Keras 训练误差和测试误差可视化

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# 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()

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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()

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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)))