The Secret of Strand Hill (Episode 113)

 
The monolithic Strand Hill ruins in Ireland was one of Earth's most revered historical sites. Local tour guide and roustabout Tim O'Malley (Chris Wiggins) lead a group around the stone circle, then down into a passage grave. As he told the tourists the local legend of a mystical king, the ground suddenly rocked by a blast from nearby construction. As the groups fled, O'Malley saw a startling mosaic uncovered by a crumbling wall. He pocketed a fragment that had broken loose.

Word spread to the Taelons about the mosaic, which they believed was a marker for the grave of Ma'el, a Taelon scout sent to Earth thousands of years ago. The Taelon Synod ordered Da'an (Leni Parker) to step up the search for Ma'el's datalog of research, which they had so far been unable to find. The datalog's contents would determine whether the Taelons would continue to remain on Earth or move on to another planet. Da'an was forced to accept the help of his nemesis Zo'or (Anita La Selva), who the Synod appointed as Companion Representative to the United Nations.

As O'Malley schemed about selling the mosaic artifact, Siobhan Beckett (Kari Matchett), the security Implant of the United Kingdom's Companion, burst into the White Horse Tavern to arrest him. His bar mates refused to reveal O'Malley's hiding place, but Beckett promised to return. Captain Lili Marquette (Lisa Howard), William Boone (Kevin Kilner) and Agent Sandoval (Von Flores) arrived at Strand Hill and began the search for Ma'el's tomb. They examined the mosaic, which showed images of Christ, Buddha and other religious leaders of the past, plus ancient Taelon writing. Boone asked Da'an to interpret the writing while plotting to find Ma'el's datalog for the Resistance, knowing it would provide invaluable insight to the Taelon's agenda on Earth. But they find that O'Malley's missing piece holds the key to the Taelon riddle. Sandoval and Beckett began excavations with a sophisticated seismic survey, determined to find Ma'el's final resting place.

 

Boone and Lili stepped up the search for O'Malley and staged a drinking contest in the White Horse Tavern. If Boone won, the bar patrons would lead him to O'Malley and since his CVI prevents him from becoming intoxicated, O'Malley's hiding place under the bar was revealed.

Boone retrieved the missing piece of the mosaic, enabling him to decipher the last of Ma'el's riddle. Meanwhile, Sandoval and Beckett were sidetracked by an ancient Celtic grave, thinking that it was Ma'el's. But Boone and Lili found the real Taelon tomb under the White Horse Tavern. When they opened the casket, they found the datalog and a hologram of Ma'el appeared. The Ma'el hologram spoke in Taelon, and Boone translated for Lili as the alien scout urged the Synod to leave Earth alone. Ma'el stated that humans are not the cruel and aggressive beings the Taelons had believed and that given time, they would be the aliens' equals. As Lili and Boone tried to comprehend Ma'el's message, Sandoval and Beckett arrived and took the datalog to Da'an and Zo'or.

Da'an and Zo'or met with Quo'on and the Taelon Synod, where it is revealed that Ma'el has destroyed his centuries of research. After consultation, and despite Da'an's dissent, the Synod decided to proceed with their hidden agenda and their occupation of Earth.

