def test_history_save_load_cycle_file_path(self, history, tmpdir):
history_f = tmpdir.mkdir('skorch').join('history.json')
history.to_file(str(history_f))
new_history = History.from_file(str(history_f))
assert history == new_history
def test_history_save_load_cycle_file_obj(self, history, tmpdir):
history_f = tmpdir.mkdir('skorch').join('history.json')
with open(str(history_f), 'w') as f:
history.to_file(f)
with open(str(history_f), 'r') as f:
new_history = History.from_file(f)
assert history == new_history
def load_params(self,
f_params=None,
f_optimizer=None,
f_history=None,
checkpoint=None):
"""Loads the the module's parameters, history, and optimizer,
not the whole object.
To save and load the whole object, use pickle.
``f_params`` and ``f_optimizer`` uses PyTorchs'
:func:`~torch.save`.
Parameters
----------
f_params : file-like object, str, None (default=None)
Path of module parameters. Pass ``None`` to not load.
f_optimizer : file-like object, str, None (default=None)
Path of optimizer. Pass ``None`` to not load.
f_history : file-like object, str, None (default=None)
Path to history. Pass ``None`` to not load.
checkpoint : :class:`.Checkpoint`, None (default=None)
Checkpoint to load params from. If a checkpoint and a ``f_*``
path is passed in, the ``f_*`` will be loaded. Pass
``None`` to not load.
"""
def _get_state_dict(f):
map_location = get_map_location(self.device)
self.device = self._check_device(self.device, map_location)
return torch.load(f, map_location=map_location)
if f_history is not None:
self.history = History.from_file(f_history)
if checkpoint is not None:
if not self.initialized_:
self.initialize()
if f_history is None and checkpoint.f_history is not None:
self.history = History.from_file(checkpoint.f_history_)
formatted_files = checkpoint.get_formatted_files(self)
f_params = f_params or formatted_files['f_params']
f_optimizer = f_optimizer or formatted_files['f_optimizer']
if f_params is not None:
msg = ("Cannot load parameters of an un-initialized model. "
"Please initialize first by calling .initialize() "
"or by fitting the model with .fit(...).")
self.check_is_fitted(msg=msg)
state_dict = _get_state_dict(f_params)
state_dict_critic = _get_state_dict(f_params + '_critic')
self.module_.load_state_dict(state_dict)
self.critic_.load_state_dict(state_dict_critic)
if f_optimizer is not None:
msg = ("Cannot load state of an un-initialized optimizer. "
"Please initialize first by calling .initialize() "
"or by fitting the model with .fit(...).")
self.check_is_fitted(attributes=['optimizer_'], msg=msg)
state_dict = _get_state_dict(f_optimizer)
state_dict_critic = _get_state_dict(f_optimizer + '_critic')
self.optimizer_.load_state_dict(state_dict)
self.critic_optimizer_.load_state_dict(state_dict_critic)
5,
new_train_set,
n_perturbations,
batch_size=200)
amp_pert_mdiff = perturbation_correlation(amp_perturbation_additive,
mean_diff_feature_maps,
pred_fn,
5,
new_train_set,
n_perturbations,
batch_size=200)
freqs = np.fft.rfftfreq(new_train_set.shape[2], d=1 / 250.)
history = History()
history = history.from_file(
home +
'/logs/model_1_lr_0.001/histories/history_{last_epoch[epoch]}.json')
plot_history(history, None)
correlation_monitor = CorrelationMonitor1D(
input_time_length=input_time_length, setname='idk')
all_preds = []
all_targets = []
dataset = test_set
for X, y in zip(train_set.X, train_set.y):
preds = model(np_to_var(X).double())
all_preds.append(preds)
all_targets.append(y)
preds_2d = [p[:, None] for p in all_preds]