下面的seed_everything函数可以帮助我们在深度学习训练过程中固定随机数种子,方便代码复现。 AI检测代码解析 def seed_everything(seed): random.seed(seed) os.environ['PYTHONHASHSEED'] = str(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(...
control = torch.from_numpy(detected_map.copy()).float().cpu() /255.0control = torch.stack([controlfor_inrange(num_samples)], dim=0) control = einops.rearrange(control,'b h w c -> b c h w').clone()ifseed == -1: seed = random.randint(0,65535) seed_everything(seed)ifconfig....
六、多臂老虎机问题 在前面的章节中,我们学习了强化学习(RL)的基本概念和几种 RL 算法,以及如何将 RL 问题建模为马尔可夫决策过程(MDP)。 我们还看到了用于解决 MDP 的不同的基于模型和无模型的算法。 在本章中,我们将看到 RL 中的经典问题之一,称为多臂老虎机(MAB)问题。 我们将看到什么是 MAB 问题,以及...
() self.seed = random.randint(0, 65535) seed_everything(self.seed) if self.save_memory: self.model.low_vram_shift(is_diffusing=False) cond = {"c_concat": [control], "c_crossattn": [self.model.get_learned_conditioning([prompt + ', ' + self.a_prompt] * self.num_samples)]} un...
trials.trials - a list of dictionaries representing everything about the search trials.results - a list of dictionaries returned by ‘objective’ during the search trials.losses() - a list of losses (float for each ‘ok’ trial) trials.statuses() - a list of status strings ...
The package is a superset of venv, which allows you to do everything that you can do using venv, and more. Virtualenv allows you to: Create virtual environments more quickly Discover installed versions of Python without needing to provide the absolute path Upgrade the tool using pip Extend the...
(t_stat), df)) * 2.0# return everythingreturn t_stat, df, cv, p# seed the random number generatorseed(1)# generate two independent samplesdata1 = 5 * randn(100) + 50data2 = 5 * randn(100) + 51# calculate the t testalpha = 0.05t_stat, df, cv, p = independent_ttest(data...
# To make the example reproducible rng = np.random.default_rng(seed=12345) def dnorm(mean, variance, size=1): if isinstance(size, int): size = size, return mean + np.sqrt(variance) * rng.standard_normal(*size) N = 100 X = np.c_[dnorm(0, 0.4, size=N), dnorm(0, 0.6, size...
plt.subplot(3,1,1) plt.title('Original sine wave') # Need to add empty space, else everything looks scrunched up! plt.subplots_adjust(hspace=.5) plt.plot(sine_wave[:500]) plt.subplot(3,1,2) plt.title('Noise wave') plt.plot(sine_noise[:4000]) plt.subplot(3,1,3) plt.title(...
() if seed == -1: seed = random.randint(0, 65535) seed_everything(seed) if config.save_memory: model.low_vram_shift(is_diffusing=False) cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]} un_cond = ...