diff --git a/.travis.yml b/.travis.yml
index 9ccc4436..60d771eb 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -53,19 +53,19 @@ script:
        # check patch using single precision, this check will create the net_current.h5 file for testing loading
        - cd python
        # test affinity training patch, network initialization, network with even field of view
-       - python train.py -c ../testsuit/affinity/config.cfg -d single -k yes
+       - python ztrain.py -c ../testsuit/affinity/config.cfg -d single -k yes
        # test boundary map training, patch matching, network initialization
-       - python train.py -c ../testsuit/boundary/config.cfg -d single -k yes
+       - python ztrain.py -c ../testsuit/boundary/config.cfg -d single -k yes
        # second check to test the network loading
-       - python train.py -c ../testsuit/boundary/config.cfg -d single -k yes
+       - python ztrain.py -c ../testsuit/boundary/config.cfg -d single -k yes
        # check the double precision
        # compile the core with double precision
        - cd core; make double -j 4
        # return to `python`
        - cd ..
-       - python train.py -c ../testsuit/boundary/config.cfg -d double -k yes
+       - python ztrain.py -c ../testsuit/boundary/config.cfg -d double -k yes
        # test forward pass
-       - python forward.py -c ../testsuit/forward/config.cfg
+       - python zforward.py -c ../testsuit/forward/config.cfg
        # return to root directory
        - cd ..
 
diff --git a/python/tifffile.py b/python/emirt/tifffile.py
similarity index 100%
rename from python/tifffile.py
rename to python/emirt/tifffile.py
diff --git a/python/front_end/__init__.py b/python/front_end/__init__.py
index 98d85cd3..65575ce6 100644
--- a/python/front_end/__init__.py
+++ b/python/front_end/__init__.py
@@ -3,4 +3,9 @@
 import zlog
 import znetio
 import zsample
-import zshow
\ No newline at end of file
+import zshow
+import zutils
+import zaws
+import zcost
+import ztest
+import zcheck
\ No newline at end of file
diff --git a/python/front_end/zaws.py b/python/front_end/zaws.py
new file mode 100644
index 00000000..754cbe0b
--- /dev/null
+++ b/python/front_end/zaws.py
@@ -0,0 +1,30 @@
+import os
+
+def s3download(s3fname, tmpdir="/tmp/"):
+    """
+    download aws s3 file
+
+    params:
+    - fname: string, file name in s3
+    - tmpdir: string, temporary directory
+
+    return:
+    - lcfname: string, local file name
+    """
+    if s3fname and "s3://" in s3fname:
+        # base name
+        bn = os.path.basename(s3fname)
+        # local directory
+        lcdir = os.path.dirname( s3fname )
+        lcdir = lcdir.replace("s3://", "")
+        lcdir = os.path.join(tmpdir, lcdir)
+        # make directory
+        os.makedirs(lcdir)
+        # local file name
+        lcfname = os.path.join( tmpdir, bn )
+        # copy file from s3
+        os.system("aws s3 cp {} {}".format(s3fname, lcfname))
+        return lcfname
+    else:
+        # this is not a s3 file, just a local file
+        return s3fname
\ No newline at end of file
diff --git a/python/zcheck.py b/python/front_end/zcheck.py
similarity index 98%
rename from python/zcheck.py
rename to python/front_end/zcheck.py
index 90550027..57e8c3ca 100644
--- a/python/zcheck.py
+++ b/python/front_end/zcheck.py
@@ -5,7 +5,7 @@
 """
 import os
 import numpy as np
-import utils
+import zutils
 import emirt
 
 def check_gradient(pars, net, smp, h=0.00001):
@@ -21,7 +21,7 @@ def check_gradient(pars, net, smp, h=0.00001):
 
     # numerical gradient
     # apply the transformations in memory rather than array view
-    vol_ins = utils.make_continuous(vol_ins)
+    vol_ins = zutils.make_continuous(vol_ins)
     # shift the input to compute the analytical gradient
     vol_ins1 = dict()
     vol_ins2 = dict()
diff --git a/python/front_end/zconfig.py b/python/front_end/zconfig.py
index aec16105..bdda2ba2 100644
--- a/python/front_end/zconfig.py
+++ b/python/front_end/zconfig.py
@@ -10,9 +10,10 @@
 import ConfigParser
 import numpy as np
 import os
-import cost_fn
-import utils
+import zcost
+import zutils
 from emirt import volume_util
+import zaws
 
 def parser(conf_fname):
     # parse config file to get parameters
@@ -78,11 +79,11 @@ def parse_cfg( conf_fname ):
     #TRAINING OPTIONS
     #Samples to use for training
     if config.has_option('parameters', 'train_range'):
-        pars['train_range'] = utils.parseIntSet( config.get('parameters',   'train_range') )
+        pars['train_range'] = zutils.parseIntSet( config.get('parameters',   'train_range') )
 
