--- a
+++ b/configs_seg_patch/luna_p5.py
@@ -0,0 +1,164 @@
+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 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': [-16, 16],
+    'translation_range_y': [-16, 16],
+    'translation_range_x': [-16, 16],
+    'rotation_range_z': [-180, 180],
+    'rotation_range_y': [-180, 180],
+    'rotation_range_x': [-180, 180]
+}
+
+zmuv_mean, zmuv_std = 0.36, 0.31
+
+
+# data preparation function
+def data_prep_function(data, patch_center, luna_annotations, pixel_spacing, luna_origin, p_transform,
+                       p_transform_augment, **kwargs):
+    x, patch_annotation_tf, annotations_tf = data_transforms.transform_patch3d(data=data,
+                                                                               luna_annotations=luna_annotations,
+                                                                               patch_center=patch_center,
+                                                                               p_transform=p_transform,
+                                                                               p_transform_augment=p_transform_augment,
+                                                                               pixel_spacing=pixel_spacing,
+                                                                               luna_origin=luna_origin)
+    x = data_transforms.hu2normHU(x)
+    x = data_transforms.zmuv(x, zmuv_mean, zmuv_std)
+    y = data_transforms.make_3d_mask_from_annotations(img_shape=x.shape, annotations=annotations_tf, shape='sphere')
+    return x, y
+
+
+data_prep_function_train = partial(data_prep_function, p_transform_augment=p_transform_augment, p_transform=p_transform)
+data_prep_function_valid = partial(data_prep_function, p_transform_augment=None, p_transform=p_transform)
+
+# 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.PatchPositiveLunaDataGenerator(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)
+
+valid_data_iterator = data_iterators.ValidPatchPositiveLunaDataGenerator(data_path=pathfinder.LUNA_DATA_PATH,
+                                                                         transform_params=p_transform,
+                                                                         data_prep_fun=data_prep_function_valid,
+                                                                         patient_ids=valid_pids)
+if zmuv_mean is None or zmuv_std is None:
+    print 'estimating ZMUV parameters'
+    x_big = None
+    for i, (x, _, _) in zip(xrange(4), train_data_iterator.generate()):
+        print i
+        x_big = x if x_big is None else np.concatenate((x_big, x), axis=0)
+    zmuv_mean = x_big.mean()
+    zmuv_std = x_big.std()
+    print 'mean:', zmuv_mean
+    print 'std:', zmuv_std
+
+nchunks_per_epoch = train_data_iterator.nsamples / chunk_size
+max_nchunks = nchunks_per_epoch * 30
+
+validate_every = int(2. * nchunks_per_epoch)
+save_every = int(0.5 * nchunks_per_epoch)
+
+learning_rate_schedule = {
+    0: 1e-5,
+    int(max_nchunks * 0.4): 5e-6,
+    int(max_nchunks * 0.5): 2e-6,
+    int(max_nchunks * 0.8): 1e-6,
+    int(max_nchunks * 0.9): 5e-7
+}
+
+# model
+conv3d = partial(dnn.Conv3DDNNLayer,
+                 filter_size=3,
+                 pad='valid',
+                 W=nn.init.Orthogonal('relu'),
+                 b=nn.init.Constant(0.0),
+                 nonlinearity=nn.nonlinearities.identity)
+
+max_pool3d = partial(dnn.MaxPool3DDNNLayer,
+                     pool_size=2)
+
+
+def conv_prelu_layer(l_in, n_filters):
+    l = conv3d(l_in, n_filters)
+    l = nn.layers.ParametricRectifierLayer(l)
+    return l
+
+
+def build_model():
+    l_in = nn.layers.InputLayer((None, 1,) + p_transform['patch_size'])
+    l_target = nn.layers.InputLayer((None, 1,) + p_transform['patch_size'])
+
+    net = {}
+    base_n_filters = 128
+    net['contr_1_1'] = conv_prelu_layer(l_in, base_n_filters)
+    net['contr_1_2'] = conv_prelu_layer(net['contr_1_1'], base_n_filters)
+    net['contr_1_3'] = conv_prelu_layer(net['contr_1_2'], base_n_filters)
+    net['pool1'] = max_pool3d(net['contr_1_3'])
+
+    net['encode_1'] = conv_prelu_layer(net['pool1'], base_n_filters)
+    net['encode_2'] = conv_prelu_layer(net['encode_1'], base_n_filters)
+    net['encode_3'] = conv_prelu_layer(net['encode_2'], base_n_filters)
+    net['encode_4'] = conv_prelu_layer(net['encode_3'], base_n_filters)
+
+    net['upscale1'] = nn.layers.Upscale3DLayer(net['encode_4'], 2)
+    net['concat1'] = nn.layers.ConcatLayer([net['upscale1'], net['contr_1_3']],
+                                           cropping=(None, None, "center", "center", "center"))
+
+    net['dropout_1'] = nn.layers.DropoutLayer(net['concat1'])
+
+    net['expand_1_1'] = conv_prelu_layer(net['dropout_1'], 2 * base_n_filters)
+    net['expand_1_2'] = conv_prelu_layer(net['expand_1_1'], base_n_filters)
+    net['expand_1_3'] = conv_prelu_layer(net['expand_1_2'], base_n_filters)
+    net['expand_1_4'] = conv_prelu_layer(net['expand_1_3'], base_n_filters / 2)
+    net['expand_1_5'] = conv_prelu_layer(net['expand_1_4'], base_n_filters / 2)
+
+    l_out = dnn.Conv3DDNNLayer(net['expand_1_5'], num_filters=1,
+                               filter_size=1,
+                               nonlinearity=nn.nonlinearities.sigmoid)
+
+    return namedtuple('Model', ['l_in', 'l_out', 'l_target'])(l_in, l_out, l_target)
+
+
+def build_objective(model, deterministic=False, epsilon=1e-12):
+    network_predictions = nn.layers.get_output(model.l_out)
+    target_values = nn.layers.get_output(model.l_target)
+    network_predictions, target_values = nn.layers.merge.autocrop([network_predictions, target_values],
+                                                                  [None, None, 'center', 'center', 'center'])
+    y_true_f = target_values
+    y_pred_f = network_predictions
+
+    intersection = T.sum(y_true_f * y_pred_f)
+    dice = (2 * intersection + epsilon) / (T.sum(y_true_f) + T.sum(y_pred_f) + epsilon)
+    return -1. * dice
+
+
+def build_updates(train_loss, model, learning_rate):
+    updates = nn.updates.adam(train_loss, nn.layers.get_all_params(model.l_out), learning_rate)
+    return updates