import keras
import numpy as np


keras.utils.set_random_seed(42)

x_train = np.linspace(-1.0, 1.0, 120, dtype="float32").reshape(-1, 1)
y_train = (2.0 * x_train) + 0.5
x_val = np.linspace(-0.8, 0.8, 40, dtype="float32").reshape(-1, 1)
y_val = (2.0 * x_val) + 0.5

model = keras.Sequential(
    [
        keras.layers.Input(shape=(1,)),
        keras.layers.Dense(8, activation="relu"),
        keras.layers.Dense(1),
    ]
)
model.compile(
    optimizer=keras.optimizers.SGD(learning_rate=0.35),
    loss="mse",
    metrics=["mae"],
)

early_stop = keras.callbacks.EarlyStopping(
    monitor="val_loss",
    mode="min",
    min_delta=0.0001,
    patience=2,
    restore_best_weights=True,
    verbose=1,
)

history = model.fit(
    x_train,
    y_train,
    validation_data=(x_val, y_val),
    epochs=20,
    batch_size=16,
    callbacks=[early_stop],
    verbose=0,
)

best_epoch = int(np.argmin(history.history["val_loss"])) + 1
best_val_loss = min(history.history["val_loss"])
restored_val_loss, restored_val_mae = model.evaluate(x_val, y_val, verbose=0)

print("epochs requested: 20")
print(f"epochs run: {len(history.history['loss'])}")
print(f"best epoch: {best_epoch}")
print(f"best val_loss: {best_val_loss:.4f}")
print(f"restored val_loss: {restored_val_loss:.4f}")
print(f"history keys: {sorted(history.history.keys())}")