     #Samples to use for cross-validation
     if config.has_option('parameters', 'test_range'):
-        pars['test_range']  = utils.parseIntSet( config.get('parameters',   'test_range') )
+        pars['test_range']  = zutils.parseIntSet( config.get('parameters',   'test_range') )
     #Learning Rate
     if config.has_option('parameters', 'eta'):
         pars['eta']         = config.getfloat('parameters', 'eta')
@@ -241,7 +242,7 @@ def parse_cfg( conf_fname ):
 
     #FULL FORWARD PASS PARAMETERS
     #Which samples to use
-    pars['forward_range'] = utils.parseIntSet( config.get('parameters', 'forward_range') )
+    pars['forward_range'] = zutils.parseIntSet( config.get('parameters', 'forward_range') )
     #Which network file to load
     pars['forward_net']   = config.get('parameters', 'forward_net')
     #Output Patch Size
@@ -258,26 +259,36 @@ def autoset_pars(pars):
         # automatic choosing of cost function
         if 'boundary' in pars['out_type']:
             pars['cost_fn_str'] = 'softmax_loss'
-            pars['cost_fn'] = cost_fn.softmax_loss
+            pars['cost_fn'] = zcost.softmax_loss
         elif 'affin' in pars['out_type']:
             pars['cost_fn_str'] = 'binomial_cross_entropy'
-            pars['cost_fn'] = cost_fn.binomial_cross_entropy
+            pars['cost_fn'] = zcost.binomial_cross_entropy
         elif 'semantic' in pars['out_type']:
             pars['cost_fn_str'] = 'softmax_loss'
-            pars['cost_fn'] = cost_fn.softmax_loss
+            pars['cost_fn'] = zcost.softmax_loss
         else:
             raise NameError("no matching cost function for out_type!")
     elif "square-square" in pars['cost_fn_str']:
-        pars['cost_fn'] = cost_fn.square_square_loss
+        pars['cost_fn'] = zcost.square_square_loss
     elif "square" in pars['cost_fn_str']:
-        pars['cost_fn'] = cost_fn.square_loss
+        pars['cost_fn'] = zcost.square_loss
     elif  "binomial" in pars['cost_fn_str']:
-        pars['cost_fn'] = cost_fn.binomial_cross_entropy
+        pars['cost_fn'] = zcost.binomial_cross_entropy
     elif "softmax" in pars['cost_fn_str']:
-        pars['cost_fn'] = cost_fn.softmax_loss
+        pars['cost_fn'] = zcost.softmax_loss
     else:
         raise NameError('unknown type of cost function')
 
+    # aws s3 filehandling
+    pars['fnet_spec']  = zaws.s3download( pars['fnet_spec'] )
+    pars['fdata_spec'] = zaws.s3download( pars['fdata_spec'] )
+    # local file name
+    if "s3://" in pars['train_net_prefix']:
+        # copy the path as a backup
+        pars['s3_train_net_prefix'] = pars['train_net_prefix']
+        bn = os.path.basename( pars['train_net_prefix'] )
+        # replace with local path
+        pars['train_net_prefix'] = "/tmp/{}".format(bn)
     return pars
 
 def check_pars(pars):
@@ -353,6 +364,9 @@ def autoset_dspec(pars, dspec):
 
 # parse args
 def parse_args(args):
+    # s3 to local
+    args['config'] = zaws.s3download( args['config'] )
+    args['seed'] = zaws.s3download( args['seed'] )
     #%% parameters
     if not os.path.exists( args['config'] ):
         raise NameError("config file not exist!")
diff --git a/python/cost_fn.py b/python/front_end/zcost.py
similarity index 92%
rename from python/cost_fn.py
rename to python/front_end/zcost.py
index 98e82060..29ad340f 100644
--- a/python/cost_fn.py
+++ b/python/front_end/zcost.py
@@ -5,7 +5,7 @@
 """
 import numpy as np
 import emirt
-import utils
+import zutils
 from core import pyznn
 
 def get_cls(props, lbls, mask=None):
@@ -25,8 +25,8 @@ def get_cls(props, lbls, mask=None):
     c = 0.0
 
