本文整理汇总了Python中config.random_seed方法的典型用法代码示例。如果您正苦于以下问题：Python config.random_seed方法的具体用法？Python config.random_seed怎么用？Python config.random_seed使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类config的用法示例。
在下文中一共展示了config.random_seed方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: predict_gan
# 需要导入模块: import config [as 别名]
# 或者: from config import random_seed [as 别名]
def predict_gan():
separate_funcs = False
drange_net = [-1,1]
drange_viz = [-1,1]
image_grid_size = None
image_grid_type = 'default'
resume_network = 'pre-trained_weight'
np.random.seed(config.random_seed)
if resume_network:
print("Resuming weight from:"+resume_network)
G = Generator(num_channels=3, resolution=128, label_size=0, **config.G)
G = load_G_weights(G,resume_network,True)
print(G.summary())
# Misc init.
if image_grid_type == 'default':
if image_grid_size is None:
w, h = G.output_shape[1], G.output_shape[2]
print("w:%d,h:%d"%(w,h))
image_grid_size = np.clip(int(1920 // w), 3, 16).astype('int'), np.clip(1080 / h, 2, 16).astype('int')
print("image_grid_size:",image_grid_size)
else:
raise ValueError('Invalid image_grid_type', image_grid_type)
result_subdir = misc.create_result_subdir('pre-trained_result', config.run_desc)
for i in range(1,6):
snapshot_fake_latents = random_latents(np.prod(image_grid_size), G.input_shape)
snapshot_fake_images = G.predict_on_batch(snapshot_fake_latents)
misc.save_image_grid(snapshot_fake_images, os.path.join(result_subdir, 'pre-trained_%03d.png'%i), drange=drange_viz, grid_size=image_grid_size)