利用tensorflow训练简单的生成对抗网络GAN

前言

对抗网络是14年Goodfellow Ian在论文Generative Adversarial Nets中提出来的。 原理方面,对抗网络可以简单归纳为一个生成器(generator)和一个判断器(discriminator)之间博弈的过程。整个网络训练的过程中, 两个模块的分工:

  • 判断网络,直观来看就是一个简单的神经网络结构,输入就是一副图像,输出就是一个概率值,用于判断真假使用(概率值大于0.5那就是真,小于0.5那就是假)
  • 生成网络,同样也可以看成是一个神经网络模型,输入是一组随机数Z,输出是一个图像。

两个模块的训练目的:

  • 判别网络的目的:就是能判别出来属于的一张图它是来自真实样本集还是假样本集。假如输入的是真样本,网络输出就接近1,输入的是假样本,网络输出接近0,那么很完美,达到了很好判别的目的。
  • 生成网络的目的:生成网络是造样本的,它的目的就是使得自己造样本的能力尽可能强,强到判别网络没法判断是真样本还是假样本。

GAN的训练

需要注意的是生成模型与对抗模型可以说是完全独立的两个模型,好比就是完全独立的两个神经网络模型,他们之间没有什么联系。

那么训练这样的两个模型的大方法就是:单独交替迭代训练。因为是2个网络,不好一起训练,所以才去交替迭代训练,我们一一来看。

  首先我们先随机产生一个生成网络模型(当然可能不是最好的生成网络),那么给一堆随机数组,就会得到一堆假的样本集(因为不是最终的生成模型,那么现在生成网络可能就处于劣势,导致生成的样本很糟糕,可能很容易就被判别网络判别出来了说这货是假冒的),但是先不管这个,假设我们现在有了这样的假样本集,真样本集一直都有,现在我们人为的定义真假样本集的标签,因为我们希望真样本集的输出尽可能为1,假样本集为0,很明显这里我们就已经默认真样本集所有的类标签都为1,而假样本集的所有类标签都为0.

  对于生成网络,回想下我们的目标,是生成尽可能逼真的样本。那么原始的生成网络生成的样本你怎么知道它真不真呢?就是送到判别网络中,所以在训练生成网络的时候,我们需要联合判别网络一起才能达到训练的目的。就是如果我们单单只用生成网络,那么想想我们怎么去训练?误差来源在哪里?细想一下没有,但是如果我们把刚才的判别网络串接在生成网络的后面,这样我们就知道真假了,也就有了误差了。所以对于生成网络的训练其实是对生成-判别网络串接的训练,就像图中显示的那样。好了那么现在来分析一下样本,原始的噪声数组Z我们有,也就是生成了假样本我们有,此时很关键的一点来了,我们要把这些假样本的标签都设置为1,也就是认为这些假样本在生成网络训练的时候是真样本。这样才能起到迷惑判别器的目的,也才能使得生成的假样本逐渐逼近为正样本。

下面是代码部分,这里,我们利用训练的两个数据集分别是:

  • mnist
  • Celeba

来生成手写数字以及人脸 首先是数据集的下载

import math
import os
import hashlib
from urllib.request import urlretrieve
import zipfile
import gzip
import shutil

data_dir = './data'

def download_extract(database_name, data_path):
     """
     Download and extract database
     :param database_name: Database name
     """
     DATASET_CELEBA_NAME = 'celeba'
     DATASET_MNIST_NAME = 'mnist'

     if database_name == DATASET_CELEBA_NAME:
         url = 'https://s3-us-west-1.amazonaws.com/udacity-dlnfd/datasets/celeba.zip'
         hash_code = '00d2c5bc6d35e252742224ab0c1e8fcb'
         extract_path = os.path.join(data_path, 'img_align_celeba')
         save_path = os.path.join(data_path, 'celeba.zip')
         extract_fn = _unzip
     elif database_name == DATASET_MNIST_NAME:
         url = 'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz'
         hash_code = 'f68b3c2dcbeaaa9fbdd348bbdeb94873'
         extract_path = os.path.join(data_path, 'mnist')
         save_path = os.path.join(data_path, 'train-images-idx3-ubyte.gz')
         extract_fn = _ungzip

     if os.path.exists(extract_path):
         print('Found {} Data'.format(database_name))
         return

     if not os.path.exists(data_path):
         os.makedirs(data_path)

     if not os.path.exists(save_path):
         with DLProgress(unit='B', unit_scale=True, miniters=1, desc='Downloading {}'.format(database_name)) as pbar:
             urlretrieve(
                 url,
                 save_path,
                 pbar.hook)

     assert hashlib.md5(open(save_path, 'rb').read()).hexdigest() == hash_code, \
         '{} file is corrupted.  Remove the file and try again.'.format(save_path)

     os.makedirs(extract_path)
     try:
         extract_fn(save_path, extract_path, database_name, data_path)
     except Exception as err:
         shutil.rmtree(extract_path)  # Remove extraction folder if there is an error
         raise err

