Use Tensorflow structures from the encoder (Autoencoder)

From the encoder is a data compression algorithm, in which the data compression and decompression of data compression function is related, from the training sample came. Most from the encoder, the compression and decompression functions are implemented by the neural network.

 

1. convolutional neural network structures from the encoder

 

• Import MNIST data set (grayscale pixel range of 0 to 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)

  View Code
• Build networks
  

    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)

  View Code

  The model used in the decoding section is not upsample + convolution transposed convolution ( Reference )

• Training Network
  

    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))

  View Code
• Network test
  

    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()

  View Code

  

2. Noise from the encoder

• Network structures (ibid., But the number of feature map by 16-8-8-8-8-16 becomes 32-32-16-16-32-32)
• Training Network
  

    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))

  View Code
• 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()

  View Code