受此github 存储库的启发，我构建了一个 DCGAN（深度卷积生成对抗网络）。它是用更底层的 Tensorflow 代码编写的，我尝试将其转换为 Keras 语法。
现在，我认为网络非常重（大约 400 万个参数），并且我遇到了这个问题，即在训练期间生成器网络比鉴别器网络要好很多。我没有找到任何关于这个问题的类似帖子，因为大多数时候是鉴别器击败了生成器（实际上被愚弄了），或者我们有模式崩溃。所以我认为代码中可能有问题（也许鉴别器网络在不应该训练的时候进行训练，或者损失函数错误等）。我试图找出错误但失败了。
我的代码如下：
from keras.models import Sequential
from keras.layers import Dense, Reshape, ReLU, LeakyReLU, BatchNormalization as BN#, tanh, sigmoid
from keras.layers.core import Activation, Flatten
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import UpSampling2D, Conv2D, MaxPooling2D, Conv2DTranspose
from keras.optimizers import SGD, Adam
from keras.datasets import mnist
import time
import numpy as np
import math
from utils import load_mnist, load_lines, load_celebA
class dcgan(object):
def __init__(self, config):
"""
Args:
batch_size: The size of batch. Should be specified before training.
y_dim: (optional) Dimension of dim for y. [None]
z_dim: (optional) Dimension of dim for Z. [100]
gf_dim: (optional) Dimension of G filters in first conv layer. [64]
df_dim: (optional) Dimension of D filters in first conv layer. [64]
gfc_dim: (optional) Dimension of G units for for fully connected layer. [1024]
dfc_dim: (optional) Dimension of D units for fully connected layer. [1024]
c_dim: (optional) Dimension of image color. For grayscale input, set to 1. [3]
"""
self.build_model(config)
def build_model(self,config):
self.D = self.discriminator(config)
self.G = self.generator(config)
self.GAN = Sequential()
self.GAN.add(self.G)
self.D.trainable = False
self.GAN.add(self.D)
def discriminator(self,config):
input_shape = (config.x_h,config.x_w,config.x_d)
D = Sequential()
D.add(Conv2D(filters=config.df_dim,strides=2,padding='same',kernel_size=5,input_shape=input_shape))
D.add(LeakyReLU(alpha=0.2))
D.add(Conv2D(filters=config.df_dim*2,strides=2,padding='same',kernel_size=5))
D.add(BN(momentum=0.9,epsilon=1e-5))
D.add(LeakyReLU(alpha=0.2))
D.add(Conv2D(filters=config.df_dim*4,strides=2,padding='same',kernel_size=5))
D.add(BN(momentum=0.9,epsilon=1e-5))
D.add(LeakyReLU(alpha=0.2))
D.add(Conv2D(filters=config.df_dim*8,strides=2,padding='same',kernel_size=5))
D.add(BN(momentum=0.9,epsilon=1e-5))
D.add(LeakyReLU(alpha=0.2))
D.add(Flatten())
D.add(Dense(1))
D.add(Activation('sigmoid'))
print('D:')
D.summary()
return D
def generator(self,config):
G = Sequential()
G.add(Dense(input_dim=config.z_dim, units=config.gf_dim*8*4*4))
G.add(Reshape((4,4,config.gf_dim*8)))
G.add(BN(momentum=0.9,epsilon=1e-5))
G.add(ReLU())
G.add(Conv2DTranspose(filters=config.gf_dim*4,strides=2,padding='same',kernel_size=5))
G.add(BN(momentum=0.9,epsilon=1e-5))
G.add(ReLU())
G.add(Conv2DTranspose(filters=config.gf_dim*2,strides=2,padding='same',kernel_size=5))
G.add(BN(momentum=0.9,epsilon=1e-5))
G.add(ReLU())
if config.dataset not in ['mnist','lines']:
#more layers could (and should) be added in order to get correct output size of G
G.add(Conv2DTranspose(filters=config.gf_dim,strides=2,padding='same',kernel_size=5))
G.add(BN(momentum=0.9,epsilon=1e-5))
G.add(ReLU())
G.add(Conv2DTranspose(filters=config.c_dim,strides=2,padding='same',kernel_size=5))
G.add(Activation('tanh'))
