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#! -*- coding: utf-8 -*-    ## by Colie (lijixiang)    import tensorflow as tf  from tensorflow.examples.tutorials.mnist import input_data  import numpy as np  from PIL import Image    train_epochs = 35  ## int(1e5+1)    INPUT_HEIGHT = 28  INPUT_WIDTH = 28    batch_size = 256    noise_factor = 0.5  ## (0~1)    ## 原始输入是28×28*3  input_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*64  weight_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7×7×64  weight_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*32  deconv_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*64  deconv_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*64  deconv_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*1  weight_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*28  output = tf.reshape(conv_final, shape=[-1, INPUT_HEIGHT * INPUT_WIDTH])    ## loss and optimizer  loss = 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')

初始loss值为大概0.90 

训练有问题，最终训练的结果越来越差