Hello everyone, I am Wei Xue AI. Today I will introduce to you Deep Learning Skills Application 2-'Residual Connection' in Neural Networks.
1. Introduction to residual connection
Residual connection is a technique used in neural networks. Since deep networks are prone to gradient disappearance or gradient explosion problems, the depth of the network can be extended to more than dozens of layers through residual connections, thereby improving the performance of the model. The basic idea of residual connections is to directly connect the input signal to the output in some layers of the network, thus introducing " cross-layer connections " in the network.
2. Residual connection problem solving
The residual connection technique is mainly used in the training of deep neural networks, especially when the number of deep network layers is large. Specifically, the residual connection technique can be applied to the following scenarios:
Solve the vanishing gradient problem: When the number of layers of a deep neural network increases, the propagation of gradients is easily affected by the vanishing gradient, making training difficult. The residual connection technique can alleviate the vanishing gradient problem by providing shortcuts through direct connections across layers.
Improve model performance: Residual connection techniques can increase the depth of the model, thereby improving the model's expressive ability and performance. In addition, since residual connections can provide the effect of direct connections across layers, they can help the model learn features better, thereby further improving model performance.
Reduce training difficulty: The residual connection technique can speed up the training of the model, thereby reducing the training difficulty. Since the residual connection can provide the effect of direct connection across layers, it can make the model converge more easily and reduce the consumption of training time and computing resources. In short, the residual connection technique has extensive application value in the field of deep learning. It can help deep neural networks learn features better and improve performance, and can also reduce training difficulty and consumption.
3. Principle of residual connection
Residual Connection means that in a neural network, the output of the previous layer is directly added to the input of the next layer, thus forming a cross-layer connection. How this cross-layer connection is calculated:
Assuming that the output of the previous layer is and the input of the next layer is
, the residual connection can be expressed as:
where is the nonlinear transformation of the latter layer (such as ReLU or sigmoid), and + represents the element-level addition operation. The main purpose of this cross-layer connection is to solve the vanishing and exploding gradient problems in deep neural networks.
In traditional neural networks, the input of each layer is the output of the previous layer, and high-level features are gradually extracted through continuous nonlinear transformation. However, as the number of network layers increases, the gradient will gradually become smaller during the backpropagation process, causing difficulties in model training. Residual connections make it easier to train deep neural networks by directly adding the output of the previous layer to the input of the next layer, making it easier to transfer the gradient to the previous layer. In addition, residual connections can also reduce the training error of the model and improve the generalization ability of the model to better adapt to unseen data. Therefore, in the field of deep learning, residual connection has become a widely used technology and is used in various neural network models, such as ResNet, DenseNet, etc.
4. Residual connection code example
The following is a simple fully connected neural network, which contains several residual blocks (ResidualBlock) using residual connections. These residual blocks form a residual network (ResidualNet) through nn.ModuleList. In each residual block, the output of the previous layer (i.e. identity) is directly added to the input (i.e. out) of the next layer, thus achieving the effect of residual connection. The residual network performs forward propagation by calling the forward method. Code example:
import torch.nn as nn
import torch
class ResidualBlock(nn.Module):
def __init__(self, in_features, out_features):
super(ResidualBlock, self).__init__()
self.linear1 = nn.Linear(in_features, out_features)
self.relu = nn.ReLU(inplace=True)
self.linear2 = nn.Linear(out_features, out_features)
def forward(self, x):
identity = x
out = self.linear1(x)
out = self.relu(out)
out = self.linear2(out)
out += identity
out = self.relu(out)
return out
class ResidualNet(nn.Module):
def __init__(self, in_features, hidden_features, out_features, num_blocks):
super(ResidualNet, self).__init__()
self.linear1 = nn.Linear(in_features, hidden_features)
self.relu = nn.ReLU(inplace=True)
self.blocks = nn.ModuleList([ResidualBlock(hidden_features, hidden_features) for _ in range(num_blocks)])
self.linear2 = nn.Linear(hidden_features, out_features)
def forward(self, x):
out = self.linear1(x)
out = self.relu(out)
for block in self.blocks:
out = block(out)
out = self.linear2(out)
return outNext, a random input data of size is constructed
, and then the residual network model defined above is used to predict this input data. Finally, we output the prediction result y_pred, whose size is
, where each row represents the prediction result of a sample, with a total of 2 categories. The prediction results can be trained by calculating the cross-entropy loss and performing backpropagation.
# 构造输入数据
x = torch.randn(10, 5) # 输入数据大小为 10x5
# 构造残差网络模型
model = ResidualNet(in_features=5, hidden_features=10, out_features=2, num_blocks=2)
# 进行模型预测
y_pred = model(x)
# 输出预测结果
print(y_pred)For more details, you can follow Weixue AI. Private messages and cooperation are welcome.