How to extract the image names and labels in the training set after completing the active learning loop and write them to a CSV file

[sivaramakrishnan-rajaraman]

I am using the Keras script at https://modal-python.readthedocs.io/en/latest/content/examples/Keras_integration.html for my classification task. After completing the active learning loop, how do we extract the image names and labels in the training set that gives the optimal test performance and write them to a CSV file?

# read training data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(60000, 28, 28, 1).astype('float32') / 255
X_test = X_test.reshape(10000, 28, 28, 1).astype('float32') / 255
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)

# assemble initial data: random sampling of 1000 samples
n_initial = 1000
initial_idx = np.random.choice(range(len(X_train)), size=n_initial, replace=False)
X_initial = X_train[initial_idx]
y_initial = y_train[initial_idx]

# generate the pool
# remove the initial data from the training dataset
X_pool = np.delete(X_train, initial_idx, axis=0)
y_pool = np.delete(y_train, initial_idx, axis=0)

"""
Training the ActiveLearner
"""

# initialize ActiveLearner
learner = ActiveLearner(
    estimator=classifier,
    query_strategy=entropy_sampling,
    X_training=X_initial, y_training=y_initial,
    verbose=1
)

# the active learning loop
n_queries = 100
for idx in range(n_queries):
    query_idx, query_instance = learner.query(X_pool, n_instances=100, verbose=0)
    print(query_idx)
    learner.teach(
        X=X_pool[query_idx], y=y_pool[query_idx], only_new=True,
        verbose=1
    )
    # remove queried instance from pool
    X_pool = np.delete(X_pool, query_idx, axis=0)
    y_pool = np.delete(y_pool, query_idx, axis=0)

# the final accuracy score
print(learner.score(X_test, y_test, verbose=1))