Training and Callbacks

class EarlyStopping:
    """
    Stop training when monitored metric stops improving.
    
    Args:
        monitor (str): Metric to monitor
        min_delta (float): Minimum change to qualify as improvement
        patience (int): Number of epochs with no improvement to wait
        verbose (int): Verbosity mode
        mode (str): 'auto', 'min', or 'max'
        baseline (float, optional): Baseline value for monitored metric
        restore_best_weights (bool): Whether to restore best weights
        start_from_epoch (int): Epoch to start monitoring from
    """
    def __init__(self, monitor='val_loss', min_delta=0, patience=0, verbose=0,
                 mode='auto', baseline=None, restore_best_weights=False,
                 start_from_epoch=0, **kwargs): ...

class ReduceLROnPlateau:
    """
    Reduce learning rate when metric stops improving.
    
    Args:
        monitor (str): Metric to monitor
        factor (float): Factor to reduce learning rate by
        patience (int): Number of epochs with no improvement to wait
        verbose (int): Verbosity mode  
        mode (str): 'auto', 'min', or 'max'
        min_delta (float): Minimum change to qualify as improvement
        cooldown (int): Number of epochs to wait before resuming normal operation
        min_lr (float): Lower bound on learning rate
    """
    def __init__(self, monitor='val_loss', factor=0.1, patience=10, verbose=0,
                 mode='auto', min_delta=1e-4, cooldown=0, min_lr=0, **kwargs): ...

class LearningRateScheduler:
    """
    Learning rate scheduler with custom schedule function.
    
    Args:
        schedule (callable): Function that takes epoch index and current learning rate
        verbose (int): Verbosity mode
    """
    def __init__(self, schedule, verbose=0, **kwargs): ...

class TerminateOnNaN:
    """Terminate training when loss becomes NaN."""
    def __init__(self, **kwargs): ...

class ModelCheckpoint:
    """
    Save model checkpoints during training.
    
    Args:
        filepath (str): Path to save model files
        monitor (str): Metric to monitor for best model
        verbose (int): Verbosity mode
        save_best_only (bool): Only save when model improves
        save_weights_only (bool): Only save model weights
        mode (str): 'auto', 'min', or 'max'  
        save_freq (str or int): Frequency to save ('epoch' or integer steps)
        options (SaveOptions, optional): Options for saving
        initial_value_threshold (float, optional): Initial threshold for metric
    """
    def __init__(self, filepath, monitor='val_loss', verbose=0, save_best_only=False,
                 save_weights_only=False, mode='auto', save_freq='epoch', **kwargs): ...

class BackupAndRestore:
    """
    Backup and restore training state for fault tolerance.
    
    Args:
        backup_dir (str): Directory to store backup files
        save_freq (str or int): Frequency to save backups
        delete_checkpoint (bool): Whether to delete old checkpoints
    """
    def __init__(self, backup_dir, save_freq='epoch', delete_checkpoint=True, **kwargs): ...

class History:
    """
    Record training history (automatically added to model.fit).
    
    Attributes:
        history (dict): Dictionary containing training metrics by epoch
    """
    def __init__(self, **kwargs): ...

class CSVLogger:
    """
    Log training progress to CSV file.
    
    Args:
        filename (str): Path to CSV file
        separator (str): Field separator
        append (bool): Whether to append to existing file
    """
    def __init__(self, filename, separator=',', append=False, **kwargs): ...

class TensorBoard:
    """
    Log training metrics for TensorBoard visualization.
    
    Args:
        log_dir (str): Directory to save TensorBoard log files
        histogram_freq (int): Frequency to compute activation histograms
        write_graph (bool): Whether to visualize computation graph
        write_images (bool): Whether to write model weights as images
        write_steps_per_second (bool): Whether to log training speed
        update_freq (str or int): Frequency to write logs ('batch', 'epoch', or integer)
        profile_batch (int or tuple): Batch(es) to profile for performance
        embeddings_freq (int): Frequency to save embeddings
        embeddings_metadata (dict, optional): Metadata for embeddings
    """
    def __init__(self, log_dir='./logs', histogram_freq=0, write_graph=True,
                 write_images=False, write_steps_per_second=False, update_freq='epoch',
                 profile_batch=0, embeddings_freq=0, **kwargs): ...

class ProgbarLogger:
    """
    Display training progress bar (automatically added to model.fit).
    
    Args:
        count_mode (str): 'steps' or 'samples'
        stateful_metrics (set, optional): Metrics that shouldn't be averaged
    """
    def __init__(self, count_mode='samples', stateful_metrics=None, **kwargs): ...

class RemoteMonitor:
    """
    Send training events to remote monitoring server.
    
    Args:
        root (str): Root URL of monitoring server
        path (str): Path to send events to
        field (str): Field name for data
        headers (dict, optional): HTTP headers
        send_as_json (bool): Whether to send data as JSON
    """
    def __init__(self, root='http://localhost:9000', path='/publish/epoch/end/',
                 field='data', headers=None, send_as_json=False, **kwargs): ...

class LambdaCallback:
    """
    Create custom callback using lambda functions.
    
    Args:
        on_epoch_begin (callable, optional): Function called at epoch start
        on_epoch_end (callable, optional): Function called at epoch end
        on_batch_begin (callable, optional): Function called at batch start  
        on_batch_end (callable, optional): Function called at batch end
        on_train_begin (callable, optional): Function called at training start
        on_train_end (callable, optional): Function called at training end
    """
    def __init__(self, on_epoch_begin=None, on_epoch_end=None, on_batch_begin=None,
                 on_batch_end=None, on_train_begin=None, on_train_end=None, **kwargs): ...

class SwapEMAWeights:
    """
    Swap Exponential Moving Average weights for evaluation.
    
