from __future__ import division, print_function, absolute_import

import tensorflow as tf

import numpy as np

import matplotlib.pyplot as plt

# Import MNIST data

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data", one_hot=False)

# # Visualize decoder setting

# # Parameters

# learning_rate = 0.01

# training_epochs = 5

# batch_size = 256

# display_step = 1

examples_to_show = 10

#

# # Network Parameters

# n_input = 784 # MNIST data input (img shape: 28*28)

#

# # tf Graph input (only pictures)

# X = tf.placeholder("float", [None, n_input])

#

# # hidden layer settings

# n_hidden_1 = 256 # 1st layer num features

# n_hidden_2 = 128 # 2nd layer num features

# weights = {

# 'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),

# 'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),

# 'decoder_h1': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])),

# 'decoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_input])),

# }

# biases = {

# 'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),

# 'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),

# 'decoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),

# 'decoder_b2': tf.Variable(tf.random_normal([n_input])),

# }

#

# # Building the encoder

# def encoder(x):

# # Encoder Hidden layer with sigmoid activation #1

# layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),

# biases['encoder_b1']))

# # Decoder Hidden layer with sigmoid activation #2

# layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']),

# biases['encoder_b2']))

# return layer_2

#

#

# # Building the decoder

# def decoder(x):

# # Encoder Hidden layer with sigmoid activation #1

# layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']),

# biases['decoder_b1']))

# # Decoder Hidden layer with sigmoid activation #2

# layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']),

# biases['decoder_b2']))

# return layer_2

# Visualize encoder setting

# Parameters

learning_rate = 0.01 # 0.01 this learning rate will be better! Tested

training_epochs = 10

batch_size = 256

display_step = 1

# Network Parameters

n_input = 784 # MNIST data input (img shape: 28*28)

# tf Graph input (only pictures)

X = tf.placeholder("float", [None, n_input])

# hidden layer settings

n_hidden_1 = 128

n_hidden_2 = 64

n_hidden_3 = 10

n_hidden_4 = 2

weights = {

}

biases = {

}

def encoder(x):

return layer_4

def decoder(x):

return layer_4

# Construct model

encoder_op = encoder(X)

decoder_op = decoder(encoder_op)

# Prediction

y_pred = decoder_op

# Targets (Labels) are the input data.

y_true = X

# Define loss and optimizer, minimize the squared error

cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))

optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)

# Launch the graph

with tf.Session() as sess:

# tf.initialize_all_variables() no long valid from

# 2017-03-02 if using tensorflow >= 0.12

if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:

init = tf.initialize_all_variables()

else:

init = tf.global_variables_initializer()

sess.run(init)

total_batch = int(mnist.train.num_examples/batch_size)

# Training cycle

for epoch in range(training_epochs):

# Loop over all batches

for i in range(total_batch):

batch_xs, batch_ys = mnist.train.next_batch(batch_size) # max(x) = 1, min(x) = 0

# Run optimization op (backprop) and cost op (to get loss value)

_, c = sess.run([optimizer, cost], feed_dict={X: batch_xs})

# Display logs per epoch step

if epoch % display_step == 0:

print("Epoch:", '%04d' % (epoch+1),

"cost=", "{:.9f}".format(c))

print("Optimization Finished!")

# # Applying encode and decode over test set

encode_decode = sess.run(

y_pred, feed_dict={X: mnist.test.images[:examples_to_show]})

# Compare original images with their reconstructions

f, a = plt.subplots(2, 10, figsize=(10, 2))

for i in range(examples_to_show):

a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28)))

a[1][i].imshow(np.reshape(encode_decode[i], (28, 28)))

plt.show()

encoder_result = sess.run(encoder_op, feed_dict={X: mnist.test.images})

plt.scatter(encoder_result[:, 0], encoder_result[:, 1], c=mnist.test.labels)

plt.colorbar()

plt.show()