sampled_Javax.sound.sampled.LineUnavailableException：_如何使用javax.sound.sampled.LineListener？ - 腾讯云开发者社区

sampled softmax原论文：On Using Very Large Target Vocabulary for Neural Machine Translation 以及tensorflow...（关于其它的解决方法，作者也有提，感兴趣的可以看原文，本篇博客只关注Sampled Softmax） 2...., labels=labels, inputs=inputs, num_sampled=num_sampled, num_classes=num_classes..., num_true=num_true, sampled_values=sampled_values, subtract_log_q=True, remove_accidental_hits...( true_classes=labels,# 真实的label num_true=num_true, num_sampled=num_sampled

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问 ChatGPT 关于GPT的事情：压缩篇

) print("采样后 wk 的形状:", wk_sampled.shape) print("采样后 wv 的形状:", wv_sampled.shape) print("采样后 wot 的形状:",...(-1, -2) @ head_sample wk_sampled = wk_sampled.swapaxes(-1, -2) @ head_sample wv_sampled = wv_sampled.swapaxes...(-1, -2) @ head_sample wot_sampled = wot_sampled.swapaxes(-1, -2) @ head_sample # 交换后两维，合并 wq_sampled...= wq_sampled.swapaxes(-1, -2).reshape((HiddenSize, -1)) wk_sampled = wk_sampled.swapaxes(-1, -2).reshape...((HiddenSize, -1)) wv_sampled = wv_sampled.swapaxes(-1, -2).reshape((HiddenSize, -1)) wot_sampled = wot_sampled.swapaxes

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tensorflow 0.10 word2vec 源码解析

# sampled_losses is batch_size x {true_loss, sampled_losses......# sampled_expected_count shape = [num_sampled] tensor if sampled_values is None: sampled_values...=unpacking-non-sequence sampled, true_expected_count, sampled_expected_count = sampled_values...# sampled_w shape is [num_sampled, dim] # sampled_b is a [num_sampled] float tensor...[batch_size, dim] # sampled_w has shape [num_sampled, dim] # sampled_b has shape [num_sampled

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Tensorflow 的 NCE-Loss 的实现和 word2vec

❞ 先看看tensorflow的nce-loss的API： def nce_loss(weights, biases, inputs, labels, num_sampled, num_classes,...: 采样出多少个负样本 num_classes = N sampled_values: 采样出的负样本，如果是None，就会用不同的sampler去采样。...这个函数把最后的问题转化为了num_sampled+num_real个两类分类问题，然后每个分类问题用了交叉熵的损伤函数，也就是logistic regression常用的损失函数。..., vocabulary_size)) 可以看到，它这里并没有传sampled_values，那么它的负样本是怎么得到的呢？...继续看nce_loss的实现，可以看到里面处理sampled_values=None的代码如下： if sampled_values is None: sampled_values

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matplotlib基础绘图命令之pie

设置饼图上的标记信息，有两种设置方式，第一种，设置字符串格式化，用法如下 plt.pie(x=[1, 2, 3, 4], labels=['sampleA', 'sampleB', 'sampleC', 'sampleD...第二种，用函数来进行设置，用法如下 data=[1,2,3,4] plt.pie(x=data, labels=['sampleA', 'sampleB', 'sampleC', 'sampleD'],...explode用于突出线似乎饼图中的子集，用间隔突出的方式进行显示，用法如下 plt.pie(x=[1, 2, 3, 4], labels=['sampleA', 'sampleB', 'sampleC', 'sampleD...startangle startangle指定起始角度，用法如下 plt.pie(x=[1, 2, 3, 4], labels=['sampleA', 'sampleB', 'sampleC', 'sampleD...上的坐标，其值为4个元素的元组，分别表示x,y,width,height, 代码如下 data=[1,2,3,4] labels=['sampleA', 'sampleB', 'sampleC', 'sampleD

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java实现MP3

package org.mp3; import java.io.File; import javax.sound.sampled.AudioFormat; import javax.sound.sampled.AudioInputStream...; import javax.sound.sampled.AudioSystem; import javax.sound.sampled.DataLine; import javax.sound.sampled.SourceDataLine

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如何避免LLM的“幻觉”(Hallucination)

下面的函数将最初生成的句子output和一个包含3个示例输出的列表sampled_passages作为输入。这里使用了all-MiniLM-L6-v2轻量模型。...= sampled_passages, # list of sampled passages ) df = pd.DataFrame({ 'Sentence Number...= sampled_passages, # list of sampled passages ) df = pd.DataFrame({ 'Sentence Number...\n\n \ Context: {sampled_passages[0]} \n\n \ {sampled_passages[1]}...\n\n \ {sampled_passages[2]}."""

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Zipkin客户端链路追踪源码解析

span); } Span nextSpan(TraceContextOrSamplingFlags extracted, Req request) { if (extracted.sampled...= null) { Boolean sampled = traceIdContext.sampled(); if (sampled == null) sampled = sampler.isSampled...(traceIdContext.traceId()); return toSpan(TraceContext.newBuilder() .sampled(sampled)...= String.valueOf(currentSpan.sampled()); MDC.put("spanExportable", sampled);...MDC.put(LEGACY_EXPORTABLE_NAME, sampled); log("Starting scope for span: {}", currentSpan)

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Jaeger知识点补充

http-nio-8080-exec-1] INFO c.b.j.p.c.HelloController [traceId=49476da841cd354b spanId=b595271a496cb0cb sampled...http-nio-8080-exec-1] INFO c.b.j.p.c.HelloController [traceId=49476da841cd354b spanId=b595271a496cb0cb sampled...http-nio-8080-exec-1] INFO i.j.i.r.LoggingReporter [traceId=49476da841cd354b spanId=b595271a496cb0cb sampled...http-nio-8080-exec-1] INFO i.j.i.r.LoggingReporter [traceId=49476da841cd354b spanId=b595271a496cb0cb sampled...http-nio-8080-exec-1] INFO i.j.i.r.LoggingReporter [traceId=49476da841cd354b spanId=b595271a496cb0cb sampled

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Tungsten Fabric入门宝典丨主流监控系统工具的集成

is_client_session = 1 is_si = 0 vrouter_ip = 172.31.6.218 local_ip = 10.47.255.250 service_port = 443 protocol = 6 sampled_forward_bytes... = 132 sampled_forward_pkts = 2 sampled_reverse_bytes = 1140 sampled_reverse_pkts = 2 ip = 10.96.0.1 ...port = 35420 forward_flow_info= [ sampled_bytes = 132 sampled_pkts = 2 flow_uuid = 130bbb8d-9be7-46bc...nw_ace_uuid = 00000000-0000-0000-0000-000000000004 underlay_source_port = 55670 ] reverse_flow_info= [ sampled_bytes... = 1140 sampled_pkts = 2 flow_uuid = e09e2579-7764-472b-8a35-b300c5ec34e7 tcp_flags = 120 setup_time

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tensorflow学习笔记（十一）：seq2seq Model相关接口介绍

调用外部的函数介绍 tf.sampled_softmax_loss() tf.sampled_softmax_loss()中调用了_compute_sampled_logits() 关于__compute_sampled_logits...() #此函数和nce_loss是差不多的, 取样求loss def sampled_softmax_loss(weights, #[num_classes, dim]...batch_size, dim] labels, #[batch_size, num_true] num_sampled...num_classes, num_true=1, sampled_values...), outputs: T*batch_size*output_size. states:batch_size*state seq2seqModel 创建映射参数 proj_w, proj_b 声明：sampled_loss

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Impala HDFS_SCAN_NODE之AverageHdfsReadThreadConcurrency

和num_sampled，分别表示采集的value总和、采集次数。...= sampling_counters_.end(); ++it) { ++it->second.num_sampled; int64_t value;...= NULL); value = it->second.sample_fn(); } it->second.total_sampled_value +...，active_hdfs_read_thread_counter_的total_sampled_value和num_sampled就会进行更新、累加。...并且first的值（这里就是AverageHdfsReadThreadConcurrency）也会被更新为最新的average，即total_sampled_value/num_sampled。

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百度语音识别pom引入jar工具类

; import com.baidu.aip.speech.TtsResponse; import com.baidu.aip.util.Util; import javazoom.spi.mpeg.sampled.file.MpegAudioFileReader...; import lombok.extern.slf4j.Slf4j; import org.json.JSONObject; import javax.sound.sampled.AudioFileFormat...; import javax.sound.sampled.AudioFormat; import javax.sound.sampled.AudioInputStream; import javax.sound.sampled.AudioSystem

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深入理解MySQL8.0直方图

sampled_pages_read和sampled_pages_skip INNODB_METRICS计数器可用于监视InnoDB数据页的采样。...计算公式如下： sampling rate = sampled_page_read/(sampled_pages_read + sampled_pages_skipped) 通过以下方式确认INNODB_METRICS...信息： mysql> SET GLOBAL innodb_monitor_enable = 'sampled%'; Query OK, 0 rows affected (0.00 sec) mysql...> SELECT NAME, COUNT FROM INNODB_METRICS WHERE NAME LIKE 'sampled%'\G *************************** 1....row *************************** NAME: sampled_pages_read COUNT: 342 *************************** 2. row

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当可变形注意力机制引入Vision Transformer

