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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() | |