Toy example of single-layer autoencoder in TensorFlow

ae_toy_example.py

import tensorflow as tf
import numpy as np
import math
#import pandas as pd
#import sys

input = np.array([[2.0, 1.0, 1.0, 2.0],
                 [-2.0, 1.0, -1.0, 2.0],
                 [0.0, 1.0, 0.0, 2.0],
                 [0.0, -1.0, 0.0, -2.0],
                 [0.0, -1.0, 0.0, -2.0]])

# Code here for importing data from file

noisy_input = input + .2 * np.random.random_sample((input.shape)) - .1
output = input

# Scale to [0,1]
scaled_input_1 = np.divide((noisy_input-noisy_input.min()), (noisy_input.max()-noisy_input.min()))
scaled_output_1 = np.divide((output-output.min()), (output.max()-output.min()))
# Scale to [-1,1]
scaled_input_2 = (scaled_input_1*2)-1
scaled_output_2 = (scaled_output_1*2)-1

input_data = scaled_input_2
output_data = scaled_output_2

# Autoencoder with 1 hidden layer
n_samp, n_input = input_data.shape 
n_hidden = 2

x = tf.placeholder("float", [None, n_input])
# Weights and biases to hidden layer
Wh = tf.Variable(tf.random_uniform((n_input, n_hidden), -1.0 / math.sqrt(n_input), 1.0 / math.sqrt(n_input)))
bh = tf.Variable(tf.zeros([n_hidden]))
h = tf.nn.tanh(tf.matmul(x,Wh) + bh)
# Weights and biases to hidden layer
Wo = tf.transpose(Wh) # tied weights
bo = tf.Variable(tf.zeros([n_input]))
y = tf.nn.tanh(tf.matmul(h,Wo) + bo)
# Objective functions
y_ = tf.placeholder("float", [None,n_input])
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
meansq = tf.reduce_mean(tf.square(y_-y))
train_step = tf.train.GradientDescentOptimizer(0.05).minimize(meansq)

init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)

n_rounds = 5000
batch_size = min(50, n_samp)

for i in range(n_rounds):
    sample = np.random.randint(n_samp, size=batch_size)
    batch_xs = input_data[sample][:]
    batch_ys = output_data[sample][:]
    sess.run(train_step, feed_dict={x: batch_xs, y_:batch_ys})
    if i % 100 == 0:
        print i, sess.run(cross_entropy, feed_dict={x: batch_xs, y_:batch_ys}), sess.run(meansq, feed_dict={x: batch_xs, y_:batch_ys})

print "Target:"
print output_data
print "Final activations:"
print sess.run(y, feed_dict={x: input_data})
print "Final weights (input => hidden layer)"
print sess.run(Wh)
print "Final biases (input => hidden layer)"
print sess.run(bh)
print "Final biases (hidden layer => output)"
print sess.run(bo)
print "Final activations of hidden layer"
print sess.run(h, feed_dict={x: input_data})

Isn't this actually a Denoising Autoencoders?

Is there anything specific about data distribution or nature of input array that is given above ? Because when I changed the array values of variable input, I am not able to get the same output as input. Actually, I am trying to use this code in a generated multivariate normal data.

how to view the reconstructed input?