train_mnist.py

from keras.datasets import mnist
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras import backend as K
#from sklearn.metrics import accuracy_score
import numpy as np
import sys
import cPickle
import os

os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"   
os.environ["CUDA_VISIBLE_DEVICES"]="1"  #Replace i with the number of the GPU you want to use (typically 0,1,2,3) 

import gzip
f = gzip.open('mnist.pkl.gz', 'rb')
if sys.version_info < (3,):
    data = cPickle.load(f)
else:
    data = cPickle.load(f, encoding='bytes')
f.close()

(x_train, y_train), (x_test, y_test ) = data

x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))  # adapt this if using `channels_first` image data format
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))  # adapt this if using `channels_first` image data format


input_img = Input(shape=(28, 28, 1))  # adapt this if using `channels_first` image data format

x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
encoded1 = MaxPooling2D((2, 2), padding='same')(x)

#model1 = Model(inputs=[input_img], outputs=[encoded1], name="encoder1")
# at this point the representation is (4, 4, 8) i.e. 128-dimensional

x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded1)
x = UpSampling2D((2, 2))(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(16, (3, 3), activation='relu')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)

model = Model(input_img, decoded, name='decoder')


model.compile(optimizer='adam',
          loss='mean_squared_error')


print y_train.shape
model.fit(x_train, x_train,
	epochs=3, batch_size=32, verbose=1)

model.save_weights("model.h5")