# Notebook from daydayup-hb-cj/Morpho
Path: sneaker1.ipynb

# Test Different Network Performance on Sampled GroundTruth Dataset_____no_output_____

<code>
import os
import shutil
import random
import numpy as np
import cv2
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers # build layer
from tensorflow.keras.models import Sequential # build model
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing.image import ImageDataGenerator # preprocessing data

import matplotlib.pyplot as plt #draw diagram
import PIL
from PIL import Image
import pathlib
_____no_output_____from google.colab import drive
drive.mount('/content/drive')Mounted at /content/drive
# destination of positive sneaker images
determination = r"./drive/MyDrive/Colab Notebooks/training/positive"
determination_n = r"./drive/MyDrive/Colab Notebooks/testing/positive"
if not os.path.exists(determination):
  os.makedirs(determination)
if not os.path.exists(determination_n):
  os.makedirs(determination_n)_____no_output_____# handle the sneaker dataset directory
snk_path = "./drive/MyDrive/Colab Notebooks/train"
dirlst = os.listdir(snk_path)
print(len(dirlst))53659
# collect training data
for folder in dirlst:
  dir = snk_path + '/' + str(folder) # folder address
  image1 = dir + '/' + '0.jpg'
  new1 = determination + '/' + str(folder).replace(" ","_")+ '0.jpg'
  shutil.copy(image1,new1)_____no_output_____# collect testing data
for i in range(len(dirlst)//4):
  folder = dirlst[i]
  dir = snk_path + '/' + str(folder) # folder address
  image2 = dir + '/' + '1.jpg'
  new2 = determination_n + '/' + str(folder).replace(" ","_")+ '1.jpg'
  shutil.copy(image2,new2)_____no_output_____d = "./drive/MyDrive/Colab Notebooks/training/positive/Air_Max_Plus_OG_Hyper_Blue_(2018)0.jpg"
img = Image.open(d)
print(np.array(img).shape)_____no_output_____imagelist = os.listdir(determination)
for image in imagelist:
  img_folder = determination+"/"+image
  print(img_folder)
  image = cv2.imdecode(np.fromfile(img_folder, dtype=np.uint8), -1)
  try:
    image.shape
  except:
    print('fail to read',img_folder)
    os.remove(img_folder)
    continue
positive_size = len(os.listdir(determination))
print(positive_size)./drive/MyDrive/Colab Notebooks/training/positive/Air_Max_Plus_OG_Hyper_Blue_(2018)0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_XXXIII_Travis_Scott0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_1_Retro_Fragment0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_2_Retro_Eminem_(The_Way_I_Am)0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Converse_Chuck_Taylor_All-Star_70s_Ox_Comme_des_Garcons_PLAY_Black0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_4_Retro_Kaws_Black0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/adidas_Ultra_Boost_Lux_Sneakersnstuff_x_Social_Status_Vintage_White0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_11_Retro_Low_IE_Space_Jam0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_Force_1_Flyleather_Steve_Harrington_Earth_Day_(2019)0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Converse_Chuck_Taylor_All-Star_70s_Hi_Kith_Coca-Cola_Denim_(2019)0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_11_Retro_Low_Snake_Navy_(2019)0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Vans_Old_Skool_Yacht_Club0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_Future_Bred_Jumpman0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_4_Retro_Flight_Nostalgia0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_1_Retro_High_Court_Purple0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/New_Balance_997.5_Phantaci0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Nike_Daybreak_Vegas_Gold_College_Orange0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_1_Retro_High_Clay_Green0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_4_Retro_Cool_Grey_(2019)0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_Presto_Mid_Acronym_Cool_Grey0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_1_Mid_White_Metallic_Gold_Obsidian0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_3_Retro_JTH_Bio_Beige0.jpg
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./drive/MyDrive/Colab Notebooks/training/positive/Air_Fear_Of_God_Moccasin_Pure_Platinum0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_11_Retro_Low_Cool_Grey0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/adidas_Yeezy_Boost_350_V2_Cream0.jpg
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./drive/MyDrive/Colab Notebooks/training/positive/adidas_NMD_Hu_Pharrell_Solar_Pack_Orange0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_More_Uptempo_Supreme___Suptempo___Red_0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_VaporMax_2019_Cactus_Plant_Flea_Market_(W)0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_Max_97_Shipping_Box_Ale_Brown0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_Max_1_Windbreaker0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_11_Retro_Low_Snake_Light_Bone0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/adidas_NMD_R1_Japan_Triple_Black0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_Max_97_Country_Camo_(Italy)0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_2_Retro_Just_Don_Beach0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_1_Low_Gold_Toe0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_5_Retro_Fire_Red_Black_Tongue_(2013)0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_Force_1_Low_Retro_Ivory_Snake0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_Max_1_Inside_Out_Phantom_Black0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Vans_Style_36_Blends_Bones0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_Max_90_OG_Infrared_(2015)0.jpg
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./drive/MyDrive/Colab Notebooks/training/positive/LeBron_15_Lifestyle_KITH_Stained_Glass0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_Force_1_Low_Utility_Black_White0.jpg
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./drive/MyDrive/Colab Notebooks/training/positive/Jordan_8_Retro_Tinker_Air_Raid0.jpg
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./drive/MyDrive/Colab Notebooks/training/positive/Revenge_X_Storm_Low_Top_Blue0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_VaporMax_Flyknit_2_Orca0.jpg
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./drive/MyDrive/Colab Notebooks/training/positive/Vans_Style_36_Marshmallow_Racing_Red0.jpg
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./drive/MyDrive/Colab Notebooks/training/positive/adidas_Yeezy_Boost_350_V2_Static_Black0.jpg
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./drive/MyDrive/Colab Notebooks/training/positive/Nike_Dunk_SB_Low_Yellow_Lobster0.jpg
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./drive/MyDrive/Colab Notebooks/training/positive/Air_Foamposite_One_ParaNorman0.jpg
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./drive/MyDrive/Colab Notebooks/training/positive/Nike_SB_Dunk_Low_Concepts_Purple_Lobster0.jpg
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./drive/MyDrive/Colab Notebooks/training/positive/Jordan_1_Retro_High_Guava_Ice0.jpg
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./drive/MyDrive/Colab Notebooks/training/positive/Jordan_1_Mid_Quai54_(2019)0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/adidas_Yeezy_Boost_700_Analog0.jpg
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./drive/MyDrive/Colab Notebooks/training/positive/Air_Max_90_Green_Python0.jpg
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./drive/MyDrive/Colab Notebooks/training/positive/Air_Force_1_Low_White_Deep_Royal0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_Presto_Acronym_Volt0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/ASICS_Gel-Lyte_III_Hanon___Wildcats___0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/adidas_Yeezy_Boost_350_V2_Blue_Tint0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_Fear_Of_God_Raid_Black0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/adidas_Yeezy_Boost_350_V2_Core_Black_Red0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_1_Retro_Satin___Banned___0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_1_Retro_High_Off-White_University_Blue0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/adidas_NMD_Hu_Pharrell_Solar_Pack_Mother0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_Force_1_Foamposite_Cup_University_Red0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_Force_1_Flyknit_2_White_Pure_Platinum0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_1_Retro_High_OG_Defiant_SB_NYC_to_Paris0.jpg
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./drive/MyDrive/Colab Notebooks/training/positive/Jordan_6_Retro_Black_Cat0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/adidas_Yeezy_Boost_350_V2_Glow0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_Presto_Mid_Acronym_Racer_Pink0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_VaporMax_2_Black_Dark_Grey0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Nike_ZoomX_Vista_Grind_Volt_(W)0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_Max_98_Supreme_Black0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_4_Retro_Silt_Red_Splatter_(W)0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_1_Low_White_Gum_Hyper_Pink0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/adidas_Ultra_Boost_Legend_Ivy0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/adidas_Ultra_Boost_4.0_Black_White_Speckle0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/adidas_Alphaedge_4D_Black0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/adidas_Yung-1_Hi-Res_Orange0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/A_Bathing_Ape_Bapesta_Kanye_West_College_Dropout0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_Max_90_Triple_Black_(2018)0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Puma_Bow_Slide_Rihanna_Fenty_Olive_(W)0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Nike_Hyperdunk_Marty_McFly_20150.jpg
./drive/MyDrive/Colab Notebooks/training/positive/adidas_Yeezy_Boost_700_Wave_Runner_Solid_Grey0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Nike_Shox_TL_Triple_Black0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_3_Retro_Hall_of_Fame0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_Max_1_Master0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_13_Retro_Ray_Allen_PE0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Rick_Owens_Geobasket0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_More_Uptempo_96_Camo0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_1_Retro_High_Blue_Moon0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Jordan_1_Retro_High_Hyper_Royal0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Vans_Era_95_Fear_of_God0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/adidas_Ultra_Boost_1.0_Undefeated_Stars_and_Stripes0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Reebok_Instapump_Fury_Aape_By_a_Bathing_Ape0.jpg
./drive/MyDrive/Colab Notebooks/training/positive/Air_VaporMax_Plus_Triple_Black0.jpg
894
imagelist = os.listdir(determination_n)
for image in imagelist:
  img_folder = determination_n+"/"+image
  print(img_folder)
  image = cv2.imdecode(np.fromfile(img_folder, dtype=np.uint8), -1)
  try:
    image.shape
  except:
    print('fail to read',img_folder)
    os.remove(img_folder)
    continue
positive_size = len(os.listdir(determination_n))
print(positive_size)./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_Plus_OG_Hyper_Blue_(2018)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_XXXIII_Travis_Scott1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Retro_Fragment1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_2_Retro_Eminem_(The_Way_I_Am)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Converse_Chuck_Taylor_All-Star_70s_Ox_Comme_des_Garcons_PLAY_Black1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_4_Retro_Kaws_Black1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_Ultra_Boost_Lux_Sneakersnstuff_x_Social_Status_Vintage_White1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_11_Retro_Low_IE_Space_Jam1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Force_1_Flyleather_Steve_Harrington_Earth_Day_(2019)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Converse_Chuck_Taylor_All-Star_70s_Hi_Kith_Coca-Cola_Denim_(2019)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_11_Retro_Low_Snake_Navy_(2019)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Vans_Old_Skool_Yacht_Club1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_Future_Bred_Jumpman1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_4_Retro_Flight_Nostalgia1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Retro_High_Court_Purple1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/New_Balance_997.5_Phantaci1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Nike_Daybreak_Vegas_Gold_College_Orange1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Retro_High_Clay_Green1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_4_Retro_Cool_Grey_(2019)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Presto_Mid_Acronym_Cool_Grey1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Mid_White_Metallic_Gold_Obsidian1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_3_Retro_JTH_Bio_Beige1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/KD_12_EYBL1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Fear_Of_God_Moccasin_Pure_Platinum1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_11_Retro_Low_Cool_Grey1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_Yeezy_Boost_350_V2_Cream1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_Ultra_Boost_2.0_Silver_Medal_(2018)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Retro_High_Black_Crimson_Tint1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Force_1_Low_Travis_Scott_Sail1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_Ultra_Boost_1.0_Triple_Black1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Flightposite_1_Black_Topaz_Mist1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_NMD_Hu_Pharrell_Solar_Pack_Orange1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_More_Uptempo_Supreme___Suptempo___Red_1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_VaporMax_2019_Cactus_Plant_Flea_Market_(W)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_97_Shipping_Box_Ale_Brown1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_1_Windbreaker1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_11_Retro_Low_Snake_Light_Bone1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_NMD_R1_Japan_Triple_Black1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_97_Country_Camo_(Italy)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_2_Retro_Just_Don_Beach1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Low_Gold_Toe1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_5_Retro_Fire_Red_Black_Tongue_(2013)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Force_1_Low_Retro_Ivory_Snake1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_1_Inside_Out_Phantom_Black1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Vans_Style_36_Blends_Bones1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_90_OG_Infrared_(2015)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Vans_Sk8-Hi_Odd_Future_Donut1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/LeBron_15_Lifestyle_KITH_Stained_Glass1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Force_1_Low_Utility_Black_White1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/LeBron_16_Low_Air_Max_Trainer_21.