TensorFlowでAutoEncoderを可視化してみたよ ref: http://qiita.com/piyo7/items/355510a1c7ec061d9aff

autoencoder.py

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_integer('batch_size', 256, '')
flags.DEFINE_integer('epoch_num', 10, '')
flags.DEFINE_integer('hidden_num', 64, '')
flags.DEFINE_float('learning_rate', 0.01, '')
flags.DEFINE_float('noise_rate', 0.3, '')
flags.DEFINE_float('noise_strength', 0.2, '')
flags.DEFINE_string('summary_dir', 'summary', '')


def main():
    mnist_data = input_data.read_data_sets('MNIST_data', one_hot=True)

    with tf.name_scope('input'):
        input_layer = tf.placeholder(tf.float32, shape=[None, 784])
        input_summary = tf.image_summary('tag', tf.reshape(input_layer, [-1, 28, 28, 1]), 10)

    with tf.name_scope('noisy_input'):
        noise_layer = tf.maximum(tf.random_uniform(shape=tf.shape(input_layer),
                                                   minval=-FLAGS.noise_strength * (2 / FLAGS.noise_rate - 1),
                                                   maxval=FLAGS.noise_strength),
                                 -FLAGS.noise_strength)
        noisy_input_layer = tf.minimum(tf.maximum(input_layer + noise_layer, 0.0), 1.0)
        noisy_input_summary = tf.image_summary('tag', tf.reshape(noisy_input_layer, [-1, 28, 28, 1]), 10)

    with tf.name_scope('hidden'):
        w1 = tf.Variable(tf.random_uniform([784, FLAGS.hidden_num], minval=-1, maxval=1))
        b1 = tf.Variable(tf.zeros([FLAGS.hidden_num]))
        hidden_layer = tf.sigmoid(tf.matmul(noisy_input_layer, w1) + b1)

    with tf.name_scope('output'):
        w2 = tf.transpose(w1)
        b2 = tf.Variable(tf.zeros([784]))
        output_layer = tf.sigmoid(tf.matmul(hidden_layer, w2) + b2)
        output_summary = tf.image_summary('tag', tf.reshape(output_layer, [-1, 28, 28, 1]), 10)

    with tf.name_scope('loss'):
        loss = tf.nn.l2_loss(input_layer - output_layer)
        loss_summary = tf.scalar_summary('loss', loss)

    train_step = tf.train.GradientDescentOptimizer(FLAGS.learning_rate).minimize(loss)

    sess = tf.InteractiveSession()
    sess.run(tf.initialize_all_variables())

    if tf.gfile.Exists(FLAGS.summary_dir):
        tf.gfile.DeleteRecursively(FLAGS.summary_dir)

    input_writer = tf.train.SummaryWriter(FLAGS.summary_dir + '/input', sess.graph)
    noisy_input_writer = tf.train.SummaryWriter(FLAGS.summary_dir + '/noisy_input')
    output_writer = tf.train.SummaryWriter(FLAGS.summary_dir + '/output')

    step = 0
    while mnist_data.train.epochs_completed < FLAGS.epoch_num:
        step += 1
        images, _ = mnist_data.train.next_batch(FLAGS.batch_size)
        loss_result, _ = sess.run([loss_summary, train_step], feed_dict={input_layer: images})
        input_writer.add_summary(loss_result, step)

    input_result, noisy_input_result, output_result =\
        sess.run([input_summary, noisy_input_summary, output_summary], feed_dict={input_layer: images})
    input_writer.add_summary(input_result)
    noisy_input_writer.add_summary(noisy_input_result)
    output_writer.add_summary(output_result)


if __name__ == '__main__':
    main()

file1.txt

$ python autoencoder.py 
Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz

$ tensorboard --logdir=summary/

file2.txt

$ python --version
Python 3.5.1

requirements.txt

numpy==1.11.1             # via tensorflow
protobuf==3.0.0b2         # via tensorflow
six==1.10.0               # via protobuf, tensorflow
tensorflow==0.9.0
wheel==0.29.0             # via tensorflow