Hello everyone, I am Weixue AI. Today I will introduce to you the application of deep learning skills 24-the method of linking and memorizing the deep learning hand-torn code and the training process. Everyone knows that the training process of the deep learning model is a complicated process. This process includes data collection, data processing, model building, optimizer selection, loss function selection, model training, model evaluation and other steps. The model cannot be trained without a link. The purpose of this article is to give everyone a better understanding and The whole process of memorizing model training, using virtual data to quickly get started training, memorizing the connection between deep learning hand-torn code and training process requires understanding basic concepts, learning code implementation, mastering common frameworks and tools, understanding training process, and through repeated practice and summary to improve coding and training capabilities.
Contents
1. Deep learning model training process
2. Vivid associative memory training process
3. Hand tearing code
4. Summary
Deep learning model training process
Next, I will train the CNN model based on PyTorch and introduce the specific steps:
1. Import necessary libraries: first import related modules in torch.nn and torchvision, and other necessary libraries.
2. Create a dataset: Use the FakeData class to create a dummy dataset with 500 samples, each with a size of (3, 28, 28), and a total of 5 categories. Also use transforms.ToTensor() to convert the data into tensor form.
3. Create a data loader: Create a data loader using DataLoader, specify a batch size of 64, and turn on the shuffle option to shuffle the data in each epoch.
4. Define the CNN model: Create a CNN class that inherits from nn.Module and define it in the constructor