2, memory: 20149, loss_prob: 0.3275, loss_cls: 0.5341, loss_lovasz: 0.5319, loss: 1.3935, grad_norm: 3.4085 2025-03-26 20:17:10,404 - mmdet - INFO - Saving checkpoint at 28130 iterations 100%|??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????| 6019/6019 [00:52<00:00, 114.81it/s] Finished. Traceback (most recent call last): File "tools/occ_train.py", line 266, in <module> main() File "tools/occ_train.py", line 255, in main custom_train_model( File "/root/Desktop/ViewFormer-Occ-main/projects/mmdet3d_plugin/bevformer/apis/train.py", line 27, in custom_train_model custom_train_detector( File "/root/Desktop/ViewFormer-Occ-main/projects/mmdet3d_plugin/bevformer/apis/mmdet_train.py", line 212, in custom_train_detector runner.run(data_loaders, cfg.workflow) File "/root/miniconda3/envs/myconda/lib/python3.8/site-packages/mmcv/runner/iter_based_runner.py", line 144, in run iter_runner(iter_loaders[i], **kwargs) File "/root/miniconda3/envs/myconda/lib/python3.8/site-packages/mmcv/runner/iter_based_runner.py", line 70, in train self.call_hook('after_train_iter') File "/root/miniconda3/envs/myconda/lib/python3.8/site-packages/mmcv/runner/base_runner.py", line 317, in call_hook getattr(hook, fn_name)(self) File "/root/miniconda3/envs/myconda/lib/python3.8/site-packages/mmcv/runner/hooks/evaluation.py", line 266, in after_train_iter self._do_evaluate(runner) File "/root/miniconda3/envs/myconda/lib/python3.8/site-packages/mmdet/core/evaluation/eval_hooks.py", line 63, in _do_evaluate key_score = self.evaluate(runner, results) File "/root/miniconda3/envs/myconda/lib/python3.8/site-packages/mmcv/runner/hooks/evaluation.py", line 367, in evaluate eval_res = self.dataloader.dataset.evaluate( File "/root/Desktop/ViewFormer-Occ-main/mmdetection3d/mmdet3d/datasets/nuscenes_dataset.py", line 503, in evaluate result_files, tmp_dir = self.format_results(results, jsonfile_prefix) TypeError: cannot unpack non-iterable NoneType object

architecture: PicoDet #pretrain_weights: LCNet_x1_5_pretrained.pdparams PicoDet: backbone: LCNet neck: LCPAN head: PicoHeadV2 LCNet: scale: 0.75 feature_maps: [3, 4, 5] LCPAN: out_channels: 96 use_depthwise: True num_features: 4 PicoHeadV2: conv_feat: name: PicoFeat feat_in: 96 feat_out: 96 num_convs: 2 num_fpn_stride: 4 norm_type: bn share_cls_reg: True use_se: True fpn_stride: [4,8, 16, 32] feat_in_chan: 96 prior_prob: 0.01 reg_max: 7 cell_offset: 0.5 grid_cell_scale: 5.0 static_assigner_epoch: 100 use_align_head: True static_assigner: name: ATSSAssigner topk: 4 force_gt_matching: False assigner: name: TaskAlignedAssigner topk: 13 alpha: 1.0 beta: 6.0 loss_class: name: VarifocalLoss use_sigmoid: False iou_weighted: True loss_weight: 1.0 loss_dfl: name: DistributionFocalLoss loss_weight: 0.5 loss_bbox: name: SIoULoss loss_weight: 2 nms: name: MultiClassNMS nms_top_k: 100 keep_top_k: 10 score_threshold: 0.025 nms_threshold: 0.6 在这份文件中该怎么修改anchor的大小和数量

在这份文件中，修改anchor的大小和数量需要在PicoHeadV2中进行修改。具体来说，可以修改下面几个参数： - fpn_stride：这是一个列表，表示每个特征图的步长。可以根据需要修改步长来调整anchor的大小和数量。 - ...

