Autoencoder1.py

##########Autoencoder##############
### using Keras on MNIST data
import keras
# import tensorflow
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras import backend as K

(x_train, y_train), (x_test, y_test) = mnist.load_data()

img_rows, img_cols = 28, 28

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

import numpy as np

temp = []
for img in x_train:
    t = []
    for row in img:
        for i in row:
            t.append(i)
    temp.append(t)
x_train = []
x_train = temp

x_train = np.array(x_train)

x_train = x_train.reshape(60000,784)

model = Sequential()
model.add(Dense(784,activation='relu',input_dim=784))
model.add(Dense(256,activation='relu'))
model.add(Dense(128,activation='relu'))
model.add(Dense(256,activation='relu'))
model.add(Dense(784,activation='relu'))

model.compile(loss=keras.losses.mean_squared_error,
             optimizer=keras.optimizers.RMSprop(lr=0.0001, rho=0.9, epsilon=None, decay=0.0),
             metrics = ['accuracy'])

model.fit(x_train,x_train,verbose=1,epochs=5,batch_size=200)
model.save('auto_en.h5')
#del model

from keras.models import load_model
import cv2

model = load_model('auto_en.h5')

test = x_train[1].reshape(1,784)
y_test = model.predict(test)

for j in range(len(test)):
	inp_img = []
	temp = []
	for i in range(len(test[j])):
	    if((i+1)%28 == 0):
	        temp.append(test[j][i])
	        inp_img.append(temp)
	        temp = []
	    else:
	        temp.append(test[j][i])
	out_img = []
	temp = []
	for i in range(len(y_test[j])):
	    if((i+1)%28 == 0):
	        temp.append(y_test[j][i])
	        out_img.append(temp)
	        temp = []
	    else:
	        temp.append(y_test[0][i])

	inp_img = np.array(inp_img)
	out_img = np.array(out_img)

	cv2.imshow('Test Image',inp_img)
	cv2.imshow('Output Image',out_img)
	cv2.waitKey(15)