深度学习训练中的eps是什么？

在深度学习训练中，eps通常是指"epsilon"，它是一个非常小的数值，通常取值为10的负八次方或更小，用于防止数值计算时出现除0错误或非常接近0的数值。eps在很多深度学习算法中都会用到，例如在Batch Normalization和Adam优化器中，都会使用eps来保证计算的数值稳定性。在实际应用中，eps的具体取值可能会根据具体情况而有所不同，但通常都会取一个非常小的数值。

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深度学习优化器Ranger代码

以下是使用 PyTorch 实现的深度学习优化器 Ranger 的代码：

import math
from torch.optim.optimizer import Optimizer
import torch.optim as optim


class Ranger(Optimizer):
    def __init__(self, params, lr=1e-3, alpha=0.5, k=6, N_sma_threshhold=5, betas=(0.95, 0.999), eps=1e-5, weight_decay=0):
        defaults = dict(lr=lr, alpha=alpha, k=k, N_sma_threshhold=N_sma_threshhold, betas=betas, eps=eps, weight_decay=weight_decay)
        super().__init__(params, defaults)

    def __setstate__(self, state):
        super().__setstate__(state)

    def step(self, closure=None):
        loss = None
        if closure is not None:
            loss = closure()

        # Gradient centralization
        for group in self.param_groups:
            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data
                if grad.is_sparse:
                    raise RuntimeError('Ranger optimizer does not support sparse gradients')
                grad_data = grad.data
                if len(grad_data.shape) > 1:
                    mean = torch.mean(grad_data, dim=tuple(range(1, len(grad_data.shape))), keepdim=True)
                    var = torch.var(grad_data, dim=tuple(range(1, len(grad_data.shape))), keepdim=True)
                    grad_data = (grad_data - mean) / (torch.sqrt(var) + group['eps'])
                p.grad.data = grad_data

        # Perform optimization step
        beta1, beta2 = group['betas']
        N_sma_threshhold = group['N_sma_threshhold']
        grad_ema_beta = 1 - beta1
        sqr_ema_beta = 1 - beta2
        step_size = group['lr']
        eps = group['eps']
        k = group['k']
        alpha = group['alpha']
        weight_decay = group['weight_decay']

        for group in self.param_groups:
            for p in group['params']:
                if p.grad is None:
                    continue

                grad = p.grad.data
                if grad.is_sparse:
                    raise RuntimeError('Ranger optimizer does not support sparse gradients')

                state = self.state[p]

                # State initialization
                if len(state) == 0:
                    state['step'] = 0
                    state['exp_avg'] = torch.zeros_like(p.data)
                    state['exp_avg_sq'] = torch.zeros_like(p.data)
                    state['SMA'] = 0

                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                SMA = state['SMA']
                state['step'] += 1

                # Gradient centralization
                grad_data = grad.data
                if len(grad_data.shape) > 1:
                    mean = torch.mean(grad_data, dim=tuple(range(1, len(grad_data.shape))), keepdim=True)
                    var = torch.var(grad_data, dim=tuple(range(1, len(grad_data.shape))), keepdim=True)
                    grad_data = (grad_data - mean) / (torch.sqrt(var) + eps)
                    grad = grad_data

                bias_correction1 = 1 - beta1 ** state['step']
                bias_correction2 = 1 - beta2 ** state['step']
                step_size = step_size * math.sqrt(bias_correction2) / bias_correction1

                # Compute exponential moving average of gradient and squared gradient
                exp_avg = beta1 * exp_avg + grad_ema_beta * grad
                exp_avg_sq = beta2 * exp_avg_sq + sqr_ema_beta * grad * grad

                # Compute SMA
                SMA_prev = SMA
                SMA = alpha * SMA + (1 - alpha) * exp_avg_sq.mean()

                # Update parameters
                if state['step'] <= k:
                    # Warmup
                    p.data.add_(-step_size * exp_avg / (torch.sqrt(exp_avg_sq) + eps))
                else:
                    if SMA > SMA_prev or state['step'] <= N_sma_threshhold:
                        # If SMA is increasing, skip lookahead and perform RAdam step
                        denom = torch.sqrt(exp_avg_sq) + eps
                        p.data.add_(-step_size * exp_avg / denom)
                    else:
                        # Lookahead
                        slow_state = state['slow_buffer']
                        if len(slow_state) == 0:
                            slow_state['step'] = 0
                            slow_state['exp_avg'] = torch.zeros_like(p.data)
                            slow_state['exp_avg_sq'] = torch.zeros_like(p.data)
                            slow_state['SMA'] = 0
                            for key in state.keys():
                                if key != 'slow_buffer':
                                    slow_state[key] = state[key].clone()

                        slow_exp_avg, slow_exp_avg_sq = slow_state['exp_avg'], slow_state['exp_avg_sq']
                        slow_SMA = slow_state['SMA']
                        slow_state['step'] += 1

                        # Gradient centralization
                        grad_data = grad.data
                        if len(grad_data.shape) > 1:
                            mean = torch.mean(grad_data, dim=tuple(range(1, len(grad_data.shape))), keepdim=True)
                            var = torch.var(grad_data, dim=tuple(range(1, len(grad_data.shape))), keepdim=True)
                            grad_data = (grad_data - mean) / (torch.sqrt(var) + eps)
                            grad = grad_data

