import numpy as np
from random import random

neurons_per_layer = 4
layers = 2
inputs = 2
outputs = 1

grid_min = -10
grid_max = 10

biases = [[] for _ in range(layers)]

for i in range(0, neurons_per_layer)

weights = [[] for _ in range(layers+2)]

# Input layer to first hidden layer weights
weights[0] = [random() for _ in range(inputs*neurons_per_layer)]

for i in range(1, layers+1):
    # Weights between hidden layers
    weights[i] = [random() for _ in range(neurons_per_layer ** 2)]

# Weights from last hidden layer to output layer
weights[-1] = [random() for _ in range(outputs*neurons_per_layer)]

def sigmoid(x):  
    return np.exp(-np.logaddexp(0, -x))