print('G:')
G.summary()
return G
def train(self,config):
if config.dataset == 'mnist':
(X_train, y_train), (X_test, y_test) = load_mnist()
X_train = (X_train.astype(np.float32) - 127.5)/127.5
elif config.dataset == 'lines':
(X_train, y_train), (X_test, y_test) = load_lines()
elif config.dataset == 'celebA':
(X_train, y_train), (X_test, y_test) = load_celebA()
D_optim = Adam(learning_rate=config.learning_rate, beta_1=config.beta_1)
G_optim = Adam(learning_rate=config.learning_rate, beta_1=config.beta_1)
loss_f = 'binary_crossentropy'
#Compile models
self.D.compile(loss=loss_f,optimizer=D_optim)
self.D.trainable = True
self.G.compile(loss=loss_f,optimizer=G_optim)
self.GAN.compile(loss=loss_f,optimizer=G_optim)
batches = int(len(X_train)/config.batch_size) #int always rounds down --> no problem with running out of data
counter = 1
print('\n' * 1)
print('='*42)
print('-'*10,'Training initialized.','-'*10)
print('='*42)
print('\n' * 2)
start_time = time.time()
for epoch in range(config.epochs):
for batch in range(batches):
batch_X_real = X_train[int(batch*config.batch_size/2):int((batch+1)*config.batch_size/2)][np.newaxis].transpose(1,2,3,0)
batch_z = np.random.normal(0,1,size=(config.batch_size,config.z_dim))
batch_X_fake = self.G.predict(batch_z[0:int(config.batch_size/2)])
batch_X = np.concatenate((batch_X_real,batch_X_fake),axis=0)
batch_yd = np.concatenate((np.ones(int(config.batch_size/2)),np.zeros((int(config.batch_size/2)))))
batch_yg = np.ones((config.batch_size))
#maybe normalize values in X?
#Update D network
self.D.trainable = True
D_loss = self.D.train_on_batch(batch_X, batch_yd)
#Update G network
self.D.trainable = False
G_loss = self.GAN.train_on_batch(batch_z, batch_yg)
#Update G network again according to https://github.com/carpedm20/DCGAN-tensorflow.git
#G_loss = self.GAN.train_on_batch(batch_z, batch_yg)
# Ta tid på körningen
# print("[%8d Epoch:[%2d/%2d] [%4d/%4d] time: %4.4f, d_loss: %.8f, g_loss: %.8f" \
# % (counter, epoch, config.epoch, idx, batch_idxs,
# time.time() - start_time, errD_fake+errD_real, errG))
#Save losses to vectors in order to plot
#Print status and save images for each config.sample_freq iterations
if np.mod(counter,config.sample_freq) == 0:
print('Epoch: {}/{} | Batch: {}/{} | D-loss {} | G-loss {} | Time: {}'.format(epoch+1,config.epochs,batch+1,batches,D_loss,G_loss,time.time() - start_time))
counter += 1
print('\n' * 2)
print('='*38)
print('-'*10,'Training complete.','-'*10)
print('='*38)
该程序运行缓慢，但如果您尝试使用这段代码运行它：
#import model
from setup import model_config
#create configuration object
config = model_config(dataset='mnist',loadmodel=False, interpolation=False,epochs=20,batch_size=64,
z_dim=100,gf_dim=64,df_dim=64,gfc_dim=1024,dfc_dim=1024,
c_dim=1,sample_freq=10) # >> model=None << ny parameter!
if config.loadmodel:
# Pass model to model parameter in config, vet inte hur man gör
# model1 = LoadModel('Generator')
# model2 = LoadModel('Discriminator')
# model3 = LoadModel('DG')
#load existing model
pass
else:
dcgan = dcgan(config)
dcgan.train(config)
if config.interpolation:
#do interpolation
pass
它将开始打印进度和损失。我确定某处存在一些明显的错误！如果我遗漏了什么，请告诉我我可以添加什么以使这篇文章更好！