     # Remove compressed data
     os.remove(save_path)

# download mnist
download_extract('mnist', data_dir)
# download celeba
download_extract('celeba', data_dir

我们先看看我们的mnist还有celeba数据集是什么样子

# the number of images
show_n_images =16

%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

def get_batch(image_files, width, height, mode):
    data_batch = np.array(
        [get_image(sample_file, width, height, mode) for sample_file in image_files]).astype(np.float32)

    # Make sure the images are in 4 dimensions
    if len(data_batch.shape) < 4:
        data_batch = data_batch.reshape(data_batch.shape + (1,))

    return data_batch

def images_square_grid(images, mode):
    # Get maximum size for square grid of images
    save_size = math.floor(np.sqrt(images.shape[0]))

    # Scale to 0-255
    images = (((images - images.min()) * 255) / (images.max() - images.min())).astype(np.uint8)

    # Put images in a square arrangement
    images_in_square = np.reshape(
            images[:save_size*save_size],
            (save_size, save_size, images.shape[1], images.shape[2], images.shape[3]))
    if mode == 'L':
        images_in_square = np.squeeze(images_in_square, 4)

    # Combine images to grid image
    new_im = Image.new(mode, (images.shape[1] * save_size, images.shape[2] * save_size))
    for col_i, col_images in enumerate(images_in_square):
        for image_i, image in enumerate(col_images):
            im = Image.fromarray(image, mode)
            new_im.paste(im, (col_i * images.shape[1], image_i * images.shape[2]))

    return new_im

mnist_images = get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(images_square_grid(mnist_images, 'L'), cmap='gray')

mninst:

mninst

 show_n_images = 9

 mnist_images = get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
 pyplot.imshow(images_square_grid(mnist_images, 'RGB'))

celeba:

celeba

现在我们开始搭建网络

这里我建议用GPU来训练,tensorflow的版本最好是1.1.0

from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))

接着我们要做的是构建输入

def model_inputs(image_width, image_height, image_channels, z_dim):
    ## Real imag
    inputs_real = tf.placeholder(tf.float32,(None, image_width,image_height,image_channels), name = 'input_real')

    ## input z

    inputs_z = tf.placeholder(tf.float32,(None, z_dim), name='input_z')

    ## Learning rate 
    learning_rate = tf.placeholder(tf.float32, name = 'lr')

    return inputs_real, inputs_z, learning_rate

构建Discriminator

def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function

    ## scope here

    with tf.variable_scope('discriminator', reuse=reuse):

        alpha = 0.2  ### leak relu coeff

        # drop out probability 
        keep_prob = 0.8

        # input layer 28 * 28 * color channel
        x1 = tf.layers.conv2d(images, 128, 5, strides=2, padding='same',
                              kernel_initializer= tf.contrib.layers.xavier_initializer(seed=2))
        ## No batch norm here
        ## leak relu here / alpha = 0.2
        relu1 = tf.maximum(alpha * x1, x1)
        # applied drop out here
        drop1 = tf.nn.dropout(relu1, keep_prob= keep_prob)
        # 14 * 14 * 128

        # Layer 2
        x2 = tf.layers.conv2d(drop1, 256, 5, strides=2, padding='same',
                             kernel_initializer= tf.contrib.layers.xavier_initializer(seed=2))
        ## employ batch norm here
        bn2 = tf.layers.batch_normalization(x2, training=True)
        ## leak relu 
        relu2 = tf.maximum(alpha * bn2, bn2)
        drop2 = tf.nn.dropout(relu2, keep_prob=keep_prob)

        # 7 * 7 * 256 

        # Layer3
        x3 = tf.layers.conv2d(drop2, 512, 5, strides=2, padding='same',
                             kernel_initializer= tf.contrib.layers.xavier_initializer(seed=2))
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        drop3 = tf.nn.dropout(relu3, keep_prob=keep_prob)
        # 4 * 4 * 512

