AutoEncoder_MNIST.py

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
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=10,batch_size=256)
model.save('C:\\Users\\Rohith\\Documents\\Rohith_Stuff\\Datasets\\auto_en.h5')
#del model

from keras.models import load_model
import cv2

model = load_model('C:\\Users\\Rohith\\Documents\\Rohith_Stuff\\Datasets\\auto_en.h5')

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

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