vladk-neural-network 0.1.21

Mini-library that implements a simple version of a feedforward neural network (FNN) and convolutional neural network (CNN) from scratch using Python and PyTorch

Custom Neural Network Library

This repository is a mini-library that implements a simple version of a fully connected neural network (FCNN) and convolutional neural network (CNN) from scratch using Python and PyTorch. PyTorch is used solely for mathematical and element-wise operations on tensors (without using autograd), and for speeding up computations by utilizing the GPU. Simply put, PyTorch is used as a replacement for NumPy but with GPU acceleration. The library provides basic functionalities for building, training, and evaluating custom neural network models for both regression and classification tasks. Practical examples of applying the library for creating neural networks for regression and classification using datasets like Iris, MNIST and synthetic datasets can be found in the notebooks/ folder.

Features

• Fully Connected Neural Network (FCNN): A fully connected neural network suitable for regression and classification tasks.
• Convolutional Neural Network (CNN): A simplified CNN implementation with convolutional and max-pooling layers.
• Activation Functions: Includes ReLU, Leaky ReLU, Sigmoid, and Linear activations.
• Loss Functions: Support for mean squared error (MSE), binary cross-entropy (BCE) and categorical cross-entropy (CCE) losses.
• Optimizers: Implementations of stochastic gradient descent (SGD) and Adam optimizers.
• Metrics: Accuracy metric for classification tasks, especially useful for one-hot encoded data, and R2 score for regression tasks.

Installation

pip install vladk-neural-network

Usage

Data Format examples:

Example for regression:

# sample shape (2, 1) - 2 input values, 1 output value
dataset = [
    {
        "input": [0.1, 0.2],
        "output": [0.15],
    },
    {
        "input": [0.8, 0.9],
        "output": [0.7],
    },
]

Example for classification, output values one-hot encoded:

# sample shape (4, 2) - 4 input values, 2 output one-hot encoded values
dataset = [
    {
        "input": [0.13, 0.22, 0.37, 0.41],
        "output": [1.0, 0.0],
    },
    {
        "input": [0.76, 0.87, 0.91, 0.93],
        "output": [0.0, 1.0],
    },
]

Model creation examples:

Fully Connected Neural Network for regression:

from vladk_neural_network.model.activation import Linear, Relu
from vladk_neural_network.model.base import NeuralNetwork
from vladk_neural_network.model.layer import FullyConnected, Input
from vladk_neural_network.model.loss import MeanSquaredError
from vladk_neural_network.model.metric import R2Score
from vladk_neural_network.model.optimizer import SGD

# Build model
layers = [
    FullyConnected(64, Relu()),
    FullyConnected(64, Relu()),
    FullyConnected(1, Linear()),
]
nn = NeuralNetwork(
    Input(2),
    layers,
    optimizer=SGD(),
    loss=MeanSquaredError(),
    metric=R2Score()
)

# Train model
history = nn.fit(train_dataset, test_dataset, epochs=20, batch_size=1, verbose=True)

# Using model for prediction
prediction = nn.predict(test_dataset)

Convolutional Neural Network for classification:

from vladk_neural_network.model.activation import LeakyRelu, Linear
from vladk_neural_network.model.base import NeuralNetwork
from vladk_neural_network.model.layer import (
    Convolutional,
    Flatten,
    FullyConnected,
    Input3D,
    MaxPool2D,
)
from vladk_neural_network.model.loss import CategoricalCrossEntropy
from vladk_neural_network.model.metric import AccuracyOneHot
from vladk_neural_network.model.optimizer import Adam

# Build model using gpu acceleration and applying argmax convert to raw prediction probabilities
layers = [
    Convolutional(LeakyRelu(), filters_num=4, kernel_size=3, padding_type="same"),
    Convolutional(LeakyRelu(), filters_num=8, kernel_size=3),
    Convolutional(LeakyRelu(), filters_num=16, kernel_size=3),
    MaxPool2D(),
    Flatten(),
    FullyConnected(64, LeakyRelu()),
    FullyConnected(10, Linear()),
]
cnn = NeuralNetwork(
    Input3D((1, 28, 28)),
    layers,
    optimizer=Adam(
        learning_rate=0.001, 
        weight_decay=0.001,
        clipping=True,
        max_grad_norm=5.0,
    ),
    loss=CategoricalCrossEntropy(),
    metric=AccuracyOneHot(),
    convert_prediction='argmax',
    use_gpu=True,
    weights_init="uniform"
)

# Train model
cnn.fit(train_dataset, test_dataset, epochs=10, batch_size=1, verbose=True)

# Using model for prediction
prediction = cnn.predict(test_dataset)

Several examples, including training fully connected and convolutional neural networks, are available in the form of Jupyter notebooks in the notebooks/ folder. You can view and run these examples to understand how to use the library for different tasks.

License

This project is licensed under the MIT License. See the LICENSE file for more details.