                        # Compute exponential moving average of gradient and squared gradient
                        slow_exp_avg = beta1 * slow_exp_avg + grad_ema_beta * grad
                        slow_exp_avg_sq = beta2 * slow_exp_avg_sq + sqr_ema_beta * grad * grad

                        # Compute SMA
                        slow_SMA_prev = slow_SMA
                        slow_SMA = alpha * slow_SMA + (1 - alpha) * slow_exp_avg_sq.mean()

                        # Update parameters
                        if slow_state['step'] <= k:
                            # Warmup
                            pass
                        else:
                            if slow_SMA > slow_SMA_prev or slow_state['step'] <= N_sma_threshhold:
                                # If SMA is increasing, skip lookahead and perform RAdam step
                                denom = torch.sqrt(slow_exp_avg_sq) + eps
                                p.data.add_(-step_size * slow_exp_avg / denom)
                            else:
                                # Lookahead
                                p.data.add_(-step_size * (exp_avg + slow_exp_avg) / (2 * torch.sqrt((beta2 * exp_avg_sq + sqr_ema_beta * slow_exp_avg_sq) / (1 - bias_correction2 ** state['step'])) + eps))

                # Weight decay
                if weight_decay != 0:
                    p.data.add_(-step_size * weight_decay * p.data)

        return loss

以上的代码实现了 Ranger 优化器，其中包括了 RAdam 和 LookAhead 的结合，以及动态学习率和权重衰减等技巧。可以将其应用于 PyTorch 中的深度学习模型训练中。

深度强化学习代码gpt

GPT (Generative Pre-trained Transformer) 是一种基于 Transformer 模型的语言生成模型。如果你想要使用深度强化学习来训练一个 GPT 模型，可以考虑使用 PPO 算法 (Proximal Policy Optimization)。

以下是一个使用 PyTorch 和 OpenAI 的 GPT-2 模型实现 PPO 算法的示例代码：

import torch
import numpy as np
from transformers import GPT2Tokenizer, GPT2LMHeadModel
from torch.distributions.categorical import Categorical

class GPT2Agent:
    def __init__(self, model_name='gpt2'):
        self.tokenizer = GPT2Tokenizer.from_pretrained(model_name)
        self.model = GPT2LMHeadModel.from_pretrained(model_name)
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.model.to(self.device)
        self.model.eval()

    def act(self, state):
        input_ids = self.tokenizer.encode(state, return_tensors='pt').to(self.device)
        with torch.no_grad():
            logits = self.model(input_ids)[0][:, -1, :]
            probs = torch.softmax(logits, dim=-1)
        dist = Categorical(probs)
        action = dist.sample()
        log_prob = dist.log_prob(action)
        return action.item(), log_prob

    def learn(self, states, actions, log_probs, rewards, gamma=0.99, eps_clip=0.2, batch_size=32, epochs=10):
        states = np.asarray(states)
        actions = np.asarray(actions)
        log_probs = np.asarray(log_probs)
        rewards = np.asarray(rewards)

        for epoch in range(epochs):
            for i in range(0, len(states), batch_size):
                batch_states = states[i:i + batch_size]
                batch_actions = actions[i:i + batch_size]
                batch_log_probs = log_probs[i:i + batch_size]
                batch_rewards = rewards[i:i + batch_size]

                returns = self._compute_returns(batch_rewards, gamma)
                advantages = self._compute_advantages(batch_rewards, returns, batch_log_probs)

                logits = self.model(torch.LongTensor(batch_states).to(self.device))[0]
                dist = Categorical(logits=logits)
                new_log_probs = dist.log_prob(torch.LongTensor(batch_actions).to(self.device))

                ratio = torch.exp(new_log_probs - torch.FloatTensor(batch_log_probs).to(self.device))
                surr1 = ratio * advantages
                surr2 = torch.clamp(ratio, 1 - eps_clip, 1 + eps_clip) * advantages
                actor_loss = -torch.min(surr1, surr2).mean()

                critic_loss = torch.mean((returns - self.model(torch.LongTensor(batch_states).to(self.device))[0].squeeze(1)) ** 2)

                loss = actor_loss + 0.5 * critic_loss

                self.optimizer.zero_grad()
                loss.backward()
                self.optimizer.step()

    def _compute_returns(self, rewards, gamma):
        returns = np.zeros_like(rewards)
        R = 0
        for t in reversed(range(len(rewards))):
            R = rewards[t] + gamma * R
            returns[t] = R
        return returns

    def _compute_advantages(self, rewards, returns, log_probs):
        advantages = returns - rewards
        advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
        return advantages

在上述代码中，我们首先定义了一个 `GPT2Agent` 类，这个类包含了一个 `act` 方法和一个 `learn` 方法，用于执行模型的预测和强化学习的训练。

在 `act` 方法中，我们将当前状态 `state` 输入到 GPT-2 模型中，得到模型对所有可能的下一个字符的概率分布。我们使用 `torch.distributions.categorical.Categorical` 类来从这个概率分布中采样一个动作，并计算其对数概率。

在 `learn` 方法中，我们首先将输入的数据转换为 NumPy 数组，并使用 `self._compute_returns` 和 `self._compute_advantages` 方法计算出每个状态动作对的收益和优势。然后我们使用 PPO 算法来更新模型的参数。

注意，在这个示例代码中，我们使用了 OpenAI 的 GPT-2 模型，并使用了 PyTorch 和 transformers 库来构建模型和进行预处理。如果你需要使用其他的 GPT 模型，你需要相应地修改代码。