使用python实现CNN-GRU故障诊断的代码示例

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Conv1D, MaxPooling1D, GlobalAveragePooling1D, GRU, Dense
from tensorflow.keras.models import Sequential
from sklearn.metrics import classification_report, confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns

train_X = np.load('train_X.npy')  # 加载训练集特征数据
train_Y = np.load('train_Y.npy')  # 加载训练集标签数据
test_X = np.load('test_X.npy')  # 加载测试集特征数据
test_Y = np.load('test_Y.npy')  # 加载测试集标签数据

model = Sequential()
model.add(Conv1D(64, 3, activation='relu', input_shape=train_X.shape[1:]))
model.add(MaxPooling1D(2))
model.add(Conv1D(128, 3, activation='relu'))
model.add(MaxPooling1D(2))
model.add(GRU(64, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['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()
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper right')
plt.show()

pred_Y = model.predict(test_X)
pred_Y = np.round(pred_Y).flatten()
accuracy = np.mean(pred_Y == test_Y)
print("Test Accuracy: {:.2f}%".format(accuracy * 100))
cm = confusion_matrix(test_Y, pred_Y)
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", xticklabels=['Normal', 'Fault'], yticklabels=['Normal', 'Fault'])
plt.title("Confusion Matrix")
plt.xlabel("Predicted Labels")
plt.ylabel("True Labels")
plt.show()