diff --git a/Dataset.py b/Dataset.py
index 42e0c0d..ef4af8b 100644
--- a/Dataset.py
+++ b/Dataset.py
@@ -1,4 +1,4 @@
-from S2parser import S2parser
+from S2parser_africa import S2parser
 import tensorflow as tf
 import os
 import configparser
@@ -8,7 +8,7 @@
 class Dataset():
     """ A wrapper class around Tensorflow Dataset api handling data normalization and augmentation """
 
-    def __init__(self, datadir, verbose=False, temporal_samples=None, section="dataset", augment=False):
+    def __init__(self, datadir, verbose=False, temporal_samples=None, section="dataset", augment=False, country=None):
         self.verbose = verbose
 
         self.augment = augment
@@ -53,6 +53,7 @@ def __init__(self, datadir, verbose=False, temporal_samples=None, section="datas
                 id,cl = row.split('|')
                 self.ids.append(int(id))
                 self.classes.append(cl)
+        print('classes: ', self.classes)
 
         ## create a lookup table to map labelids to dimension ids
 
@@ -76,14 +77,16 @@ def __init__(self, datadir, verbose=False, temporal_samples=None, section="datas
         datacfg.read(cfgpath)
         cfg = datacfg[section]
 
-        self.tileidfolder = os.path.join(dataroot, "tileids")
+        self.country = country
+        if self.country is None:
+            self.tileidfolder = os.path.join(dataroot, "tileids")
+        else:
+            self.tileidfolder = os.path.join(dataroot, "tileids/" + self.country + "/tileids")
         self.datadir = os.path.join(dataroot, cfg["datadir"])
 
         assert 'pix10' in cfg.keys()
         assert 'nobs' in cfg.keys()
         assert 'nbands10' in cfg.keys()
-        assert 'nbands20' in cfg.keys()
-        assert 'nbands60' in cfg.keys()
 
         self.tiletable=cfg["tiletable"]
 
@@ -91,26 +94,25 @@ def __init__(self, datadir, verbose=False, temporal_samples=None, section="datas
 
         self.expected_shapes = self.calc_expected_shapes(int(cfg["pix10"]),
                                                          int(cfg["nobs"]),
-                                                         int(cfg["nbands10"]),
-                                                         int(cfg["nbands20"]),
-                                                         int(cfg["nbands60"])
+                                                         int(cfg["nbands10"])
                                                          )
 
 
         # expected datatypes as read from disk
-        self.expected_datatypes = (tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.int64)
+        self.expected_datatypes = (tf.float32, tf.float32, tf.float32, tf.int64)
 
-    def calc_expected_shapes(self, pix10, nobs, bands10, bands20, bands60):
-        pix20 = pix10 / 2;
-        pix60 = pix10 / 6;
+    def calc_expected_shapes(self, pix10, nobs, bands10):
         x10shape = (nobs, pix10, pix10, bands10)
-        x20shape = (nobs, pix20, pix20, bands20)
-        x60shape = (nobs, pix60, pix60, bands60)
         doyshape = (nobs,)
         yearshape = (nobs,)
         labelshape = (nobs, pix10, pix10)
 
-        return [x10shape, x20shape, x60shape, doyshape, yearshape, labelshape]
+        print('x10shape: ', x10shape)
+        print('doyshape: ', doyshape)
+        print('yearshape: ', yearshape)
+        print('labelshape: ', labelshape)
+
+        return [x10shape, doyshape, yearshape, labelshape]
 
     def transform_labels(self,feature):
         """
@@ -118,32 +120,49 @@ def transform_labels(self,feature):
         2. perform label lookup as stored label ids might be not sequential labelid:[0,3,4] -> dimid:[0,1,2]
         """
 
-        x10, x20, x60, doy, year, labels = feature
+        x10, doy, year, labels = feature
 
         # take first label time [46,24,24] -> [24,24]
         # labels are not supposed to change over the time series
         #labels = labels[0]
+        #print('unique_labels: ', tf.unique(tf.stack(tf.reshape(labels, -1))))
         labels = self.id_lookup_table.lookup(labels)
 
-        return x10, x20, x60, doy, year, labels
+        return x10, doy, year, labels
 
     def normalize(self, feature):
-
-        x10, x20, x60, doy, year, labels = feature
-        x10 = tf.scalar_mul(1e-4, tf.cast(x10, tf.float32))
-        x20 = tf.scalar_mul(1e-4, tf.cast(x20, tf.float32))
-        x60 = tf.scalar_mul(1e-4, tf.cast(x60, tf.float32))
-
+        """
+        Normalizes between 0 and 1.
+        """ 
+        x10, doy, year, labels = feature
+        
         doy = tf.cast(doy, tf.float32) / 365
-
         # year = (2016 - tf.cast(year, tf.float32)) / 2017
         year = tf.cast(year, tf.float32) - 2016
+        
+        if self.country in ['ghana', 'southsudan', 'germany']:
+            S2_BAND_MEANS = { 'ghana': tf.constant([2620.00, 2519.89, 2630.31, 2739.81, 3225.22, 3562.64, 3356.57, 3788.05, 2915.40, 2102.65]),
+                              'southsudan': tf.constant([2119.15, 2061.95, 2127.71, 2277.60, 2784.21, 3088.40, 2939.33, 3308.03, 2597.14, 1834.81]),
+                              'germany': tf.constant([1991.37, 2026.92, 2136.22, 6844.82, 9951.98, 11638.58, 3664.66, 12375.27, 7351.99, 5027.96, 0., 0., 0.])}
+
+            S2_BAND_STDS = { 'ghana': tf.constant([2171.62, 2085.69, 2174.37, 2084.56, 2058.97, 2117.31, 1988.70, 2099.78, 1209.48, 918.19]),
+                             'southsudan': tf.constant([2113.41, 2026.64, 2126.10, 2093.35, 2066.81, 2114.85, 2049.70, 2111.51, 1320.97, 1029.58]),
+                             'germany': tf.constant([1943.62, 1755.82, 1841.09, 5703.38, 5104.90, 5136.54, 1663.27, 5125.05, 3682.57, 3273.71, 10000., 10000., 10000.])}
+
+            band_means = S2_BAND_MEANS[self.country]
+            band_stds = S2_BAND_STDS[self.country]
 
