![nn](https://camo.githubusercontent.com/e25d8c0d9c8234f7fb2f7b02c0c68021bd6e305518a18ea457b64baa6946074a/68747470733a2f2f692e6962622e636f2f647235533450482f6e6e2e706e67)
Step 0. Install neural-net-numpy
$ pip install -i https://test.pypi.org/simple/ neural-net-numpy
Step 1. Import modules from neural_net package
from neural_net.architecture import Sequential
from neural_net.layers import Fullyconnected,Activation
from neural_net.init_funcs import zeros,XavierHe
from neural_net.activation import σ,Softmax,LeakyReLU,Tanh,ELU,ReLU
from neural_net.cost import BinaryCrossEntropy,CrossEntropy
from neural_net.metrics import accuracy
from neural_net.pipeline import onehot,scaler,shuffle,Batch
from neural_net.utils import IrisDatasetDownloader
Step 2. Define Your Model
NNN = Sequential(
[
Fullyconnected(2,50,XavierHe("Uniform","ReLU").init_func),
Activation(LeakyReLU),
Fullyconnected(50,1,XavierHe("Uniform","Sigmoid").init_func),
Activation(σ)
],
BinaryCrossEntropy
)
Step 3. Import or create your training dataset
import numpy
n,k = 5000,2
X = numpy.random.uniform(-100,100,size=(n,k))
y =( (X[:, 0]**2 + X[:, 1]**2)/numpy.pi < 1000).reshape(-1,1)+0
NNN.train(scaler(X),y,metrics=accuracy)