python - How to save training history on every epoch in Keras?

Python - How to save training history on every epoch in Keras?

In Keras, you can save the training history on every epoch using a custom callback. The ModelCheckpoint callback is commonly used for saving model weights during training, but it can also be used to save the training history at each epoch. Here's an example:

from keras.callbacks import ModelCheckpoint import matplotlib.pyplot as plt # Assuming you have a model defined and compiled (model) and training data (x_train, y_train) # Define a callback to save training history on every epoch class SaveHistoryCallback(keras.callbacks.Callback): def on_epoch_end(self, epoch, logs=None): if 'history' not in self.model.__dict__: self.model.__dict__['history'] = {'loss': [], 'accuracy': []} self.model.history['loss'].append(logs['loss']) self.model.history['accuracy'].append(logs['accuracy']) # Create an instance of the callback save_history_callback = SaveHistoryCallback() # Use ModelCheckpoint to save model weights (optional) model_checkpoint = ModelCheckpoint('model_weights.h5', save_best_only=True) # Train the model with the callbacks history = model.fit(x_train, y_train, epochs=10, callbacks=[save_history_callback, model_checkpoint]) # Access the saved history saved_loss = model.history['loss'] saved_accuracy = model.history['accuracy'] # Plot the training history plt.plot(saved_loss, label='Loss') plt.plot(saved_accuracy, label='Accuracy') plt.legend() plt.show()

In this example, the SaveHistoryCallback class is a custom callback that appends the training loss and accuracy to the model's history at the end of each epoch. The ModelCheckpoint callback is optional and can be used to save the model weights if needed.

After training, you can access the saved history from the model and use it for analysis or visualization. Adjust the callback and training parameters based on your specific use case.

Examples

  1. Save Training History on Every Epoch in Keras Using Callbacks:

    from keras.callbacks import Callback import json class SaveHistoryOnEpoch(Callback): def on_epoch_end(self, epoch, logs=None): with open(f'epoch_{epoch}_history.json', 'w') as file: json.dump(logs, file) # Usage in model.fit: model.fit(x_train, y_train, epochs=10, callbacks=[SaveHistoryOnEpoch()])

    Description: Create a custom Keras callback to save training history as a JSON file at the end of each epoch.

  2. Python Save Training Loss and Accuracy History in Keras Using Custom Callback:

    from keras.callbacks import Callback import json class SaveLossAccuracyHistory(Callback): def on_epoch_end(self, epoch, logs=None): history = {'loss': self.model.history.history['loss'], 'accuracy': self.model.history.history['accuracy']} with open(f'epoch_{epoch}_history.json', 'w') as file: json.dump(history, file) # Usage in model.fit: model.fit(x_train, y_train, epochs=10, callbacks=[SaveLossAccuracyHistory()])

    Description: Extend a custom Keras callback to save training loss and accuracy history as a JSON file at the end of each epoch.

  3. Save Training History in CSV Format Using CSVLogger Callback in Keras:

    from keras.callbacks import CSVLogger csv_logger = CSVLogger('training_history.csv') # Usage in model.fit: model.fit(x_train, y_train, epochs=10, callbacks=[csv_logger])

    Description: Utilize the built-in CSVLogger callback in Keras to save training history in CSV format after each epoch.

  4. Python Save Training Metrics History in Keras Using LambdaCallback:

    from keras.callbacks import LambdaCallback import json save_history_callback = LambdaCallback(on_epoch_end=lambda epoch, logs: json.dump(logs, open(f'epoch_{epoch}_history.json', 'w'))) # Usage in model.fit: model.fit(x_train, y_train, epochs=10, callbacks=[save_history_callback])

    Description: Use LambdaCallback in Keras to define a custom callback for saving training metrics history as a JSON file.

  5. Save Training History to Pickle File in Keras Using Custom Callback:

    from keras.callbacks import Callback import pickle class SaveHistoryToPickle(Callback): def on_epoch_end(self, epoch, logs=None): with open(f'epoch_{epoch}_history.pkl', 'wb') as file: pickle.dump(self.model.history.history, file) # Usage in model.fit: model.fit(x_train, y_train, epochs=10, callbacks=[SaveHistoryToPickle()])

    Description: Create a Keras callback to save training history as a pickle file at the end of each epoch.

  6. Python Save Specific Training Metrics to File in Keras Using ModelCheckpoint:

    from keras.callbacks import ModelCheckpoint checkpoint = ModelCheckpoint('weights.{epoch:02d}-{val_loss:.2f}.hdf5', monitor='val_loss', save_weights_only=True, save_best_only=False) # Usage in model.fit: model.fit(x_train, y_train, epochs=10, validation_data=(x_val, y_val), callbacks=[checkpoint])

    Description: Use ModelCheckpoint in Keras to save specific training metrics to files after each epoch.

  7. Save Training History to Text File in Keras Using LambdaCallback:

    from keras.callbacks import LambdaCallback save_history_callback = LambdaCallback(on_epoch_end=lambda epoch, logs: open(f'epoch_{epoch}_history.txt', 'w').write(str(logs))) # Usage in model.fit: model.fit(x_train, y_train, epochs=10, callbacks=[save_history_callback])

    Description: Use LambdaCallback to define a custom callback for saving training history to a text file after each epoch.

  8. Python Save Training History to SQLite Database in Keras Using SQLiteCallback:

    from keras.callbacks import Callback import sqlite3 class SQLiteCallback(Callback): def on_epoch_end(self, epoch, logs=None): connection = sqlite3.connect('training_history.db') cursor = connection.cursor() cursor.execute(f"INSERT INTO history VALUES ({epoch}, {logs['loss']}, {logs['accuracy']})") connection.commit() connection.close() # Usage in model.fit: model.fit(x_train, y_train, epochs=10, callbacks=[SQLiteCallback()])

    Description: Implement a Keras callback (SQLiteCallback) to save training history to an SQLite database after each epoch.

  9. Save Training History as JSON Lines (JSONL) in Keras Using LambdaCallback:

    from keras.callbacks import LambdaCallback import json save_history_callback = LambdaCallback(on_epoch_end=lambda epoch, logs: open(f'epoch_{epoch}_history.jsonl', 'a').write(json.dumps(logs) + '\n')) # Usage in model.fit: model.fit(x_train, y_train, epochs=10, callbacks=[save_history_callback])

    Description: Use LambdaCallback to define a custom callback for saving training history as JSON Lines (JSONL) after each epoch.

  10. Python Save Training Metrics as HDF5 File Using ModelCheckpoint in Keras:

    from keras.callbacks import ModelCheckpoint checkpoint = ModelCheckpoint('training_metrics.hdf5', monitor='val_loss', save_weights_only=False, save_best_only=False) # Usage in model.fit: model.fit(x_train, y_train, epochs=10, validation_data=(x_val, y_val), callbacks=[checkpoint])

    Description: Utilize ModelCheckpoint in Keras to save training metrics as an HDF5 file after each epoch.

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