解释代码fully_train: pipe_step: type: TrainPipeStep models_folder: "{local_base_path}/output/nas/" trainer: ref: nas.trainer epochs: 600 lr_scheduler: type: CosineAnnealingLR params: T_max: 600.0 eta_min: 0 loss: type: MixAuxiliaryLoss params: loss_base: type: CrossEntropyLoss aux_weight: 0.4 seed: 100 drop_path_prob: 0.2 evaluator: type: Evaluator host_evaluator: type: HostEvaluator metric: type: accuracy

- loss: 损失函数的配置，这里使用的是混合辅助损失函数，其中 loss_base 是交叉熵损失函数，aux_weight 是辅助损失函数的权重。 - evaluator: 评估器的配置，这里使用的是主机评估器，评估指标为准确率。 ...

class FocalLoss(nn.Module): # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) def init(self, loss_fcn, gamma=1.5, alpha=0.25): super(FocalLoss, self).init() self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() self.gamma = gamma self.alpha = alpha self.reduction = loss_fcn.reduction self.loss_fcn.reduction = 'none' # required to apply FL to each element def forward(self, pred, true): loss = self.loss_fcn(pred, true) # p_t = torch.exp(-loss) # loss = self.alpha (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py pred_prob = torch.sigmoid(pred) # prob from logits p_t = true * pred_prob + (1 - true) * (1 - pred_prob) alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) modulating_factor = (1.0 - p_t) ** self.gamma loss = alpha_factor modulating_factor if self.reduction == 'mean': return loss.mean() elif self.reduction == 'sum': return loss.sum() else: # 'none' return loss

这个代码实现了一个 Focal Loss 损失函数，它是对二分类问题中的交叉熵损失函数的一种改进。它主要通过增加一个可调的超参数 $\gamma$，来调整难易样本的权重，从而解决类别不平衡问题。在代码中，它被实现为一个 ...

import aip import time import pandas as pd from tqdm import tqdm content_list = [] positive_prob_list = [] negative_prob_list = [] def sentiment_classify(txt): client_appid = '34468201' client_ak = 'U3tHxNzzm3z89wXRpjSswVv3' client_sk = 'qlbLtgMcUg75Mmfd4ei1puzLXPpNgDfC' my_nlp = aip.nlp.AipNlp(client_appid, client_ak, client_sk) results = my_nlp.sentimentClassify(txt) positive_prob = results['items'][0]['positive_prob'] negative_prob = results['items'][0]['negative_prob'] content_list.append(txt) positive_prob_list.append(positive_prob) negative_prob_list.append(negative_prob) return positive_prob df = pd.read_excel('/Users/26921/Desktop/shuju/20000-30000.xlsx') txt_content = df["content"] positive_times = 0 negative_times = 0 for txt in tqdm(txt_content): if sentiment_classify(txt) > 0.5: positive_times += 1 else: negative_times += 1 df_res = pd.DataFrame({"content": content_list, "positive_prob":positive_prob_list, "negative_prob":negative_prob_list}) df_res.to_excel("/Users/26921/Desktop/shuju/20000-30000(2).xlsx") print("分析完成，正向{}条，负向{}条!".format(positive_times, negative_times))优化这段代码，使其出现'items'错误时，跳过错误继续执行程序

positive_prob = results['items'][0]['positive_prob'] negative_prob = results['items'][0]['negative_prob'] content_list.append(txt) positive_prob_list.append(positive_prob) negative_prob_list....

acc, _, summary_str, p_loss = self.sess.run( [self.pre_acc, self.g_pre_optim, self.g_pretrain_sum, self.pretrain_loss], feed_dict={ self.z: batch_z, self.x: incomplete, self.m: m, self.deltaPre: delta, self.lastvalues: last_values, self.complete: complete, self.keep_prob: 0.7})

其中，self.pre_acc 是预训练模型的准确率，self.g_pre_optim 是预训练模型的优化器，self.g_pretrain_sum 是预训练模型的汇总信息，self.pretrain_loss 是预训练模型的损失函数。在 self.sess.run 函数中...

