Auto Encoder

import tensorflow as tf

mnist = tf.keras.datasets.mnist

(x_train, _), (x_test, _) = mnist.load_data()
x_train, x_test = x_train/255.0, x_test/255.0
x_train = x_test.reshape((10000, 784))
x_test = x_test.reshape((10000,784))

model = tf.keras.Sequential([
    tf.keras.layers.InputLayer(input_shape=(784, )),
    tf.keras.layers.Dense(256, activation='relu'),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(784, activation='sigmoid'),
])

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

model.fit(x_train, x_train, epochs=1)

p_test = model.predict(x_test) 

import matplotlib.pyplot as plt

x_test = x_test.reshape(10000, 28, 28)
p_test = p_test.reshape(10000, 28, 28)

plt.figure()
plt.imshow(x_test[0])
plt.show()

plt.figure()
plt.imshow(p_test[0])
plt.show()

plt.figure(figsize=(10, 10))
for i in range(100):
    plt.subplot(10, 10, i+1)
    plt.xticks([])
    plt.yticks([])
    plt.imshow(x_test[i])
plt.show()