import numpy as np

attribute_File_Path = "D:/Simulation_Raw_Data/CelebA/list_attr_celeba.txt"
image_Files_Dir = "D:/Simulation_Raw_Data/CelebA/img"
select_Attribute_List = ["Black_Hair", "Blond_Hair", "Brown_Hair", "Male", "Young", "Pale_Skin"]   #If 'None', model use all attributes
image_Size = 128
initial_Filter_Count = 64   #For bottleneck
batch_Size = 16
gan_Loss_Type = 'WGAN'  #'WGAN' or 'LSGAN'
learning_Rate = 0.0001
extract_Dir = 'E:/GAN_Result/StarGAN'


test_File_Name_List = [
    'D:/Simulation_Raw_Data/CelebA/Img/188712.jpg',
    'D:/Simulation_Raw_Data/CelebA/Img/188673.jpg',
    'D:/Simulation_Raw_Data/CelebA/Img/189052.jpg',
    'D:/Simulation_Raw_Data/CelebA/Img/201436.jpg',
    'D:/Simulation_Raw_Data/CelebA/Img/201881.jpg',
    'D:/Simulation_Raw_Data/CelebA/Img/201434.jpg',
    ]

test_Attribute_Pattern_List = [
    np.array([1, 0, 0, 0, 0, 0]),
    np.array([0, 1, 0, 0, 0, 0]),
    np.array([0, 0, 1, 0, 0, 0]),
    np.array([0, 0, 0, 1, 0, 0]),
    np.array([0, 0, 0, 0, 1, 0]),
    np.array([0, 0, 0, 0, 0, 1]),            
    np.array([0, 1, 0, 1, 0, 0]),
    np.array([0, 1, 0, 0, 1, 0]),
    np.array([0, 0, 0, 1, 1, 0]),
    np.array([0, 0, 1, 1, 1, 0]),
    ]   #Each select_Attribute_List

test_Step = 1000
checkpoint_Step = 1000
2eb8f7bf18b9b50cbd74ab542ea775a861431536
<mask token>

<mask token>
attribute_File_Path = 'D:/Simulation_Raw_Data/CelebA/list_attr_celeba.txt'
image_Files_Dir = 'D:/Simulation_Raw_Data/CelebA/img'
select_Attribute_List = ['Black_Hair', 'Blond_Hair', 'Brown_Hair', 'Male',
    'Young', 'Pale_Skin']
image_Size = 128
initial_Filter_Count = 64
batch_Size = 16
gan_Loss_Type = 'WGAN'
learning_Rate = 0.0001
extract_Dir = 'E:/GAN_Result/StarGAN'
test_File_Name_List = ['D:/Simulation_Raw_Data/CelebA/Img/188712.jpg',
    'D:/Simulation_Raw_Data/CelebA/Img/188673.jpg',
    'D:/Simulation_Raw_Data/CelebA/Img/189052.jpg',
    'D:/Simulation_Raw_Data/CelebA/Img/201436.jpg',
    'D:/Simulation_Raw_Data/CelebA/Img/201881.jpg',
    'D:/Simulation_Raw_Data/CelebA/Img/201434.jpg']
test_Attribute_Pattern_List = [np.array([1, 0, 0, 0, 0, 0]), np.array([0, 1,
    0, 0, 0, 0]), np.array([0, 0, 1, 0, 0, 0]), np.array([0, 0, 0, 1, 0, 0]
    ), np.array([0, 0, 0, 0, 1, 0]), np.array([0, 0, 0, 0, 0, 1]), np.array
    ([0, 1, 0, 1, 0, 0]), np.array([0, 1, 0, 0, 1, 0]), np.array([0, 0, 0, 
    1, 1, 0]), np.array([0, 0, 1, 1, 1, 0])]
test_Step = 1000
checkpoint_Step = 1000

import numpy as np
attribute_File_Path = 'D:/Simulation_Raw_Data/CelebA/list_attr_celeba.txt'
image_Files_Dir = 'D:/Simulation_Raw_Data/CelebA/img'
select_Attribute_List = ['Black_Hair', 'Blond_Hair', 'Brown_Hair', 'Male',
    'Young', 'Pale_Skin']
image_Size = 128
initial_Filter_Count = 64
batch_Size = 16
gan_Loss_Type = 'WGAN'
learning_Rate = 0.0001
extract_Dir = 'E:/GAN_Result/StarGAN'
test_File_Name_List = ['D:/Simulation_Raw_Data/CelebA/Img/188712.jpg',
    'D:/Simulation_Raw_Data/CelebA/Img/188673.jpg',
    'D:/Simulation_Raw_Data/CelebA/Img/189052.jpg',
    'D:/Simulation_Raw_Data/CelebA/Img/201436.jpg',
    'D:/Simulation_Raw_Data/CelebA/Img/201881.jpg',
    'D:/Simulation_Raw_Data/CelebA/Img/201434.jpg']
test_Attribute_Pattern_List = [np.array([1, 0, 0, 0, 0, 0]), np.array([0, 1,
    0, 0, 0, 0]), np.array([0, 0, 1, 0, 0, 0]), np.array([0, 0, 0, 1, 0, 0]
    ), np.array([0, 0, 0, 0, 1, 0]), np.array([0, 0, 0, 0, 0, 1]), np.array
    ([0, 1, 0, 1, 0, 0]), np.array([0, 1, 0, 0, 1, 0]), np.array([0, 0, 0, 
    1, 1, 0]), np.array([0, 0, 1, 1, 1, 0])]
test_Step = 1000
checkpoint_Step = 1000

import numpy as np

attribute_File_Path = "D:/Simulation_Raw_Data/CelebA/list_attr_celeba.txt"
image_Files_Dir = "D:/Simulation_Raw_Data/CelebA/img"
select_Attribute_List = ["Black_Hair", "Blond_Hair", "Brown_Hair", "Male", "Young", "Pale_Skin"]   #If 'None', model use all attributes
image_Size = 128
initial_Filter_Count = 64   #For bottleneck
batch_Size = 16
gan_Loss_Type = 'WGAN'  #'WGAN' or 'LSGAN'
learning_Rate = 0.0001
extract_Dir = 'E:/GAN_Result/StarGAN'


test_File_Name_List = [
    'D:/Simulation_Raw_Data/CelebA/Img/188712.jpg',
    'D:/Simulation_Raw_Data/CelebA/Img/188673.jpg',
    'D:/Simulation_Raw_Data/CelebA/Img/189052.jpg',
    'D:/Simulation_Raw_Data/CelebA/Img/201436.jpg',
    'D:/Simulation_Raw_Data/CelebA/Img/201881.jpg',
    'D:/Simulation_Raw_Data/CelebA/Img/201434.jpg',
    ]

test_Attribute_Pattern_List = [
    np.array([1, 0, 0, 0, 0, 0]),
    np.array([0, 1, 0, 0, 0, 0]),
    np.array([0, 0, 1, 0, 0, 0]),
    np.array([0, 0, 0, 1, 0, 0]),
    np.array([0, 0, 0, 0, 1, 0]),
    np.array([0, 0, 0, 0, 0, 1]),            
    np.array([0, 1, 0, 1, 0, 0]),
    np.array([0, 1, 0, 0, 1, 0]),
    np.array([0, 0, 0, 1, 1, 0]),
    np.array([0, 0, 1, 1, 1, 0]),
    ]   #Each select_Attribute_List

test_Step = 1000
checkpoint_Step = 1000