     #Applying mask if it exists
-    props = utils.mask_dict_vol(props, mask)
-    lbls = utils.mask_dict_vol(lbls, mask)
+    props = zutils.mask_dict_vol(props, mask)
+    lbls = zutils.mask_dict_vol(lbls, mask)
 
     for name, prop in props.iteritems():
         lbl = lbls[name]
@@ -53,8 +53,8 @@ def square_loss(props, lbls, mask=None):
     err = 0
 
     #Applying mask if it exists
-    props = utils.mask_dict_vol(props, mask)
-    lbls = utils.mask_dict_vol(lbls, mask)
+    props = zutils.mask_dict_vol(props, mask)
+    lbls = zutils.mask_dict_vol(lbls, mask)
 
     for name, prop in props.iteritems():
         lbl = lbls[name]
@@ -74,8 +74,8 @@ def square_square_loss(props, lbls, mask=None, margin=0.2):
     error = 0
 
     #Applying mask if it exists
-    props = utils.mask_dict_vol(props, mask)
-    lbls = utils.mask_dict_vol(lbls, mask)
+    props = zutils.mask_dict_vol(props, mask)
+    lbls = zutils.mask_dict_vol(lbls, mask)
 
     for name, propagation in props.iteritems():
         lbl = lbls[name]
@@ -120,8 +120,8 @@ def binomial_cross_entropy(props, lbls, mask=None):
         entropy[name] = -lbl*np.log(prop) - (1-lbl)*np.log(1-prop)
 
     #Applying mask if it exists
-    grdts = utils.mask_dict_vol(grdts, mask)
-    entropy = utils.mask_dict_vol(entropy, mask)
+    grdts = zutils.mask_dict_vol(grdts, mask)
+    entropy = zutils.mask_dict_vol(entropy, mask)
 
     for name, vol in entropy.iteritems():
         err += np.sum( vol )
@@ -186,7 +186,7 @@ def multinomial_cross_entropy(props, lbls, mask=None):
         entropy[name] = -lbl * np.log(prop)
 
     #Applying mask if it exists
-    entropy = utils.mask_dict_vol(entropy, mask)
+    entropy = zutils.mask_dict_vol(entropy, mask)
 
     for name, vol in entropy.iteritems():
         cost += np.sum( vol )
@@ -399,7 +399,7 @@ def get_grdt(pars, history, props, lbl_outs, msks, wmsks, vn):
       #  history['re']  += pyznn.get_rand_error( props.values(), lbl_outs.values() )
        # print  're: {}'.format( history['re'] )
 
-    num_mask_voxels = utils.sum_over_dict(msks)
+    num_mask_voxels = zutils.sum_over_dict(msks)
     if num_mask_voxels > 0:
         history['err'] += cerr / num_mask_voxels
         history['cls'] += get_cls(props, lbl_outs) / num_mask_voxels
@@ -408,24 +408,24 @@ def get_grdt(pars, history, props, lbl_outs, msks, wmsks, vn):
         history['cls'] += get_cls(props, lbl_outs) / vn
 
     if pars['is_debug']:
-        c2 = utils.check_dict_nan(lbl_outs)
-        c3 = utils.check_dict_nan(msks)
-        c4 = utils.check_dict_nan(wmsks)
-        c5 = utils.check_dict_nan(props)
-        c6 = utils.check_dict_nan(grdts)
+        c2 = zutils.check_dict_nan(lbl_outs)
+        c3 = zutils.check_dict_nan(msks)
+        c4 = zutils.check_dict_nan(wmsks)
+        c5 = zutils.check_dict_nan(props)
+        c6 = zutils.check_dict_nan(grdts)
         if  not ( c2 and c3 and c4 and c5 and c6):
             # stop training
             raise NameError('nan encountered!')
 
