neural-monkey 0.3.3

A neural network library for beginners.

Monkey – Simple Neural Networks for Beginners

Monkey is a lightweight Python library for building, training, and experimenting with simple neural networks.
It is designed for beginners who want to understand how neural networks work internally without heavy dependencies.

---

Features

• Fully connected neural networks (Dense layers)
• Activation functions: ReLU, Sigmoid, Tanh, Linear
• Train networks using gradient descent with multiple optimizers
• Supports SGD, Adam, RMSProp, and AdaGrad
• Works with Python lists or NumPy arrays
• Autoencoder-style training (no labels required)
• Sequence prediction using next-step training
• Lightweight AttentionBlock for sequence inputs
• Save and load models using .mon format
• Minimal and beginner-friendly API

---

Installation

pip install neural-monkey

---

Quick Start

Predict the sum of two numbers

from monkey import NeuralNet

x_train = [[2, 8], [9, 3], [7, 4], [1, 1]]
y_train = [[sum(pair)] for pair in x_train]

nn = NeuralNet(input_size=2)
nn.add_layer(neurons=5, activation='relu')
nn.add_layer(neurons=1, activation='relu', layer='output')

nn.train(x_train, y_train, epochs=500, lr=0.1)

print(nn.predict([3, 5])[0])

---

Using different optimizer

from monkey import NeuralNet

x_train = [[2, 8], [9, 3], [7, 4], [1, 1]]
y_train = [[sum(pair)] for pair in x_train]

nn = NeuralNet(input_size=2, optimizer="adam")

nn.add_layer(4, activation="relu")
nn.add_layer(1, activation="linear", layer="output")

nn.train(x_train, y_train, epochs=500)

print(nn.predict([3, 5])[0])

---

Autoencoder (no labels)

from monkey import NeuralNet

data = [[0], [1], [2], [3], [4], [5]]

nn = NeuralNet(input_size=1)
nn.add_layer(3, activation="relu")
nn.add_layer(1, activation="linear", layer="output")

nn.train(data, epochs=200)

print(nn.predict([2]))

---

Sequence prediction (next-step learning)

from monkey import NeuralNet

sequence = [1, 2, 3, 4, 5, 6]

nn = NeuralNet(input_size=1)
nn.add_layer(5, activation="relu")
nn.add_layer(1, activation="linear", layer="output")

nn.train(sequence, epochs=300, next_step=True)

print(nn.predict([6]))

---

AttentionBlock example

from monkey import AttentionBlock

seq_input = [
    [0.8, 0.2, 0.1],
    [0.5, 0.1, 0.3],
    [0.2, 0.7, 0.6]
]

attn = AttentionBlock(input_size=3, output_size=3)
output = attn.forward(seq_input)

print(output)

---

Model Saving and Loading

from monkey import save, load

save(nn, "model.mon")

loaded = load("model.mon", use_numpy=True)

print(loaded.predict([3, 5]))

---

Available API (Public)

Core

• NeuralNet → Create and train networks
• Dense → Internal fully connected layer

Activations

• relu
• sigmoid
• tanh
• linear
• activation_map

Attention

• AttentionBlock

Optimizers

• SGD
• Adam
• RMSProp
• AdaGrad

Models

• save
• load

Global

• useNumpy → Toggle NumPy usage (True / False)

---

Notes

• Only .mon model format is supported
• Works with both Python lists and NumPy arrays
• If NumPy is unavailable, pure Python mode is used
• next_step=True enables sequence learning
• If y_train=None, autoencoder training is used automatically

---

Learning Tips

• Start with small datasets
• Use fewer neurons to understand behavior
• Try different activations to observe changes
• Experiment with optimizers
• Use AttentionBlock for sequence understanding

---

Repository

https://github.com/19919rohit/Neural-Monkey

---

License

MIT License