from dataset import *
from wgan import *
from generators import *
from critics import *
dataset = FacesData(img_size=64)
# dataset = MNISTData()
generator = ConvGenerator(img_size=dataset.img_size,
channels=dataset.channels)
critic = ConvCritic(img_size=dataset.img_size,
channels=dataset.channels)
wgan = WGAN(generator=generator,
critic=critic,
dataset=dataset,
z_size=100)
wgan(batch_size=8, steps=100000, model_path=project_path.model_path)
from dataset import *
from wgan import *
from generators import *
from critics import *
dataset = PianoRollData(img_size=(16, 128))
# dataset = MNISTData()
generator = DCGANGenerator(img_size=dataset.img_size,
channels=dataset.channels,
prev_x=dataset.prev_x)
critic = DCGANCritic(img_size=dataset.img_size,
channels=dataset.channels,
image=dataset.x)
wgan = WGAN(generator=generator,
critic=critic,
dataset=dataset,
epoches=100,
z_size=100)
wgan(batch_size=64, model_path=project_path.model_path)
import wgan
import sys
import os
if (len(sys.argv) < 2):
wgan = wgan.GAN()
wgan(100000, 256, 1000)
elif (len(sys.argv) == 5):
if (sys.argv[1] == "load"):
model_name = sys.argv[2]
x = int(sys.argv[3])
y = int(sys.argv[4])
model = os.path.join("models", model_name)
wgan = wgan.GAN()
wgan.generate(model, x, y)
elif (len(sys.argv) == 4):
if (sys.argv[1] == "load"):
model_name = sys.argv[2]
x = int(sys.argv[3])
model = os.path.join("models", model_name)
wgan = wgan.GAN()
wgan.generate(model, x, 0)