### Autoencoder Implementation in TensorFlow/Keras
import numpy as np
import matplotlib.pyplot as plt
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import mnist

# Load MNIST dataset
(x_train, _), (x_test, _) = mnist.load_data()

# Normalize the images to [0, 1] range and reshape them to (num_samples, 28*28)
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), -1))
x_test = x_test.reshape((len(x_test), -1))

# Define the Autoencoder Model
input_dim = x_train.shape[1]
encoding_dim = 64  # Dimension of the encoding layer

# Encoder
input_img = layers.Input(shape=(input_dim,))
encoded = layers.Dense(256, activation='relu')(input_img)
encoded = layers.Dense(encoding_dim, activation='relu')(encoded)

# Decoder
decoded = layers.Dense(256, activation='relu')(encoded)
decoded = layers.Dense(input_dim, activation='sigmoid')(decoded)  # Use sigmoid since we normalized input between 0 and 1.

# Autoencoder Model
autoencoder = keras.Model(input_img, decoded)

# Compile the model
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')

# Train the model
autoencoder.fit(x_train, x_train,
                epochs=10,
                batch_size=128,
                shuffle=True,
                validation_data=(x_test, x_test))

# Visualize some results after training
decoded_imgs = autoencoder.predict(x_test)

n = 8  # Number of digits to display
plt.figure(figsize=(9,4))
for i in range(n):
    # Display original images on top row 
    ax = plt.subplot(2,n,i+1)
    plt.imshow(x_test[i].reshape(28, 28), cmap='gray')
    ax.axis('off')

    # Display reconstructed images on bottom row 
    ax = plt.subplot(2,n,i+n+1)
    plt.imshow(decoded_imgs[i].reshape(28, 28), cmap='gray')
    ax.axis('off')

plt.show()