Что означает, когда потери проверки увеличиваются в течение нескольких эпох ⇐ Python

[Anonymous]

Я обучаю модель LSTM для прогнозирования цены акций. Это то, что я делаю с моим модельным обучением: < /p>

Код: Выделить всё

def build_and_train_lstm_model(X_train, y_train, X_validate, y_validate,
num_layers=4, units=100, dropout_rate=0.2,
epochs=200, batch_size=64,
model_name="lstm_google_price_predict_model.keras"):

"""
Builds and trains an LSTM model for time series prediction.
Parameters:
- X_train, y_train: Training data
- X_validate, y_validate: Validation data
- num_layers: Number of LSTM layers
- units: Number of LSTM units per layer
- dropout_rate: Dropout rate for regularization
- epochs: Training epochs
- batch_size: Batch size
- model_name: Name of the model file (stored in _local_config.models_dir)
Returns:
- history: Training history object
"""

global _local_config
if _local_config is None:
raise RuntimeError("Config not loaded yet! Call load_google first.")

# Try to get model_location from _local_config if available
if hasattr(_local_config, 'models_dir'):
print(f"Model will be saved to ${_local_config.models_dir}")
else:
raise ValueError("Model location not provided and not found in configg (_local_config)")

# Ensure the model directory exists
model_dir = Path(_local_config.models_dir)
model_dir.mkdir(parents=True, exist_ok=True)
model_path = model_dir / model_name

# Initialize model
regressor = Sequential()
regressor.add(Input(shape=(X_train.shape[1], X_train.shape[2])))

# Add LSTM + Dropout layers
for i in range(num_layers):
return_seq = i < (num_layers - 1)
regressor.add(LSTM(units=units, return_sequences=return_seq))
regressor.add(Dropout(rate=dropout_rate))

# Add output layer
regressor.add(Dense(units=1))

# Compile model
regressor.compile(optimizer="adam", loss="mean_squared_error")

# Create checkpoint
checkpoint_callback = ModelCheckpoint(
filepath=str(model_path),
monitor="val_loss",
save_best_only=True,
mode="min",
verbose=0
)

# Train the model
history = regressor.fit(
x=X_train,
y=y_train,
validation_data=(X_validate, y_validate),
epochs=epochs,
batch_size=batch_size,
callbacks=[checkpoint_callback]
)

return history

When I ran my training and then plot the loss function from my training and validation dataset, here is what I see:


I do not understand 2 things:

Как может быть, что потеря тренировок довольно последовательна?>

Подробнее здесь: https://stackoverflow.com/questions/797 ... ral-epochs