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_4_Retro_Motorsports_(2017)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/ASICS_Gel-Lyte_III_Ronnie_Fieg_Super_Green1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_90_OFF-WHITE1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_1_Tinker_Schematic1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Kyrie_4_Lucky_Charms1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Foamposite_One_Obsidian_Glitter_(W)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Nike_LD_Waffle_Sacai_Blue_Multi1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Kyrie_5_Bandulu1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_Ultra_Boost_OG_(2018)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Retro_High_Track_Red1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_97_Black_White1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_Human_Race_NMD_Pharrell_Multi-Color1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Nike_Blazer_Mid_Off-White_All_Hallow's_Eve1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Mid_Pine_Green1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/UA_Curry_3Zer0_LTD_Gold_(Signed)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_8_Retro_Doernbecher1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_Ultra_Boost_1.0_Multi-Color_Toe_Navy1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Low_SB_UNC1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Saucony_Shadow_5000_END___Burger___1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_Ultra_Boost_4.0_Chinese_New_Year_(2018)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Vans_Authentic_Supreme_Black_Checker_Logo1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_NMD_CS1_Parley_Blue_Spirit1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Nike_Zoom_Vomero_5_A_Cold_Wall_Solarized1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_6_Retro_Gatorade_Like_Mike_White1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/ASICS_Gel-Lyte_III_Ronnie_Fieg_Militia_(Special_Box)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Kobe_10_Elite_HTM_Shark_Jaw1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_12_Retro_Gym_Red1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_6_Retro_White_Laser_Fuchsia_Green_Abyss_(GS)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_1_White_Photo_Blue_Black1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/New_Balance_999_Concepts___The_Kennedy___1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/A_Bathing_Ape_Bapesta_Ronnie_Fieg_Fiegsta_Sand1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_Ultra_Boost_4.0_Non_Dye_Cloud_White1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_11_Retro_Low_Golf_Concord1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_NMD_R1_STLT_Grey_Black1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_NMD_Hu_Pharrell_x_BBC_Blue_Plaid1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_5_Retro_Grape_(2013)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_Ultra_Boost_4.0_Core_Black1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_VaporMax_Run_Utility_Medium_Olive1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Fear_Of_God_Moccasin_Black1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_11_Retro_Legend_Blue_(2014)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_12_Retro_Bordeaux1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Foamposite_One_Navy_Gum1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/ASICS_Gel-Lyte_III_Woei___Cervidae___1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_Spizike_Black_Gym_Red1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Retro_Top_31.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_3_Retro_Pure_White_(2018)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/LeBron_8_112_Pack_(Clark_Kent)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Diadora_N9000_Packer_Shoes_x_Raekwon___Purple_Tape___1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_12_Retro_OVO_White1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_VaporMax_Off-White1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_90_Triple_Red1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_720_Saturn_White_Black_Red1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_Yeezy_Boost_350_V2_Semi_Frozen_Yellow1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_VaporMax_Plus_White1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Converse_Chuck_Taylor_All-Star_Vulcanized_Hi__Off-White1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_2_Retro_Just_Don_Blue1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_Yeezy_Boost_700_Utility_Black1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_1_97_Sean_Wotherspoon1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Nike_Dunk_SB_Low_Supreme__Red_Cement_(2012)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Nike_Tailwind_79_Stranger_Things_Hawkins_High_School1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_NMD_R1_Nice_Kicks1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Retro_High_Nigel_Sylvester1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_11_Retro_Low_Golf_White_Metallic_Gold1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Force_1_Low_Carhartt_WIP_Ale_Brown1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_NMD_R1_NYC_Red_Apple1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Retro_High___Not_for_Resale___Varsity_Maize_1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_720_Sunrise1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Mid_Bred_Multi-Color1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_Consortium_Runner_4D_Mid_White1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/LeBron_16_Air_Trainer_Medicine_Ball1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Revenge_X_Storm_Low_Top_Black_Rag1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_1_Inside_Out_Club_Gold_Black1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_4_Retro_Oregon_Ducks1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Retro_High_Pine_Green1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Diadora_Intrepid_Kith_From_Seoul_To_Rio1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Retro_Black_Metallic_Gold_(2013)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Mid_Team_Orange_Black1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Nike_React_Element_87_Sail_Light_Bone1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_901.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Kyrie_5_Friends1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_8_Retro_Tinker_Air_Raid1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_ZX_Flux_Clot_RWB1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_Yeezy_Boost_350_Moonrock1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_Yeezy_Boost_700_V2_Vanta1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Kobe_4_Protro_Carpe_Diem1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Louis_Vuitton_Jaspers_Kanye_Patchwork_Grey1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Kobe_1_Protro_81_Pt_Game1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_Ultra_Boost_Clima_Missoni_Multi-Color1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_11_Retro_Low_Rose_Gold_(W)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Vans_Sk8-Hi_Supreme_Public_Enemy_Black_White1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Kyrie_5_Taco_PE1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_VaporMax_Flyknit_3_Oreo1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_7_Retro_Miro1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_Ultra_Boost_4.0_Breast_Cancer_Awareness1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_Plus_OG_Voltage_Purple1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Retro_Shattered_Backboard1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_VaporMax_2019_Triple_Black1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Revenge_X_Storm_Low_Top_Teal_(with_Flames)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Nike_React_Element_87_Royal_Tint1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Nike_Cortez_Los_Angeles_Blue1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_3_Retro_Tinker_Black_Cement_Gold1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Off-White_Vulc_Low_White_(Updated_Stripes)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Converse_Chuck_Taylor_All-Star_70s_Hi_Off-White1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Nike_React_Element_55_Black_Aurora_Green1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Vans_Old_Skool_Checkerboard_Racing_Red1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_97_Metallic_Gold1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_NMD_XR1_OG_Black1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Kobe_6_Grinch1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Retro_High_Aleali_May_Court_Lux_(W)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Force_1_Low_VLONE_(2017)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_14_Retro_Ferrari_University_Gold1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Huarache_Black_Black_White1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/ACE_16_PureControl_Ultra_Boost_Kith_Flamingos1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Fear_Of_God_1_Sail_Black1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_97_Moon_Particle_Bright_Crimson1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Skylon_2_Fear_of_God_White1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Skylon_2_Fear_of_God_Light_Bone1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_13_Retro_Atmosphere_Grey1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_3_Retro_Sport_Blue1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Nike_SB_Dunk_Low_Pro_Dark_Russet_Cedar1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Retro_High_Season_of_Her_Mint_Foam_(W)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_VaporMax_Run_Utility_Black_Anthracite1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Retro_High_Bred_Toe1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Nike_PG_3_NASA1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_90_Mars_Landing1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_LXCON_Black_Market1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Retro_High_Gatorade_Orange_Peel1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Yeezy_2_Red_October1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_Ultra_Boost_4.0_White_Multi-Color1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_NMD_R1_Pharrell_HU_Friends_and_Family_Burgundy1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Nike_Blazer_High_sacai_White_Black_Legend_Blue1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Force_1_LX_Vandalised_White_(W)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_11_Retro_Low_University_Blue_(2017)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Nike_SB_Dunk_Low_Concepts_Green_Lobster1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_Yeezy_700_V2_Static1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_6_Retro_PSG_Paris_Saint-Germain1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_Copa_Mundial_17_Ultra_Boost_Kith_Cobras1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_NMD_R1_Marble_Aero_Green1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_4_Retro_Levi's_Black_(Levi's_Tag)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_Ultra_Boost_19_Black_Glow1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_11_Retro_Playoffs_(2012)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_13_OG_Flint_(1997)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_5_Retro_JSP_Laney_Varsity_Royal1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Fear_Of_God_1_Frosted_Spruce1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_97_UNDFTD_Black1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Mid_Lakers_Top_31.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Kyrie_5_Concepts_Ikhet1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Retro_Black_Toe_(2016)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_9_Retro_Pearl_Blue1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_More_Uptempo_University_Red_Black_White_(GS)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Fear_Of_God_Military_Sneaker_Kith_Blue1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Flyknit_Trainer_White_Black_(2017)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Converse_Chuck_Taylor_All-Star_70s_Hi_Fear_of_God_Black1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_1_Tokyo_Maze1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_12_Retro_Fiba_(2019)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_1_Retro_High_UNC_Patent_(W)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_NMD_R1_STLT_Parley_Core_Black1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_NMD_R1_White_Gum_(2018)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Nike_SB_Dunk_Low_Staple_Panda_Pigeon1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/adidas_NMD_R1_Core_Black_Lush_Red1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_1_Just_Do_It_White_(W)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/LeBron_16_Buzz_Lightyear1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_1_White_Pure_Platinum1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Nike_Blazer_High_sacai_Snow_Beach1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/New_Balance_1500_Hanon_Chosen_Few1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_VaporMax_Flyknit_3_Blue_Fury1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_More_Uptempo_Supreme___Suptempo___Black_1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Nike_Dunk_SB_Low_Supreme__White_Cement_(2002)1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Jordan_5_Retro_Trophy_Room_Ice_Blue1.jpg
./drive/MyDrive/Colab Notebooks/testing/positive/Air_Max_1_112_Pack1.jpg
220
# destination of negative non-sneaker images(imagenet)
determination2 = r"./drive/MyDrive/Colab Notebooks/training/negative"
determination2_n = r"./drive/MyDrive/Colab Notebooks/testing/negative"
if not os.path.exists(determination2):
  os.makedirs(determination2)
if not os.path.exists(determination2_n):
  os.makedirs(determination2_n)_____no_output_____d = "./drive/MyDrive/Colab Notebooks/training/negative/ILSVRC2012_val_00000063.JPEG"
img = Image.open(d)
print(np.array(img).shape)(500, 375, 3)
imagelist = os.listdir(determination2)
for image in imagelist:
  img_folder = determination2+"/"+image
  print(img_folder)
  im = cv2.imdecode(np.fromfile(img_folder, dtype=np.uint8), -1)
  try:
    np.array(image).shape
  except:
    print('fail to read',img_folder)
    # os.remove(img_folder)
    continue
negative_size = len(os.listdir(determination2))
print(negative_size)./drive/MyDrive/Colab Notebooks/training/negative/ILSVRC2012_val_00000011.JPEG
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894
imagelist = os.listdir(determination2_n)
for image in imagelist:
  img_folder = determination2_n+"/"+image
  print(img_folder)
  im = cv2.imdecode(np.fromfile(img_folder, dtype=np.uint8), -1)
  try:
    np.array(image).shape
  except:
    print('fail to read',img_folder)
    # os.remove(img_folder)
    continue
negative_size = len(os.listdir(determination2_n))
print(negative_size)./drive/MyDrive/Colab Notebooks/testing/negative/ILSVRC2012_val_00000904.JPEG
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./drive/MyDrive/Colab Notebooks/testing/negative/ILSVRC2012_val_00001077.JPEG
./drive/MyDrive/Colab Notebooks/testing/negative/ILSVRC2012_val_00001085.JPEG
./drive/MyDrive/Colab Notebooks/testing/negative/ILSVRC2012_val_00001083.JPEG
./drive/MyDrive/Colab Notebooks/testing/negative/ILSVRC2012_val_00001084.JPEG
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### training procedure
### training procedure
### training procedure_____no_output_____train_dir = './drive/MyDrive/daydayup/dataset/sneaker_nonsneaker/sneaker_nonsneaker/training'
test_dir = './drive/MyDrive/daydayup/dataset/sneaker_nonsneaker/sneaker_nonsneaker/testing'
batch_size = 64
epochs = 10
IMG_HEIGHT = 112
IMG_WIDTH = 112_____no_output_____train_image_generator = ImageDataGenerator(rescale=1./255,#归一化
                                           horizontal_flip=True, #图片翻转, augmentation technology
                                           width_shift_range=.15, #宽变化
                                           height_shift_range=.15, #高变化
                                           rotation_range=45,#旋转45度
                                           zoom_range=0.5 #缩放0.5倍
)
test_image_generator = ImageDataGenerator(rescale=1./255) #test dataset without augmentation

