picograd 1.0.5

Description:

picograd 1.0.5

picograd


A lightweight machine learning framework

Description •
Features •
Examples •
References •
License

Description
A PyTorch-like lightweight deep learning framework written from scratch in Python.
The library has a built-in auto-differentiation engine that dynamically builds a computational graph. The framework is
built with basic features to train neural nets: optimizers, training API, data utilities, metrics
and loss functions.
Additional tools are developed to visualize forward computational graph.
Features

PyTorch-like auto-differentiation engine (dynamically constructed computational graph)
Keras-like simple training API
Neural networks API
Activations: ReLU, Sigmoid, tanh
Optimizers: SGD, Adam
Loss: Mean squared error
Accuracy: Binary accuracy
Data utilities
Computational graph visualizer

Examples
The demo notebook showcases what picograd is all about.
Example Usage
from picograd.engine import Var
from picograd.graph_viz import ForwardGraphViz

graph_builder = ForwardGraphViz()

x = Var(1.0, label='x')
y = (x * 2 + 1).relu();
y.label = 'y'
y.backward()

graph_builder.create_graph(y)

Output:



Training MLP
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import make_moons

from picograd.nn import MLP
from picograd.engine import Var
from picograd.data import BatchIterator
from picograd.trainer import Trainer
from picograd.optim import SGD, Adam
from picograd.metrics import binary_accuracy, mean_squared_error

# Generate moon-shaped, non-linearly separable data
x_train, y_train = make_moons(n_samples=200, noise=0.10, random_state=0)

model = MLP(in_features=2, layers=[16, 16, 1], activations=['relu', 'relu', 'linear']) # 2 hidden layers
print(model)
print(f"Number of parameters: {len(model.parameters())}")

optimizer = SGD(model.parameters(), lr=0.05)
data_iterator = BatchIterator(x_train, list(map(Var, y_train)))
trainer = Trainer(model, optimizer, loss=mean_squared_error, acc_metric=binary_accuracy)

history = trainer.fit(data_iterator, num_epochs=70, verbose=True)

Decision boundary:



References

Andrej Karpathy's micrograd library and intro explanation
on training neural nets, which is the
foundation of picograd's autograd engine.
Baptiste Pesquet's pyfit library, from which training API was borrowed.

License
MIT