小编GIR*_*yal的帖子

我使用sklean使用命令as计算文档中术语的TFIDF值

from sklearn.feature_extraction.text import CountVectorizer
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(documents)
from sklearn.feature_extraction.text import TfidfTransformer
tf_transformer = TfidfTransformer(use_idf=False).fit(X_train_counts)
X_train_tf = tf_transformer.transform(X_train_counts)

X_train_tf是scipy稀疏形状矩阵

from sklearn.feature_extraction.text import CountVectorizer
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(documents)
from sklearn.feature_extraction.text import TfidfTransformer
tf_transformer = TfidfTransformer(use_idf=False).fit(X_train_counts)
X_train_tf = tf_transformer.transform(X_train_counts)

输出为(2257,35788).如何在特定文档中获取TF-IDF？更具体地说,如何在给定文档中获取具有最大TF-IDF值的单词？

在这个 pytorch ResNet 代码示例中，他们在第 44 行将下采样定义为变量。第 58 行将其用作函数。作为 CNN 的观点和 Python 代码的观点，这个下采样在这里是如何工作的。

代码示例：pytorch ResNet

我搜索了下采样是否是任何 pytorch 内置函数。但事实并非如此。

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, norm_layer=None):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1:
            raise ValueError('BasicBlock only supports groups=1')
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes) …