--- a
+++ b/train_props_patch.py
@@ -0,0 +1,188 @@
+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
+
+nn.random.set_rng(np.random.RandomState(317070))
+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_luna_props_patch', config_name)
+expid = utils.generate_expid(config_name)
+print
+print "Experiment ID: %s" % expid
+print
+
+# 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))
+
+idx = T.lscalar('idx')
+givens_train = {}
+givens_train[model.l_in.input_var] = x_shared[idx * config().batch_size:(idx + 1) * config().batch_size]
+givens_train[model.l_target.input_var] = y_shared[idx * config().batch_size:(idx + 1) * config().batch_size]
+
+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([idx], train_loss, givens=givens_train, updates=updates)
+iter_validate = theano.function([], valid_loss, givens=givens_valid)
+
+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 = []
+
+# use buffering.buffered_gen_threaded()
+for chunk_idx, (x_chunk_train, y_chunk_train, id_train) in izip(chunk_idxs, buffering.buffered_gen_threaded(
+        train_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)
+
+    # make nbatches_chunk iterations
+    for b in xrange(config().nbatches_chunk):
+        loss = iter_train(b)
+        # print loss
+        tmp_losses_train.append(loss)
+        losses_train_print.append(loss)
+
+    if (chunk_idx + 1) % 10 == 0:
+        print 'Chunk %d/%d' % (chunk_idx + 1, config().max_nchunks), np.mean(losses_train_print)
+        losses_train_print = []
+
+    if ((chunk_idx + 1) % config().validate_every) == 0:
+        print
+        print 'Chunk %d/%d' % (chunk_idx + 1, config().max_nchunks)
+        # calculate mean train loss since the last validation phase
+        mean_train_loss = np.mean(tmp_losses_train)
+        print 'Mean train loss: %7f' % mean_train_loss
+        losses_eval_train.append(mean_train_loss)
+        tmp_losses_train = []
+
+        # load validation data to GPU
+        tmp_losses_valid = []
+        for i, (x_chunk_valid, y_chunk_valid, ids_batch) in enumerate(
+                buffering.buffered_gen_threaded(valid_data_iterator.generate(),
+                                                buffer_size=2)):
+            x_shared.set_value(x_chunk_valid)
+            y_shared.set_value(y_chunk_valid)
+            l_valid = iter_validate()
+            print i, l_valid
+            tmp_losses_valid.append(l_valid)
+
+        # calculate validation loss across validation set
+        valid_loss = np.mean(tmp_losses_valid)
+        print 'Validation loss: ', valid_loss
+        losses_eval_valid.append(valid_loss)
+
+        now = time.time()
+        time_since_start = now - start_time
+        time_since_prev = now - prev_time
+        prev_time = now
+        est_time_left = time_since_start * (config().max_nchunks - chunk_idx + 1.) / (chunk_idx + 1. - start_chunk_idx)
+        eta = datetime.now() + timedelta(seconds=est_time_left)
+        eta_str = eta.strftime("%c")
+        print "  %s since start (%.2f s)" % (utils.hms(time_since_start), time_since_prev)
+        print "  estimated %s to go (ETA: %s)" % (utils.hms(est_time_left), eta_str)
+        print
+
+    if ((chunk_idx + 1) % config().save_every) == 0:
+        print
+        print 'Chunk %d/%d' % (chunk_idx + 1, config().max_nchunks)
+        print 'Saving metadata, parameters'
+
+        with open(metadata_path, 'w') as f:
+            pickle.dump({
+                'configuration_file': config_name,
+                'git_revision_hash': utils.get_git_revision_hash(),
+                'experiment_id': expid,
+                'chunks_since_start': chunk_idx,
+                'losses_eval_train': losses_eval_train,
+                'losses_eval_valid': losses_eval_valid,
+                'param_values': nn.layers.get_all_param_values(model.l_out)
+            }, f, pickle.HIGHEST_PROTOCOL)
+            print '  saved to %s' % metadata_path
+            print
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