Neural Net

A Deep Learning library made from scratch with just Python and Numpy. Gradients are fully automated, both forward and backward propagation. Deriving the math from scratch by hand using only online resources and textbooks helped me understand the math deeper, and how to take advantage of deep learning.

Installation

python3 -m pip install [email protected]:slackroad/neuralnet.git

Usage

An example below using the MNIST dataset:

Import modules

from keras.datasets import mnist # type: ignore
from keras import utils # type: ignore
import numpy as np
from optimizer import RMSProp
from optimizer import SGD
from layers import Dense
from loss import BinaryCrossEntropy
from train import train
from layers import Convolution, Reshape, Sigmoid

from nn import NeuralNet
from tensor import Tensor

Make Sequential Model

After preprocessing data, we can create a Neural Net from an array of layers:

# neural network
network = NeuralNet([
    Convolution((1, 28, 28), 3, 5),
    Sigmoid(),
    Reshape((5, 26, 26), (5 * 26 * 26, 1)),
    Dense(5 * 26 * 26, 100),
    Sigmoid(),
    Dense(100, 10),
    Sigmoid()
])

Then, we can train the model using CrossEntropy and RMSProp

train(
    net = network,
    inputs = x_train,
    targets = y_train,
    loss = CrossEntropy(),
    optimizer = RMSProp(),
    num_epochs=5000
)

Optimizers:

• SGD
• SGD with Momentum
• Rmsprop

Layers:

• Conv2D
• Linear
• Reshape
• Dense
• Activation

Loss functions:

• CrossEntropy
• MSE

Activation Functions:

• sigmoid
• relu
• leakyRelu
• elu
• tanh