    Args:
        swap_on_epoch (bool): Whether to swap weights at epoch end
    """
    def __init__(self, swap_on_epoch=False, **kwargs): ...

def save_model(model, filepath, overwrite=True, save_format=None, **kwargs):
    """
    Save complete model to file.
    
    Args:
        model: Keras model to save
        filepath (str): Path to save model
        overwrite (bool): Whether to overwrite existing file
        save_format (str, optional): Format to save in ('tf', 'h5', or None for auto)
        include_optimizer (bool): Whether to save optimizer state
        save_traces (bool): Whether to save function traces
        options (SaveOptions, optional): Platform-specific save options
        signatures (callable or dict, optional): Model signatures for SavedModel
    """

def load_model(filepath, custom_objects=None, compile=True, safe_mode=True):
    """
    Load saved model from file.
    
    Args:
        filepath (str): Path to saved model
        custom_objects (dict, optional): Custom objects for deserialization
        compile (bool): Whether to compile loaded model
        safe_mode (bool): Whether to load in safe mode
        
    Returns:
        Model: Loaded Keras model
    """

def save_weights(model, filepath, overwrite=True, save_format=None, options=None):
    """
    Save model weights to file.
    
    Args:
        model: Keras model
        filepath (str): Path to save weights
        overwrite (bool): Whether to overwrite existing file
        save_format (str, optional): Format to save in
        options (SaveOptions, optional): Platform-specific save options
    """

def load_weights(model, filepath, skip_mismatch=False, by_name=False, options=None):
    """
    Load model weights from file.
    
    Args:
        model: Keras model
        filepath (str): Path to saved weights
        skip_mismatch (bool): Whether to skip layers with mismatched shapes
        by_name (bool): Whether to load weights by layer name
        options (SaveOptions, optional): Platform-specific load options
    """

class Callback:
    """
    Base class for callbacks.
    
    Attributes:
        params (dict): Training parameters
        model (Model): Reference to training model
    """
    def __init__(self, **kwargs): ...
    
    def set_params(self, params): ...
    def set_model(self, model): ...
    
    def on_train_begin(self, logs=None): ...
    def on_train_end(self, logs=None): ...
    def on_epoch_begin(self, epoch, logs=None): ...
    def on_epoch_end(self, epoch, logs=None): ...
    def on_train_batch_begin(self, batch, logs=None): ...
    def on_train_batch_end(self, batch, logs=None): ...
    def on_test_batch_begin(self, batch, logs=None): ...
    def on_test_batch_end(self, batch, logs=None): ...
    def on_predict_batch_begin(self, batch, logs=None): ...
    def on_predict_batch_end(self, batch, logs=None): ...

import keras
from keras import layers, callbacks

# Build model
model = keras.Sequential([
    layers.Dense(64, activation='relu', input_shape=(784,)),
    layers.Dropout(0.2),
    layers.Dense(10, activation='softmax')
])

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

# Configure callbacks
callback_list = [
    callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True),
    callbacks.ModelCheckpoint('best_model.keras', save_best_only=True),
    callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3),
    callbacks.TensorBoard(log_dir='./logs')
]

# Train with callbacks
history = model.fit(
    x_train, y_train,
    epochs=100,
    validation_data=(x_val, y_val),
    callbacks=callback_list
)

import keras
from keras import callbacks
import numpy as np

class ValidationMetrics(callbacks.Callback):
    def __init__(self, validation_data, **kwargs):
        super().__init__(**kwargs)
        self.validation_data = validation_data
        
    def on_epoch_end(self, epoch, logs=None):
        val_x, val_y = self.validation_data
        predictions = self.model.predict(val_x, verbose=0)
        
        # Calculate custom metrics
        accuracy = np.mean(np.argmax(predictions, axis=1) == val_y)
        print(f'Custom validation accuracy: {accuracy:.4f}')
        
        # Log custom metrics
        logs = logs or {}
        logs['custom_val_acc'] = accuracy

# Use custom callback
custom_callback = ValidationMetrics((x_val, y_val))
model.fit(x_train, y_train, epochs=10, callbacks=[custom_callback])

import keras
from keras import callbacks
import math

def step_decay(epoch, lr):
    """Step decay schedule."""
    drop_rate = 0.5
    epochs_drop = 10
    return lr * math.pow(drop_rate, math.floor(epoch / epochs_drop))

def cosine_decay(epoch, lr):
    """Cosine annealing schedule."""
    max_epochs = 100
    return 0.001 * 0.5 * (1 + math.cos(math.pi * epoch / max_epochs))

# Use scheduling callback
lr_scheduler = callbacks.LearningRateScheduler(step_decay, verbose=1)

model.fit(
    x_train, y_train,
    epochs=50,
    validation_data=(x_val, y_val),
    callbacks=[lr_scheduler]
)

import keras
from keras import callbacks

# Save best model based on validation loss
checkpoint_best = callbacks.ModelCheckpoint(
    filepath='models/best_model_{epoch:02d}_{val_loss:.2f}.keras',
    monitor='val_loss',
    save_best_only=True,
    save_weights_only=False,
    verbose=1
)

# Save model every 5 epochs
checkpoint_regular = callbacks.ModelCheckpoint(
    filepath='models/model_epoch_{epoch:02d}.keras',
    save_freq=5,
    verbose=1
)

# Backup and restore for fault tolerance
backup_restore = callbacks.BackupAndRestore(backup_dir='./backup')

model.fit(
    x_train, y_train,
    epochs=100,
    validation_data=(x_val, y_val),
    callbacks=[checkpoint_best, checkpoint_regular, backup_restore]
)