méthode python3 pour empaqueter le code python dans un fichier exe

import keras
from keras.models import Sequential
import numpy as np
import pandas as pd
from keras.layers import Dense
import random
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
from tkinter import filedialog
import tkinter.messagebox #这个是消息框，对话框的关键
file_path = filedialog.askdirectory()

mnist = input_data.read_data_sets(file_path, validation_size=0)

#随机挑选其中一个手写数字并画图
num = random.randint(1, len(mnist.train.images))
img = mnist.train.images[num]
plt.imshow(img.reshape((28, 28)), cmap='Greys_r')
plt.show()

x_train = mnist.train.images
y_train = mnist.train.labels
x_test = mnist.test.images
y_test = mnist.test.labels

#reshaping the x_train, y_train, x_test and y_test to conform to MLP input and output dimensions
x_train = np.reshape(x_train, (x_train.shape[0], -1))
x_test = np.reshape(x_test, (x_test.shape[0], -1))
y_train = pd.get_dummies(y_train)
y_test = pd.get_dummies(y_test)

#performing one-hot encoding on target variables for train and test
y_train=np.array(y_train)
y_test=np.array(y_test)
#defining model with one input layer[784 neurons], 1 hidden layer[784 neurons] with dropout rate 0.4 and 1 output layer [10 #neurons]
model=Sequential()
model.add(Dense(784, input_dim=784, activation='relu'))
keras.layers.core.Dropout(rate=0.4)
model.add(Dense(10,input_dim=784,activation='softmax'))
# compiling model using adam optimiser and accuracy as metric
model.compile(loss='categorical_crossentropy', optimizer="adam", metrics=['accuracy'])
# fitting model and performing validation
model.fit(x_train, y_train, epochs=20, batch_size=200, validation_data=(x_test, y_test))
y_test1 = pd.DataFrame(model.predict(x_test, batch_size=200))
y_pre = y_test1.idxmax(axis = 1)
result = pd.DataFrame({'test': y_test, 'pre': y_pre})
tkinter.messagebox.showinfo('Message', 'Completed!')