AIET Training and Inference Models

Model Training

Definition:

Model training refers to the process of using a large amount of data to train artificial intelligence algorithms, enabling the model to learn patterns and features within the data. This process involves adjusting the parameters of the model, such as weights and biases, to minimize prediction errors.

Code Example:

Here is a simple example of neural network model training:

import torch
import torch.nn as nn
import torch.optim as optim

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(10, 5) 
        self.fc2 = nn.Linear(5, 2)  

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

net = Net()
optimizer = optim.SGD(net.parameters(), lr=0.01)
criterion = nn.CrossEntropyLoss()


inputs = torch.randn(1, 10)  
targets = torch.tensor([1])  

optimizer.zero_grad()        
outputs = net(inputs)       
loss = criterion(outputs, targets)  
loss.backward()             
optimizer.step()             

Model Inference

Definition:

Model inference is the process of using a trained model to make predictions on new data. This step typically does not involve adjusting model parameters; rather, it involves using the model for prediction.

Code Example:

An example of using the trained model for inference:

new_inputs = torch.randn(1, 10)  

net.eval() 
with torch.no_grad():
    predictions = net(new_inputs)
    predicted_class = torch.argmax(predictions, dim=1)
    print(f"Predicted class: {predicted_class.item()}")