"""Blocking out the structure of the Neural Network
"""

import numpy as np

class SimpleNN:
    def __init__(self, architecture):
        self.architecture = architecture
        self.weights = []
        self.biases = []

        # Initialize weights and biases
        np.random.seed(99)
        for i in range(len(architecture) - 1):
            self.weights.append(np.random.uniform(
                low=-1, high=1,
                size=(architecture[i], architecture[i+1])
            ))
            self.biases.append(np.zeros((1, architecture[i+1])))

architecture = [2, 64, 64, 64, 1]  # Two inputs, two hidden layers, one output
model = SimpleNN(architecture)

print('weight dimensions:')
for w in model.weights:
    print(w.shape)

print('nbias dimensions:')
for b in model.biases:
    print(b.shape)