madrin 1.0.0

A Neural Network Library

Madrin

A cute Neural Network library with Keras-like API. Build for fun and educational purposes. Because the code is so simple, it is very easy to change to your needs. Still under active development.

Dependencies

• numpy

Installation

pip install madrin

Demo

Create a neural network:
You can create a Neural Network by passing a list of layers to the Network constructor.
Currently it supports the following layers:

Linear(no_of_neurons, input_size)
Relu()
LeakyRelu()
Sigmoid()
Tanh()
Softmax()

import numpy as np

# Import the necessary classes from the madrin library
from madrin import Linear, Sigmoid, Relu, LeakyRelu, Tanh, Softmax, Network

# Generate some dummy data for training
np.random.seed(0)  # For reproducibility
X_train = np.random.randn(1000, 3)  # 1000 samples, 3 features each
y_train = np.random.randint(0, 3, 1000)  # 1000 labels (3 classes)

# Create the network
model = Network([
    Linear(no_of_neurons=5, input_size=3),  # First layer: 3 input features, 5 neurons
    Relu(),  # ReLU activation
    Linear(no_of_neurons=3, input_size=5),  # Second layer: 5 input features, 3 neurons (output layer)
    Softmax()  # Softmax activation for multi-class classification
])

# See the total number of trainable parameters(i.e., weights and biases)
print(model.n_parameters())

# Compile the network with loss function and learning rate
model.compile(loss='categorical_crossentropy', lr=0.01)

# Train the network
model.fit(X_train, y_train, epochs=1000, batch_size=100, track_loss = True)

# Make predictions
predictions = model.forward(X_train)

# Print the predictions
print(predictions)

# Print the training costs
import matplotlib.pyplot as plt
plt.plot(np.arange(len(model.costs)),model.costs)
plt.xlabel('Epochs')
plt.ylabel('Cost')
plt.title('Training Cost Over Time')
plt.show()

Contributing

Contributions are welcome! Please open an issue or submit a pull request on Github.

License

Madrin is released under the MIT License.