Why is my DCGAN not converging?
I'm training a tf DCGAN on the MVTec hazelnut dataset and I found some difficulties. The problem is that after a lot of epochs the generate does not produce some quality images.
My model is the following:
``` def build_generator(self, noise_dim): # # Input shape (None, 100) # Output shape (None, 64, 64, 3) # model = Sequential(name = "Generator")
model.add(Dense(4*4*512, input_dim = noise_dim, activation = "relu", use_bias = False))
model.add(BatchNormalization())
model.add(Reshape((4,4,512)))
model.add(Conv2DTranspose(512, (5, 5), strides = (2, 2), padding = "same", activation = "relu", use_bias = False))
model.add(BatchNormalization())
model.add(Conv2DTranspose(256, (5, 5), strides = (2, 2), padding = "same", activation = "relu", use_bias = False))
model.add(BatchNormalization())
model.add(Conv2DTranspose(128, (5, 5), strides = (2, 2), padding = "same", activation = "relu", use_bias = False))
model.add(BatchNormalization())
model.add(Conv2DTranspose(64, (5, 5), strides = (2, 2), padding = "same", activation = "relu", use_bias = False))
model.add(BatchNormalization())
model.add(Conv2DTranspose(3, (5, 5), activation = "tanh", padding = "same", use_bias = False))
assert model.output_shape == (None, 64, 64, 3)
return model
def build_discriminator(self, image_shape):
model = Sequential(name = "Discriminator")
model.add(Conv2D(64, kernel_size = (5, 5), strides = (2, 2), input_shape = image_shape, padding = "same"))
model.add(BatchNormalization())
model.add(LeakyReLU(0.2))
model.add(Dropout(0.3))
model.add(Conv2D(128, kernel_size = (5, 5), strides = (2, 2), padding = "same"))
model.add(BatchNormalization())
model.add(LeakyReLU(0.2))
model.add(Dropout(0.3))
model.add(Conv2D(256, kernel_size = (5, 5), strides = (2, 2), padding = "same"))
model.add(BatchNormalization())
model.add(LeakyReLU(0.2))
model.add(Dropout(0.3))
model.add(Conv2D(512, kernel_size = (5, 5), strides = (2, 2), padding = "same"))
model.add(BatchNormalization())
model.add(LeakyReLU(0.2))
model.add(Dropout(0.3))
model.add(Flatten())
model.add(Dense(1))
return model
```
I tried a few configurations of the previous one and i think this is the best and it seems to learn faster than models with less filters.
About the training set, it has only 391 images and it seems to be a very poor dataset to train a GAN, so I tried some data augmentation to gather more images. 338
In particular I've used the RandomTranslation, RandomRotation and RandomZoom tensorflow's layers. After that with 1564 images (normalized betweem -1 and 1) the training seems, again, a little faster: after 100 epochs the G_loss has a value between 1 and 2 and the D_loss is between 0.5 and 1. However, the generated images don't seems real.
Can anyone help out on how to make my GAN to converge? Thanks in advance. 338