tensorflow 卷积、反卷积形式的去噪自编码器

对于去噪自编码器，网上好多都是利用全连接神经网络进行构建，我自己写了一个卷积、反卷积形式的去噪自编码器，其中的参数调优如果有兴趣的话，可以自行修改查看结果。

数据集我使用最简单的mnist：

网络结构：

mnist输入（28*28=784向量） => 28*28*1矩阵 => 卷积层1 => 14*14*64 => 卷积层2 => 7*7*64 => 卷积层3 => 4*4*32 => 反卷积层1 => 7×7*32 => 反卷积层2 => 14*14*64 => 反卷积层3 => 28*28*64 => 卷积层X => 28×28*1

训练：

我用train集训练train_epochs轮，然后用test集对训练好的模型进行评测，同时保存加噪图像及对应的去噪图像。

Code:

#! -*- coding: utf-8 -*-## by Colie (lijixiang)import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataimport numpy as npfrom PIL import Imagetrain_epochs = 35  ## int(1e5+1)INPUT_HEIGHT = 28INPUT_WIDTH = 28batch_size = 256noise_factor = 0.5  ## (0~1)## 原始输入是28×28*3input_x = tf.placeholder(tf.float32, [None, INPUT_HEIGHT * INPUT_WIDTH], name='input_with_noise')input_matrix = tf.reshape(input_x, shape=[-1, INPUT_HEIGHT, INPUT_WIDTH, 1])input_raw = tf.placeholder(tf.float32, shape=[None, INPUT_HEIGHT * INPUT_WIDTH], name='input_without_noise')## 1 conv layer## 输入28*28*3## 经过卷积、激活、池化，输出14*14*64weight_1 = tf.Variable(tf.truncated_normal(shape=[3, 3, 1, 64], stddev=0.1, name = 'weight_1'))bias_1 = tf.Variable(tf.constant(0.0, shape=[64], name='bias_1'))conv1 = tf.nn.conv2d(input=input_matrix, filter=weight_1, strides=[1, 1, 1, 1], padding='SAME')conv1 = tf.nn.bias_add(conv1, bias_1, name='conv_1')acti1 = tf.nn.relu(conv1, name='acti_1')pool1 = tf.nn.max_pool(value=acti1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='max_pool_1')## 2 conv layer## 输入14*14*64## 经过卷积、激活、池化，输出7×7×64weight_2 = tf.Variable(tf.truncated_normal(shape=[3, 3, 64, 64], stddev=0.1, name='weight_2'))bias_2 = tf.Variable(tf.constant(0.0, shape=[64], name='bias_2'))conv2 = tf.nn.conv2d(input=pool1, filter=weight_2, strides=[1, 1, 1, 1], padding='SAME')conv2 = tf.nn.bias_add(conv2, bias_2, name='conv_2')acti2 = tf.nn.relu(conv2, name='acti_2')pool2 = tf.nn.max_pool(value=acti2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='max_pool_2')## 3 conv layer## 输入7*7*64## 经过卷积、激活、池化，输出4×4×32## 原始输入是28*28*3=2352，转化为4*4*32=512，大量噪声会在网络中过滤掉weight_3 = tf.Variable(tf.truncated_normal(shape=[3, 3, 64, 32], stddev=0.1, name='weight_3'))bias_3 = tf.Variable(tf.constant(0.0, shape=[32]))conv3 = tf.nn.conv2d(input=pool2, filter=weight_3, strides=[1, 1, 1, 1], padding='SAME')conv3 = tf.nn.bias_add(conv3, bias_3)acti3 = tf.nn.relu(conv3, name='acti_3')pool3 = tf.nn.max_pool(value=acti3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='max_pool_3')## 1 deconv layer## 输入4*4*32## 经过反卷积，输出7*7*32deconv_weight_1 = tf.Variable(tf.truncated_normal(shape=[3, 3, 32, 32], stddev=0.1), name='deconv_weight_1')deconv1 = tf.nn.conv2d_transpose(value=pool3, filter=deconv_weight_1, output_shape=[batch_size, 7, 7, 32], strides=[1, 2, 2, 1], padding='SAME', name='deconv_1')## 2 deconv layer## 输入7*7*32## 经过反卷积，输出14*14*64deconv_weight_2 = tf.Variable(tf.truncated_normal(shape=[3, 3, 64, 32], stddev=0.1), name='deconv_weight_2')deconv2 = tf.nn.conv2d_transpose(value=deconv1, filter=deconv_weight_2, output_shape=[batch_size, 14, 14, 64], strides=[1, 2, 2, 1], padding='SAME', name='deconv_2')## 3 deconv