help_ = "Number of training epochs"
parser.add_argument("-e", "--epochs", help=help_, default=101, type=int)
return parser.parse_args()
if __name__ == '__main__':
# parse arguments
args = parse_args()
if args is None:
exit()
x_train, y_train = create_dataset(
128, 128, nSlices=1000, resize=1,
directory='FluidArt/') # 3 channels = RGB
assert (x_train.shape[0] > 0)
x_train /= 255
# plot results to make sure data looks good!
fig, axs = plt.subplots(4, 4)
for i in range(4):
for j in range(4):
axs[i, j].imshow(x_train[np.random.randint(x_train.shape[0])])
axs[i, j].axis('off')
plt.show()
dcgan = DCGAN(img_rows=x_train[0].shape[0],
img_cols=x_train[0].shape[1],
help_ = "Number of training epochs"
parser.add_argument("-e", "--epochs", help=help_, default=101, type=int)
return parser.parse_args()
if __name__ == '__main__':
# parse arguments
args = parse_args()
if args is None:
exit()
x_train, y_train = create_dataset(
64, 64, nSlices=20, resize=0.75,
directory='SoapBubble/output/') # 3 channels = RGB
assert (x_train.shape[0] > 0)
x_train /= 255
stds = np.array(
[np.std(x_train[i].mean(2)) for i in range(x_train.shape[0])])
gmask = stds > np.percentile(stds, 25)
x_train = x_train[gmask]
# plot results to make sure data looks good!
fig, axs = plt.subplots(10, 10)
for i in range(10):
for j in range(10):
from dcgan import DCGAN, create_dataset
def parse_args():
parser = argparse.ArgumentParser()
help_ = "Load h5 model trained weights"
parser.add_argument("-w", "--weights", help=help_)
help_ = "Number of training epochs"
parser.add_argument("-e", "--epochs", help=help_, default=10 ,type=int)
return parser.parse_args()
if __name__ == '__main__':
# parse arguments
args = parse_args()
if args is None:
exit()
x_train, y_train = create_dataset(128,128, nSlices=150, resize=0.75, directory='space/') # 3 channels = RGB
assert(x_train.shape[0]>0)
x_train /= 255
dcgan = DCGAN(img_rows = x_train[0].shape[0],
img_cols = x_train[0].shape[1],
channels = x_train[0].shape[2],
latent_dim=32,
name='nebula_32_128')
dcgan.train(x_train, epochs=args.epochs, batch_size=32, save_interval=100)
help_ = "Number of training epochs"
parser.add_argument("-e", "--epochs", help=help_, default=101, type=int)
return parser.parse_args()
if __name__ == '__main__':
# parse arguments
args = parse_args()
if args is None:
exit()
x_train, y_train = create_dataset(
128, 128, nSlices=1000, resize=0.5,
directory='Space/Galaxy/') # 3 channels = RGB
assert (x_train.shape[0] > 0)
x_train /= 255
# plot results to make sure data looks good!
fig, axs = plt.subplots(4, 4)
for i in range(4):
for j in range(4):
axs[i, j].imshow(x_train[np.random.randint(x_train.shape[0])])
axs[i, j].axis('off')
plt.show()
dcgan = DCGAN(img_rows=x_train[0].shape[0],
img_cols=x_train[0].shape[1],