自编码器是一种数据压缩算法，其中数据的压缩和解压缩函数是数据相关的、从样本中训练而来的。大部分自编码器中，压缩和解压缩的函数是通过神经网络实现的。
1. 使用卷积神经网络搭建自编码器
- 导入MNIST数据集（灰度图，像素范围0~1）
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0) - 搭建网络
inputs_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='inputs')
targets_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='targets')
### Encoder
conv1 = tf.layers.conv2d(inputs_, 16, (3,3), padding='same', activation=tf.nn.relu) # 28x28x16
maxpool1 = tf.layers.max_pooling2d(conv1, (2,2), (2,2), padding='same') # 14x14x16
conv2 = tf.layers.conv2d(maxpool1, 8, (3,3), padding='same', activation=tf.nn.relu) # 14x14x8
maxpool2 = tf.layers.max_pooling2d(conv2, (2,2), (2,2), padding='same') # 7x7x8
conv3 = tf.layers.conv2d(maxpool2, 8, (3,3), padding='same', activation=tf.nn.relu) # 7x7x8
encoded = tf.layers.max_pooling2d(conv3, (2,2), (2,2), padding='same') # 4x4x8
### Decoder
upsample1 = tf.image.resize_nearest_neighbor(encoded, (7,7)) # 7x7x8
conv4 = tf.layers.conv2d(upsample1, 8, (3,3), padding='same', activation=tf.nn.relu) # 7x7x8
upsample2 = tf.image.resize_nearest_neighbor(conv4, (14,14)) # 14x14x8
conv5 = tf.layers.conv2d(upsample2, 8, (3,3), padding='same', activation=tf.nn.relu) # 14x14x8
upsample3 = tf.image.resize_nearest_neighbor(conv5, (28,28)) # 28x28x8
conv6 = tf.layers.conv2d(upsample3, 16, (3,3), padding='same', activation=tf.nn.relu) # 28x28x16
logits = tf.layers.conv2d(conv6, 1, (3,3), padding='same', activation=None) # 28x28x1
decoded = tf.nn.sigmoid(logits, name='decoded') # 28x28x1
### Loss and Optimization:
loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=targets_, logits=logits)
cost = tf.reduce_mean(loss)
opt = tf.train.AdamOptimizer(0.001).minimize(cost)模型在解码部分使用的是upsample+convolution而不是transposed convolution（参考文献）
- 训练网络
sess = tf.Session()
epochs = 20
batch_size = 200
sess.run(tf.global_variables_initializer())
for e in range(epochs):
for ii in range(mnist.train.num_examples//batch_size):
batch = mnist.train.next_batch(batch_size)
imgs = batch[0].reshape((-1, 28, 28, 1))
batch_cost, _ = sess.run([cost, opt], feed_dict={inputs_: imgs, targets_: imgs})
print("Epoch: {}/{}...".format(e+1, epochs), "Training loss: {:.4f}".format(batch_cost)) - 检验网络
fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))
in_imgs = mnist.test.images[:10]
reconstructed, compressed = sess.run([decoded, encoded], feed_dict={inputs_: in_imgs.reshape((10, 28, 28, 1))})
# plot
for images, row in zip([in_imgs, reconstructed], axes):
for img, ax in zip(images, row):
ax.imshow(img.reshape((28, 28)), cmap='Greys_r')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
fig.tight_layout(pad=0.1)
sess.close()
2. 使用自编码器降噪
- 搭建网络（同上但feature map的个数由16-8-8-8-8-16变为32-32-16-16-32-32）
- 训练网络
sess = tf.Session()
epochs = 100
batch_size = 200
# Set's how much noise we're adding to the MNIST images
noise_factor = 0.5
sess.run(tf.global_variables_initializer())
for e in range(epochs):
for ii in range(mnist.train.num_examples//batch_size):
batch = mnist.train.next_batch(batch_size)
# Get images from the batch
imgs = batch[0].reshape((-1, 28, 28, 1))
# Add random noise to the input images
noisy_imgs = imgs + noise_factor * np.random.randn(*imgs.shape)
# Clip the images to be between 0 and 1
noisy_imgs = np.clip(noisy_imgs, 0., 1.)
# Noisy images as inputs, original images as targets
batch_cost, _ = sess.run([cost, opt], feed_dict={inputs_: noisy_imgs, targets_: imgs})
print("Epoch: {}/{}...".format(e+1, epochs), "Training loss: {:.4f}".format(batch_cost)) - 检验网络
fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))
in_imgs = mnist.test.images[:10]
noisy_imgs = in_imgs + noise_factor * np.random.randn(*in_imgs.shape)
noisy_imgs = np.clip(noisy_imgs, 0., 1.)
reconstructed = sess.run(decoded, feed_dict={inputs_: noisy_imgs.reshape((10, 28, 28, 1))})
for images, row in zip([noisy_imgs, reconstructed], axes):
for img, ax in zip(images, row):
ax.imshow(img.reshape((28, 28)), cmap='Greys_r')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
fig.tight_layout(pad=0.1)
sess.close()