layer## 输入14*14*64## 经过反卷积，输出28*28*64deconv_weight_3 = tf.Variable(tf.truncated_normal(shape=[3, 3, 64, 64], stddev=0.1, name='deconv_weight_3'))deconv3 = tf.nn.conv2d_transpose(value=deconv2, filter=deconv_weight_3, output_shape=[batch_size, 28, 28, 64], strides=[1, 2, 2, 1], padding='SAME', name='deconv_3')## conv layer## 输入28*28*64## 经过卷积，输出为28*28*1weight_final = tf.Variable(tf.truncated_normal(shape=[3, 3, 64, 1], stddev=0.1, name = 'weight_final'))bias_final = tf.Variable(tf.constant(0.0, shape=[1], name='bias_final'))conv_final = tf.nn.conv2d(input=deconv3, filter=weight_final, strides=[1, 1, 1, 1], padding='SAME')conv_final = tf.nn.bias_add(conv_final, bias_final, name='conv_final')## output## 输入28*28*1## reshape为28*28output = tf.reshape(conv_final, shape=[-1, INPUT_HEIGHT * INPUT_WIDTH])## loss and optimizerloss = tf.reduce_mean(tf.pow(tf.subtract(output, input_raw), 2.0))optimizer = tf.train.AdamOptimizer(0.01).minimize(loss)with tf.Session() as sess:    mnist = input_data.read_data_sets('MNIST_data', one_hot=True)    n_samples = int(mnist.train.num_examples)    print('train samples: %d' % n_samples)    print('batch size: %d' % batch_size)    total_batch = int(n_samples / batch_size)    print('total batchs: %d' % total_batch)    init = tf.global_variables_initializer()    sess.run(init)    for epoch in range(train_epochs):        for batch_index in range(total_batch):            batch_x, _ = mnist.train.next_batch(batch_size)            noise_x = batch_x + noise_factor * np.random.randn(*batch_x.shape)            noise_x = np.clip(noise_x, 0., 1.)            _, train_loss = sess.run([optimizer, loss], feed_dict={input_x: noise_x, input_raw: batch_x})            print('epoch: %04d\tbatch: %04d\ttrain loss: %.9f' % (epoch + 1, batch_index + 1, train_loss))    ## 训练结束后，用测试集测试，并保存加噪图像、去噪图像    n_test_samples = int(mnist.test.num_examples)    test_total_batch = int(n_test_samples / batch_size)    for i in range(test_total_batch):        batch_test_x, _ = mnist.test.next_batch(batch_size)        noise_test_x = batch_test_x + noise_factor * np.random.randn(*batch_test_x.shape)        noise_test_x = np.clip(noise_test_x, 0., 1.)        test_loss, pred_result = sess.run([loss, conv_final], feed_dict={input_x: noise_test_x, input_raw: batch_test_x})        print('test batch index: %d\ttest loss: %.9f' % (i + 1, test_loss))        for index in range(batch_size):            array = np.reshape(pred_result[index], newshape=[INPUT_HEIGHT, INPUT_WIDTH])            array = array * 255            image = Image.fromarray(array)            if image.mode != 'L':                image = image.convert('L')            image.save('./pred/' + str(i * batch_size + index) + '.png')            array_raw = np.reshape(noise_test_x[index], newshape=[INPUT_HEIGHT, INPUT_WIDTH])            array_raw = array_raw * 255            image_raw = Image.fromarray(array_raw)            if image_raw.mode != 'L':                image_raw = image_raw.convert('L')            image_raw.save('./pred/' + str(i * batch_size + index) + '_raw.png')        #break

去噪效果：