lazytorch

The easiest ML library you'll ever use. Currently in early beta. Do not use for production yet.

What is LazyTorch?

LazyTorch is a ML library that lets you make neural networks and models with ease. Just like the name suggests, LazyTorch is for lazy people like me.

Code Examples

Brain - General classification and neural networks.

model = Brain(input={"text": ""}, output={"is_positive": True})

ds = Dataset(
    {"text": [
        "i love this",
        "this is great",
        "absolute perfection",
        "i hate this",
        "this is terrible",
        "this is awful"
    ]},
    {"is_positive": [True, True, True, False, False, False]}
)

model.teach(ds)

print(model.infer({"text": "i love this"})) # outputs: {'is_positive': True}
print(model.infer({"text": "this is terrible"})) # outputs: {'is_positive': False}

AutoregressiveBrain - Brain but for autoregressive text generation and word prediction.

model = AutoregressiveBrain(context_size=2)
model.teach("the capital of russia is moscow. the capital of britan is london. the capital of france is paris. the capital of greece is athens.")
print(model.generate("the capital of france is", max_words=1)) # output: paris

Dataset - Datasets.

ds = Dataset({"text": ["hello", "world"]}, {"is_positive": [True, True]})
model = Brain(input={"text": ""}, output={"is_positive": True})
model.teach(ds)

BrainTrust - Sample results from multiple Brain models to get a general consensus.

ensemble = BrainTrust([model_a, model_b])
print(ensemble.infer({"text": "spam message"})) # output: {'is_spam': True}

BrainScanner - Evaluate Brain models on datasets.

accuracy = BrainScanner.evaluate(model, ds)
print(accuracy) # output: 1.0 (100%)

These are all of the functions available in the beta of LazyTorch. These are not final.