from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.callbacks import EarlyStopping

# 1. Build the model
model = Sequential([
    Dense(128, activation='relu', input_shape=(input_dim,)),
    Dense(64, activation='relu'),
    Dense(1, activation='sigmoid')  # for binary classification
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# 2. Define EarlyStopping callback
early_stop = EarlyStopping(
    monitor='val_loss',     # What to monitor
    patience=3,             # Number of epochs to wait after no improvement
    restore_best_weights=True # Restore best weights after stopping
)

# 3. Fit the model with validation data
history = model.fit(
    X_train, y_train,
    validation_data=(X_val, y_val),
    epochs=100,
    callbacks=[early_stop],  # Add callback here
    batch_size=32
)