autoencode

1.  autoencode

from __future__ import division, print_function, absolute_import

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
#%matplotlib inline

# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

# Parameters
learning_rate = 0.01
training_epochs = 20
batch_size = 256
display_step = 1
examples_to_show = 10

# Network Parameters
n_hidden_1 = 256 # 1st layer num features
n_hidden_2 = 128 # 2nd layer num features
n_input = 784 # MNIST data input (img shape: 28*28)

# tf Graph input (only pictures)
X = tf.placeholder("float", [None, n_input])

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

# 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.RMSPropOptimizer(learning_rate).minimize(cost)

# Initializing the variables
init = tf.global_variables_initializer()

# Launch the graph
with tf.Session() as sess:
    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)
            # 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(f)
    plt.draw()
    plt.waitforbuttonpress()

Extracting /tmp/data/train-images-idx3-ubyte.gz
Extracting /tmp/data/train-labels-idx1-ubyte.gz
Extracting /tmp/data/t10k-images-idx3-ubyte.gz
Extracting /tmp/data/t10k-labels-idx1-ubyte.gz
Epoch: 0001 cost= 0.202572390
Epoch: 0002 cost= 0.170586497
Epoch: 0003 cost= 0.147145674
Epoch: 0004 cost= 0.134183317
Epoch: 0005 cost= 0.129831925
Epoch: 0006 cost= 0.125962004
Epoch: 0007 cost= 0.118559957
Epoch: 0008 cost= 0.112685442
Epoch: 0009 cost= 0.109631285
Epoch: 0010 cost= 0.105650857
Epoch: 0011 cost= 0.103282064
Epoch: 0012 cost= 0.101963483
Epoch: 0013 cost= 0.099665105
Epoch: 0014 cost= 0.100522719
Epoch: 0015 cost= 0.097489521
Epoch: 0016 cost= 0.093438946
Epoch: 0017 cost= 0.093155362
Epoch: 0018 cost= 0.091413260
Epoch: 0019 cost= 0.090430483
Epoch: 0020 cost= 0.090419836
Optimization Finished!

/home/wgb/anaconda3/lib/python3.6/site-packages/matplotlib/figure.py:402: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure
  "matplotlib is currently using a non-GUI backend, "

---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
 in ()
    108     plt.show(f)
    109     plt.draw()
--> 110     plt.waitforbuttonpress()

/home/wgb/anaconda3/lib/python3.6/site-packages/matplotlib/pyplot.py in waitforbuttonpress(*args, **kwargs)
    724     If *timeout* is negative, does not timeout.
    725     """
--> 726     return gcf().waitforbuttonpress(*args, **kwargs)
 727 
 728 

/home/wgb/anaconda3/lib/python3.6/site-packages/matplotlib/figure.py in waitforbuttonpress(self, timeout)  1681   1682 blocking_input = BlockingKeyMouseInput(self) -> 1683 return blocking_input(timeout=timeout)  1684   1685 def get_default_bbox_extra_artists(self): /home/wgb/anaconda3/lib/python3.6/site-packages/matplotlib/blocking_input.py in __call__(self, timeout)  374 """  375 self.keyormouse = None --> 376 BlockingInput.__call__(self, n=1, timeout=timeout)  377   378 return self.keyormouse /home/wgb/anaconda3/lib/python3.6/site-packages/matplotlib/blocking_input.py in __call__(self, n, timeout)  115 try:  116 # Start event loop --> 117 self.fig.canvas.start_event_loop(timeout=timeout)  118 finally: # Run even on exception like ctrl-c  119 # Disconnect the callbacks /home/wgb/anaconda3/lib/python3.6/site-packages/matplotlib/backend_bases.py in start_event_loop(self, timeout)  2412 This is implemented only for backends with GUIs.  2413 """ -> 2414 raise NotImplementedError  2415   2416 def stop_event_loop(self): NotImplementedError: