哪惧明天，风高路斜

RMSNorm 是一种简单而有效的归一化技术，通过去除均值中心化步骤，在保持性能的同时大幅提升了计算效率。它在现代大语言模型中得到了广泛应用，是深度学习中归一化技术的重要发展。核心价值简单高效的设计哲学良好的性能表现广泛的应用前景选择 RMSNorm 还是 LayerNorm 应该基于具体的应用场景、计算资源和性能需求来决定。

在PyTorch中，这两个乘法操作的核心区别在于。

十种学习率方法对比

函数功能是否修改形状rearrange重排、reshape、transpose✅reduce降维（如 mean、max）✅repeat张量复制✅asnumpy转换为 numpy array❌解析张量形状❌。

multi-head Self-attention实现，attention_mask维度解释，matmul/transpose/view/masked_fill等算子解释

torch.nn.utils.rnn.pad_sequence(sequences, batch_first=False, padding_value=0)用padding_value 填充一系列可变长度的tensor，把它们填充到等长Example>>> from torch.nn.utils.rnn import pad_sequence>>> a...

could not find conda environment

1.在终端上先启用环境$ conda activate py362.启用环境以后，再输入jupyter notebook3.在新打开的notebook页面，进行import，就可以使用py36环境下的包

javaimport org.apache.commons.lang3.StringUtils;public class TestOperator { public static void main(String[] args) { System.out.println(StringUtils.repeat("-", 10)); }}scalascala> print("-" * 10)----------Python>>> print("-"

文章目录需求解决需求将通过for循环得到的多个tensor，最终拼接起来。解决>>> import pytorch>>> input = torch.randn(2,5)>>> input.unsqueeze_(1)tensor([[[-0.1127, 0.1031, -1.7152, -0.1951, 0.8266]], ...

链接：packing-unpacking-pytorch-minimal-tutorialsorry，翻译有空再补

>>> from torch.nn.utils.rnn import pad_sequence>>> input_x =[[1,2,3],[4,5,6,7,8],[8,9]]>>> norm_data_pad = pad_sequence([torch.from_numpy(np.array(x)) for x in input_x], b...

the-incredible-pytorch

解决方法检查输入的数据是否为float32，如果不是的话，要将其改为float32参考：https://github.com/pytorch/pytorch/issues/2267

import torchx=torch.Tensor([1,2])print('x: ',x)print('type(x): ',type(x))print('x.dtype: ',x.dtype) # x的具体类型y=x.int()print('y: ',y)print('type(y): ',type(y))print('y.dtype: ',y.d...

import torchx = torch.randn(2,1,7,3)conv = torch.nn.conv2d(1,8,(2,3))res = conv(x)print(res.shape) # shape = (2, 8, 6, 1)输入：x[ batch_size, channels, height_1, width_1 ]batch_size ...

举例：拿maxpool操作来说import torchx_input = torch.randn(20, 16, 50)# 先定义一个model，再把数据传进去# pool of size=3, stride=2m = torch.nn.MaxPool1d(3, stride=2)x_output = m(x_input)print(x_output.shape) # torc...

文章目录一个简单的例子MSELoss的直观实现方法SGD的直观实现方法一个简单的例子import torchimport torch.nn as nnx = torch.randn(10, 3)y = torch.randn(10, 2)# Build a fully connected layer.linear = nn.Linear(3, 2)# Build loss fu...

文章目录linear_regression.pydetach()的用法参考linear_regression.pyimport torchimport torch.nn as nnimport numpy as npimport matplotlib.pyplot as plt# Hyper-parametersinput_size = 1output_size = 1num_...

文章目录convolutional_neural_network代码代码报错出错原因参考convolutional_neural_network代码import torchimport torch.nn as nnimport torchvisionimport torchvision.transforms as transformsdevice = torch.device('cu...

import torchimport torch.nn as nnlstm = nn.LSTM(10, 20, 2)x = torch.randn(5, 3, 10)h0 = torch.randn(2, 3, 20)c0 = torch.randn(2, 3, 20)output, (hn, cn)=lstm(x, (h0, c0))# output.shape torch.Si...

>>> import subprocess>>> out = subprocess.check_output(['ls', '-l'])>>> outb'\xe6\x80\xbb\xe7\x94\xa8\xe9\x87\x8f 36\n-rw-rw-r-- 1 root root 3713 Mar 15 09:54 dataloader....

有两种方式直接把模型的参数梯度设成0：model.zero_grad()optimizer.zero_grad()#当optimizer=optim.Optimizer(model.parameters())时，两者等效如果想要把某一Variable的梯度置为0，只需用以下语句：Variable.grad.data.zero_()作者：CodeTutor来源：优快云原文：ht...

torch repeat>>> x=torch.randn(1,2)>>> xtensor([[1.2059, 2.4903]])>>> x.repeat(3,1)tensor([[1.2059, 2.4903], [1.2059, 2.4903], [1.2059, 2.4903]])numpy ...

>>> y=torch.range(1,6)>>> ytensor([1., 2., 3., 4., 5., 6.])>>> y.dtypetorch.float32>>> z=torch.arange(1,6)>>> ztensor([1, 2, 3, 4, 5])>>>...

import torchx = torch.randn(128, 20) # 输入的维度是（128，20）m = torch.nn.Linear(20, 30) # 20,30是指维度output = m(x)print('m.weight.shape:\n ', m.weight.shape)print('m.bias.shape:\n', m.bias.shape)print...