neural_network/neural_network.py

from dataclasses import dataclass, field
from typing import Callable
import numpy as np


@dataclass
class NeuralNetwork:
    epochs: int
    learning_rate: float
    activation_func: Callable
    func_prime: Callable
    hidden_size: int
    w1: np.array
    w2: np.array
    b1: np.array
    b2: np.array

    mse: float = 0
    loss_history: list = field(
        default_factory=lambda: [],
    )

    def predict(self, x: np.array) -> np.array:
        n1 = self.compute_node(x, self.w1, self.b1, self.activation_func)
        return self.compute_node(n1, self.w2, self.b2, self.activation_func)

    def set_loss_hist(self, loss_hist: list) -> None:
        assert (isinstance(loss_hist, list))
        self.loss_history = loss_hist

    def eval(self, X_test, y_test) -> None:
        self.mse = np.mean((self.predict(X_test) - y_test) ** 2)

    @staticmethod
    def compute_node(arr, w, b, func) -> np.array:
        return func(np.dot(arr, w) + b)

    @classmethod
    def from_dict(cls, dct):
        return cls(**dct)

    def to_dict(self) -> dict:
        return {
            "w1": self.w1.tolist(),
            "w2": self.w2.tolist(),
            "b1": self.b1.tolist(),
            "b2": self.b2.tolist(),
            "epochs": self.epochs,
            "learning_rate": self.learning_rate,
            "activation_func": self.activation_func.__name__,
            "func_prime": self.func_prime.__name__,
            "hidden_size": self.hidden_size,
            "mse": self.mse,
            "loss_history": self.loss_history,
        }