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, )