#!/usr/bin/env python
encoding: utf-8
import matplotlib.pylab as plt
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Dropout
import numpy as np
from sklearn import datasets
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from keras.models import Sequential
import sys
import tensorflow as tf
sys.path.append("../process")#添加其他文件夹
import data_input#导入其他模块
from traffic_network import LeNet
import numpy as np
from keras.models import Sequential
from keras.layers import Dropout
from keras.layers import Dense
from keras.constraints import max_norm
from keras.optimizers import SGD
sgd = SGD(lr=0.01, momentum=0.0, decay=0.0, nesterov=False)
def train(aug, model,train_x,train_y,test_x,test_y):
model.add(Dropout(rate=0.0, input_shape=(4,)))
model.compile(loss="categorical_crossentropy",
optimizer="sgd",metrics=["accuracy"])#配置
#model.fit(train_x,train_y,batch_size,epochs,validation_data=(test_x,test_y))
_history = model.fit_generator(aug.flow(train_x,train_y,batch_size=batch_size),
validation_data=(test_x,test_y),steps_per_epoch=len(train_x)//batch_size,
epochs=epochs,verbose=1)
#拟合，具体fit_generator请查阅其他文档,steps_per_epoch是每次迭代，需要迭代多少个batch_size，validation_data为test数据，直接做验证，不参与训练
model.save("../predict/traffic_model.h5")
print(_history.history.keys())
plt.style.use("ggplot")#matplotlib的美化样式
plt.figure()
N = epochs
plt.plot(np.arange(0,N),_history.history["loss"],label ="train_loss")#model的history有四个属性，loss,val_loss,acc,val_acc
plt.plot(np.arange(0,N),_history.history["val_loss"],label="val_loss")
plt.plot(np.arange(0,N),_history.history["acc"],label="train_acc")
plt.plot(np.arange(0,N),_history.history["val_acc"],label="val_acc")
plt.title("loss and accuracy")
plt.xlabel("epoch")
plt.ylabel("loss/acc")
plt.legend(loc="best")
plt.savefig("../result/result.png")
plt.show()
if name =="__main__":
channel = 3
height = 32
width = 32
class_num = 8
norm_size = 32#参数
batch_size = 64
epochs = 1000#40
DROPOUT = 0.3
model = LeNet.neural(channel=channel, height=height,
width=width, classes=class_num)#网络
train_x, train_y = data_input.load_data("../data/train", norm_size, class_num)
test_x, test_y = data_input.load_data("../data/test", norm_size, class_num)#生成数据
aug = ImageDataGenerator(rotation_range=30,width_shift_range=0.1,
height_shift_range=0.1,shear_range=0.2,zoom_range=0.2,
horizontal_flip=True,fill_mode="nearest")#数据增强，生成迭代器
train(aug,model,train_x,train_y,test_x,test_y)#训练
我这里怎么加dropout啊,rate太高val_acc就会变成恒定是1.0然后train_acc识别率也也上不去了,求大神指导啊