     # gradient reweighting
-    grdts = utils.dict_mul( grdts, msks  )
+    grdts = zutils.dict_mul( grdts, msks  )
     if pars['rebalance_mode']:
-        grdts = utils.dict_mul( grdts, wmsks )
+        grdts = zutils.dict_mul( grdts, wmsks )
 
     if pars['is_malis'] :
         malis_weights, rand_errors, num_non_bdr = cost_fn.malis_weight(pars, props, lbl_outs)
-        grdts = utils.dict_mul(grdts, malis_weights)
-        dmc, dme = utils.get_malis_cost( props, lbl_outs, malis_weights )
+        grdts = zutils.dict_mul(grdts, malis_weights)
+        dmc, dme = zutils.get_malis_cost( props, lbl_outs, malis_weights )
         if num_mask_voxels > 0:
             history['mc'] += dmc.values()[0] / num_mask_voxels
             history['me'] += dme.values()[0] / num_mask_voxels
@@ -433,5 +433,5 @@ def get_grdt(pars, history, props, lbl_outs, msks, wmsks, vn):
             history['mc'] += dmc.values()[0] / vn
             history['me'] += dme.values()[0] / vn
 
-    grdts = utils.make_continuous(grdts)
+    grdts = zutils.make_continuous(grdts)
     return props, grdts, history
diff --git a/python/front_end/zdataset.py b/python/front_end/zdataset.py
index 7165dae0..155f4bff 100644
--- a/python/front_end/zdataset.py
+++ b/python/front_end/zdataset.py
@@ -10,7 +10,7 @@
 import sys
 import numpy as np
 import emirt
-import utils
+import zutils
 
 class CDataset(object):
 
@@ -383,7 +383,7 @@ def __init__(self, dspec, pars, sec_name, \
         if pars['is_bd_mirror']:
             if self.pars['is_debug']:
                 print "data shape before mirror: ", self.data.shape
-            self.data = utils.boundary_mirror(self.data, self.mapsz)
+            self.data = zutils.boundary_mirror(self.data, self.mapsz)
             #Modifying the deviation boundaries for the modified dataset
             self.calculate_sizes( )
             if self.pars['is_debug']:
@@ -450,9 +450,6 @@ def __init__(self, dspec, pars, sec_name, outsz, setsz, mapsz ):
         self.sublbl = None
         self.submsk = None
 
-        # rename data as lbl
-        self.lbl = self.data
-
         # deal with mask
         self.msk = np.array([])
         if dspec[sec_name].has_key('fmasks'):
@@ -498,7 +495,10 @@ def get_dataset(self):
         return the whole label for examination
         """
         return self.data
-
+    def get_lbl(self):
+        return self.data
+    def get_msk(self):
+        return self.msk
     def get_candidate_loc( self, low, high ):
         """
         find the candidate location of subvolume
diff --git a/python/front_end/zlog.py b/python/front_end/zlog.py
index 28b62fc1..20e59ca4 100644
--- a/python/front_end/zlog.py
+++ b/python/front_end/zlog.py
@@ -10,8 +10,7 @@
 import ConfigParser
 import numpy as np
 import os
-import cost_fn
-import utils
+import zutils
 
 from emirt import volume_util
 
@@ -28,7 +27,7 @@ def record_config_file(params=None, config_filename=None, net_save_filename=None
 
     #Need to specify either a params object, or all of the other optional args
     #"ALL" optional args excludes train
-    utils.assert_arglist(params,
+    zutils.assert_arglist(params,
         [config_filename, net_save_filename]
         )
 
@@ -57,7 +56,7 @@ def record_config_file(params=None, config_filename=None, net_save_filename=None
 
     #Deriving destination filename information
     if timestamp is None:
-        timestamp = utils.timestamp()
+        timestamp = zutils.timestamp()
     mode = "train" if train else "forward"
 
     #Actually saving
@@ -76,7 +75,7 @@ def make_logfile_name(params=None, net_save_filename=None, timestamp = None, tra
     '''
 
