Deep-Learning

Implementation of simple back-propagation using numpy.

Installation

You can install Deep-Learning using pip:

pip install git+https://github.com/Mathiasotnes/Deep-Learning.git

Usage

Quickly set up a neural network with multiple layers, including a softmax output layer, using the Deep Learning Library.

Example: Multi-Layer Network with Softmax Output

import numpy as np
from brain_of_mathias.models import Layer, Network
from brain_of_mathias.activations import ReLU, Softmax
from brain_of_mathias.losses import MSE

# Sample data - replace with actual data
X_train = np.array([...])  # Input features
y_train = np.array([...])  # Target labels

# Define a network with desired layers
layer1 = Layer(input_size=..., number_of_neurons=..., activation=ReLU())
layer2 = Layer(input_size=..., number_of_neurons=..., activation=ReLU())
output_layer = Layer(input_size=..., number_of_neurons=..., activation=Softmax())

# Initialize the network with the layers
network = Network([layer1, layer2, output_layer], loss_function=MSE())

# Train the network
network.fit(X_train, y_train, learning_rate=0.01, epochs=500)

# Predict
network.predict(X_test)

Features

• Custom activation and loss functions.
• Extensible model architecture.
• Utilities for common operations.

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