Deep Learning avec Keras et TensorFlow

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Chapitre 1. Les fondamentaux du Machine Learning

Voici une implémentation simple de l’arrêt précoce:

from sklearn.base import clone

# Prépare les données

poly_scaler = Pipeline([

("poly_features", PolynomialFeatures(degree=90, include_bias=False)),

("std_scaler", StandardScaler())

])

X_train_poly_scaled = poly_scaler.fit_transform(X_train)

X_val_poly_scaled = poly_scaler.transform(X_val)

sgd_reg = SGDRegressor(max_iter=1, tol=-np.infty, warm_start=True,

penalty=None, learning_rate="constant", eta0=0.0005)

minimum_val_error = float("inf")

best_epoch = None

best_model = None

for epoch in range(1000):

sgd_reg.fit(X_train_poly_scaled, y_train) # Reprend ...