DataCheese is a Python library with implementations of popular data science and machine learning algorithms.
1️⃣ Install Python
Python 3.10 or above required.
2️⃣ Install package using pip
Install directly from the repository:
pip3 install git+https://github.com/alvii147/DataCheese.gitThe MultiLayerPerceptron model can be used to train a feed-forward neural network using data:
import numpy as np
from datacheese.neural_networks import (
MultiLayerPerceptron,
SigmoidLayer,
ReLULayer,
)
# number of data patterns
n_patterns = 5
# number of feature dimensions
n_dimensions = 3
# number of target classes
n_classes = 2
# generate random data
rng = np.random.default_rng()
X = rng.random(size=(n_patterns, n_dimensions))
Y = rng.random(size=(n_patterns, n_classes))
# initialize multi-layer perceptron model
model = MultiLayerPerceptron(lr=0.5)
# add relu layer
model.add_layer(ReLULayer(n_dimensions, 4))
# add sigmoid layer
model.add_layer(SigmoidLayer(4, n_classes))
# fit model to data
model.fit(X, Y, epochs=20, verbose=1)When verbose is non-zero, progress is logged:
Epoch: 0, Loss: 0.15181599599950849
Epoch: 4, Loss: 0.13701115369406147
Epoch: 8, Loss: 0.11337662383705667
Epoch: 12, Loss: 0.10121139637335393
Epoch: 16, Loss: 0.09388681525946835
The model can then be used to make predictions:
# predict target values
Y_pred = model.predict(X)
# compute mean squared loss
print(np.mean((Y_pred - Y) ** 2))This outputs the following:
0.05310463606057757
For more details, visit the documentation pages.