# ---
# jupyter:
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#     text_representation:
#       extension: .py
#       format_name: light
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# +
import os
import tensorflow as tf
import cv2
import matplotlib.pyplot as plt
import numpy as np

test_dir='../dataset\\cifar_10_small\\test'

test_aeroplane_dir= os.path.join(test_dir,'aeroplane')
test_bird_dir=os.path.join(test_dir,'bird')
test_car_dir= os.path.join(test_dir,'car')
test_cat_dir=os.path.join(test_dir,'cat')


# +

names= [  ] ## class 작성하기.

fnames = sorted([os.path.join(test_aeroplane_dir, fname) for fname in os.listdir(test_aeroplane_dir)])


# +

# 증식할 이미지 선택합니다
img_path =

# 이미지를 읽고 크기를 변경합니다
img=
img_resize=

# (150, 150, 3) 크기의 넘파이 배열로 변환합니다
M=
test_image =
print(test_image.shape)
test_image_reshape = test_image.reshape(, , , )


# +


model =


# +

##모델 예측하는 코드 짜기
y=

index=np.argmax(y)
plt.imshow(test_image)
plt.xlabel(names[np.squeeze(int(y))])
plt.show()


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