请使用Python 代码来查找文件名为 staticprob.txt中指定字符串'staticprob = '并打印字符串后面8个字符串，以及打印字符串前面最接近字符串的时间,打印信息如下： time = 00:04:29, prob = 0.967633 time = 00:11:14, prob = 0.937645 其中文件名为 staticprob.txt 的文本文件，其中包含以下内容： [00:04:29]A7_TRACE: [ ALG_DBG ] [radar_alg_stru.c:293] ALG_TAG begin(43952[00:04:29]) [00:04:29]A7_TRACE: [ ALG_DBG ] [radar_alg_path.c:9701] sclu[0][x y z p]=[0.62 3.08 0.90 13.82] A7_TRACE: [ ALG_DBG ] [radar_alg_path.c:9701] sclu[1][x y z p]=[3.27 2.57 0.76 12.99] A7_TRACE: [ ALG_DBG ] [radar_alg_path.c:4984] alltrc[0] [report][x y z] = [1][0.79 2.70 1.14 0 41] [0 0 0.000000] A7_TRACE: [ ALG_DBG ] [radar_alg_static.c:2477] max noise=39921.98, indx indy= 1 6 A7_TRACE: [ ALG_DBG ] [radar_alg_path.c:5415] alltrcblk[0] [rep][x y z pre sc on] = [0][0.17 2.91 0.91 0 1 0] A7_TRACE: [ ALG_DBG ] [radar_alg_static.c:1423] Static condition check state1, 0, 0, 0, 5 A7_TRACE: [ ALG_DBG ] [radar_alg_night.c:839] TRC: 0 IS STICA7_TRACE: [ ALG_DBG ] [radar_alg_night.c:261] Frame 21792 bdPos 1.00 A7_TRACE: [ ALG_DBG ] [radar_alg_night.c:577] [night]trc 1 mVzIdx 12 mVz 0.11 maxIdx 0 minIdx 17 maxZ 1.31 minZ 1.25 A7_TRACE: [ ALG_DBG ] [radar_alg_night.c:651] [night]trc 1 bdside 3 bdpos 1 bspos 0 thrVzSitup 0.05 thrZDiffSitup 0.20 A7_TRACE: [ ALG_DBG ] [radar_alg_alm.c:409] Alm Cond: start[cntN][cntA][virW] = [1 0 0 0 0 50] A7_TRACE: [ ALG_DBG ] [radar_alg_alm.c:1041] obj Alm, 0, 0, 0 A7_TRACE: [ ALG_DBG ] [radar_alg_stru.c:409] trc handle[err]=[0][0] [seq,bb,cnt]=[43952 43845 43845] A7_TRACE: [ ALG_DBG ] [radar_alg_path.c:11658] obj[0][zM vzM pre st sp gtup act] = [1.15 0.00 0 0 0 1 5] A7_TRACE: [ ALG_DBG ] [radar_alg_path.c:11699] obj[0] fncId[alg rpt app][x y z] = [0 0 1][0.79 2.70 1.14] score=4 A7_TRACE: [ ALG_DBG ] [radar_alg_stru.c:364] ALG_TAG end(43952) A7_TRACE: [AI_ALG_LOG]: PhaAIPrediction finished. A7_TRACE: [AI_ALG_LOG]: staticprob = 0.967633 [00:11:14]A7_TRACE: [ ALG_DBG ] [radar_alg_path.c:9701] sclu[0][x y z p]=[0.66 2.97 0.88 12.46] A7_TRACE: [ ALG_DBG ] [radar_alg_path.c:5415] alltrcblk[0] [rep][x y z pre sc on] = [0][0.66 3.01 1.08 0 1 0] A7_TRACE: [ ALG_DBG ] [radar_alg_static.c:1423] Static condition check state1, 0, 0, 8, 5 A7_TRACE: [ ALG_DBG ] [radar_alg_path.c:11658] obj[0][zM vzM pre st sp gtup act] = [1.06 0.00 0 0 0 1 5] A7_TRACE: [ ALG_DBG [00:11:14]] [radar_alg_stru.c:293] ALG_TAG begin(48003) [00:11:14]A7_TRACE: [AI_ALG_LOG]: PhaAIPrediction finished. A7_TRACE: [AI_ALG_LOG]: staticprob = 0.937645

可以使用以下代码实现： python import re filename = "staticprob.txt" search_str = "staticprob = " with open(filename, "r") as ...time = 00:04:29, prob = 0.967633 time = 00:11:14, prob = 0.937645

