--- a
+++ b/test_a6_dsb.py
@@ -0,0 +1,145 @@
+import cPickle as pickle
+import string
+import sys
+import time
+from itertools import izip
+import lasagne as nn
+import numpy as np
+import theano
+from datetime import datetime, timedelta
+import utils
+import logger
+import theano.tensor as T
+import buffering
+from configuration import config, set_configuration
+import pathfinder
+import utils_plots
+
+theano.config.warn_float64 = 'raise'
+
+if len(sys.argv) < 2:
+    sys.exit("Usage: train.py <configuration_name>")
+
+config_name = sys.argv[1]
+set_configuration('configs_class_dsb', config_name)
+expid = utils.generate_expid(config_name)
+print
+print "Experiment ID: %s" % expid
+print
+
+predictions_dir = utils.get_dir_path('analysis', pathfinder.METADATA_PATH)
+outputs_path = predictions_dir + '/%s' % config_name
+utils.auto_make_dir(outputs_path)
+
+# metadata
+metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
+metadata_path = metadata_dir + '/%s.pkl' % expid
+
+# logs
+logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
+sys.stdout = logger.Logger(logs_dir + '/%s.log' % expid)
+sys.stderr = sys.stdout
+
+print 'Build model'
+model = config().build_model()
+all_layers = nn.layers.get_all_layers(model.l_out)
+all_params = nn.layers.get_all_params(model.l_out)
+num_params = nn.layers.count_params(model.l_out)
+print '  number of parameters: %d' % num_params
+print string.ljust('  layer output shapes:', 36),
+print string.ljust('#params:', 10),
+print 'output shape:'
+for layer in all_layers:
+    name = string.ljust(layer.__class__.__name__, 32)
+    num_param = sum([np.prod(p.get_value().shape) for p in layer.get_params()])
+    num_param = string.ljust(num_param.__str__(), 10)
+    print '    %s %s %s' % (name, num_param, layer.output_shape)
+
+train_loss = config().build_objective(model, deterministic=False)
+valid_loss = config().build_objective(model, deterministic=True)
+
+learning_rate_schedule = config().learning_rate_schedule
+learning_rate = theano.shared(np.float32(learning_rate_schedule[0]))
+updates = config().build_updates(train_loss, model, learning_rate)
+
+x_shared = nn.utils.shared_empty(dim=len(model.l_in.shape))
+y_shared = nn.utils.shared_empty(dim=len(model.l_target.shape))
+
+givens_train = {}
+givens_train[model.l_in.input_var] = x_shared
+givens_train[model.l_target.input_var] = y_shared
+
+givens_valid = {}
+givens_valid[model.l_in.input_var] = x_shared
+givens_valid[model.l_target.input_var] = y_shared
+
+# theano functions
+iter_train = theano.function([], train_loss, givens=givens_train, updates=updates)
+iter_validate = theano.function([], nn.layers.get_output(model.l_out, deterministic=True), givens=givens_valid,
+                                on_unused_input='ignore')
+
+if config().restart_from_save:
+    print 'Load model parameters for resuming'
+    resume_metadata = utils.load_pkl(config().restart_from_save)
+    nn.layers.set_all_param_values(model.l_out, resume_metadata['param_values'])
+    start_chunk_idx = resume_metadata['chunks_since_start'] + 1
+    chunk_idxs = range(start_chunk_idx, config().max_nchunks)
+
+    lr = np.float32(utils.current_learning_rate(learning_rate_schedule, start_chunk_idx))
+    print '  setting learning rate to %.7f' % lr
+    learning_rate.set_value(lr)
+    losses_eval_train = resume_metadata['losses_eval_train']
+    losses_eval_valid = resume_metadata['losses_eval_valid']
+else:
+    chunk_idxs = range(config().max_nchunks)
+    losses_eval_train = []
+    losses_eval_valid = []
+    start_chunk_idx = 0
+
+train_data_iterator = config().train_data_iterator
+valid_data_iterator = config().valid_data_iterator
+
+print
+print 'Data'
+print 'n train: %d' % train_data_iterator.nsamples
+print 'n validation: %d' % valid_data_iterator.nsamples
+print 'n chunks per epoch', config().nchunks_per_epoch
+
+print
+print 'Train model'
+chunk_idx = 0
+start_time = time.time()
+prev_time = start_time
+tmp_losses_train = []
+losses_train_print = []
+
+for chunk_idx, (x_chunk_train, y_chunk_train, id_train) in izip(chunk_idxs, buffering.buffered_gen_threaded(
+        valid_data_iterator.generate())):
+    if chunk_idx in learning_rate_schedule:
+        lr = np.float32(learning_rate_schedule[chunk_idx])
+        print '  setting learning rate to %.7f' % lr
+        print
+        learning_rate.set_value(lr)
+
+    # load chunk to GPU
+    x_shared.set_value(x_chunk_train)
+    y_shared.set_value(y_chunk_train)
+
+    predictions = iter_validate()
+    print predictions.shape
+
+    if predictions.shape != x_chunk_train.shape:
+        pad_width = (np.asarray(x_chunk_train.shape) - np.asarray(predictions.shape)) / 2
+        pad_width = [(p, p) for p in pad_width]
+        predictions = np.pad(predictions, pad_width=pad_width, mode='constant')
+
+    for i in xrange(x_chunk_train.shape[0]):
+        pid = id_train[i]
+        for j in xrange(x_chunk_train.shape[1]):
+            utils_plots.plot_slice_3d_3(input=x_chunk_train[i, j, 0],
+                                        mask=predictions[i, j, 0],
+                                        prediction=predictions[i, j, 0],
+                                        pid='-'.join([str(pid), str(j)]),
+                                        img_dir=outputs_path,
+                                        idx=np.array(x_chunk_train[i, j, 0].shape) / 2,
+                                        axis=0)