问题描述
我需要将一个对象保存在文件中，然后在以后检索它。对象本身实现接口# split into train-test set
size = int(len(X) * 0.75)
train,test = hospitalization_diff[:size],hospitalization_diff[size:]
# Build Model
model = ARIMA(train,order=(0,1))
fitted = model.fit(disp=-1)
# Forecast
fc,se,conf = fitted.forecast(len(test),alpha=0.05) # 95% conf
# Make as pandas series
fc_series = pd.Series(fc,index=test.index)
lower_series = pd.Series(conf[:,0],index=test.index)
upper_series = pd.Series(conf[:,1],index=test.index)
# Plot
plt.figure(figsize=(12,5),dpi=100)
plt.plot(train,label='training')
plt.plot(test,label='actual')
plt.plot(fc_series,label='forecast')
plt.fill_between(lower_series.index,lower_series,upper_series,color='k',alpha=.15)
plt.title('Forecast vs Actuals')
plt.legend(loc='upper left',fontsize=8)
plt.show()
，但是其字段之一包含lambda表达式。显然，这算作一个未实现Serializable接口的字段，我得到了Serializable。
我不想彻底更改我的代码，但是我不知道在这种情况下该怎么做。有人有建议吗？
以下是重复此问题的示例代码：
java.io.NotSerializableException}
解决方法
您不能保留一个函数-它不代表数据，它代表行为。
使用# importing libraries
import tkinter as tk
from tkinter import *
# import PIL
from tkinter import filedialog
import numpy
from PIL import Image,ImageTk
import torch
# importing model
model=torch.load('corelK_model_0.pt')
classes = {
0:'africa',1: 'beach',2: 'tallbuilding',3: 'buses',4: 'dinosaurs',5: 'elephants',6: 'Roses',7: 'horses',8: 'mountains',9: 'food'
}
def upload_image():
file_path = filedialog.askopenfilename()
uploaded = Image.open(file_path)
uploaded.thumbnail(((top.winfo_width() / 2.25,(top.winfo_height() / 2.25))))
im = ImageTk.PhotoImage(uploaded)
sign_image.configure(image=im)
sign_image.image = im
label.configure(text=' ')
show_classify_button(file_path)
def show_classify_button(file_path):
classify_btn = Button(top,text="Classify Image",command=lambda: classify(file_path),padx=10,pady=5)
classify_btn.configure(background="#364156",foreground="white",font=('arial',10,'bold'))
classify_btn.place(relx=0.79,rely=0.46)
def classify(file_path):
image = Image.open(file_path)
image = image.resize((32,32))
image = numpy.expand_dims(image,axis=0)
image = numpy.array(image)
pred = model.predict_classes([image])[0]
sign = classes[pred]
print(sign)
label.configure(foreground='#011638',text=sign)
# initialize GUI
top = tk.Tk() # calling the constructor or creating the object of tk class
top.geometry('800x600') # set height and width
top.title("Image Classification CIFAR10")
top.configure(background="#CDCDCD")
# set Heading
heading = Label(top,text="Image Classifier",pady=20,20,'bold'))
heading.configure(background="#CDCDCD",foreground='#364156')
heading.pack()
upload = Button(top,text="Upload an image",command=upload_image,pady=5)
heading.configure(background="#364156",foreground='white','bold'))
upload.pack(side=BOTTOM,pady=50)
# upload image
sign_image = Label(top)
sign_image.pack(side=BOTTOM,expand=True)
# predicted class
label = Label(top,background="#CDCDCD",15,'bold'))
label.pack(side=BOTTOM,expand=True)
top.mainloop()
关键字可以防止其在文件中持续存在。
transient