私はTensorflowを使ってGANを構築しています。
最初は、32x32イメージを生成するGANを作成しました。
128x128イメージを作成するためにレイヤーを追加するようにモデルを修正しました。
ちなみに、32x32 GAN G、D損失値は問題ありませんでしたが、レイヤーサイズと画像サイズが大きくなるにつれて損失値は非常に高くなります。
私はレイヤーを変更し、他のハイパーパラメータを修正して損失を下げましたが、まだ高いです。
私はどのようにGとDの損失を減らすのだろうか。
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()
結果:
エポック:0、D損失:8.065221479096389、AM損失:14.9227381388189171
エポック:1、D損失:8.052544213793604、AM損失:14.836829509831928
エポック:2、D損失:8.02602034776949、AM損失:14.889192866794954
エポック:3、D損失:8.05762272074743、AM損失:14.88101108667209
エポック:4、D損失:8.045719083795692、AM損失:14.863829361000642
エポック:5、D損失:8.052135099614333、AM損失:14.872829325913173
エポック:6、D損失:8.026918762226396、AM損失:14.90064733766623
エポック:7、D損失:8.091860083759133、AM損失:14.83682948562694
エポック:8、D損失:8.05686701130746、AM損失:14.935828973799188
エポック:9、D損失:8.038368832641448、AM損失:14.832738677862332
エポック:10、D損失:8.061731440169、AM損失:14.904738174477204
エポック:11、D損失:8.03249556749064、AM損失:14.926010857983893 はい はい はい