autoloss 0.1.0

AutoLoss is a complex optimizer that uses Artifical Intelligence to optimize your neural network

AutoLoss

AutoLoss is an optimizer that uses Artificial Intelligence to optimize your neural networks as well as possible.

Usage

First, import the package and other dependencies:

from AutoLoss import *
import torch
import torch.nn as nn

Then, create your AI Model. For the sake of simplicity, let's just name it model for this example.

Next, initialize a loss function. For this example, we'll use CrossEntropyLoss:

loss_fn = nn.CrossEntropyLoss()

Afterwards, create an instance of the AutoLoss optimizer

optimizer = AutoLoss(model, loss_fn, patience=25)

Note: For more advanced details on the patience argument, check Internal Functioning.

Now, to train the model, just call optimizer.step(x, target), where x is the input data and target is the expected output data.

Internal Functioning

• AutoLoss uses its own AI model to predict what parameters would work best for your neural network, therefore constantly getting better at predicting better parameters and training your AI better.
• If AutoLoss's AI can not find any better parameters, it switches to SGD (Stochastic Gradient Descent).
• The amount of tries AutoLoss's AI has at finding better parameters until it switches to SGD is equal to the patience parameter from earlier.