1dcnntest1_1DCNN_故障诊断_轴承故障诊断_tensorflowcnn_故障诊断轴承_

# -*- coding: utf-8 -*- """ Spyder Editor This is a temporary script file. """ import numpy as np import pandas as pd import keras from keras.models import Sequential from keras.wrappers.scikit_learn import KerasClassifier from keras.utils import np_utils,plot_model from sklearn.model_selection import cross_val_score,train_test_split,KFold from sklearn.preprocessing import LabelEncoder from keras.models import model_from_json import matplotlib.pyplot as plt from sklearn.metrics import confusion_matrix import itertools from keras.optimizers import SGD from keras.layers import Dense, Activation, Flatten, Convolution1D, Dropout,MaxPooling1D,BatchNormalization from keras.models import load_model # 载入数据 df = pd.read_csv(r'C:/Users/Administrator/Desktop/14改.csv') X = np.expand_dims(df.values[:, 0:1024].astype(float), axis=2) Y = df.values[:, 1024] # 湿度分类编码为数字 encoder = LabelEncoder() Y_encoded = encoder.fit_transform(Y) Y_onehot = np_utils.to_categorical(Y_encoded) # 划分训练集，测试集 X_train, X_test, Y_train, Y_test = train_test_split(X, Y_onehot, test_size=0.3, random_state=0) # 定义神经网络 def baseline_model(): model = Sequential() model.add(Convolution1D(16, 128,1, input_shape=(1024, 1),padding="same")) model.add(Activation('tanh')) model.add(MaxPooling1D(2,strides=2,padding='same')) #model.add(BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None)) model.add(Convolution1D(32,3,padding='same')) model.add(BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None)) model.add(Activation('tanh')) model.add(MaxPooling1D(2,strides=2,padding='same')) model.add(Flatten()) model.add(Dropout(0.3)) model.add(Dense(60, activation='tanh')) model.add(Dense(2, activation='softmax')) print(model.summary()) sgd = SGD(lr=0.01, nesterov=True, decay=1e-6, momentum=0.9) model.compile(loss='categorical_crossentropy',optimizer='adam', metrics=['accuracy']) return model # 训练分类器 estimator = KerasClassifier(build_fn=baseline_model, epochs=3, batch_size=1, verbose=1) history=estimator.fit(X_train, Y_train) import matplotlib.pyplot as plt plt.rcParams['font.sans-serif'] = ['SimHei'] plt.plot(history.history['loss'],label=u'train_loss') #plt.plot(history.history['val_loss'],label=u'val_loss') plt.plot(history.history['accuracy'],label=u'train_acc') #plt.plot(history.history['val_accuracy'],label=u'val_acc') plt.show() # 卷积网络可视化 def visual(model, data, num_layer=1): layer = keras.backend.function([model.layers[0].input], [model.layers[num_layer].output]) f1 = layer([data])[0] print(f1.shape) num = f1.shape[-1] print(num) plt.figure(figsize=(8, 8)) for i in range(num): plt.subplot(np.ceil(np.sqrt(num)), np.ceil(np.sqrt(num)), i+1) plt.imshow(f1[:, :, i] * 255, cmap='gray') plt.axis('off') plt.show() # 混淆矩阵定义 def plot_confusion_matrix(cm, classes,title='Confusion matrix',cmap=plt.cm.jet): cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] plt.imshow(cm, interpolation='nearest', cmap=cmap) plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 plt.colorbar() tick_marks = np.arange(len(classes)) plt.xticks(tick_marks,('good','bad')) plt.yticks(tick_marks,('good','bad')) thresh = cm.max() / 2. for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, '{:.2f}'.format(cm[i, j]), horizontalalignment="center",color="white" if cm[i, j] > thresh else "black") plt.tight_layout() plt.ylabel('真实类别') plt.xlabel('预测类别') plt.savefig('test_xx.png', dpi=200, bbox_inches='tight', transparent=False) plt.show() # 显示混淆矩阵 def plot_confuse(model, x_val, y_val): predictions = model.predict_classes(x_val) truelabel = y_val.argmax(axis=-1) # 将one-hot转化为label conf_mat = confusion_matrix(y_true=truelabel, y_pred=predictions) plt.figure() plot_confusion_matrix(conf_mat, range(np.max(truelabel)+1)) # 将其模型转换为json model_json = estimator.model.to_json() with open('C:/Users/Administrator/Desktop/model', 'w')as json_file: json_file.write(model_json)# 权重不在json中,只保存网络结构 estimator.model.save_weights('C:/Users/Administrator/Desktop/model.json.h5') # 加载模型用做预测 json_file = open(r'C:/Users/Administrator/Desktop/model') loaded_model_json = json_file.read() json_file.close() loaded_model = model_from_json(loaded_model_json) loaded_model.load_weights('C:/Users/Administrator/Desktop/model.json.h5') print("loaded model from disk") loaded_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # 分类准确率 print("The accuracy of the classification model:") scores = loaded_model.evaluate(X_test, Y_test, verbose=0) print('%s: %.2f%%' % (loaded_model.metrics_names[1], scores[1] * 100)) # 输出预测类别 predicted = loaded_model.predict(X) predicted_label = loaded_model.predict_classes(X) print("predicted label:\n " + str(predicted_label)) #显示混淆矩阵 plot_confuse(estimator.model, X_test, Y_test) # 可视化卷积层 visual(estimator.model, X_train, 1)