请使用Python 代码来查找文件名为 staticprob.txt中指定字符串'staticprob = '并打印字符串后面8个字符串，以及打印字符串前面最接近字符串的时间,打印信息如下： time = 00:04:29, prob = 0.967633 time = 00:11:14, prob = 0.937645 其中文件名为 staticprob.txt 的文本文件，其中包含以下内容： [00:04:29]A7_TRACE: [ ALG_DBG ] [radar_alg_stru.c:293] ALG_TAG begin(43952[00:04:29]) [00:04:29]A7_TRACE: [ ALG_DBG ] [radar_alg_path.c:9701] sclu[0][x y z p]=[0.62 3.08 0.90 13.82] A7_TRACE: [ ALG_DBG ] [radar_alg_path.c:9701] sclu[1][x y z p]=[3.27 2.57 0.76 12.99] A7_TRACE: [ ALG_DBG ] [radar_alg_path.c:4984] alltrc[0] [report][x y z] = [1][0.79 2.70 1.14 0 41] [0 0 0.000000] A7_TRACE: [ ALG_DBG ] [radar_alg_static.c:2477] max noise=39921.98, indx indy= 1 6 A7_TRACE: [ ALG_DBG ] [radar_alg_path.c:5415] alltrcblk[0] [rep][x y z pre sc on] = [0][0.17 2.91 0.91 0 1 0] A7_TRACE: [ ALG_DBG ] [radar_alg_static.c:1423] Static condition check state1, 0, 0, 0, 5 A7_TRACE: [ ALG_DBG ] [radar_alg_night.c:839] TRC: 0 IS STICA7_TRACE: [ ALG_DBG ] [radar_alg_night.c:261] Frame 21792 bdPos 1.00 A7_TRACE: [ ALG_DBG ] [radar_alg_night.c:577] [night]trc 1 mVzIdx 12 mVz 0.11 maxIdx 0 minIdx 17 maxZ 1.31 minZ 1.25 A7_TRACE: [ ALG_DBG ] [radar_alg_night.c:651] [night]trc 1 bdside 3 bdpos 1 bspos 0 thrVzSitup 0.05 thrZDiffSitup 0.20 A7_TRACE: [ ALG_DBG ] [radar_alg_alm.c:409] Alm Cond: start[cntN][cntA][virW] = [1 0 0 0 0 50] A7_TRACE: [ ALG_DBG ] [radar_alg_alm.c:1041] obj Alm, 0, 0, 0 A7_TRACE: [ ALG_DBG ] [radar_alg_stru.c:409] trc handle[err]=[0][0] [seq,bb,cnt]=[43952 43845 43845] A7_TRACE: [ ALG_DBG ] [radar_alg_path.c:11658] obj[0][zM vzM pre st sp gtup act] = [1.15 0.00 0 0 0 1 5] A7_TRACE: [ ALG_DBG ] [radar_alg_path.c:11699] obj[0] fncId[alg rpt app][x y z] = [0 0 1][0.79 2.70 1.14] score=4 A7_TRACE: [ ALG_DBG ] [radar_alg_stru.c:364] ALG_TAG end(43952) A7_TRACE: [AI_ALG_LOG]: PhaAIPrediction finished. A7_TRACE: [AI_ALG_LOG]: staticprob = 0.967633 [00:11:14]A7_TRACE: [ ALG_DBG ] [radar_alg_path.c:9701] sclu[0][x y z p]=[0.66 2.97 0.88 12.46] A7_TRACE: [ ALG_DBG ] [radar_alg_path.c:5415] alltrcblk[0] [rep][x y z pre sc on] = [0][0.66 3.01 1.08 0 1 0] A7_TRACE: [ ALG_DBG ] [radar_alg_static.c:1423] Static condition check state1, 0, 0, 8, 5 A7_TRACE: [ ALG_DBG ] [radar_alg_path.c:11658] obj[0][zM vzM pre st sp gtup act] = [1.06 0.00 0 0 0 1 5] A7_TRACE: [ ALG_DBG [00:11:14]] [radar_alg_stru.c:293] ALG_TAG begin(48003) [00:11:14]A7_TRACE: [AI_ALG_LOG]: PhaAIPrediction finished. A7_TRACE: [AI_ALG_LOG]: staticprob = 0.937645

pattern = r'\[(\d{2}:\d{2}:\d{2})\].*staticprob = (\d{1}\.\d{6})' matches = re.findall(pattern, content) for i in range(len(matches)): time = matches[i][0] prob = float(matches[i][1]) print(f"time...

def init_params(self, data): self.data = data self.n_dim = data.shape[1] self.n_sample = data.shape[0] ## 1.采用了Kmeans初始化 km = KMeans(self.n_class) km.fit(self.data) self.mus = [] for ind in range(self.n_class): self.mus.append(np.mean(self.data[km.labels_ == ind], axis=0)) self.vars = [] for ind in range(self.n_class): self.vars.append(np.cov(self.data[km.labels_ == ind], rowvar=False)) self.class_prob = np.random.rand(self.n_class) self.class_prob = self.class_prob / np.sum(self.class_prob)这段代码作用

这段代码实现了一个高斯混合模型（GMM）的参数初始化过程。GMM是一种用于聚类和密度估计的模型，它将数据看作是由多个高斯分布组成的混合体，每个高斯分布对应一个聚类中心。参数初始化过程中，该代码采用了Kmeans...

