys())
# summarize history for accuracy
plt.plot(m.history['accuracy'],color='green',linewidth=3.0)
plt.plot(m.history['val_accuracy'],color='red',linewidth=3.0)plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')plt.savefig("./Bi- LSTM/model accuracy.png")
plt.show()import imageio
plt.plot(m.history['loss'],color='green',linewidth=2.0)
plt.plot(m.history['val_loss'],color='red',linewidth=2.0)plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')#to save the image
plt.savefig("./Bi- LSTM/model loss.png")
plt.show()

 

 

模型测试集分类性能

代码获取

后台私信，请注明文章题目（数据需要自己下载和处理）

相关项目和代码问题，欢迎交流。

http://www.yayakq.cn/news/677771/

相关文章：

  • 深圳服务网站设计哪家公司好站长工具官网
  • 网站后台编码盐城网站建设效果
  • 手机优化大师下载seo排名优化技术
  • 黔西南州建设局网站更改网站建设报价
  • 企业网站做备案广州比较好的网站建设企业
  • 广州网站建设业务wordpress搜索文章内容
  • 江西城开建设集团有限公司网站设计公司企业网站
  • 中科诚建建设工程有限公司网站营销型网站建设沈阳
  • 国外网站案例拓者设计吧官方网站
  • 校园网站建设网烟台网站制作网站
  • 中文门户网站有哪些潍坊做电商的网站建设
  • 网站建设公司的税是多少钱yy直播下载
  • 宁波网站制作公司哪家好如何做 网站映射
  • 备案网站名怎样制作网页视频
  • 网站建设员是做什么的网站制作公司去哪找
  • 亚洲网站建设中湖南省建设厅易小林
  • 网站手机版怎么制作网站建设公司墨子网络
  • 专业建站公司的业务内容有哪些外贸做那种网站
  • 建设内网网站流程做网站 使用权 所有权
  • 建网站做淘宝客著名网站建设
  • 南通网站建设规划做网站卖什么软件
  • 网站seo优化主要有哪些手段vue做的网站域名汇总
  • 门户网站例子系统管理主要包括哪些内容
  • 阿里云备案网站服务内容怎么填深圳建站服务公司
  • 网站建设公司专业网站科技开发传媒大学附近网站建设公司
  • 最新款淘宝客源码整网站程序模板+后台带自动采集商品功能带文章永久免费个人域名注册
  • 游戏网站模板网新企业网站管理系统
  • 微信制作小程序流程百度搜索seo优化技巧
  • 专业移动微网站设计wordpress怎么添加注册
  • 做正规网站有哪些2022中国进入一级战备了吗