GAN Converges in Just a Few Epochs

Question

I implemented a genrative adversarial network in Keras. My training data size is about 16,000, where each image is of 32*32 size. All of my training images are the resized versions of the imageds from the imagenet dataset with regard to the object detection task. I fed the image matrix directly into the network without doing the center crop. I used the AdamOptimizer with the learning rate being 1e-4, and beta1 being 0.5 and I also set the dropout rate to be 0.1. I first trained the discrimator on 3000 real images and 3000 fake images and it achieved a 93% accuracy. Then, I trained for 500 epochs with the batch size being 32. However, my model seemed to converge in only a few epochs(<10), and the images it generated were ugly.

Plot of the Loss Function

Random Samples Generated by the Generator

I was wondering whether my training dataset is too small(compared to those in the paper of DCGAN, which are more than 300,000) or my model configuration is not correct. What's more, should I train the SGD on D for k iterations (where k is small, perhaps 1) and then training with SGD on G for one iteration as suggested by Ian Goodfellow in the original paper?(I have just tried to train them one at a time)

Below is the configuration of the generator.

g_input = Input(shape=[100])
H = Dense(1024*4*4, init='glorot_normal')(g_input)
H = BatchNormalization(mode=2)(H)
H = Activation('relu')(H)
H = Reshape( [4, 4,1024] )(H)
H = UpSampling2D(size=( 2, 2))(H)
H = Convolution2D(512, 3, 3, border_mode='same', init='glorot_uniform')(H)
H = BatchNormalization(mode=2)(H)
H = Activation('relu')(H)
H = UpSampling2D(size=( 2, 2))(H)
H = Convolution2D(256, 3, 3, border_mode='same', init='glorot_uniform')(H)
H = BatchNormalization(mode=2)(H)
H = Activation('relu')(H)
H = UpSampling2D(size=( 2, 2))(H)
H = Convolution2D(3, 3, 3, border_mode='same', init='glorot_uniform')(H)
g_V = Activation('tanh')(H)
generator = Model(g_input,g_V)
generator.compile(loss='binary_crossentropy', optimizer=opt)
generator.summary()

Below is the configuration of the discriminator：

d_input = Input(shape=shp)
H = Convolution2D(64, 5, 5, subsample=(2, 2), border_mode = 'same', init='glorot_normal')(d_input)
H = LeakyReLU(0.2)(H)
#H = Dropout(dropout_rate)(H)
H = Convolution2D(128, 5, 5, subsample=(2, 2), border_mode = 'same', init='glorot_normal')(H)
H = BatchNormalization(mode=2)(H)
H = LeakyReLU(0.2)(H)
#H = Dropout(dropout_rate)(H)
H = Flatten()(H)
H = Dense(256, init='glorot_normal')(H)
H = LeakyReLU(0.2)(H)
d_V = Dense(2,activation='softmax')(H)
discriminator = Model(d_input,d_V)
discriminator.compile(loss='categorical_crossentropy', optimizer=dopt)
discriminator.summary()

Below is the configuration of GAN as a whole:

gan_input = Input(shape=[100])
H = generator(gan_input)
gan_V = discriminator(H)
GAN = Model(gan_input, gan_V)
GAN.compile(loss='categorical_crossentropy', optimizer=opt)
GAN.summary()

Answer 1

I suspect that your generator is trainable while you training the gan. You can verify by using generator.layers[-1].get_weights() to see if the parameters changed during training process of gan.

You should freeze discriminator before you assemble it to gan:

generator.trainnable = False
gan_input = Input(shape=[100])
H = generator(gan_input)
gan_V = discriminator(H)
GAN = Model(gan_input, gan_V)
GAN.compile(loss='categorical_crossentropy', optimizer=opt)
GAN.summary()

see this discussion: https://github.com/fchollet/keras/issues/4674

Answer 2

I think problem is with loss function Try

loss='categorical_crossentropy',