train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,
                                                           directory=train_dir,#训练集路径
                                                           shuffle=True, #打乱图片顺序
                                                           target_size=(IMG_HEIGHT,IMG_WIDTH),#修改图片尺寸
                                                           class_mode='binary')
test_data_gen = test_image_generator.flow_from_directory(batch_size=batch_size,
                                                             directory=test_dir,#测试集路径
                                                             target_size=(IMG_HEIGHT,IMG_WIDTH), #修改图片尺寸
                                                             class_mode='binary')Found 1788 images belonging to 2 classes.
Found 440 images belonging to 2 classes.
# plot images we obtain using plt library
def plotImages(images_arr):
    fig,axes = plt.subplots(1,5,figsize=(20,20))
    axes = axes.flatten()
    for img,ax in zip(images_arr,axes):
        ax.imshow(img)
        ax.axis('off')
    plt.tight_layout()
    plt.show()
augemted_images=[train_data_gen[0][0][0] for i in range(5)]
plotImages(augemted_images)_____no_output_____##### build our mobile model #####
mobile = tf.keras.applications.MobileNet(input_shape=(224,224,3),include_top=False,weights=None)
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
# dense1 = tf.keras.layers.Dense(128,activation='relu')
# dense2 = tf.keras.layers.Dense(64,activation='relu')
prediction_layer = layers.Dense(1,activation='sigmoid')

