dcase_framework.learners.SceneClassifierKerasSequential

class dcase_framework.learners.SceneClassifierKerasSequential(*args, **kwargs)

Sequential Keras model for Acoustic scene classification

__init__(*args, **kwargs)

Methods

__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_hash_for_path([dotted_path])
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)
itervalues(...)
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)
viewitems(...)
viewkeys(...)
viewvalues(...)

Attributes

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
valid_formats