1. 解释什么是全局平均池化(Global Average Pooling, GAP) 全局平均池化(Global Average Pooling, GAP)是一种特殊的池化操作,它对整个特征图进行平均池化,生成一个全局的特征向量。具体而言,对于输入的特征图(feature map),GAP会计算每个通道(channel)上所有元素的平均值,从而得到一个维度降低的特征表示。这种池化方式...
global average pooling 与 average pooling 的差别就在 "global" 这一个字眼上。global 与 local 在字面上都是用来形容 pooling 窗口区域的。 local 是取 feature map 的一个子区域求平均值,然后滑动这个子区域; global 显然就是对整个 feature map 求平均值了。
>>> assert parse_shape(x, 'b _ h w') == parse_shape(y2, 'b _ h w') # Adaptive 2d max-pooling to 3 * 4 grid >>> reduce(x, 'b c (h1 h2) (w1 w2) -> b c h1 w1', 'max', h1=3, w1=4).shape (10, 20, 3, 4) # Global average pooling >>> reduce(x, 'b c...
我们可以使用全局平均池化层(Global AveragePooling,GAP)来取代全连接层,这种思想最早见于NIN(Network in Network)网络中,总体上,使用GAP有如下3点好处: ·利用池化实现了降维,极大地减少了网络的参数量。 ·将特征提取与分类合二为一,一定程度上可以防止过拟合。 ·由于去除了全连接层,可以实现任意图像尺度的输入。
0GAP(Global average pooling)层 gap= torch.nn.AdaptiveAvgPool2d(output_size=1) 双线性汇合(bilinear pooling) X = torch.reshape(N, D, H * W)# Assume X has shape N*D*H*WX = torch.bmm(X, torch.transpose(X, 1, 2)) / (H * W)# Bilinear poolingassert X.size() == (N, D, D...
block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2, dropRate) # global average pooling and classifier self.bn1 = nn.BatchNorm2d(nChannels[3]) self.relu = nn.ReLU(inplace=True) self.fc = nn.Linear(nChannels[3], num_classes) self.nChannels = nChannels[3] for m in self...
self.inferencing =False# use global average pooling to aggregate responses if peak stimulation is disabledself.enable_peak_stimulation = kargs.get('enable_peak_stimulation',True)# return only the class response maps in inference mode if peak backpropagation is disabledself.enable_peak_backprop = k...
-- Global Average Pooling Layer local final_mlpconv_layer = nn.TemporalConvolution(1024, 100, 1, 1) model:add(final_mlpconv_layer) if self.batchNormalize then model:add(nn.Reshape(math.floor(structure.nInputs / (3 * 5 * 2 * 3)) * 100)); model:add(nn.BatchNormalization(math.floor...
Learn about nn.BatchNorm2d, nn.AdaptiveAvgPool2d (Global Average Pooling) Deep Learning for Text Basics [↩] D2L_Text_Basics.ipynb: Creating a tokenizer and vocabulary, random & sequential sampling and Sequence Data Loader Learn about corpus statistics (unigrams,bigrams,trigrams) D2L_Seq_Model...
目前由于全连接层参数冗余(仅全连接层参数就可占整个网络参数80%左右),近期一些性能优异的网络模型如ResNet和GoogLeNet等均用全局平均池化(global average pooling,GAP)取代FC来融合学到的深度特征,最后仍用softmax等损失函数作为网络目标函数来指导学习过程。