How does the model training process work in the Caffe framework?
The typical process of training a model in the Caffe framework usually includes the following steps:
- Data preparation: Firstly, it is necessary to prepare the training dataset, which typically consists of image data. The dataset should include training samples along with their corresponding labels.
- Define the network structure: Use Caffe to define the network architecture, including the input data size, types of network layers (such as convolutional layers, pooling layers, fully connected layers, etc.), parameters for each layer (such as kernel size, stride, activation function, etc.), and the overall structure and connections of the network.
- Configure the solver: setting up the solver includes selecting optimization algorithms (such as stochastic gradient descent SGD), setting learning rate, momentum, weight decay and other hyperparameters, as well as specifying the number of training iterations and batch size used for each iteration.
- Start training: Begin training the model using the defined network structure and solver. During each iteration, feed the input data into the network, calculate the loss function, update the network parameters, until reaching the specified number of training iterations or the stopping condition is met.
- Assessing model performance: After the training is complete, the trained model can be evaluated using a test dataset to calculate performance metrics such as accuracy and precision.
- Model optimization: Improving the performance of the model based on evaluation results, such as adjusting the network structure, tuning hyperparameters, etc.
- Prediction: Eventually, the trained model can be used to predict, classify, and recognize new data.