_BASE_: [ '../datasets/coco_detection.yml', '../runtime.yml', '_base_/picodet_v2.yml', '_base_/optimizer_300e.yml', '_base_/picodet_320_reader.yml', ] weights: output/picodet_m_320_coco/best_model find_unused_parameters: True use_ema: true epoch: 800 snapshot_epoch: 20 TrainReader: batch_size: 96 LearningRate: base_lr: 0.3 schedulers: - name: CosineDecay max_epochs: 400 - name: LinearWarmup start_factor: 0.1 steps: 400 训练数据集，共有 2416张，包含10种类别，给我推荐个参数

bias_init = -np.log((1 - prior_prob) / prior_prob) # prior_prob建议0.02-0.05 四、实践验证方法 1. 分阶段调参验证： mermaid graph TD A[16 batch试运行] --> B[验证loss收敛] B -->|正常| C[32 ...

def call(self, state): #熵log_prob x = self.fc1(state) x = self.fc2(x) mu = self.mu(x) log_std = self.log_std(x) log_std = tf.clip_by_value(log_std, -20, 2) std = tf.math.exp(log_std) dist = tfp.distributions.Normal(mu, std) action = dist.sample() log_prob = dist.log_prob(action) log_prob -= tf.reduce_sum(2 * (np.log(2) - action - tf.nn.softplus(-2 * action)), axis=1, keepdims=True) return action, log_prob

这段代码是一个Actor网络的前向传播函数，它接收一个状态state作为输入，返回一个动作action和相应的对数概率log_prob。具体来说，这个函数首先通过两个全连接层（fc1和fc2）将状态state转换为一个mu和log_std向量...

这段代码构建了计算图吗？log_prob = torch.log(self.policy_net(state).gather(1, action)) G = reward + self.gamma * G loss = -log_prob * G loss.backward()

然后，损失函数是负的log_prob乘以G，最后执行loss.backward()反向传播。用户可能想知道，为什么需要这些操作来构建计算图，或者是否隐式地创建了计算图。根据PyTorch的特性，所有的张量操作都会自动记录计算图，...

with tf.Session() as sess: sess.run(init) saver = tf.train.Saver() train_writer = tf.summary.FileWriter(log_dir+'/train',sess.graph) test_writer = tf.summary.FileWriter(log_dir+'/test') step = 1 coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess, coord) print('doing1') # Keep training until reach max iterations try: while step * batch_size <= training_iters: # print('doing2') batch_xs, batch_ys = sess.run([hrrp_batch, label_batch]) # print(batch_xs.shape) # print(batch_ys.shape) # 获取批数据 sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout}) if step % display_step == 0: summary= sess.run(merged, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.}) test_writer.add_summary(summary, step) # 计算精度 acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.}) # 计算损失值 loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.}) 所选择的特征是

在这段代码中，没有涉及到特征的选择。这段代码是用于在 TensorFlow 中训练一个神经网络模型的。在这段代码中，使用了一个数据队列来读取数据，其中 hrrp_batch 和 label_batch 是从数据队列中读取的批量数据。...

def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the argument. """ if drop_prob == 0. or not training: return x keep_prob = 1 - drop_prob shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets random_tensor = x.new_empty(shape).bernoulli_(keep_prob) if keep_prob > 0.0 and scale_by_keep: random_tensor.div_(keep_prob) return x * random_tensor

具体实现中，首先判断drop_prob是否为0或者是否处于非训练模式，如果是，则直接返回输入张量x，不进行路径丢弃操作。然后，计算保留概率keep_prob，即1减去丢弃概率drop_prob。接着，根据输入张量x的...