        # Output
        # Flatten
        flatten = tf.reshape(relu3, (-1, 4 * 4 * 512))
        logits = tf.layers.dense(flatten,1)
        # activation
        out = tf.nn.sigmoid(logits)

    return out, logits

接着是 Generator

def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function

    with tf.variable_scope('generator', reuse = not is_train):
        # First Fully connect layer
        x0 = tf.layers.dense(z, 4 * 4 * 512)
        # Reshape 
        x0 = tf.reshape(x0,(-1,4,4,512))
        # Use the batch norm
        bn0 = tf.layers.batch_normalization(x0, training= is_train)
        # Leak relu
        relu0 = tf.nn.relu(bn0)
        # 4 * 4 * 512

        # Conv transpose here
        x1 = tf.layers.conv2d_transpose(relu0, 256, 4, strides=1, padding='valid')
        bn1 = tf.layers.batch_normalization(x1, training=is_train)
        relu1 = tf.nn.relu(bn1)
        # 7 * 7 * 256 

        x2 = tf.layers.conv2d_transpose(relu1, 128, 3, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=is_train)
        relu2 = tf.nn.relu(bn2)
        # 14 * 14 * 128

        # Last cov
        logits = tf.layers.conv2d_transpose(relu2, out_channel_dim, 3, strides=2, padding='same')
        ## without batch norm here
        out = tf.tanh(logits)


        return out

然后我们来定义loss,这里,加入了smoother

def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function


    g_model = generator(input_z, out_channel_dim, is_train=True)

    d_model_real, d_logits_real = discriminator(input_real, reuse = False)

    d_model_fake, d_logits_fake = discriminator(g_model, reuse= True)

    ## add smooth here

    smooth = 0.1
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, 
                                                labels=tf.ones_like(d_model_real) * (1 - smooth)))

    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))

    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
                                                labels= tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake



    return d_loss, g_loss

接着我们需要定义网络优化的过程,这里我们需要用到batch_normlisation, 不懂的话去搜下文档

def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """

    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')] 



    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)

    with tf.control_dependencies(update_ops):
        d_train_opt = tf.train.AdamOptimizer(learning_rate,beta1=beta1).minimize(d_loss,var_list = d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate,beta1=beta1).minimize(g_loss,var_list = g_vars)

    return d_train_opt, g_train_opt

现在,我们网络的模块,损失函数,以及优化的过程都定义好了,现在我们就要开始训练我们的网络了,我们的训练过程定义如下。

def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    losses = []
    samples = []

    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)

    d_loss, g_loss = model_loss(input_real,input_z,data_shape[-1])

    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)

    steps = 0

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1

                # Reshape the image and pass to Discriminator 
                batch_images = batch_images.reshape(batch_size, 
                                                    data_shape[1], 
                                                    data_shape[2],
                                                    data_shape[3])
                # Rescale the data to -1 and 1
                batch_images = batch_images * 2

                # Sample the noise 
                batch_z = np.random.uniform(-1,1,size = (batch_size, z_dim))


                ## Run optimizer
                _ = sess.run(d_opt, feed_dict = {input_real:batch_images, 
                                                 input_z:batch_z,
                                                 lr:learning_rate
                                                 })
                _ = sess.run(g_opt, feed_dict = {input_real:batch_images,
                                                 input_z:batch_z,
                                                 lr:learning_rate})

                if steps % 10 == 0:

                    train_loss_d = d_loss.eval({input_real:batch_images, input_z:batch_z})
                    train_loss_g = g_loss.eval({input_real:batch_images, input_z:batch_z})

                    losses.append((train_loss_d,train_loss_g))

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % 100 == 0:

                    show_generator_output(sess, 25, input_z, data_shape[-1], data_image_mode)

开始训练,超参数的设置 对于MNIST

batch_size = 64
z_dim = 100
learning_rate = 0.001
beta1 = 0.5
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)

训练效果如下

开始的时候,网络的参数很差,我们生成的手写数字的效果自然就不好

mninst_gen1

随着训练的进行,轮廓逐渐清晰,效果如下,到最后:

mninst_gen2

我们看到数字的轮廓基本是清晰可以辨认的,当然,这只是两个epoch的结果,如果有足够的时间经过更长时间的训练,效果会更好。 我们同样展示下对celeba人脸数据集的训练结果

batch_size = 32
z_dim = 100
learning_rate = 0.001
beta1 = 0.4
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)

训练开始:

celeba_gen1

经过一个epoch之后:

celeba_gen2

人脸的轮廓基本清晰了。

这里我们就是用了DCGAN最简单的方式来实现,原理过程说的不是很详细,同时,可能这个参数设置也不是很合理,训练的也不够成分,但是我想可以帮大家快速掌握实现一个简单的DCGAN的方法了。

本文来源:

参考链接:

results matching ""

    No results matching ""