# 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)))