neuralnetlib 3.1.1

A flexible deep learning framework built from scratch using only NumPy

Neuralnetlib

📝 Description

This is a handmade convolutional neural network library, made in python, using numpy as the only dependency.

I made it to challenge myself and to learn more about deep neural networks, how they work in depth.

The big part of this project, meaning the Multilayer (MLP) part, was made in 4 hours and a half.

I then decided to push it even further by adding Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).

And, of course, I intend to improve the neural networks and add more features in the future (Transformers? Autoencoders? Who knows?).

📦 Features

• Many layers (wrappers, dense, dropout, conv1d/2d, pooling1d/2d, flatten, embedding, batchnormalization, lstm, attention and more) 🧠
• Many activation functions (sigmoid, tanh, relu, leaky relu, softmax, linear, elu, selu) 📈
• Many loss functions (mean squared error, mean absolute error, categorical crossentropy, binary crossentropy, huber loss) 📉
• Many optimizers (sgd, momentum, rmsprop, adam) 📊
• Supports binary classification, multiclass classification and regression 📖
• Save and load models 📁
• Simple to use 📚

⚙️ Installation

You can install the library using pip:

pip install neuralnetlib

💡 How to use

See this file for a simple example of how to use the library.
For a more advanced example, see this file for using CNN.
You can also check this file for text classification using RNN.

More examples in this folder.

You are free to tweak the hyperparameters and the network architecture to see how it affects the results.

I used the MNIST dataset to test the library, but you can use any dataset you want.

🚀 Quick examples (more here)

Binary Classification

from neuralnetlib.model import Model
from neuralnetlib.layers import Input, Dense
from neuralnetlib.activations import Sigmoid
from neuralnetlib.losses import BinaryCrossentropy
from neuralnetlib.optimizers import SGD
from neuralnetlib.metrics import accuracy_score

# ... Preprocess x_train, y_train, x_test, y_test if necessary (you can use neuralnetlib.preprocess and neuralnetlib.utils)

# Create a model
model = Model()
model.add(Input(10))  # 10 features
model.add(Dense(8))
model.add(Dense(1))
model.add(Activation(Sigmoid()))  # many ways to tell the model which Activation Function you'd like, see the next example

# Compile the model
model.compile(loss_function='bce', optimizer='sgd')

# Train the model
model.fit(X_train, y_train, epochs=10, batch_size=32, metrics=['accuracy'])

Multiclass Classification

from neuralnetlib.activations import Softmax
from neuralnetlib.losses import CategoricalCrossentropy
from neuralnetlib.optimizers import Adam
from neuralnetlib.metrics import accuracy_score

# ... Preprocess x_train, y_train, x_test, y_test if necessary (you can use neuralnetlib.preprocess and neuralnetlib.utils)

# Create and compile a model
model = Model()
model.add(Input(28, 28, 1)) # For example, MNIST images
model.add(Conv2D(32, kernel_size=3, padding='same'), activation='relu')  # activation supports both str...
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=2))
model.add(Dense(64, activation='relu'))
model.add(Dense(10, activation=Softmax()))  # ... and ActivationFunction objects
model.compile(loss_function='categorical_crossentropy', optimizer=Adam())


model.compile(loss_function='categorical_crossentropy', optimizer=Adam())  # same for loss_function and optimizer

# Train the model
model.fit(X_train, y_train_ohe, epochs=5, metrics=['accuracy'])

Regression

from neuralnetlib.losses import MeanSquaredError
from neuralnetlib.metrics import accuracy_score

# ... Preprocess x_train, y_train, x_test, y_test if necessary (you can use neuralnetlib.preprocess and neuralnetlib.utils)

# Create and compile a model
model = Model()
model.add(Input(13))
model.add(Dense(64, activation='leakyrelu'))
model.add(Dense(1), activation="linear")

model.compile(loss_function="mse", optimizer='adam')  # you can either put acronyms or full name

# Train the model
model.fit(X_train, y_train, epochs=100, batch_size=128, metrics=['accuracy'])

You can also save and load models:

# Save a model
model.save('my_model.json')

# Load a model
model = Model.load('my_model.json')

📜 Output of the example file

Here is the decision boundary on a Binary Classification (breast cancer dataset):

[!NOTE] PCA (Principal Component Analysis) was used to reduce the number of features to 2, so we could plot the decision boundary. Representing n-dimensional data in 2D is not easy, so the decision boundary may not be always accurate. I also tried with t-SNE, but the results were not good.

Here is an example of a model training on the mnist using the library

Here is an example of a loaded model used with Tkinter:

Here, I decided to print the first 10 predictions and their respective labels to see how the network is performing.

You can of course use the library for any dataset you want.

✍️ Authors

• Marc Pinet - Initial work - marcpinet