Mobile = tf.keras.Sequential([
    mobile,
    global_average_layer,
    # dense1,
    # dense2,
    prediction_layer
])
Mobile.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
##### train our mobilenet model #####
history = Mobile.fit_generator(
    train_data_gen, #训练集
    epochs=epochs,
    validation_data=test_data_gen
)/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
/usr/local/lib/python3.7/dist-packages/PIL/Image.py:960: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images
  "Palette images with Transparency expressed in bytes should be "
##### build our mobile2 model #####
mobile2 = tf.keras.applications.MobileNetV2(input_shape=(224,224,3),include_top=False,weights=None)
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
# dense1 = tf.keras.layers.Dense(128,activation='relu')
# dense2 = tf.keras.layers.Dense(64,activation='relu')
prediction_layer = layers.Dense(1,activation='sigmoid')

Mobile2 = tf.keras.Sequential([
    mobile2,
    global_average_layer,
    # dense1,
    # dense2,
    prediction_layer
])
Mobile2.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
##### train our mobilenet model #####
history = Mobile2.fit_generator(
    train_data_gen, #训练集
    epochs=epochs,
    validation_data=test_data_gen
)/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
##### build our densenet1 model #####
densenet1 = tf.keras.applications.DenseNet121(input_shape=(224,224,3),include_top=False,weights=None)
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
# dense1 = tf.keras.layers.Dense(128,activation='relu')
# dense2 = tf.keras.layers.Dense(64,activation='relu')
prediction_layer = layers.Dense(1,activation='sigmoid')

Dense1 = tf.keras.Sequential([
    densenet1,
    global_average_layer,
    # dense1,
    # dense2,
    prediction_layer
])
Dense1.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])

history = Dense1.fit_generator(
    train_data_gen, #训练集
    epochs=epochs,
    validation_data=test_data_gen
)/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
##### build our densenet2 model #####
densenet2 = tf.keras.applications.DenseNet201(input_shape=(224,224,3),include_top=False,weights=None)
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
# dense1 = tf.keras.layers.Dense(128,activation='relu')
# dense2 = tf.keras.layers.Dense(64,activation='relu')
prediction_layer = layers.Dense(1,activation='sigmoid')

Dense2 = tf.keras.Sequential([
    densenet2,
    global_average_layer,
    # dense1,
    # dense2,
    prediction_layer
])
Dense2.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])

history = Dense2.fit_generator(
    train_data_gen, #训练集
    epochs=epochs,
    validation_data=test_data_gen
)/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
##### build our nasnetmobile model #####
nasnetmobile = tf.keras.applications.NASNetMobile(input_shape=(224,224,3),include_top=False,weights=None)
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
# dense1 = tf.keras.layers.Dense(128,activation='relu')
# dense2 = tf.keras.layers.Dense(64,activation='relu')
prediction_layer = layers.Dense(1,activation='sigmoid')

Nasnetmobile = tf.keras.Sequential([
    nasnetmobile,
    global_average_layer,
    # dense1,
    # dense2,
    prediction_layer
])
Nasnetmobile.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])

history = Nasnetmobile.fit_generator(
    train_data_gen, #训练集
    epochs=epochs,
    validation_data=test_data_gen
)/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
##### build our Xception model #####
xception = tf.keras.applications.Xception(input_shape=(224,224,3),include_top=False,weights=None)
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
# dense1 = tf.keras.layers.Dense(128,activation='relu')
# dense2 = tf.keras.layers.Dense(64,activation='relu')
prediction_layer = layers.Dense(1,activation='sigmoid')

X = tf.keras.Sequential([
    xception,
    global_average_layer,
    # dense1,
    # dense2,
    prediction_layer
])
X.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])

history = X.fit_generator(
    train_data_gen, #训练集
    epochs=epochs,
    validation_data=test_data_gen
)/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
##### build our Inception model #####
inception = tf.keras.applications.InceptionV3(input_shape=(224,224,3),include_top=False,weights=None)
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
# dense1 = tf.keras.layers.Dense(128,activation='relu')
# dense2 = tf.keras.layers.Dense(64,activation='relu')
prediction_layer = layers.Dense(1,activation='sigmoid')

inceptionv3 = tf.keras.Sequential([
    inception,
    global_average_layer,
    # dense1,
    # dense2,
    prediction_layer
])
inceptionv3.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])

history = inceptionv3.fit_generator(
    train_data_gen, #训练集
    epochs=epochs,
    validation_data=test_data_gen
)/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
##### build our resnet model #####
resnet = tf.keras.applications.ResNet50(input_shape=(224,224,3),include_top=False,weights=None)
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
# dense1 = tf.keras.layers.Dense(128,activation='relu')
# dense2 = tf.keras.layers.Dense(64,activation='relu')
prediction_layer = layers.Dense(1,activation='sigmoid')

res = tf.keras.Sequential([
    resnet,
    global_average_layer,
    # dense1,
    # dense2,
    prediction_layer
])
res.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])

history = res.fit_generator(
    train_data_gen, #训练集
    epochs=epochs,
    validation_data=test_data_gen
)/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
##### build our vgg model #####
vgg = tf.keras.applications.VGG16(input_shape=(224,224,3),include_top=False,weights=None)
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
# dense1 = tf.keras.layers.Dense(128,activation='relu')
# dense2 = tf.keras.layers.Dense(64,activation='relu')
prediction_layer = layers.Dense(1,activation='sigmoid')

vgg16 = tf.keras.Sequential([
    vgg,
    global_average_layer,
    # dense1,
    # dense2,
    prediction_layer
])
vgg16.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])

history = vgg16.fit_generator(
    train_data_gen, #训练集
    epochs=epochs,
    validation_data=test_data_gen
)/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
##### build our incepres model #####
incepres = tf.keras.applications.InceptionResNetV2(input_shape=(224,224,3),include_top=False,weights=None)
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
# dense1 = tf.keras.layers.Dense(128,activation='relu')
# dense2 = tf.keras.layers.Dense(64,activation='relu')
prediction_layer = layers.Dense(1,activation='sigmoid')

InceptionRes = tf.keras.Sequential([
    incepres,
    global_average_layer,
    # dense1,
    # dense2,
    prediction_layer
])
InceptionRes.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])

history = InceptionRes.fit_generator(
    train_data_gen, #训练集
    epochs=epochs,
    validation_data=test_data_gen
)/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:20: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.

