如何降低GAN中G和D的损失率？

Question

我正在使用 Tensorflow 构建 GAN。

起初，我创建了一个生成 32x32 图像的 GAN。

修改模型以添加图层以创建 128x128 图像。

顺便说一句，32x32 GAN G,D损失值还可以，但是随着层尺寸和图像尺寸的增加，损失值非常高。

我修改了图层并修改了其他超参数以降低损失，但它仍然很高

请问如何减少G和D的损失

import os.path
import numpy as np
from keras.models import *
from keras.layers import *
from keras.optimizers import *
from keras.layers.convolutional import Conv2DTranspose, MaxPooling2D, UpSampling2D, Conv2D
from keras.layers.core import Reshape, Dense, Dropout, Flatten
from keras.layers.advanced_activations import LeakyReLU, ReLU
from keras.layers.normalization import BatchNormalization
import keras.backend as K
import matplotlib.pyplot as plt
from PIL import Image
from keras.models import load_model
K.set_image_data_format('channels_last')

class Gan:
    def __init__(self,img_data):
        img_size = img_data.shape[1]
        channel = img_data.shape[3] if len(img_data.shape) >= 4 else 1

        self.img_data = img_data
        self.input_shape = (img_size,img_size,channel)

        self.img_rows = img_size
        self.img_cols = img_size
        self.channel = channel
        self.noise_size = 128

        self.create_d()
        self.create_g()

        optimizer = Adam(lr=0.0008)
        self.D.compile(loss='binary_crossentropy', optimizer=optimizer)

        optimizer = Adam(lr=0.0004)
        self.D.trainable = False
        self.AM = Sequential()
        self.AM.add(self.G)
        self.AM.add(self.D)
        self.AM.compile(loss='binary_crossentropy',optimizer=optimizer)

    def create_g(self):
        self.G = Sequential()
        dropout = 0.4

        self.G.add(Dense(8 * 8 * 1024, input_dim=self.noise_size))
        self.D.add(Dropout(dropout))
        self.G.add(Activation('relu'))
        self.G.add(Reshape((8, 8, 1024)))
        self.G.add(Dropout(dropout))
        self.G.add(Conv2DTranspose(512, 5, strides=2, padding ='same'))
        self.D.add(Dropout(dropout))
        self.G.add(Activation('relu'))
        self.G.add(Conv2DTranspose(256, 5, strides=2, padding ='same'))
        self.D.add(Dropout(dropout))
        self.G.add(Activation('relu'))
        self.G.add(Conv2DTranspose(128, 5, strides=2, padding ='same'))
        self.D.add(Dropout(dropout))
        self.G.add(Activation('relu'))
        self.G.add(Conv2DTranspose(64, 5, strides=2, padding='same'))
        self.D.add(Dropout(dropout))
        self.G.add(Activation('relu'))
        self.G.add(Conv2DTranspose(self.channel, 5, strides =1,padding='same'))
        self.G.add(Activation('sigmoid'))
        self.G.summary()
        return self.G

    def create_d(self):
        self.D = Sequential()
        dropout = 0.4
        self.D.add(Conv2D(64, 5, strides=2, input_shape=self.input_shape, padding='same'))
        self.D.add(LeakyReLU(alpha=0.2))
        self.D.add(Dropout(dropout))
        self.D.add(BatchNormalization(momentum=0.9))
        self.D.add(Conv2D(128, 5, strides=2, input_shape=self.input_shape, padding='same'))
        self.D.add(LeakyReLU(alpha=0.2))
        self.D.add(Dropout(dropout))
        self.D.add(Conv2D(256, 5, strides=2, input_shape=self.input_shape, padding='same'))
        self.D.add(LeakyReLU(alpha=0.2))
        self.D.add(Dropout(dropout))
        self.D.add(Conv2D(512, 5, strides=1, input_shape=self.input_shape, padding='same'))
        self.D.add(LeakyReLU(alpha=0.2))
        self.D.add(Dropout(dropout))
        self.D.add(Conv2D(1024, 5, strides=2, input_shape=self.input_shape, padding='same'))
        self.D.add(LeakyReLU(alpha=0.2))
        self.D.add(Dropout(dropout))
        self.D.add(Flatten())
        self.D.add(Dense(1))
        self.D.add(Activation('sigmoid'))
        self.D.summary()
        return self.D


    def train(self, sess, batch_size=100):

