candle.parsing_utils.ConfigDict

candle.parsing_utils.ConfigDict#

class candle.parsing_utils.ConfigDict#

Definition of the dictionary structure expected for the configuration of parameters.

__init__(*args, **kwargs)#

Methods

__init__(*args, **kwargs)

clear()

copy()

fromkeys([value])

Create a new dictionary with keys from iterable and values set to value.

get(key[, default])

Return the value for key if key is in the dictionary, else default.

items()

keys()

pop(k[,d])

If key is not found, default is returned if given, otherwise KeyError is raised

popitem()

Remove and return a (key, value) pair as a 2-tuple.

setdefault(key[, default])

Insert key with a value of default if key is not in the dictionary.

update([E, ]**F)

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values()

Attributes

config_file

data_type

rng_seed

train_bool

eval_bool

timeout

gpus

profiling

save_path

model_name

home_dir

train_data

val_data

test_data

output_dir

data_url

experiment_id

run_id

verbose

logfile

scaling

shuffle

feature_subsample

dense

conv

locally_connected

activation

out_activation

lstm_size

recurrent_dropout

dropout

pool

batch_normalization

loss

optimizer

metrics

epochs

batch_size

learning_rate

early_stop

momentum

initialization

val_split

train_steps

val_steps

test_steps

train_samples

val_samples

clr_flag

clr_mode

clr_base_lr

clr_max_lr

clr_gamma

ckpt_restart_mode

ckpt_checksum

ckpt_skip_epochs

ckpt_directory

ckpt_save_best

ckpt_save_best_metric

ckpt_save_weights_only

ckpt_save_interval

ckpt_keep_mode

ckpt_keep_limit