</code>

# Train Binary Classification Model_____no_output_____

<code>
!pip uninstall -y tensorflow
!pip install tensorflow-gpu==1.14.0
Found existing installation: tensorflow 2.8.0
Uninstalling tensorflow-2.8.0:
  Successfully uninstalled tensorflow-2.8.0
Collecting tensorflow-gpu==1.14.0
  Downloading tensorflow_gpu-1.14.0-cp37-cp37m-manylinux1_x86_64.whl (377.1 MB)
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Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-gpu==1.14.0) (1.15.0)
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Collecting tensorboard<1.15.0,>=1.14.0
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Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py->keras-applications>=1.0.6->tensorflow-gpu==1.14.0) (1.5.2)
Installing collected packages: tensorflow-estimator, tensorboard, keras-applications, tensorflow-gpu
  Attempting uninstall: tensorflow-estimator
    Found existing installation: tensorflow-estimator 2.8.0
    Uninstalling tensorflow-estimator-2.8.0:
      Successfully uninstalled tensorflow-estimator-2.8.0
  Attempting uninstall: tensorboard
    Found existing installation: tensorboard 2.8.0
    Uninstalling tensorboard-2.8.0:
      Successfully uninstalled tensorboard-2.8.0
Successfully installed keras-applications-1.0.8 tensorboard-1.14.0 tensorflow-estimator-1.14.0 tensorflow-gpu-1.14.0
!pip install keras==2.2.5
!pip install 'h5py==2.10.0'Collecting keras==2.2.5
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Installing collected packages: keras
  Attempting uninstall: keras
    Found existing installation: keras 2.8.0
    Uninstalling keras-2.8.0:
      Successfully uninstalled keras-2.8.0
Successfully installed keras-2.2.5
import keras
import os
from keras import layers,models,optimizers
from keras.layers import Conv2D, MaxPooling2D, Dropout, Dense, Flatten
from keras.layers import *
from keras.models import Sequential, Model
import keras.backend as K
from keras import regularizers
from keras.applications.xception import Xception,preprocess_input
from keras.applications.vgg16 import VGG16
from keras.applications.inception_v3 import InceptionV3
import matplotlib.pylab as plt
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
import random
import sys
import numpy as np
from keras import backend as K
from keras.models import load_model
from keras.optimizers import SGD
import tensorflow as tf
from tensorflow import keras as ke
from keras.preprocessing.image import img_to_array, ImageDataGenerator#图片转为array
from keras.utils import to_categorical#相当于one-hot
from imutils import paths
import cv2
import numpy as np
import random
import os
from keras.callbacks import TensorBoard, ModelCheckpoint

Using TensorFlow backend.
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 14333331230153390102
, name: "/device:XLA_CPU:0"
device_type: "XLA_CPU"
memory_limit: 17179869184
locality {
}
incarnation: 3391757553641448534
physical_device_desc: "device: XLA_CPU device"
, name: "/device:XLA_GPU:0"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 2239445115401131396
physical_device_desc: "device: XLA_GPU device"
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 14949928141
locality {
  bus_id: 1
  links {
  }
}
incarnation: 16130729979968475979
physical_device_desc: "device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5"
]
seed = 42

os.environ['PYTHONHASHSEED']=str(seed)
os.environ['TF_DETERMINISTIC_OPS'] = '1'
os.environ['TF_CUDNN_DETERMINISTIC'] = '1'
os.environ['HOROVOD_FUSION_THRESHOLD']='0'
random.seed(seed)
np.random.seed(seed)
tf.random.set_random_seed(seed)
tf.set_random_seed(seed)_____no_output_____# 配置项目参数
channel = 3
height = 112 #300
width = 112 #400
class_num = 2 # 4
#norm_size = 32#参数
batch_size = 32
epochs = 200
sgd = SGD(lr=0.1,momentum=0.9) # 模型微调时使用 lr=1e-4, 模型直接训练时 lr=0.1
rmsprop_ = 'rmsprop'
FINE_LAYER = 'block5_conv1' # conv2d_73
FREEZED_LAYER = 'MaxPooling2D' #flatten max_pooling2d_5

train_dir = './drive/MyDrive/daydayup/dataset/sneaker_nonsneaker/sneaker_nonsneaker/training' # ../data/dataset/train
validation_dir = './drive/MyDrive/daydayup/dataset/sneaker_nonsneaker/sneaker_nonsneaker/testing' # ../data/dataset/val
save_tl_dir = "./drive/MyDrive/daydayup/Morpho/predict/TLCheckpoint"
save_ft_dir = "./drive/MyDrive/daydayup/Morpho/predict/FTCheckpoint"
save_Direct_dir = "./drive/MyDrive/daydayup/Morpho/predict/DirectCheckpoint"
tl_best_model_path = os.path.join(save_tl_dir, 'TL_model_32-0.95.hdf5')

totalTrain = len(list(paths.list_images(train_dir)))
totalVal = len(list(paths.list_images(validation_dir)))
print(totalTrain)

NetworkName = "selfNetwork"

JudgeFreezing = True
JudgeSaveRecord = False
switchCard = True # default:True, if change it to False, the network will not unfreezing after training with freezed conv_base1788
def dataprocess(train_dir, validation_dir,height, width, batch_size):
    train_datagen = ImageDataGenerator(
        rescale=1./255,
        rotation_range=40,
        width_shift_range=0.2,
        height_shift_range=0.2,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True,
        fill_mode='nearest')
    test_datagen = ImageDataGenerator(rescale=1./255)
    train_generator = train_datagen.flow_from_directory(
        train_dir,
        target_size=(height, width),
        batch_size= batch_size,
        class_mode='categorical')
    validation_generator = test_datagen.flow_from_directory(
        validation_dir,
        target_size=(height, width),
        batch_size= 32,
        class_mode='categorical')
    return train_generator, validation_generator_____no_output_____train_generator, validation_generator = dataprocess(train_dir, validation_dir, height, width, batch_size)Found 1788 images belonging to 2 classes.
Found 440 images belonging to 2 classes.
# 定义特征提取
def setupTransferLearning(model, choOpt, freezedLayer):
    for layer in model.layers:
        layer.trainable = True
    set_trainable = False
    for layer in model.layers:
        if layer.name == freezedLayer:
            set_trainable = True
        if set_trainable:
            layer.trainable = True
        else:
            layer.trainable = False
    for layer in model.layers:
        print("{}: {}".format(layer, layer.trainable))
    model.compile(loss="categorical_crossentropy",
            optimizer=choOpt,metrics=["accuracy"])#配置

# 定义模型微调
def setupFineTune(model, choOpt, fineLayer):
    for layer in model.layers:
        layer.trainable = True
    set_trainable = False
    for layer in model.layers:
        if layer.name == fineLayer:
            set_trainable = True
        if set_trainable:
            layer.trainable = True
        else:
            layer.trainable = False
    for layer in model.layers:
        print("{}: {}".format(layer, layer.trainable))
    model.compile(loss="categorical_crossentropy",
            optimizer=choOpt,metrics=["accuracy"])#配置

