--- a
+++ b/configs_fpred_patch/luna_c2.py
@@ -0,0 +1,150 @@
+import numpy as np
+import data_transforms
+import data_iterators
+import pathfinder
+import lasagne as nn
+from collections import namedtuple
+from functools import partial
+import lasagne.layers.dnn as dnn
+import lasagne
+import theano.tensor as T
+import utils
+
+restart_from_save = None
+rng = np.random.RandomState(42)
+
+# transformations
+p_transform = {'patch_size': (64, 64, 64),
+               'mm_patch_size': (64, 64, 64),
+               'pixel_spacing': (1., 1., 1.)
+               }
+p_transform_augment = {
+    'translation_range_z': [-4, 4],
+    'translation_range_y': [-4, 4],
+    'translation_range_x': [-4, 4],
+    'rotation_range_z': [-180, 180],
+    'rotation_range_y': [-180, 180],
+    'rotation_range_x': [-180, 180]
+}
+
+
+# data preparation function
+def data_prep_function(data, patch_center, pixel_spacing, luna_origin, p_transform,
+                       p_transform_augment, world_coord_system, **kwargs):
+    x, patch_annotation_tf = data_transforms.transform_patch3d(data=data,
+                                                               luna_annotations=None,
+                                                               patch_center=patch_center,
+                                                               p_transform=p_transform,
+                                                               p_transform_augment=p_transform_augment,
+                                                               pixel_spacing=pixel_spacing,
+                                                               luna_origin=luna_origin,
+                                                               world_coord_system=world_coord_system)
+    x = data_transforms.pixelnormHU(x)
+    return x
+
+
+data_prep_function_train = partial(data_prep_function, p_transform_augment=p_transform_augment,
+                                   p_transform=p_transform, world_coord_system=True)
+data_prep_function_valid = partial(data_prep_function, p_transform_augment=None,
+                                   p_transform=p_transform, world_coord_system=True)
+
+# data iterators
+batch_size = 4
+nbatches_chunk = 8
+chunk_size = batch_size * nbatches_chunk
+
+train_valid_ids = utils.load_pkl(pathfinder.LUNA_VALIDATION_SPLIT_PATH)
+train_pids, valid_pids = train_valid_ids['train'], train_valid_ids['valid']
+
+train_data_iterator = data_iterators.CandidatesLunaDataGenerator(data_path=pathfinder.LUNA_DATA_PATH,
+                                                                 batch_size=chunk_size,
+                                                                 transform_params=p_transform,
+                                                                 data_prep_fun=data_prep_function_train,
+                                                                 rng=rng,
+                                                                 patient_ids=train_pids,
+                                                                 full_batch=True, random=True, infinite=True,
+                                                                 positive_proportion=0.5)
+
+valid_data_iterator = data_iterators.CandidatesLunaValidDataGenerator(data_path=pathfinder.LUNA_DATA_PATH,
+                                                                      transform_params=p_transform,
+                                                                      data_prep_fun=data_prep_function_valid,
+                                                                      patient_ids=valid_pids)
+
+nchunks_per_epoch = train_data_iterator.nsamples / chunk_size
+max_nchunks = nchunks_per_epoch * 100
+
+validate_every = int(5. * nchunks_per_epoch)
+save_every = int(1. * nchunks_per_epoch)
+
+learning_rate_schedule = {
+    0: 1e-5,
+    int(max_nchunks * 0.5): 5e-6,
+    int(max_nchunks * 0.6): 2e-6,
+    int(max_nchunks * 0.8): 1e-6,
+    int(max_nchunks * 0.9): 5e-7
+}
+
+# model
+conv3 = partial(dnn.Conv3DDNNLayer,
+                filter_size=3,
+                pad='valid',
+                W=nn.init.Orthogonal(),
+                b=nn.init.Constant(0.01),
+                nonlinearity=nn.nonlinearities.very_leaky_rectify)
+
+max_pool = partial(dnn.MaxPool3DDNNLayer,
+                   pool_size=2)
+
+drop = lasagne.layers.DropoutLayer
+
+dense = partial(lasagne.layers.DenseLayer,
+                W=lasagne.init.Orthogonal(),
+                b=lasagne.init.Constant(0.01),
+                nonlinearity=lasagne.nonlinearities.very_leaky_rectify)
+
+
+def build_model():
+    l_in = nn.layers.InputLayer((None, 1,) + p_transform['patch_size'])
+    l_target = nn.layers.InputLayer((None, 1))
+
+    l = conv3(l_in, num_filters=128)
+    l = conv3(l, num_filters=128)
+
+    l = max_pool(l)
+
+    l = conv3(l, num_filters=128)
+    l = conv3(l, num_filters=128)
+
+    l = max_pool(l)
+
+    l = conv3(l, num_filters=256)
+    l = conv3(l, num_filters=256)
+    l = conv3(l, num_filters=256)
+
+    l = max_pool(l)
+
+    l_d01 = nn.layers.DenseLayer(l, num_units=1024, W=nn.init.Orthogonal(),
+                                 b=nn.init.Constant(0.01), nonlinearity=nn.nonlinearities.very_leaky_rectify)
+
+    l_d02 = nn.layers.DenseLayer(nn.layers.dropout(l_d01), num_units=1024, W=nn.init.Orthogonal(),
+                                 b=nn.init.Constant(0.01), nonlinearity=nn.nonlinearities.very_leaky_rectify)
+
+    l_out = nn.layers.DenseLayer(l_d02, num_units=2,
+                                 W=nn.init.Constant(0.),
+                                 nonlinearity=nn.nonlinearities.softmax)
+
+    return namedtuple('Model', ['l_in', 'l_out', 'l_target'])(l_in, l_out, l_target)
+
+
+def build_objective(model, deterministic=False, epsilon=1e-12):
+    predictions = nn.layers.get_output(model.l_out, deterministic=deterministic)
+    targets = T.cast(T.flatten(nn.layers.get_output(model.l_target)), 'int32')
+    p = predictions[T.arange(predictions.shape[0]), targets]
+    p = T.clip(p, epsilon, 1.)
+    loss = T.mean(T.log(p))
+    return -loss
+
+
+def build_updates(train_loss, model, learning_rate):
+    updates = nn.updates.adam(train_loss, nn.layers.get_all_params(model.l_out, trainable=True), learning_rate)
+    return updates