        images_train = self.img_data[np.random.randint(0, self.img_data.shape[0], size=batch_size), :, :, :] #shape[0] -> image data의 숫자
        noise = np.random.uniform(-1.0,1.0, size=[batch_size,self.noise_size])
        images_fake = self.G.predict(noise)

        x = np.concatenate((images_train, images_fake))
        y = np.ones([2*batch_size,1])
        y[batch_size:,:] = 0
        self.D.trainable = True
        d_loss = self.D.train_on_batch(x,y)

        y = np.ones([batch_size,1])
        noise = np.random.uniform(-1.0,1.0,size=[batch_size,self.noise_size])
        self.D.trainable = False
        a_loss = self.AM.train_on_batch(noise,y)

        return d_loss, a_loss, images_fake

    def save_weigths(self):

        self.G.save_weights('gan_g_weights')
        self.D.save_weights('gan_d_weights')


    def load(self):
        if os.path.isfile('gan_g_weights'):
            self.G.load_weights('gan_g_weights')
            print("Load G from file")
        if os.path.isfile('gan_d_weights'):
            self.D.load_weights('gan_d_weights')
            print("Load D from file")

class faceData():
    def __init__(self):
        img_data_list = []
        images = os.listdir("data_rgb1")

        for path in images:
            img = Image.open("data_rgb1/" + path)
            img_data_list.append([np.array(img).astype('float32')])

        self.x_train = np.vstack(img_data_list) / 255.0
        print(self.x_train.shape)

dataset = faceData()
x_train =dataset.x_train

gan = Gan(x_train)
gan.load()

sess = tf.Session()
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())

epochs = 1000
sample_size = 10
batch_size = 50
train_per_epoch = x_train.shape[0] // batch_size



for epoch in range(0,epochs):
    total_d_loss = 0.0
    total_a_loss = 0.0
    imgs = None

    for batch in range(0, train_per_epoch):
        d_loss, a_loss, t_imgs = gan.train(batch_size)
        total_d_loss += d_loss
        total_a_loss += a_loss
        if imgs is None:
            imgs = t_imgs


    total_d_loss /= train_per_epoch
    total_a_loss /= train_per_epoch
    print("Epoch: {}, D Loss: {}, AM loss: {} " .format(epoch, total_d_loss, total_a_loss))
    fig, ax = plt.subplots(1, sample_size, figsize = (sample_size, 1))
    if epoch == 999:
        for i in range(0, sample_size):
            ax[i].set_axis_off()
            ax[i].imshow(imgs[i].reshape((gan.img_rows, gan.img_cols, gan.channel)), interpolation='nearest');
            plt.savefig('result%d.png' % epoch)
        saver.save(sess, os.path.join('save', 'model_{}'.format(epoch)))


    plt.close('all')
    gan.save_weigths()

结果：

纪元：0，D 损失：8.065221479096389，AM 损失：14.922738138189171

纪元：1，D 损失：8.052544213793604，AM 损失：14.836829509831928

纪元：2，D 损失：8.02602034776949，AM 损失：14.889192866794954

纪元：3，D 损失：8.05762272074743，AM 损失：14.88101108667209

纪元：4，D 损失：8.045719083795692，AM 损失：14.863829361000642

纪元：5，D 损失：8.052135099614333，AM 损失：14.872829325913173

纪元：6，D 损失：8.026918762226396，AM 损失：14.900647337666623

纪元：7，D 损失：8.091860083759133，AM 损失：14.836829485626994

纪元：8，D 损失：8.05686701130746，AM 损失：14.935828973799188

纪元：9，D 损失：8.038368832641448，AM 损失：14.832738677862332

纪元：10，D 损失：8.06173144016169，AM 损失：14.904738174477204

纪元：11，D损失：8.032495556749064，AM损失：14.926010857983893 . . .

Answer 1

欢迎来到训练 GAN 的美丽世界。这不是一件容易的事，但并非不可能。我无法仅通过查看前 10 个时期的损失来判断问题出在哪里。

我高度推荐你看看这个article。您可能遇到了麻烦，因为当您添加更多层时，平衡一定已经被打破。

文章中推荐的一些技巧是：

看看渐变。
执行预训练。
不要使用硬标签。

一些个人提示是：在生成器中添加批量归一化。不要在生成器中使用密集层。

祝你好运，玩得开心！ Post你生成的一些图片我很好奇！

如何降低GAN中G和D的损失率？

How can I reduce the loss rate of G and D in GAN?

keras

tensorflow

generative-adversarial-network