def test_log_hyperparams_verbose(mock_api_post_block, capsys):
data = {'arch_name': 'cnn_1', 'lr': .001}
log_hyperparams(data)
captured = capsys.readouterr()
expected_result = '[jovian] Hyperparams logged.'
assert expected_result == captured.out.strip()
def test_log_hyperparams(self, mock_api_post_block):
data = {'arch_name': 'cnn_1', 'lr': .001}
expected_result = [('fake_slug_metrics_1', 'metrics', {}),
('fake_slug_metrics_2', 'metrics', {}),
('fake_slug_hyperparams_1', 'hyperparams', {}),
('fake_slug_hyperparams_2', 'hyperparams', {}),
('fake_slug_3', 'hyperparams', data)]
log_hyperparams(data)
self.assertEqual(jovian.utils.records._data_blocks, expected_result)
def on_train_begin(self, n_epochs: int, metrics_names: list, **ka):
if self.reset_tracking:
reset('hyperparams', 'metrics')
hyp_dict = {
'epochs': n_epochs,
'batch_size': self.learn.data.batch_size,
'loss_func': str(self.learn.loss_func.func),
'opt_func': str(self.learn.opt_func.func).split("'")[1],
'weight_decay': self.learn.wd,
'learning_rate': str(self.learn.opt.lr)
}
if self.arch_name:
hyp_dict['arch_name'] = self.arch_name
log_hyperparams(hyp_dict)
if self.valid_set:
self.met_names.extend(metrics_names)
def on_train_begin(self, logs=None):
# Reset state if required
if self.reset_tracking:
reset('hyperparams', 'metrics')
hyp_dict = {
'epochs': self.params['epochs'],
'batch_size': self.params['batch_size'],
'loss_func': self.model.loss,
'opt':
str(self.model.optimizer.__class__).split("'")[1].split('.')[-1],
'wt_decay': self.model.optimizer.initial_decay,
'lr': str(get_value(self.model.optimizer.lr))
}
if self.arch_name:
hyp_dict['arch'] = self.arch_name
log_hyperparams(hyp_dict, verbose=False)
self.hyperparams = hyp_dict