예제 8-1 컬러를 복원하는 UNET

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# %load /home/sjkim/.jupyter/head.py
# %%writefile /home/sjkim/.jupyter/head.py
# %load /home/sjkim/.jupyter/head.py
%matplotlib inline
%load_ext autoreload 
%autoreload 2
from importlib import reload

import matplotlib.pyplot as plt
import numpy as np

import pandas as pd
#import os
#os.environ["CUDA_VISIBLE_DEVICES"]="1"

# seaborn
#import seaborn as sns
#sns.set( style = 'white', font_scale = 1.7)
#sns.set( font_scale = 1.7)
#sns.set_style('ticks')
#plt.rcParams['savefig.dpi'] = 200

# font for matplotlib
#import matplotlib
#import matplotlib.font_manager as fm
#fm.get_fontconfig_fonts()
#font_location = '/usr/share/fonts/truetype/nanum/NanumGothicBold.ttf'
#font_name = fm.FontProperties(fname=font_location).get_name()
#matplotlib.rc('font', family=font_name)


    

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import ex8_1_unet_cifar10


    

    




Using TensorFlow backend.

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ex8_1_unet_cifar10.main()


    

    




(32, 32, 1) (50000, 32, 32, 1)
Train on 40000 samples, validate on 10000 samples
Epoch 1/10
40000/40000 [==============================] - 11s - loss: 0.0253 - val_loss: 0.0481
Epoch 2/10
40000/40000 [==============================] - 7s - loss: 0.0139 - val_loss: 0.0445
Epoch 3/10
40000/40000 [==============================] - 7s - loss: 0.0116 - val_loss: 0.0313
Epoch 4/10
40000/40000 [==============================] - 7s - loss: 0.0106 - val_loss: 0.0364
Epoch 5/10
40000/40000 [==============================] - 7s - loss: 0.0100 - val_loss: 0.0175
Epoch 6/10
40000/40000 [==============================] - 7s - loss: 0.0096 - val_loss: 0.0149
Epoch 7/10
40000/40000 [==============================] - 7s - loss: 0.0092 - val_loss: 0.0197
Epoch 8/10
40000/40000 [==============================] - 7s - loss: 0.0089 - val_loss: 0.0094
Epoch 9/10
40000/40000 [==============================] - 7s - loss: 0.0087 - val_loss: 0.0111
Epoch 10/10
40000/40000 [==============================] - 7s - loss: 0.0086 - val_loss: 0.0096






    













    

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