     #Need to specify either a params object, or the net save prefix
-    utils.assert_arglist(params,
+    zutils.assert_arglist(params,
         [net_save_filename])
 
     if params is not None:
@@ -94,7 +93,7 @@ def make_logfile_name(params=None, net_save_filename=None, timestamp = None, tra
     assert(save_prefix_valid)
 
     if timestamp is None:
-        timestamp = utils.timestamp()
+        timestamp = zutils.timestamp()
     mode = "train" if train else "forward"
 
     directory_name = os.path.dirname( save_prefix )
diff --git a/python/front_end/zsample.py b/python/front_end/zsample.py
index e163fa71..419d2625 100644
--- a/python/front_end/zsample.py
+++ b/python/front_end/zsample.py
@@ -13,7 +13,7 @@
 import sys
 import numpy as np
 import emirt
-import utils
+import zutils
 from zdataset import *
 import os
 
@@ -111,11 +111,11 @@ def _data_aug(self):
         if self.pars['is_data_aug']:
             rft = (np.random.rand(4)>0.5)
             for key, subinput in self.subimgs.iteritems():
-                self.subimgs[key] = utils.data_aug_transform(subinput,      rft )
+                self.subimgs[key] = zutils.data_aug_transform(subinput,      rft )
             for key, sublbl in self.sublbls.iteritems():
                 submsk = self.submsks[key]
-                self.sublbls[key]  = utils.data_aug_transform(sublbl, rft )
-                self.submsks[key]  = utils.data_aug_transform(submsk, rft )
+                self.sublbls[key]  = zutils.data_aug_transform(sublbl, rft )
+                self.submsks[key]  = zutils.data_aug_transform(submsk, rft )
 
     def get_random_sample(self):
         '''Fetches a matching random sample from all input and output volumes'''
@@ -139,7 +139,7 @@ def get_random_sample(self):
         self._data_aug()
         # make sure that the input image is continuous in memory
         # the C++ core can not deal with numpy view
-        self.subimgs = utils.make_continuous(self.subimgs)
+        self.subimgs = zutils.make_continuous(self.subimgs)
         return ( self.subimgs, self.sublbls, self.submsks )
 
     def _get_balance_weight(self, arr, msk=None):
@@ -225,8 +225,8 @@ def write_request_to_log(self, dev):
         if self.log is not None:
             log_line1 = self.name
             log_line2 = "subvolume: [{},{},{}] requested".format(dev[0],dev[1],dev[2])
-            utils.write_to_log(self.log, log_line1)
-            utils.write_to_log(self.log, log_line2)
+            zutils.write_to_log(self.log, log_line1)
+            zutils.write_to_log(self.log, log_line2)
 
 class CAffinitySample(CSample):
     """
@@ -255,8 +255,8 @@ def __init__(self, dspec, pars, sample_id, net, outsz, log=None, is_forward=Fals
         self.taffs = dict()
         self.tmsks = dict()
         for k, out in self.outs.iteritems():
-            self.taffs[k] = self._seg2aff( out.lbl )
-            self.tmsks[k] = self._msk2affmsk( out.msk )
+            self.taffs[k] = self._seg2aff( out.get_lbl() )
+            self.tmsks[k] = self._msk2affmsk( out.get_msk() )
 
         self._prepare_rebalance_weights( self.taffs, self.tmsks )
         return
@@ -415,7 +415,7 @@ def _prepare_rebalance_weights(self):
         self.wps = dict()
         self.wzs = dict()
         for key, out in self.outs.iteritems():
-            self.wps[key], self.wzs[key] = self._get_balance_weight( out.lbl,out.msk )
+            self.wps[key], self.wzs[key] = self._get_balance_weight( out.get_lbl(),out.get_msk() )
 
     def _binary_class(self, lbl):
         """
diff --git a/python/test.py b/python/front_end/ztest.py
similarity index 88%
rename from python/test.py
rename to python/front_end/ztest.py
index 031eb8f5..3f92f635 100644
--- a/python/test.py
+++ b/python/front_end/ztest.py
@@ -3,11 +3,11 @@
 
 Jingpeng Wu <jingpeng.wu@gmail.com>, 2015
 """
-import utils
-import cost_fn
+import zutils
 import numpy as np
 from core import pyznn
 import time
+import zcost
 
 def _single_test(net, pars, sample, vn):
     # return errors as a dictionary
@@ -15,20 +15,20 @@ def _single_test(net, pars, sample, vn):
     vol_ins, lbl_outs, msks, wmsks = sample.get_random_sample()
 
     # forward pass
-    vol_ins = utils.make_continuous(vol_ins)
+    vol_ins = zutils.make_continuous(vol_ins)
     props = net.forward( vol_ins )
 
