How to perform forward propagation in Caffe?

Performing forward propagation in Caffe is achieved by calling the Forward function within the network model. Initially, it is necessary to load the pre-defined network model and corresponding weight parameters. Then, the input data is passed to the input layer of the network model, and calling the Forward function completes the forward propagation process. Once the forward propagation is completed, the final prediction result can be obtained by retrieving the output from the network model’s output layer. Here is a simple example code:

import caffe

# 加载网络模型和权重参数
net = caffe.Net('path/to/deploy.prototxt', 'path/to/weights.caffemodel', caffe.TEST)

# 将输入数据传递给网络模型的输入层
input_data = # 输入数据
net.blobs['data'].data[...] = input_data

# 进行前向传播
net.forward()

# 获取网络模型输出层的结果
output_data = net.blobs['output'].data

# 输出预测结果
print(output_data)

It is important to note that before calling the Forward function, the input data needs to be passed to the input layer of the network model, and the results of the output layer of the network model can be obtained through net.blobs[‘output’].data. This way, the forward propagation process in Caffe can be completed.