class dcase_framework.learners.SceneClassifierKerasSequential(*args, **kwargs)[source]

Sequential Keras model for Acoustic scene classification

__init__(*args, **kwargs)[source]


__init__(\*args, \*\*kwargs)
clear(() -> None.  Remove all items from D.)
copy(() -> a shallow copy of D)
create_callback_list() Create list of Keras callbacks
create_external_metric_evaluators() Create external metric evaluators
create_model(input_shape) Create sequential Keras model
detect_file_format(filename) Detect file format from extension
empty() Check if file is empty
exists() Checks that file exists
fromkeys(...) v defaults to None.
get((k[,d]) -> D[k] if k in D, ...)
get_dump_content(data) Clean internal content for saving
get_file_information() Get file information, filename
get_hash([data]) Get unique hash string (md5) for given parameter dict
get_path(dotted_path[, default, data]) Get value from nested dict with dotted path
get_processing_interval() Processing interval
has_key((k) -> True if D has a key k, else False)
items(() -> list of D’s (key, value) pairs, ...)
iteritems(() -> an iterator over the (key, ...)
iterkeys(() -> an iterator over the keys of D)
keras_model_exists() Check that keras model exists on disk
keys(() -> list of D’s keys)
learn(data, annotations[, data_filenames, ...]) Learn based on data and annotations
load(\*args, \*\*kwargs) Load file
log([level]) Log container content
log_model_summary() Prints model summary to the logging interface.
merge(override[, target]) Recursive dict merge
plot_model([filename, show_shapes, ...]) Plots model topology
pop((k[,d]) -> v, ...) If key is not found, d is returned if given, otherwise KeyError is raised
popitem(() -> (k, v), ...) 2-tuple; but raise KeyError if D is empty.
predict(feature_data) Predict frame probabilities for given feature matrix
prepare_activity(activity_matrix_dict, files) Concatenate activity matrices into one activity matrix
prepare_data(data, files[, processor]) Concatenate feature data into one feature matrix
save(\*args, \*\*kwargs) Save file
set_path(dotted_path, new_value[, data]) Set value in nested dict with dotted path
set_seed([seed]) Set randomization seeds
setdefault((k[,d]) -> D.get(k,d), ...)
show() Print container content
update(([E, ...) If E present and has a .keys() method, does: for k in E: D[k] = E[k]
values(() -> list of D’s values)


class_labels Class labels
data_processor Feature processing chain
data_processor_training Feature processing chain
feature_aggregator Feature aggregator instance
feature_masker Feature masker instance
feature_normalizer Feature normalizer instance
feature_stacker Feature stacker instance
learner_params Get learner parameters from parameter container
method Learner method label
model Acoustic model
params Parameters