AllenOR灵感 发表于5个月前

• 发表于 5个月前
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`Autoencoder`（自编码）是一种非监督学习算法，如下图，就是一个简单的自编码神经网络结构。

Autoencoder.jpg

``````def encoder(x):
# Encoder Hidden layer with sigmoid activation #1
biases['encoder_b1']))
# Encoder Hidden layer with sigmoid activation #2
biases['encoder_b2']))
return layer_2``````

``````def decoder(x):
# Decoder Hidden layer with sigmoid activation #1
biases['decoder_b1']))
# Decoder Hidden layer with sigmoid activation #2
biases['decoder_b2']))
return layer_2``````

``````#!/usr/bin/env python
# -*- coding: utf-8 -*-

from __future__ import division, print_function, absolute_import

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

# Import MINST data
from tensorflow.examples.tutorials.mnist import input_data

# Parameters
learning_rate = 0.01
training_epochs = 100
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
biases['encoder_b1']))
# Decoder Hidden layer with sigmoid activation #2
biases['encoder_b2']))
return layer_2

# Building the decoder
def decoder(x):
# Encoder Hidden layer with sigmoid activation #1
biases['decoder_b1']))
# Decoder Hidden layer with sigmoid activation #2
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.initialize_all_variables()

# 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)))
f.show()
plt.draw()
plt.waitforbuttonpress()``````

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