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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:
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,
}
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