cubweb 0.1.6

Demo library

cobweb

the cobweb is a tiny neural network library that I created to gain deeper understanding of how neural nets are working underneath.

Installation

first of all checkout the requirements.txt

and simply use pip install cubweb command

basics

let's create a simple NN using cobweb

first you need to import MLP (model)

from cobweb.model import MLP

now we will create our model object.

you should pass your input (your training data) size as the first argument

and a list of tuples for each layer input size and it's activation function name

for instance imagine we want a NN with 4 layers such that the first layer has 12 neurons with tanh activation

the second layer has 8 neurons with tanh activation

the third layer has 4 neurons with the relu activation function

and the last layer has 3 neuron with the softmax activation functin and our inputs are 1*3 arrays.

our model will be created like this:

m = MLP(3,[(12,'tanh'),(8,"tanh"),(4,"relu"),(3,"softmax")])

[!TIP]

the default activation is relu so if you just pass a number or a tuple just with one elemnt like these are valid!

m = MLP(3,[(12,'tanh'),(8,"tanh"),(4,),(3,"softmax")])

m = MLP(3,[(12,'tanh'),(8,"tanh"),4,(3,"softmax")])

list of activations

you can pass these as activatins

• "relu"

• "tanh"

• "softmax"

[!WARNING]

make sure you are always using the softmax activation with the cross_entropy loss and as the last layer!

because of the backpropagation of softmax and cross_entropy loss if you use it in other ways maybe lead to error or your network doesn't learn.

in other activations and losses you are totally free.

training the NN

now let's train our NN

for this we will use **m.update()**

update method gets these as input:

• training data batch

• target datas

• loss_function (as str)

• number of epochs

• learning rate

by running this line of code our NN will be training:

m.update(x,y,loss_function="cross_entropy",epochs=100,learning_rate=0.01)

[!TIP]

the default valu for loss_function is "mse" which is MSE(mean squared error) loss.

the default value for epochs is 100

the default value for learning rate is 0.01

list of loss functions

• "mse" for MSE

• "mae" for MAE

• "cross_entropy" for categorical cross entropy

also even you can use r2 score as loss function by passing "r2"

Credit

special tahnks to Andrej Karpathy

I learned the most of these codes by his Micro Grad.