Keras: how to save the training history attribute of the history object

Keras: how to save the training history attribute of the history object

In Keras, the training history is an object returned by the fit() method when training a model. It contains information about the training process, such as loss and accuracy values over epochs. If you want to save the training history attribute of the history object, you can achieve this by using Python's pickle module or other serialization methods.

Here's how you can save the training history using the pickle module:

import pickle from keras.models import Sequential from keras.layers import Dense from keras.optimizers import SGD # Create a simple model model = Sequential() model.add(Dense(units=1, input_dim=1)) model.compile(loss='mean_squared_error', optimizer=SGD(learning_rate=0.01)) # Generate some example data import numpy as np x = np.array([1, 2, 3, 4, 5]) y = np.array([2, 4, 6, 8, 10]) # Train the model and get the history object history = model.fit(x, y, epochs=10) # Save the history object using pickle with open('history.pickle', 'wb') as file: pickle.dump(history.history, file)

In this example, the history.history attribute contains the training history, including loss and other metrics, for each epoch. We save this dictionary using the pickle.dump() function.

To load and access the saved history later:

import pickle with open('history.pickle', 'rb') as file: loaded_history = pickle.load(file) print(loaded_history)

Keep in mind that using pickle might have compatibility issues across different versions of Python and libraries. If you plan to use the serialized data in different environments or across versions, consider using other serialization methods like JSON or saving the history to a CSV file.

Examples

  1. "Keras save training history object example"

    • Description: This query might lead to examples or tutorials demonstrating how to save the training history object in Keras for later analysis or visualization.
    import pickle # Train model and get history history = model.fit(train_data, train_labels, epochs=10) # Save history object with open('training_history.pkl', 'wb') as file: pickle.dump(history.history, file)

  2. "Keras save model training metrics"

    • Description: This query could be about saving the metrics obtained during model training in Keras for further analysis.
    import numpy as np # Train model and get history history = model.fit(train_data, train_labels, epochs=10) # Save training metrics np.save('training_metrics.npy', history.history)

  3. "Keras store training history to file"

    • Description: This query may aim to understand how to store the training history of a Keras model to a file for future reference.
    import json # Train model and get history history = model.fit(train_data, train_labels, epochs=10) # Save history to JSON file with open('training_history.json', 'w') as file: json.dump(history.history, file)

  4. "Keras save training history dictionary"

    • Description: This query might focus on saving the training history dictionary obtained from the history object in Keras.
    import numpy as np # Train model and get history history = model.fit(train_data, train_labels, epochs=10) # Save history dictionary np.save('training_history.npy', history.history)

  5. "Keras save model training progress"

    • Description: This query could be about saving the training progress of a Keras model, including metrics like loss and accuracy.
    import json # Train model and get history history = model.fit(train_data, train_labels, epochs=10) # Save training progress to JSON file with open('training_progress.json', 'w') as file: json.dump(history.history, file)

  6. "Keras save training history as CSV"

    • Description: This query may seek methods to save the training history of a Keras model as a CSV file for easy analysis.
    import pandas as pd # Train model and get history history = model.fit(train_data, train_labels, epochs=10) # Convert history to DataFrame and save as CSV pd.DataFrame(history.history).to_csv('training_history.csv', index=False)

  7. "Keras save model training logs"

    • Description: This query might be about saving the training logs generated during model training in Keras.
    import json # Train model and get history history = model.fit(train_data, train_labels, epochs=10) # Save training logs to JSON file with open('training_logs.json', 'w') as file: json.dump(history.history, file)

  8. "Keras export training history to text file"

    • Description: This query could aim at exporting the training history of a Keras model to a text file for documentation or analysis.
    # Train model and get history history = model.fit(train_data, train_labels, epochs=10) # Export history to text file with open('training_history.txt', 'w') as file: for key, value in history.history.items(): file.write(f'{key}: {value}\n')

  9. "Keras save training history for plotting"

    • Description: This query might be looking for ways to save the training history of a Keras model for later plotting of training curves.
    import numpy as np # Train model and get history history = model.fit(train_data, train_labels, epochs=10) # Save history for plotting np.save('training_history.npy', history.history)

  10. "Keras save training history to HDF5 file"

    • Description: This query could be about saving the training history of a Keras model to an HDF5 file format.
    import h5py # Train model and get history history = model.fit(train_data, train_labels, epochs=10) # Save history to HDF5 file with h5py.File('training_history.h5', 'w') as file: for key, value in history.history.items(): file.create_dataset(key, data=value)

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