def __init__(
self,
layers,
update=nesterov_momentum,
loss=None, # BBB
objective=objective,
objective_loss_function=None,
batch_iterator_train=BatchIterator(batch_size=128),
batch_iterator_test=BatchIterator(batch_size=128),
regression=False,
max_epochs=100,
train_split=TrainSplit(eval_size=0.2),
custom_scores=None,
scores_train=None,
scores_valid=None,
X_tensor_type=None,
y_tensor_type=None,
use_label_encoder=False,
on_batch_finished=None,
on_epoch_finished=None,
on_training_started=None,
on_training_finished=None,
more_params=None,
check_input=True,
verbose=0,
**kwargs):
NeuralNet.__init__(
self, layers, update, loss, objective, objective_loss_function,
batch_iterator_train, batch_iterator_test, regression, max_epochs,
train_split, custom_scores, scores_train, scores_valid,
X_tensor_type, y_tensor_type, use_label_encoder, on_batch_finished,
on_epoch_finished, on_training_started, on_training_finished,
more_params, check_input, verbose, **kwargs)
def __init__(self, net_type, input_shape, output_size,
regression=False,
epochs=100,
learning_rate=0.0002,
verbose=1):
layers = self.get_layers(net_type, input_shape, output_size)
NeuralNet.__init__(self,
layers=layers,
max_epochs=epochs,
regression=regression,
update=lasagne.updates.adam,
update_learning_rate=learning_rate,
objective_l2=0.0025,
train_split=TrainSplit(eval_size=0.05),
verbose=verbose,
)