     # cost function and accumulate errors
     props, derr['err'], grdts = pars['cost_fn']( props, lbl_outs )
     # pixel classification error
-    derr['cls'] = cost_fn.get_cls(props, lbl_outs)
+    derr['cls'] = zcost.get_cls(props, lbl_outs)
     # rand error
     #derr['re'] = pyznn.get_rand_error(props.values()[0], lbl_outs.values()[0])
 
     if pars['is_malis']:
-        malis_weights, rand_errors, num_non_bdr = cost_fn.malis_weight( pars, props, lbl_outs )
+        malis_weights, rand_errors, num_non_bdr = zcost.malis_weight( pars, props, lbl_outs )
         # dictionary of malis classification error
-        dmc, dme = utils.get_malis_cost( props, lbl_outs, malis_weights )
+        dmc, dme = zutils.get_malis_cost( props, lbl_outs, malis_weights )
         derr['mc'] = dmc.values()[0]
         derr['me'] = dme.values()[0]
     # normalization
diff --git a/python/utils.py b/python/front_end/zutils.py
similarity index 94%
rename from python/utils.py
rename to python/front_end/zutils.py
index 97225d24..583bedd2 100644
--- a/python/utils.py
+++ b/python/front_end/zutils.py
@@ -325,6 +325,10 @@ def init_save(pars, lc, net, iter_last):
         os.remove( fname )
     lc.save(pars, fname)
     znetio.save_network(net, fname, pars['is_stdio'])
+    if pars.has_key('s3_train_net_prefix'):
+        # should transfer local network to s3
+        s3fname = pars['s3_train_net_prefix'] + "_init_{}.h5".format(iter_last)
+        os.system("aws cp {} {}".format(fname, s3fname))
 
 # save the intermediate networks while training
 def inter_save(pars, lc, net, vol_ins, props, lbl_outs, grdts, wmsks, it):
@@ -355,3 +359,10 @@ def inter_save(pars, lc, net, vol_ins, props, lbl_outs, grdts, wmsks, it):
 
     # Overwriting most current file with completely saved version
     shutil.copyfile(filename, filename_current)
+
+    if pars.has_key('s3_train_net_prefix'):
+        # should transfer local network to s3
+        s3fname = pars['s3_train_net_prefix'] + "_{}.h5".format(iter_last)
+        os.system("aws cp {} {}".format(filename, s3fname))
+        s3fname = pars['s3_train_net_prefix'] + "_current.h5"
+        os.system("aws cp {} {}".format(filename, s3fname))
diff --git a/python/tests/test_seg.py b/python/tests/test_seg.py
deleted file mode 100644
index d2fc9ffa..00000000
--- a/python/tests/test_seg.py
+++ /dev/null
@@ -1,19 +0,0 @@
-#!/usr/bin/env python
-__doc__ = """
-
-Jingpeng Wu <jingpeng.wu@gmail.com>, 2015
-"""
-
-# read affinity
-fname = "/usr/people/jingpeng/seungmount/research/Jingpeng/09_pypipeline/znn_merged.h5"
-import emirt
-affs = emirt.emio.imread( fname )
-
-seg = emirt.volume_util.seg_affs(affs, threshold=0.8)
-
-#%%
-com = emirt.show.CompareVol((affs[0, :,:,:], seg))
-com.vol_compare_slice()
-
-#%%
-emirt.show.random_color_show( seg[4,:,:] )
\ No newline at end of file
diff --git a/python/forward.py b/python/zforward.py
similarity index 97%
rename from python/forward.py
rename to python/zforward.py
index 60b3ad80..0001a800 100644
--- a/python/forward.py
+++ b/python/zforward.py
@@ -32,7 +32,6 @@
 import numpy as np
 import os
 from front_end import *
-import utils
 from emirt import emio
 
 def parse_args( args ):
@@ -45,7 +44,7 @@ def parse_args( args ):
     assert( os.path.exists( params['forward_net'] ) )
 
     if args['range']:
-        params['forward_range'] = utils.parseIntSet( args['range'] )
+        params['forward_range'] = zutils.parseIntSet( args['range'] )
 