# 绘制训练结果的图表
def plotResult(epochs, _history):
    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("./drive/MyDrive/daydayup/Morpho/result/result.png")
    plt.show()
_____no_output_____class changeVGG16:

    def neural(channel,height,width,classes):
        input_shape = (channel,height,width)
        if K.image_data_format() == "channels_last":#确认输入维度
            input_shape = (height,width,channel)

        conv_base = VGG16(include_top=False, weights='imagenet', input_shape=input_shape)
        x = conv_base.output

        UpSample = MaxPooling2D(pool_size=(9, 9), strides=(1, 1),padding = 'same', name='MaxPooling2D')(x)
        UpSample = Dropout(0.5)(UpSample)
        UpSample = Conv2D(256,(1,1))(UpSample)
        UpSample = BatchNormalization()(UpSample)
        UpSample = Activation('relu')(UpSample)
        UpSample = Dropout(0.5)(UpSample)
        UpSample = Conv2D(64,(1,1))(UpSample)
        UpSample = BatchNormalization()(UpSample)
        UpSample = Activation('relu')(UpSample)
        UpSample = Dropout(0.5)(UpSample)
        UpSample = Flatten(name='flatten')(UpSample)
        UpSample = Dense(classes)(UpSample)
        UpSample = BatchNormalization()(UpSample)
        predictions = Activation('softmax')(UpSample)

        model = Model(inputs=conv_base.input, outputs=predictions)

        return model

class selfNetworkMutilBlock:
    def neural(channel,height,width,classes):
        input_shape = (channel,height,width)
        if K.image_data_format() == "channels_last":
            input_shape = (height,width,channel)
        inputs = Input(shape= input_shape)
        x = Conv2D(32, (1, 3), padding='same')(inputs)
        x = BatchNormalization()(x)
        x = Activation('relu')(x)
        x = Conv2D(32, (3, 1), padding='same')(x)
        x = BatchNormalization()(x)
        x = Activation('relu')(x)
        x = Conv2D(32, (1, 3), padding='same')(x)
        x = BatchNormalization()(x)
        x = Activation('relu')(x)
        x = Conv2D(32, (3, 1), padding='same')(x)
        x = BatchNormalization()(x)
        x = Activation('relu')(x)
        x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding = 'same')(x)

        x = Conv2D(64, (3, 3), padding='same')(x)
        x = BatchNormalization()(x)
        x = Activation('relu')(x)
        x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding = 'same')(x)

        x = Conv2D(128, (3, 3), padding='same')(x)
        x = BatchNormalization()(x)
        x = Activation('relu')(x)
        x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding = 'same')(x)

        x = Conv2D(256, (3, 3), padding='same')(x)
        x = BatchNormalization()(x)
        x = Activation('relu')(x)
        x = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding = 'same')(x)

        x4 = Conv2D(64, (1, 1), padding='same')(x)
        x4 = BatchNormalization()(x4)
        x4 = Activation('relu')(x4)

        x2 = AveragePooling2D(pool_size=(2, 2), strides=(1, 1), padding = 'same')(x)
        x2 = Conv2D(32, (1, 1), padding='same')(x2)
        x2 = BatchNormalization()(x2)
        x2 = Activation('relu')(x2)

        x1 = Conv2D(96, (1, 1), padding='same')(x)
        x1 = BatchNormalization()(x1)
        x1 = Activation('relu')(x1)
        x1 = Conv2D(128, (1, 3), padding='same')(x1)
        x1 = BatchNormalization()(x1)
        x1 = Activation('relu')(x1)
        x1 = Conv2D(128, (3, 1), padding='same')(x1)
        x1 = BatchNormalization()(x1)
        x1 = Activation('relu')(x1)

        x1 = Conv2D(128, (1, 1), padding='same')(x)
        x1 = BatchNormalization()(x1)
        x1 = Activation('relu')(x1)

        x3 = Conv2D(192, (1, 3), padding='same')(x)
        x3 = BatchNormalization()(x3)
        x3 = Activation('relu')(x3)
        x3 = Conv2D(192, (3, 1), padding='same')(x3)
        x3 = BatchNormalization()(x3)
        x3 = Activation('relu')(x3)
        x3 = Conv2D(192, (1, 3), padding='same')(x3)
        x3 = BatchNormalization()(x3)
        x3 = Activation('relu')(x3)
        x3 = Conv2D(192, (3, 1), padding='same')(x3)
        x3 = BatchNormalization()(x3)
        x3 = Activation('relu')(x3)

        out1 = Concatenate(axis = -1)([x4, x1, x3, x2])

        x1 = Conv2D(256, (3, 3), padding='same', strides = 2 )(out1)
        x1 = BatchNormalization()(x1)
        x1 = Activation('relu')(x1)

        x2 = Conv2D(64, (1, 1), padding='same')(out1)
        x2 = BatchNormalization()(x2)
        x2 = Activation('relu')(x2)
        x2 = Conv2D(256, (3, 3), padding='same', strides = 2)(x2)
        x2 = BatchNormalization()(x2)
        x2 = Activation('relu')(x2)

        x3 = Conv2D(128, (1, 1), padding='same')(out1)
        x3 = BatchNormalization()(x3)
        x3 = Activation('relu')(x3)
        x3 = Conv2D(256, (3, 3), padding='same')(x3)
        x3 = BatchNormalization()(x3)
        x3 = Activation('relu')(x3)
        x3 = Conv2D(256, (3, 3), padding='same', strides = 2)(x3)
        x3 = BatchNormalization()(x3)
        x3 = Activation('relu')(x3)

        x4 = MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding = 'same')(out1)

        out2 = Concatenate(axis = -1)([x1, x2, x3, x4])

        x1 = Conv2D(256, (1, 1), padding='same')(out2)
        x1 = BatchNormalization()(x1)
        x1 = Activation('relu')(x1)

        x2 = Conv2D(768, (1, 1), padding='same')(out2)
        x2 = BatchNormalization()(x2)
        x2 = Activation('relu')(x2)
        x2 = Conv2D(640, (3, 3), padding='same')(x2)
        x2 = BatchNormalization()(x2)
        x2 = Activation('relu')(x2)

        x3 = Conv2D(1044, (1, 1), padding='same')(out2)
        x3 = BatchNormalization()(x3)
        x3 = Activation('relu')(x3)
        x3 = Conv2D(1024, (3, 3), padding='same')(x3)
        x3 = BatchNormalization()(x3)
        x3 = Activation('relu')(x3)
        x3 = Conv2D(1024, (1, 1), padding='same')(x3)
        x3 = BatchNormalization()(x3)
        x3 = Activation('relu')(x3)

        x4 = AveragePooling2D(pool_size=(2, 2), strides=(1, 1), padding = 'same')(out2)
        x4 = Conv2D(128, (1, 1), padding='same')(x4)
        x4 = BatchNormalization()(x4)
        x4 = Activation('relu')(x4)

        out3 = Concatenate(axis = -1)([x1, x2, x3, x4])

