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from sklearn.model_selection import train_test_split
from typing import Callable
from nn.nn import NN
import pandas as pd
import numpy as np
def init_weights_biases(nn: NN) -> None:
bh = np.zeros((1, nn.hidden_size))
bo = np.zeros((1, 1))
wh = np.random.randn(nn.input_size, nn.hidden_size) * \
np.sqrt(2 / nn.input_size)
wo = np.random.randn(nn.hidden_size, 1) * np.sqrt(2 / nn.hidden_size)
nn.set_bh(bh)
nn.set_bo(bo)
nn.set_wh(wh)
nn.set_wo(wo)
def train(nn: NN) -> dict:
init_weights_biases(nn=nn)
X_train, X_test, y_train, y_test = train_test_split(
nn.X,
nn.y,
test_size=nn.test_size,
)
for _ in range(nn.epochs):
# compute hidden output
hidden_output = compute_node(
data=X_train.to_numpy(),
weights=nn.wh,
biases=nn.bh,
func=nn.func,
)
# compute output layer
y_hat = compute_node(
data=hidden_output,
weights=nn.wo,
biases=nn.bo,
func=nn.func,
)
mse = mean_squared_error(y_train, y_hat)
return {"mse": mse}
def compute_node(data: np.array, weights: np.array, biases: np.array, func: Callable) -> np.array:
return func(np.dot(data, weights) + biases)
def mean_squared_error(y: np.array, y_hat: np.array) -> np.array:
return np.mean((y - y_hat) ** 2)
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