自编码器对数据进行降噪处理的代码
时间: 2024-06-10 09:04:32 浏览: 57
以下为简单的自编码器代码,用于对数据进行降噪处理:
```
import numpy as np
import tensorflow as tf
# 定义自编码器的输入输出
input_data = tf.placeholder(tf.float32, [None, 784])
output_data = tf.placeholder(tf.float32, [None, 784])
# 定义编码器
hidden_layer = tf.layers.dense(inputs=input_data, units=256, activation=tf.nn.relu)
# 定义解码器
reconstructed = tf.layers.dense(inputs=hidden_layer, units=784, activation=tf.nn.sigmoid)
# 定义损失函数
loss = tf.reduce_mean(tf.square(output_data - reconstructed))
# 定义优化器
train_step = tf.train.AdamOptimizer(0.01).minimize(loss)
# 加载数据,该代码使用MNIST数据集
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(10000):
batch_xs, batch_ys = mnist.train.next_batch(100)
_, l = sess.run([train_step, loss], feed_dict={input_data: batch_xs, output_data: batch_xs})
if i % 1000 == 0:
print("step %d, loss %g" % (i, l))
# 对输入数据进行降噪处理
noisy_images = mnist.test.images + 0.2 * np.random.randn(*mnist.test.images.shape)
denoised_images = sess.run(reconstructed, feed_dict={input_data: noisy_images})
# 输出降噪后的图像
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))
for row in range(2):
for col in range(10):
i = row * 10 + col
axes[row][col].imshow(noisy_images[i].reshape(28,28), cmap='gray')
axes[row][col].axis('off')
plt.show()
fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))
for row in range(2):
for col in range(10):
i = row * 10 + col
axes[row][col].imshow(denoised_images[i].reshape(28,28), cmap='gray')
axes[row][col].axis('off')
plt.show()
```
该代码使用MNIST数据集进行测试。输入数据为784维(28*28像素)的图像数据,输出也是784维的图像数据。通过训练自编码器,对输入数据进行降噪处理,从而得到降噪后的图像。