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from sklearn import datasets
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, precision_score, recall_score
import numpy as np
from numpyneuron import (
NN,
Relu,
Sigmoid,
CrossEntropyWithLogits,
)
RANDOM_SEED = 2
def _preprocess_digits(
seed: int,
) -> tuple[np.ndarray, ...]:
digits = datasets.load_digits(as_frame=False)
n_samples = len(digits.images)
data = digits.images.reshape((n_samples, -1))
y = OneHotEncoder().fit_transform(digits.target.reshape(-1, 1)).toarray()
X_train, X_test, y_train, y_test = train_test_split(
data,
y,
test_size=0.2,
random_state=seed,
)
return X_train, X_test, y_train, y_test
def train_nn_classifier() -> None:
X_train, X_test, y_train, y_test = _preprocess_digits(seed=RANDOM_SEED)
nn_classifier = NN(
epochs=2_000,
hidden_size=16,
batch_size=1,
learning_rate=0.01,
loss_fn=CrossEntropyWithLogits(),
hidden_activation_fn=Relu(),
output_activation_fn=Sigmoid(),
input_size=64, # 8x8 pixel grid images
output_size=10, # digits 0-9
seed=2,
)
nn_classifier.train(
X_train=X_train,
y_train=y_train,
)
pred = nn_classifier.predict(X_test=X_test)
pred = np.argmax(pred, axis=1)
y_test = np.argmax(y_test, axis=1)
accuracy = accuracy_score(y_true=y_test, y_pred=pred)
print(f"accuracy on validation set: {accuracy:.4f}")
if __name__ == "__main__":
train_nn_classifier()
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