def train_all_tanhgru(seed=0, model_dir='tanhgru'):
"""Training of all tasks with Tanh GRUs."""
model_dir = os.path.join(DATAPATH, model_dir, str(seed))
hp = {'activation': 'tanh', 'rnn_type': 'LeakyGRU'}
rule_prob_map = {'contextdm1': 5, 'contextdm2': 5}
train.train(model_dir,
hp=hp,
ruleset='all',
rule_prob_map=rule_prob_map,
seed=seed)
# Analyses
variance.compute_variance(model_dir)
log = tools.load_log(model_dir)
analysis = clustering.Analysis(model_dir, 'rule')
log['n_cluster'] = analysis.n_cluster
tools.save_log(log)
data_analysis.compute_var_all(model_dir)
setups = [1, 2, 3]
for setup in setups:
taskset.compute_taskspace(model_dir,
setup,
restore=False,
representation='rate')
taskset.compute_replacerule_performance(model_dir, setup, False)
def train_all_mixrule_softplus(seed=0, root_dir='mixrule_softplus'):
"""Training of all tasks."""
model_dir = os.path.join(DATAPATH, root_dir, str(seed))
hp = {
'activation': 'softplus',
'w_rec_init': 'diag',
'use_separate_input': True,
'mix_rule': True
}
rule_prob_map = {'contextdm1': 5, 'contextdm2': 5}
train.train(model_dir,
hp=hp,
ruleset='all',
rule_prob_map=rule_prob_map,
seed=seed)
# Analyses
variance.compute_variance(model_dir)
log = tools.load_log(model_dir)
analysis = clustering.Analysis(model_dir, 'rule')
log['n_cluster'] = analysis.n_cluster
tools.save_log(log)
setups = [1, 2, 3]
for setup in setups:
taskset.compute_taskspace(model_dir,
setup,
restore=False,
representation='rate')
taskset.compute_replacerule_performance(model_dir, setup, False)
def train_all_analysis(seed=0, root_dir='train_all'):
model_dir = os.path.join(DATAPATH, root_dir, str(seed))
# Analyses
variance.compute_variance(model_dir)
variance.compute_variance(model_dir, random_rotation=True)
log = tools.load_log(model_dir)
analysis = clustering.Analysis(model_dir, 'rule')
log['n_cluster'] = analysis.n_cluster
tools.save_log(log)
data_analysis.compute_var_all(model_dir)
for rule in ['dm1', 'contextdm1', 'multidm']:
performance.compute_choicefamily_varytime(model_dir, rule)
setups = [1, 2, 3]
for setup in setups:
taskset.compute_taskspace(model_dir,
setup,
restore=False,
representation='rate')
taskset.compute_replacerule_performance(model_dir, setup, False)