        #top0
        UpSample = MaxPooling2D(pool_size=(10, 10), strides=(1, 1),padding = 'same')(out3)
        UpSample = Dropout(0.5)(UpSample)
        UpSample = Conv2D(256,(1,1))(UpSample)
        UpSample = BatchNormalization()(UpSample)
        UpSample = Activation('relu')(UpSample)
        UpSample = Dropout(0.5)(UpSample)
        UpSample = Conv2D(256,(1,1))(UpSample)
        UpSample = BatchNormalization()(UpSample)
        UpSample = Activation('relu')(UpSample)
        UpSample = Dropout(0.5)(UpSample)
        UpSample = Flatten(name='flatten')(UpSample)
        UpSample = Dense(classes)(UpSample)
        UpSample = BatchNormalization()(UpSample)
        predictions = Activation('softmax')(UpSample)

        model = Model(inputs=inputs, outputs=predictions)

        return model
_____no_output_____if NetworkName == "VGG16":
    model = changeVGG16.neural(channel=channel, height=height,
                        width=width, classes=class_num)#网络
    model.summary()
    if switchCard == False:
        setupTransferLearning(model, rmsprop_, FREEZED_LAYER)
        checkpointerTL = ModelCheckpoint(os.path.join(save_tl_dir, 'TL_model_{epoch:02d}-{val_acc:.2f}.hdf5'),
                                verbose=1, monitor='val_acc', save_best_only=True, save_weights_only=False, period=1)
        _history = model.fit_generator(
            train_generator,
            steps_per_epoch=totalTrain // batch_size,
            epochs = epochs,
            validation_data=validation_generator,
            validation_steps=totalVal // 32, # RedDate 32 Polen 64
            callbacks=[checkpointerTL])
        history_dict = _history.history
        print(history_dict.keys())
    elif switchCard == True:
        model = load_model(tl_best_model_path)
        setupFineTune(model, sgd, FINE_LAYER)
        #Using tensorboard version
        tbCallback = TensorBoard(log_dir="./drive/MyDrive/daydayup/Morpho/result/tbResult", histogram_freq=0, write_grads=True)
        checkpointerFT = ModelCheckpoint(os.path.join(save_ft_dir, 'FT_model_{epoch:02d}-{val_acc:.2f}.hdf5'),
                                verbose=1, monitor='val_acc', save_best_only=True, save_weights_only=False, period=1)
        _history = model.fit_generator(
            train_generator,
            steps_per_epoch=totalTrain // batch_size,
            epochs = epochs,
            validation_data=validation_generator,
            validation_steps=totalVal // 32,
            callbacks=[tbCallback, checkpointerFT])

        history_dict = _history.history
        print(history_dict.keys())
        plotResult(epochs, _history)
elif NetworkName == "selfNetwork":
    model = selfNetworkMutilBlock.neural(channel=channel, height=height,
                width=width, classes=class_num)
    model.summary()
    model.compile(loss="categorical_crossentropy",
            optimizer=sgd,metrics=["accuracy"])# 最终决定使用sgd进行直接训练 学习率为0.1 比使用rmsprop效果更好
    #Using tensorboard version
    tbCallback = TensorBoard(log_dir="./drive/MyDrive/daydayup/Morpho/result/tbResult", histogram_freq=0, write_grads=True)
    checkpointerFT = ModelCheckpoint(os.path.join(save_Direct_dir, 'Direct_model_{epoch:02d}-{val_acc:.2f}.hdf5'),
                            verbose=1, monitor='val_acc', save_best_only=True, save_weights_only=False, period=1)
    _history = model.fit_generator(
        train_generator,
        steps_per_epoch=totalTrain // batch_size,  #368
        epochs = epochs,
        validation_data=validation_generator,
        validation_steps=totalVal // 32,   #60
        callbacks=[tbCallback, checkpointerFT])
    #No tensorboard version
    #_history = model.fit_generator(
    #    train_generator,
    #    steps_per_epoch=totalTrain // batch_size,  #368
    #    epochs = epochs,
    #    validation_data=validation_generator,
    #    validation_steps=totalVal // 64)  #60
    history_dict = _history.history
    print(history_dict.keys())
    plotResult(epochs, _history)WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.

WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.

WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.

WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.

WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/keras/backend/tensorflow_backend.py:2041: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.

WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.

WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/keras/backend/tensorflow_backend.py:4271: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.

WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
Model: "model_1"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to
==================================================================================================
input_1 (InputLayer)            (None, 112, 112, 3)  0
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, 112, 112, 32) 320         input_1[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 112, 112, 32) 128         conv2d_1[0][0]
__________________________________________________________________________________________________
activation_1 (Activation)       (None, 112, 112, 32) 0           batch_normalization_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, 112, 112, 32) 3104        activation_1[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 112, 112, 32) 128         conv2d_2[0][0]
__________________________________________________________________________________________________
activation_2 (Activation)       (None, 112, 112, 32) 0           batch_normalization_2[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, 112, 112, 32) 3104        activation_2[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 112, 112, 32) 128         conv2d_3[0][0]
__________________________________________________________________________________________________
activation_3 (Activation)       (None, 112, 112, 32) 0           batch_normalization_3[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, 112, 112, 32) 3104        activation_3[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 112, 112, 32) 128         conv2d_4[0][0]
__________________________________________________________________________________________________
activation_4 (Activation)       (None, 112, 112, 32) 0           batch_normalization_4[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D)  (None, 56, 56, 32)   0           activation_4[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D)               (None, 56, 56, 64)   18496       max_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 56, 56, 64)   256         conv2d_5[0][0]
__________________________________________________________________________________________________
activation_5 (Activation)       (None, 56, 56, 64)   0           batch_normalization_5[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D)  (None, 28, 28, 64)   0           activation_5[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D)               (None, 28, 28, 128)  73856       max_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 28, 28, 128)  512         conv2d_6[0][0]
__________________________________________________________________________________________________
activation_6 (Activation)       (None, 28, 28, 128)  0           batch_normalization_6[0][0]
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D)  (None, 14, 14, 128)  0           activation_6[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D)               (None, 14, 14, 256)  295168      max_pooling2d_3[0][0]
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 14, 14, 256)  1024        conv2d_7[0][0]
__________________________________________________________________________________________________
activation_7 (Activation)       (None, 14, 14, 256)  0           batch_normalization_7[0][0]
__________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D)  (None, 7, 7, 256)    0           activation_7[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D)              (None, 7, 7, 192)    147648      max_pooling2d_4[0][0]
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 7, 7, 192)    768         conv2d_14[0][0]
__________________________________________________________________________________________________
activation_14 (Activation)      (None, 7, 7, 192)    0           batch_normalization_14[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D)              (None, 7, 7, 192)    110784      activation_14[0][0]
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 7, 7, 192)    768         conv2d_15[0][0]
__________________________________________________________________________________________________
activation_15 (Activation)      (None, 7, 7, 192)    0           batch_normalization_15[0][0]
__________________________________________________________________________________________________
conv2d_16 (Conv2D)              (None, 7, 7, 192)    110784      activation_15[0][0]
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 7, 7, 192)    768         conv2d_16[0][0]
__________________________________________________________________________________________________
activation_16 (Activation)      (None, 7, 7, 192)    0           batch_normalization_16[0][0]
__________________________________________________________________________________________________
average_pooling2d_1 (AveragePoo (None, 7, 7, 256)    0           max_pooling2d_4[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D)               (None, 7, 7, 64)     16448       max_pooling2d_4[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D)              (None, 7, 7, 128)    32896       max_pooling2d_4[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D)              (None, 7, 7, 192)    110784      activation_16[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D)               (None, 7, 7, 32)     8224        average_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 7, 7, 64)     256         conv2d_8[0][0]
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 7, 7, 128)    512         conv2d_13[0][0]
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 7, 7, 192)    768         conv2d_17[0][0]
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 7, 7, 32)     128         conv2d_9[0][0]
__________________________________________________________________________________________________
activation_8 (Activation)       (None, 7, 7, 64)     0           batch_normalization_8[0][0]
__________________________________________________________________________________________________
activation_13 (Activation)      (None, 7, 7, 128)    0           batch_normalization_13[0][0]
__________________________________________________________________________________________________
activation_17 (Activation)      (None, 7, 7, 192)    0           batch_normalization_17[0][0]
__________________________________________________________________________________________________
activation_9 (Activation)       (None, 7, 7, 32)     0           batch_normalization_9[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate)     (None, 7, 7, 416)    0           activation_8[0][0]
                                                                 activation_13[0][0]
                                                                 activation_17[0][0]
                                                                 activation_9[0][0]
__________________________________________________________________________________________________
conv2d_21 (Conv2D)              (None, 7, 7, 128)    53376       concatenate_1[0][0]
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, 7, 7, 128)    512         conv2d_21[0][0]
__________________________________________________________________________________________________
activation_21 (Activation)      (None, 7, 7, 128)    0           batch_normalization_21[0][0]
__________________________________________________________________________________________________
conv2d_19 (Conv2D)              (None, 7, 7, 64)     26688       concatenate_1[0][0]
__________________________________________________________________________________________________
conv2d_22 (Conv2D)              (None, 7, 7, 256)    295168      activation_21[0][0]
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, 7, 7, 64)     256         conv2d_19[0][0]
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, 7, 7, 256)    1024        conv2d_22[0][0]
__________________________________________________________________________________________________
activation_19 (Activation)      (None, 7, 7, 64)     0           batch_normalization_19[0][0]
__________________________________________________________________________________________________
activation_22 (Activation)      (None, 7, 7, 256)    0           batch_normalization_22[0][0]
__________________________________________________________________________________________________
conv2d_18 (Conv2D)              (None, 4, 4, 256)    958720      concatenate_1[0][0]
__________________________________________________________________________________________________
conv2d_20 (Conv2D)              (None, 4, 4, 256)    147712      activation_19[0][0]
__________________________________________________________________________________________________
conv2d_23 (Conv2D)              (None, 4, 4, 256)    590080      activation_22[0][0]
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 4, 4, 256)    1024        conv2d_18[0][0]
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, 4, 4, 256)    1024        conv2d_20[0][0]
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, 4, 4, 256)    1024        conv2d_23[0][0]
__________________________________________________________________________________________________
activation_18 (Activation)      (None, 4, 4, 256)    0           batch_normalization_18[0][0]
__________________________________________________________________________________________________
activation_20 (Activation)      (None, 4, 4, 256)    0           batch_normalization_20[0][0]
__________________________________________________________________________________________________
activation_23 (Activation)      (None, 4, 4, 256)    0           batch_normalization_23[0][0]
__________________________________________________________________________________________________
max_pooling2d_5 (MaxPooling2D)  (None, 4, 4, 416)    0           concatenate_1[0][0]
__________________________________________________________________________________________________
concatenate_2 (Concatenate)     (None, 4, 4, 1184)   0           activation_18[0][0]
                                                                 activation_20[0][0]
                                                                 activation_23[0][0]
                                                                 max_pooling2d_5[0][0]
__________________________________________________________________________________________________
conv2d_27 (Conv2D)              (None, 4, 4, 1044)   1237140     concatenate_2[0][0]
__________________________________________________________________________________________________
batch_normalization_27 (BatchNo (None, 4, 4, 1044)   4176        conv2d_27[0][0]
__________________________________________________________________________________________________
activation_27 (Activation)      (None, 4, 4, 1044)   0           batch_normalization_27[0][0]
__________________________________________________________________________________________________
conv2d_25 (Conv2D)              (None, 4, 4, 768)    910080      concatenate_2[0][0]
__________________________________________________________________________________________________
conv2d_28 (Conv2D)              (None, 4, 4, 1024)   9622528     activation_27[0][0]
__________________________________________________________________________________________________
batch_normalization_25 (BatchNo (None, 4, 4, 768)    3072        conv2d_25[0][0]
__________________________________________________________________________________________________
batch_normalization_28 (BatchNo (None, 4, 4, 1024)   4096        conv2d_28[0][0]
__________________________________________________________________________________________________
activation_25 (Activation)      (None, 4, 4, 768)    0           batch_normalization_25[0][0]
__________________________________________________________________________________________________
activation_28 (Activation)      (None, 4, 4, 1024)   0           batch_normalization_28[0][0]
__________________________________________________________________________________________________
average_pooling2d_2 (AveragePoo (None, 4, 4, 1184)   0           concatenate_2[0][0]
__________________________________________________________________________________________________
conv2d_24 (Conv2D)              (None, 4, 4, 256)    303360      concatenate_2[0][0]
__________________________________________________________________________________________________
conv2d_26 (Conv2D)              (None, 4, 4, 640)    4424320     activation_25[0][0]
__________________________________________________________________________________________________
conv2d_29 (Conv2D)              (None, 4, 4, 1024)   1049600     activation_28[0][0]
__________________________________________________________________________________________________
conv2d_30 (Conv2D)              (None, 4, 4, 128)    151680      average_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, 4, 4, 256)    1024        conv2d_24[0][0]
__________________________________________________________________________________________________
batch_normalization_26 (BatchNo (None, 4, 4, 640)    2560        conv2d_26[0][0]
__________________________________________________________________________________________________
batch_normalization_29 (BatchNo (None, 4, 4, 1024)   4096        conv2d_29[0][0]
__________________________________________________________________________________________________
batch_normalization_30 (BatchNo (None, 4, 4, 128)    512         conv2d_30[0][0]
__________________________________________________________________________________________________
activation_24 (Activation)      (None, 4, 4, 256)    0           batch_normalization_24[0][0]
__________________________________________________________________________________________________
activation_26 (Activation)      (None, 4, 4, 640)    0           batch_normalization_26[0][0]
__________________________________________________________________________________________________
activation_29 (Activation)      (None, 4, 4, 1024)   0           batch_normalization_29[0][0]
__________________________________________________________________________________________________
activation_30 (Activation)      (None, 4, 4, 128)    0           batch_normalization_30[0][0]
__________________________________________________________________________________________________
concatenate_3 (Concatenate)     (None, 4, 4, 2048)   0           activation_24[0][0]
                                                                 activation_26[0][0]
                                                                 activation_29[0][0]
                                                                 activation_30[0][0]
__________________________________________________________________________________________________
max_pooling2d_6 (MaxPooling2D)  (None, 4, 4, 2048)   0           concatenate_3[0][0]
__________________________________________________________________________________________________
dropout_1 (Dropout)             (None, 4, 4, 2048)   0           max_pooling2d_6[0][0]
__________________________________________________________________________________________________
conv2d_31 (Conv2D)              (None, 4, 4, 256)    524544      dropout_1[0][0]
__________________________________________________________________________________________________
batch_normalization_31 (BatchNo (None, 4, 4, 256)    1024        conv2d_31[0][0]
__________________________________________________________________________________________________
activation_31 (Activation)      (None, 4, 4, 256)    0           batch_normalization_31[0][0]
__________________________________________________________________________________________________
dropout_2 (Dropout)             (None, 4, 4, 256)    0           activation_31[0][0]
__________________________________________________________________________________________________
conv2d_32 (Conv2D)              (None, 4, 4, 256)    65792       dropout_2[0][0]
__________________________________________________________________________________________________
batch_normalization_32 (BatchNo (None, 4, 4, 256)    1024        conv2d_32[0][0]
__________________________________________________________________________________________________
activation_32 (Activation)      (None, 4, 4, 256)    0           batch_normalization_32[0][0]
__________________________________________________________________________________________________
dropout_3 (Dropout)             (None, 4, 4, 256)    0           activation_32[0][0]
__________________________________________________________________________________________________
flatten (Flatten)               (None, 4096)         0           dropout_3[0][0]
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 2)            8194        flatten[0][0]
__________________________________________________________________________________________________
batch_normalization_33 (BatchNo (None, 2)            8           dense_1[0][0]
__________________________________________________________________________________________________
activation_33 (Activation)      (None, 2)            0           batch_normalization_33[0][0]
==================================================================================================
Total params: 21,336,430
Trainable params: 21,320,066
Non-trainable params: 16,364
__________________________________________________________________________________________________
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.

WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/keras/callbacks.py:1122: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.

WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/keras/callbacks.py:1125: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.

Epoch 1/200
_____no_output_____
</code>