In [1]:

    

import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
from keras.models import load_model
from keras.datasets import mnist
import matplotlib.pyplot as plt
import numpy as np
(X_train, _), (X_test, y_test) = mnist.load_data()


    

    




Using TensorFlow backend.
/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
  return f(*args, **kwds)
/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88
  return f(*args, **kwds)

In [2]:

    

encoder = load_model('auto-encoder.h5')
decoder = load_model('auto-decoder.h5')


    

    




/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py:269: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.
  warnings.warn('No training configuration found in save file: '

In [3]:

    

index = 10
original = X_test[index].astype("float32") / 255.
encoding = encoder.predict(np.array([original]))[0]
#encoding[0] = 0
#encoding[1] = -100
print(encoding, y_test[index])
plt.subplot(1,2,1)
plt.title("predicted")
plt.imshow(decoder.predict(np.array([encoding]))[0], cmap='gray')
plt.subplot(1,2,2)
plt.title("original")
plt.imshow(original, cmap='gray')


    

    




[ 0.         8.899152   6.920689   2.8540618  0.         3.9087083
  5.929813  16.855928   0.         6.8242755] 0






    
Out[3]:





<matplotlib.image.AxesImage at 0x7f47bc0420f0>






    

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