训练数据保存为deep_convnet_params.pkl,UI使用wxPython编写。卷积神经网络(CNN)是一种专门针对图像、视频等结构化数据设计的深度学习模型,在计算机视觉、语音识别、自然语言处理等多个领域有广泛应用。其核心设计理念源于对生物视觉系统的模拟,主要特点包括局部感知、权重共享、多层级抽象以及空间不变性。 **1. 局部感知与卷积操作** 卷积层是CNN的基本构建块,使用一组可学习的滤波器对输入图像进行扫描。每个滤波器在图像上滑动,以局部区域内的像素值与滤波器权重进行逐元素乘法后求和,生成输出值。这一过程能够捕获图像中的边缘、纹理等局部特征。 **2. 权重共享** 同一滤波器在整个输入图像上保持相同的权重。这显著减少了模型参数数量,增强了泛化能力,并体现了对图像平移不变性的内在假设。 **3. 池化操作** 池化层通常紧随卷积层之后,用于降低数据维度并引入空间不变性。常见方法有最大池化和平均池化,它们可以减少模型对微小位置变化的敏感度,同时保留重要特征。 **4. 多层级抽象** CNN通常包含多个卷积和池化层堆叠在一起。随着网络深度增加,每一层逐渐提取更复杂、更抽象的特征,从底层识别边缘、角点,到高层识别整个对象或场景,使得CNN能够从原始像素数据中自动学习到丰富的表示。 **5. 激活函数与正则化** CNN中使用非线性激活函数来引入非线性表达能力。为防止过拟合,常采用正则化技术,如L2正则化和Dropout,以增强模型的泛化性能。 **6. 应用场景** CNN在诸多领域展现出强大应用价值,包括图像分类、目标检测、语义分割、人脸识别、图像生成、医学影像分析以及自然语言处理等任务。 **7. 发展与演变** CNN的概念起源于20世纪80年代,其影响力在硬件加速和大规模数据集出现后真正显现。经典模型如LeNet-5用于手写数字识别,而AlexNet、VGG、GoogLeNet、ResNet等现代架构推动了CNN技术的快速发展。如今,CNN已成为深度学习图像处理领域的基石,并持续创新。 资源来源于网络分享,仅用于学习交流使用,请勿用于商业,如有侵权请联系我删除!
REINFORCE算法是一种基于策略梯度的无模型强化学习算法,在短走廊网格世界环境中,其性能表现有一定特点。 短走廊网格世界是一个具有挑战性的环境,该环境具有稀疏奖励和长时依赖的特性。REINFORCE算法通过蒙特卡罗采样估计策略梯度,利用整个轨迹的奖励来更新策略参数。 在短走廊网格世界中,REINFORCE算法的优点在于它能够直接优化策略,不依赖于对环境动态的建模。随着训练的进行,它能够逐渐学习到最优策略。不过,由于其采用蒙特卡罗方法,对每个轨迹的奖励进行采样估计,会存在较大的方差。这可能导致在训练初期,算法的性能不稳定,学习过程可能比较缓慢,需要较多的样本才能收敛到较好的策略。并且,长时依赖的特性使得早期动作对最终奖励的影响难以准确估计,进一步增加了学习的难度。 以下是一个简单的Python伪代码示例,模拟REINFORCE算法在短走廊网格世界中的训练过程: ```python import numpy as np # 定义短走廊网格世界环境 class ShortCorridorGridworld: def __init__(self): self.state = 0 self.actions = [0, 1] # 动作空间 def step(self, action): if self.state == 1: if action == 0: next_state = 2 else: next_state = 0 else: if action == 0: next_state = max(0, self.state - 1) else: next_state = min(3, self.state + 1) reward = -1 done = next_state == 3 self.state = next_state return next_state, reward, done # REINFORCE算法实现 def reinforce(): env = ShortCorridorGridworld() num_episodes = 1000 gamma = 0.99 theta = np.random.rand(2) # 策略参数 for episode in range(num_episodes): states = [] actions = [] rewards = [] state = env.state # 生成一个轨迹 while True: probs = np.exp(theta) / np.sum(np.exp(theta)) action = np.random.choice(env.actions, p=probs) next_state, reward, done = env.step(action) states.append(state) actions.append(action) rewards.append(reward) state = next_state if done: break # 计算折扣回报 G = [] discounted_return = 0 for r in reversed(rewards): discounted_return = r + gamma * discounted_return G.insert(0, discounted_return) # 更新策略参数 for t in range(len(states)): state = states[t] action = actions[t] prob = np.exp(theta[action]) / np.sum(np.exp(theta)) grad_log_pi = np.zeros_like(theta) grad_log_pi[action] = 1 - prob grad_log_pi[1 - action] = -prob theta += 0.01 * gamma ** t * G[t] * grad_log_pi return theta # 运行REINFORCE算法 theta = reinforce() print("Final policy parameters:", theta) ```
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