     return dspec, params
 
@@ -90,7 +89,7 @@ def forward_pass( params, Dataset, network, verbose=True ):
         if verbose:
 	    print "Output patch #{} of {}".format(i+1, num_patches) # i is just an index
         input_patches, junk = Dataset.get_next_patch()
-	vol_ins = utils.make_continuous(input_patches)
+	vol_ins = zutils.make_continuous(input_patches)
 	output = network.forward( vol_ins )
         Output.set_next_patch( output )
         if params['is_check']:
@@ -107,12 +106,10 @@ def run_softmax( sample_output ):
     Performs a softmax calculation over the output volumes for a
     given sample output
     '''
-    from cost_fn import softmax
-
     for dname, dataset in sample_output.output_volumes.iteritems():
 
         props = {'dataset':dataset.data}
-        props = softmax(props)
+        props = zcost.softmax(props)
         dataset.data = props.values()[0]
         sample_output.output_volumes[dname] = dataset
 
diff --git a/python/zstatistics.py b/python/zstatistics.py
index 30abba82..7b0718b7 100644
--- a/python/zstatistics.py
+++ b/python/zstatistics.py
@@ -6,7 +6,7 @@
 import numpy as np
 import time
 import os
-import utils
+from front_end import *
 
 class CLearnCurve:
     def __init__(self, fname=None):
@@ -194,7 +194,7 @@ def plot(self, w=3, plotmode='matplotlib'):
         fig = plt.figure()
 
         # number of subplots
-        nsp = len(self.train)-1
+        nsp = 2
         print "number of subplots: {}".format(nsp)
 
         # print the maximum iteration
@@ -214,7 +214,7 @@ def plot(self, w=3, plotmode='matplotlib'):
         # plot data
         idx = 0
         for key in self.train.keys():
-            if key == 'it':
+            if key in ['it', 'elapse', 'eta', 're', 'num_mask_voxels']:
                 continue
             idx += 1
             ax = fig.add_subplot(1,nsp,idx)
diff --git a/python/train.py b/python/ztrain.py
similarity index 88%
rename from python/train.py
rename to python/ztrain.py
index 93c3521d..7c8bc29e 100644
--- a/python/train.py
+++ b/python/ztrain.py
@@ -5,12 +5,9 @@
 """
 import time
 from front_end import *
-import cost_fn
-import utils
 import zstatistics
 import os
 import numpy as np
-import test
 
 
 def main( args ):
@@ -19,7 +16,7 @@ def main( args ):
     net, lc = znetio.create_net(pars)
 
     # total voxel number of output volumes
-    vn = utils.get_total_num(net.get_outputs_setsz())
+    vn = zutils.get_total_num(net.get_outputs_setsz())
 
     # initialize samples
     print "\n\ncreate train samples..."
@@ -28,7 +25,6 @@ def main( args ):
     smp_tst = zsample.CSamples(dspec, pars, pars['test_range'],  net, pars['train_outsz'], logfile)
 
     if pars['is_check']:
-        import zcheck
         zcheck.check_patch(pars, smp_trn)
 
     # initialize history recording
@@ -40,7 +36,7 @@ def main( args ):
     #Saving initial/seeded network
     # get file name
     fname, fname_current = znetio.get_net_fname( pars['train_net_prefix'], iter_last, suffix="init" )
-    utils.init_save(pars, lc, net, iter_last)
+    zutils.init_save(pars, lc, net, iter_last)
 
     # start time cumulation
     print "start training..."
@@ -59,7 +55,7 @@ def main( args ):
 
         #print props
         # get gradient and record history
-        props, grdts, history = cost_fn.get_grdt(pars, history, props, lbl_outs, msks, wmsks, vn)
+        props, grdts, history = zcost.get_grdt(pars, history, props, lbl_outs, msks, wmsks, vn)
         #print props
         #print lbl_outs
 
@@ -69,10 +65,10 @@ def main( args ):
         # post backward pass processing
         history, net, lc, start, total_time = zstatistics.process_history(pars, history, \
                                                             lc, net, it, start, total_time)
-        utils.inter_save(pars, lc, net, vol_ins, props, \
+        zutils.inter_save(pars, lc, net, vol_ins, props, \
                          lbl_outs, grdts, wmsks, it)
 
-        lc, start, total_time = test.run_test(net, pars, smp_tst, \
+        lc, start, total_time = ztest.run_test(net, pars, smp_tst, \
                                               vn, it, lc, start, total_time)
 
         # stop the iteration at checking mode