neurologic 0.1.2

Dead-simple AI training

neurologic Dead-simple AI training. Build and train neural networks in 3 lines of Python.

Python from neurologic import Model, Trainer

model = Model.classifier(784, [128, 64], 10) trainer = Trainer(model) trainer.fit(X_train, y_train, epochs=10) Install Bash pip install neurologic Quick Start Tabular / CSV Data

Python from neurologic import Model, Trainer, load_csv

X, y = load_csv("data.csv", target="label")

model = Model.classifier(4, [64, 32], 3) trainer = Trainer(model)

trainer.fit(X, y, epochs=20) trainer.save("my_model.pt") Production Inference

For deploying to production, use the lightweight Inference class. It loads the model and handles tensor conversion automatically.

Python from neurologic import Inference

Load and predict in 2 lines

engine = Inference("my_model.pt") prediction = engine.run([0.5, 1.2, 3.3]) Data Loaders Python from neurologic import load_csv, load_images, load_text

CSV -> Tensors

X, y = load_csv("data.csv", target="label")

Images -> Tensors (auto-resizes and normalizes)

images, labels = load_images("dataset/", image_size=224)

Text -> Tokenized Tensors

X, y = load_text("reviews.csv", text_col="text", label_col="sentiment") Advanced Training Dynamic Learning Rate

Automatically adjust how fast the model learns based on performance.

Python

Drops LR when improvement stops

trainer = Trainer(model, scheduler="plateau")

Smoothly decays LR over time

trainer = Trainer(model, scheduler="cosine") Layer Growth

Let the model architecture expand if the current size isn't enough to solve the problem.

Python model = Model.classifier(10, [32], 3) trainer = Trainer(model)

Add a layer every 10 epochs if the loss plateaus

trainer.fit(X, y, epochs=50, grow_epochs=10, grow_max=3) API Reference Inference

Python engine = Inference( model_path, # Path to .pt file device=None # "cpu", "cuda", or "mps" )

engine.run(data) # Returns class index (e.g., 1) Model.load()

If you want to load a model to continue training rather than just for inference:

Python model = Model.load("my_model.pt") trainer = Trainer(model) trainer.fit(X, y, epochs=5) Model.vision()

Python model = Model.vision( num_classes=2, # Number of output categories backbone="resnet18", # Pretrained architecture pretrained=True # Use ImageNet weights ) Trainer.fit()

Python trainer.fit( X, y, # Tensors epochs=10, # Total passes batch_size=32, # Samples per step early_stopping=5 # Stop if no improvement for 5 epochs ) Tips Inference vs Trainer: Use Inference for web APIs or mobile apps; use Trainer.load if you need to keep training.

GPU: You don't need to call .to('cuda'). Everything in neurologic detects your hardware automatically.

Growing: If your loss is stuck high, try grow_epochs=5 to let the model add more "brain power" automatically.