self.fc1 = nn.Conv2d(planes, planes//16, kernel_size=1) self.fc2 = nn.Conv2d(planes//16, planes, kernel_size=1) def forward(self, x): out = F.relu(self.bn1(x)) shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x out = self.conv1(out) out = self.conv2...
# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers importZeroPadding2D[as 别名]def_conv2d_same(x, filters, prefix, stride=1, kernel_size=3, rate=1):# 计算padding的数量,hw是否需要收缩ifstride ==1:returnConv2D(filters, (kernel_size, kernel_size), strides=(str...
else: sparse_feature_columns.append(SparseFeat(feat, vocabulary_size=364046 + 1, embedding_dim=emb_dim)) # sparse_feature_columns = [SparseFeat(feat, vocabulary_size=df[feat].max() + 1, embedding_dim=emb_dim) for feat in sparse_fea] dense_feature_columns = [DenseFeat(feat, 1, ) for ...
=0:raiseValueError('in_channels must be divisible by groups')ifout_channels%groups!=0:raiseValueError('out_channels must be divisible by groups')self.in_channels=in_channelsself.out_channels=out_channelsself.kernel_size=kernel_sizeself.stride=strideself.padding=paddingself.dilation=dilationself.tran...
Source File: RecyclerViewFragment.java From kernel_adiutor with Apache License 2.0 6 votes public void setOnScrollListener(RecyclerView recyclerView) { if (recyclerView != null) { int paddingBottom = recyclerView.getPaddingBottom() + getResources().getDimensionPixelSize(R.dimen.basecard_padding);...
*kernel_size))else:self.weight=Parameter(torch.Tensor(out_channels,in_channels// groups, *kernel_size))ifbias:self.bias=Parameter(torch.Tensor(out_channels))else:self.register_parameter('bias',None)self.reset_parameters()defreset_parameters(self):n=self.in_channelsforkinself.kernel_size:n*=k...
# import torchimportonnxruntime## Only when ceil_mode=True and padding>0, the bug occurs.model=torch.nn.MaxPool3d(kernel_size=3,stride=None,padding=1,dilation=1,return_indices=False,ceil_mode=True)x=torch.randn(1,2,3,6,5)out_torch=model(x)# torch.onnx.export(model, (x, ), '...
The support vector machine (SVM) with the kernel of the radial basis function is adopted as the discriminative model. For the equilibrium of positive samples and negative samples, 60% positive samples and 30% negative samples are chosen from the data set {Yrmpred,Dpred}{Yrmpred,Dpred} (...
according with the size of the filter used in the next layer (half of it...). Nice Ideia ! thanks . this way the kernel (cnn neuron) will travel the features maps like in the asteroid game :-) what goes up appear down :-) in a multivariable time series it will correlate all ...
}if(temp_event->childNode->window_size !=0) {padding(' ',14);printf("+Sliding Window Size: %f sec\n",temp_event->childNode->window_size); }else{padding(' ',14);printf("+Sliding Window Size: Unlimited\n"); }padding(' ',14);printf("[email protected]: %s/%d\n",temp_event-...