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# ------------------------------------------------- # 5. Train (early stopping) # ------------------------------------------------- es = tf.keras.callbacks.EarlyStopping(patience=5, restore_best_weights=True) model.fit(X, y, epochs=50, batch_size=32, validation_split=0.2, callbacks=[es]) To understand the intent behind the search, it
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# ------------------------------------------------- # 3. Prepare supervised learning windows # ------------------------------------------------- def create_dataset(series, look_back=12): X, y = [], [] for i in range(len(series)-look_back): X.append(series[i:i+look_back]) y.append(series[i+look_back]) return tf.constant(X, dtype=tf.float32), tf.constant(y, dtype=tf.float32)