import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F import numpy as np class ActorCritic(nn.Module): def init(self, state_dim, action_dim): super(ActorCritic, self).init() self.fc1 = nn.Linear(state_dim, 128) self.fc2 = nn.Linear(128, 128) self.actor = nn.Linear(128, action_dim) self.critic = nn.Linear(128, 1) def forward(self, x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) action_probs = F.softmax(self.actor(x), dim=-1) state_value = self.critic(x) return action_probs, state_value class A2CScheduler: def init(self, state_dim, action_dim, lr=0.001, gamma=0.99): self.model = ActorCritic(state_dim, action_dim) self.optimizer = optim.Adam(self.model.parameters(), lr=lr) self.gamma = gamma def select_action(self, state): state = torch.FloatTensor(state).unsqueeze(0) action_probs, _ = self.model(state) action = torch.multinomial(action_probs, 1).item() return action, action_probs[:, action] def update(self, trajectory): rewards, log_probs, state_values = [], [], [] for (state, action, reward, log_prob, state_value) in trajectory: rewards.append(reward) log_probs.append(log_prob) state_values.append(state_value) returns = [] R = 0 for r in reversed(rewards): R = r + self.gamma * R returns.insert(0, R) returns = torch.tensor(returns) log_probs = torch.stack(log_probs) state_values = torch.stack(state_values).squeeze() advantage = returns - state_values actor_loss = -log_probs * advantage.detach() critic_loss = F.mse_loss(state_values, returns) loss = actor_loss.mean() + critic_loss self.optimizer.zero_grad() loss.backward() self.optimizer.step() # 结合 mp-quic-go 使用 # 1. 获取状态信息 (如带宽、RTT、丢包等) # 2. 选择路径 (基于 select_action 方法) # 3. 收集数据并训练模型 (基于 update 方法)请详细解释每一行代码的含义和意义

2. **动作空间**：选择不同传输路径作为离散动作$ a_t \in \{0,1,...,action\_dim-1\} $ 3. **奖励设计**：可包含吞吐量、延迟、丢包率的综合指标 --- ### 四、代码改进建议 1. **Log概率计算**：应使用torch.log...

a_, _, a_dist_ = self._build_a(self.S_, reuse=True, custom_getter=ema_getter) # replaced target parameters lya_a_, _, lya_a_dist_ = self._build_a(self.S_, reuse=True) # self.cons_a_input_ = tf.placeholder(tf.float32, [None, a_dim, 'cons_a_input_']) # self.log_pis = log_pis = self.a_dist.log_prob(self.a) self.log_pis = log_pis = self.a_dist.log_prob(self.a) self.prob = tf.reduce_mean(self.a_dist.prob(self.a))代码什么意思

同时，该代码中还计算了选择动作 a 的概率 log_pis 和平均概率 prob。这些结果可以用于计算损失函数，并通过反向传播来更新 actor 神经网络模型的参数，以提高其决策的准确性。同时，该代码中还使用了经验平均...

WARNING:tensorflow:From /root/miniconda3/envs/myconda/lib/python3.5/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Colocations handled automatically by placer. WARNING:tensorflow:From /root/miniconda3/envs/myconda/lib/python3.5/site-packages/tensorflow/python/keras/layers/core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version. Instructions for updating: Please use rate instead of keep_prob. Rate should be set to rate = 1 - keep_prob.

第二个warning是因为在TensorFlow 2.0中，使用tf.nn.dropout()函数代替原来的keras.layers.Dropout()函数，而keep_prob参数已经被rate参数代替，因此会有这个warning。如果想要避免这些warning信息，可以考虑更新...

def get_probabilities(self, data, sess=None): """Returns the probabilities of belonging to the class.""" size = data.shape[0] # M = [512, C] corresponds to the fully conncetec output layer, C - number of classes samples_probabilities = np.empty([size, self.M[-1]]) sess = self._get_session(sess) for begin in range(0, size, self.batch_size): end = begin + self.batch_size end = min([end, size]) batch_data = np.zeros((self.batch_size, data.shape[1])) tmp_data = data[begin:end, :] if type(tmp_data) is not np.ndarray: tmp_data = tmp_data.toarray() # convert sparse matrices batch_data[:end - begin] = tmp_data feed_dict = {self.ph_data: batch_data, self.ph_dropout: 1} batch_prob = sess.run([self.op_probabilities], feed_dict) samples_probabilities[begin:end, :] = np.array(batch_prob[0])[:end - begin, :] return samples_probabilities解释代码

samples_probabilities[begin:end, :] = np.array(batch_prob[0])[:end - begin, :] - 利用 TensorFlow 的 sess.run() 方法运行模型，得到当前批次内每个样本的类别概率结果。 - 结果存放在 samples_...

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acc, _, summary_str, p_loss = self.sess.run( [self.pre_acc, self.g_pre_optim, self.g_pretrain_sum, self.pretrain_loss], feed_dict={ self.z: batch_z, self.x: incomplete, self.m: m, self.deltaPre: delta, self.lastvalues: last_values, self.complete: complete, self.keep_prob: 0.7})

这段代码构建了计算图吗？log_prob = torch.log(self.policy_net(state).gather(1, action)) G = reward + self.gamma * G loss = -log_prob * G loss.backward()

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