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jsp网站开发文献,cms开源框架,信宜网站建设,wordpress分类栏目关于 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#xff1a;2D 卷积神经网络#xff1b;1D卷积神经网络GRU#xff1b; LSTM网络。 工具 数据集 DEAP数据 图片来源#xff1a; 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_pickleTrue)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_pickleTrue, fix_importsTrue) np.save(/content/drive/My Drive/leading_ai/label_training, np.array(label_training), allow_pickleTrue, fix_importsTrue) 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_pickleTrue, fix_importsTrue) np.save(/content/drive/My Drive/leading_ai/label_testing, np.array(label_testing), allow_pickleTrue, fix_importsTrue) 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_size3,padding same,activationrelu, input_shapeinput_shape)) model.add(BatchNormalization()) model.add(MaxPooling1D(pool_size(2))) model.add(Conv1D(164,kernel_size3,padding same, activationrelu)) model.add(BatchNormalization()) model.add(MaxPooling1D(pool_size(2))) model.add(Conv1D(82,kernel_size3,padding same, activationrelu)) model.add(MaxPooling1D(pool_size(2))) model.add(Flatten()) model.add(Dense(82, activationtanh)) model.add(Dropout(0.2)) model.add(Dense(42, activationtanh)) model.add(Dropout(0.2)) model.add(Dense(21, activationrelu)) model.add(Dropout(0.2)) model.add(Dense(num_classes, activationsoftmax)) model.summary() 模型配置和训练 model.compile(losskeras.losses.categorical_crossentropy,optimizeradam,metrics[accuracy])historymodel.fit(x_train, y_train,batch_sizebatch_size,epochsepochs, verbose1,validation_data(x_test,y_test)) 模型测试集验证 score model.evaluate(x_test, y_test, verbose1) 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], locupper left) plt.show() 模型测试集分类混沌矩阵 cmatrixconfusion_matrix(y_test1, y_pred)import seaborn as sns figure plt.figure(figsize(8, 8)) sns.heatmap(cmatrix, annotTrue,cmapplt.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-CNNGRU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 githttps://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, activationrelu, input_shapeinput_shape)) model_2.add(MaxPooling1D(pool_size2)) model_2.add(Dropout(0.2))model_2.add(Conv1D(128, 3, activationrelu)) model_2.add(MaxPooling1D(pool_size2)) model_2.add(Dropout(0.2))model_2.add(GRU(units 256, return_sequencesTrue))
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, activationrelu)) 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,epochsepochs,batch_sizebatch_size,verbose1,validation_data(x_test, y_test),callbacks[keras.callbacks.EarlyStopping(monitorval_loss,patience20,restore_best_weightsTrue)] ) 模型训练过程可视化

summarize history for accuracy

plt.plot(history_2.history[accuracy],colorgreen,linewidth3.0) plt.plot(history_2.history[val_accuracy],colorred,linewidth3.0) plt.title(model accuracy) plt.ylabel(accuracy) plt.xlabel(epoch) plt.legend([train, test], locupper left)plt.savefig(/content/drive/My Drive/GRU/model accuracy.png) plt.show()# summarize history for loss plt.plot(history_2.history[loss],colorgreen,linewidth2.0) plt.plot(history_2.history[val_loss],colorred,linewidth2.0) plt.title(model loss) plt.ylabel(loss) plt.xlabel(epoch) plt.legend([train, test], locupper 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_sequencesTrue), input_shapeinput_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()mmodel.fit(x_train, y_train,epochs200,batch_size256,verbose1,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],colorgreen,linewidth3.0) plt.plot(m.history[val_accuracy],colorred,linewidth3.0)plt.title(model accuracy) plt.ylabel(accuracy) plt.xlabel(epoch) plt.legend([train, test], locupper left)plt.savefig(./Bi- LSTM/model accuracy.png) plt.show()import imageio plt.plot(m.history[loss],colorgreen,linewidth2.0) plt.plot(m.history[val_loss],colorred,linewidth2.0)plt.title(model loss) plt.ylabel(loss) plt.xlabel(epoch) plt.legend([train, test], locupper 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。