Keras在训练期间可视化训练误差和测试误差实例

 # Visualize training history from keras.models import Sequential from keras.layers import Dense import matplotlib.pyplot as plt import numpy # fix random seed for reproducibility seed = 7 numpy.random.seed(seed) # load pima indians dataset dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",") # split into input (X) and output (Y) variables X = dataset[:,0:8] Y = dataset[:,8] # create model model = Sequential() model.add(Dense(12, input_dim=8, kernel_initializer='uniform', activation='relu')) model.add(Dense(8, kernel_initializer='uniform', activation='relu')) model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid')) # Compile model model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # Fit the model history = model.fit(X, Y, validation_split=0.33, epochs=150, batch_size=10, verbose=0) # list all data in history print(history.history.keys()) # summarize history for accuracy plt.plot(history.history['acc']) plt.plot(history.history['val_acc']) plt.title('model accuracy') plt.ylabel('accuracy') plt.xlabel('epoch') plt.legend(['train', 'test'], loc='upper left') plt.show() # summarize history for loss 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 left') plt.show()

 train_prompt = "Train cost" cost_ploter = Ploter(train_prompt) def event_handler_plot(ploter_title, step, cost): cost_ploter.append(ploter_title, step, cost) cost_ploter.plot()

 EPOCH_NUM = 8 # 开始训练 lists = [] step = 0 for epochs in range(EPOCH_NUM): # 开始训练 for batch_id, train_data in enumerate(train_reader()):    #遍历train_reader的迭代器，并为数据加上索引batch_id train_cost,sult,lab,vgg = exe.run(program=main_program,  #运行主程序 feed=feeder.feed(train_data),    #喂入一个batch的数据 fetch_list=[avg_cost,predict,label,VGG])   #fetch均方误差和准确率 if step % 10 == 0: event_handler_plot(train_prompt,step,train_cost[0]) # print(batch_id) if batch_id % 10 == 0:         #每100次batch打印一次训练、进行一次测试 p = [np.sum(pre) for pre in sult] l = [np.sum(pre) for pre in lab] print(p,l,np.sum(sult),np.sum(lab)) print('Pass:%d, Batch:%d, Cost:%0.5f' % (epochs, batch_id, train_cost[0])) step += 1 # 保存模型 if model_save_dir is not None: fluid.io.save_inference_model(model_save_dir, ['images'], [predict], exe) print('训练模型保存完成！') end = time.time() print(time.strftime('V100训练用时：%M分%S秒',time.localtime(end-start)))