在得到参照点以后使用bilinear pooling操作将很小一部分特征图抠出来，作为k和v的输入 x_sampled = F.grid_sample( input=x.reshape(B * self.n_groups...x_sampled = F.grid_sample( input=x.reshape(B * self.n_groups, self.n_group_channels, H, W),...= x_sampled.reshape(B, C, 1, n_sample) q = q.reshape(B * self.n_heads, self.n_head_channels, H * W)...k = self.proj_k(x_sampled).reshape(B * self.n_heads, self.n_head_channels, n_sample) v = self.proj_v...(x_sampled).reshape(B * self.n_heads, self.n_head_channels, n_sample) 在positional encodding部分引入相对位置的偏置

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ac GAN MNIST代码详细解析

generated_images = generator.predict( [noise, sampled_labels.reshape((-1, 1))], verbose...soft_one] * len(image_batch) + [soft_zero] * len(image_batch)) aux_y = np.concatenate((label_batch, sampled_labels...noise = np.random.uniform(-1, 1, (2 * len(image_batch), latent_size)) sampled_labels = np.random.randint...注意这里训练时输入的第二组数[trick, sampled_labels], trick代表是否是真图片，sampled_labels代表图片的分类。...((-1, 1))], [trick, sampled_labels]))

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中文NLP笔记：13 用 Keras 实现一个简易聊天机器人

decoder_model.predict( [target_seq] + states_value) # Sample a token sampled_token_index...= np.argmax(output_tokens[0, -1, :]) sampled_char = reverse_target_char_index[sampled_token_index...] decoded_sentence += sampled_char if (sampled_char == '\n' or len(decoded_sentence...target_seq = np.zeros((1, 1, num_decoder_tokens)) target_seq[0, 0, sampled_token_index] = 1

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分布式链路追踪系统原来是这么一回事

带内数据被称为 b3-propagation，包含 TraceId,SpanId,ParentSpanId,Sampled四个字段，每个server在生成span之后会得到TraceId,SpanId,...Zipkin的采样字段Sampled有四种状态 Defer/Deny/Accept/Debug，采样的一个重要前提是下游要尊重上游的采样决定，不能随意更改sampled字段。...Defer代表该span的采样状态还未决定，下游收到该状态时则可以对sampled字段重新赋值。 Deny代表该span不上报。 Accept代表span需要上报。...Root_span的sampled字段由系统的采样率来决定。如采样率为50%，则一半的带内数据中sampled字段为accept，其他为deny。...并记录kind为CLIENT，name，timestamp，localendpoint(server-2)信息，并将traceid，id，parentid，sampled信息传递给server-3。

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AI绘画训练一个扩散模型-上集

torch.randn(8, 3, 128, 128) loss = diffusion(training_images.cuda()) loss.backward() # 采样1000步生成一张图片 sampled_images...torchvision.utils import make_grid import torchvision.transforms as transforms # 如果张量在 GPU上，需要移动到 CPU上 if sampled_images.is_cuda...: sampled_images = sampled_images.cpu() # 检查我们生成的一张图 img = sampled_images[0].clone().detach().permute

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Caffe2 - (三十二) Detectron 之 roi_data - 模型 minibatch blobs

= sampled_boxes * im_scale repeated_batch_idx = batch_idx * blob_utils.ones((sampled_rois.shape[...0], 1)) sampled_rois = np.hstack((repeated_batch_idx, sampled_rois)) # Base Fast R-CNN blobs..., weights = keypoint_utils.keypoints_to_heatmap_labels( sampled_keypoints, sampled_fg_rois )...* blob_utils.ones( (sampled_fg_rois.shape[0], 1) ) sampled_fg_rois = np.hstack((repeated_batch_idx..., sampled_fg_rois)) blobs['keypoint_rois'] = sampled_fg_rois blobs['keypoint_locations_int32

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Sampled Softmax

问 ChatGPT 关于GPT的事情：压缩篇

tensorflow 0.10 word2vec 源码解析

Tensorflow 的 NCE-Loss 的实现和 word2vec

matplotlib基础绘图命令之pie

java实现MP3

如何避免LLM的“幻觉”(Hallucination)

Zipkin客户端链路追踪源码解析

Jaeger知识点补充

Tungsten Fabric入门宝典丨主流监控系统工具的集成

tensorflow学习笔记（十一）：seq2seq Model相关接口介绍

Impala HDFS_SCAN_NODE之AverageHdfsReadThreadConcurrency

百度语音识别pom引入jar工具类

深入理解MySQL8.0直方图

当可变形注意力机制引入Vision Transformer

ac GAN MNIST代码详细解析

中文NLP笔记：13 用 Keras 实现一个简易聊天机器人

分布式链路追踪系统原来是这么一回事

AI绘画训练一个扩散模型-上集

Caffe2 - (三十二) Detectron 之 roi_data - 模型 minibatch blobs

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