-        return x10, x20, x60, doy, year, labels
+            x10 = tf.cast(x10, tf.float32)
+            x10 = tf.divide( tf.subtract( x10, tf.reshape(band_means, shape=[1, 1, 1, -1]) ), tf.reshape(band_stds, shape=[1, 1, 1, -1]) )
+
+        else:
+            x10 = tf.scalar_mul(1e-4, tf.cast(x10, tf.float32))
+
+        return x10, doy, year, labels
 
     def augment(self, feature):
 
-        x10, x20, x60, doy, year, labels = feature
+        x10, doy, year, labels = feature
 
         ## Flip UD
 
@@ -152,8 +171,6 @@ def augment(self, feature):
 
         # flip
         x10 = tf.cond(condition, lambda: tf.reverse(x10, axis=[1]), lambda: x10)
-        x20 = tf.cond(condition, lambda: tf.reverse(x20, axis=[1]), lambda: x20)
-        x60 = tf.cond(condition, lambda: tf.reverse(x60, axis=[1]), lambda: x60)
         labels = tf.cond(condition, lambda: tf.reverse(labels, axis=[1]), lambda: labels)
 
 
@@ -164,11 +181,9 @@ def augment(self, feature):
 
         # flip
         x10 = tf.cond(condition, lambda: tf.reverse(x10, axis=[2]), lambda: x10)
-        x20 = tf.cond(condition, lambda: tf.reverse(x20, axis=[2]), lambda: x20)
-        x60 = tf.cond(condition, lambda: tf.reverse(x60, axis=[2]), lambda: x60)
         labels = tf.cond(condition, lambda: tf.reverse(labels, axis=[2]), lambda: labels)
 
-        return x10, x20, x60, doy, year, labels
+        return x10, doy, year, labels
 
 
     def temporal_sample(self, feature):
@@ -180,7 +195,7 @@ def temporal_sample(self, feature):
         if n is None:
             return feature
 
-        x10, x20, x60, doy, year, labels = feature
+        x10, doy, year, labels = feature
 
         # data format 1, 2, 1, 2, -1,-1,-1
         # sequence lengths indexes are negative values.
@@ -194,21 +209,19 @@ def temporal_sample(self, feature):
         shuffled_range = tf.random_shuffle(tf.range(max_obs))[0:n]
 
         idxs = -tf.nn.top_k(-shuffled_range, k=n).values
-
+        print('idxs: ', idxs)
         x10 = tf.gather(x10, idxs)
-        x20 = tf.gather(x20, idxs)
-        x60 = tf.gather(x60, idxs)
         doy = tf.gather(doy, idxs)
         year = tf.gather(year, idxs)
 
-        return x10, x20, x60, doy, year, labels
+        return x10, doy, year, labels
 
     def get_ids(self, partition, fold=0):
 
         def readids(path):
             with open(path, 'r') as f:
                 lines = f.readlines()
-            return [int(l.replace("\n", "")) for l in lines]
+            return [l.replace("\n", "") for l in lines]
 
         traintest = "{partition}_fold{fold}.tileids"
         eval = "{partition}.tileids"
@@ -233,10 +246,9 @@ def create_tf_dataset(self, partition, fold, batchsize, shuffle, prefetch_batche
 
         # set of ids as present in database of given partition (train/test/eval) and fold (0-9)
         allids = self.get_ids(partition=partition, fold=fold)
-
+       
         # set of ids present in local folder (e.g. 1.tfrecord)
         tiles = os.listdir(self.datadir)
-
         if tiles[0].endswith(".gz"):
             compression = "GZIP"
             ext = ".tfrecord.gz"
@@ -244,7 +256,7 @@ def create_tf_dataset(self, partition, fold, batchsize, shuffle, prefetch_batche
             compression = ""
             ext = ".tfrecord"
 
-        downloaded_ids = [int(t.replace(".gz", "").replace(".tfrecord", "")) for t in tiles]
+        downloaded_ids = [t.replace(".gz", "").replace(".tfrecord", "") for t in tiles]
 
         # intersection of available ids and partition ods
         if overwrite_ids is None:
@@ -253,7 +265,6 @@ def create_tf_dataset(self, partition, fold, batchsize, shuffle, prefetch_batche
             print "overwriting data ids! due to manual input"
             ids = overwrite_ids
 
-
         filenames = [os.path.join(self.datadir, str(id) + ext) for id in ids]
 
         if self.verbose:
@@ -320,9 +331,7 @@ def main():
 
     with tf.Session() as sess:
         sess.run([iterator.initializer, tf.tables_initializer()])
-        x10, x20, x60, doy, year, labels = sess.run(iterator.get_next())
-        print x10.shape
-
+        x10, doy, year, labels = sess.run(iterator.get_next())
 
 if __name__ == "__main__":
     main()
diff --git a/Dockerfile b/Dockerfile
index 2a9891e..ef291be 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,4 +1,5 @@
-FROM tensorflow/tensorflow:1.4.0-gpu
+FROM tensorflow/tensorflow:1.12.0-gpu
+#FROM tensorflow/tensorflow:1.4.0-gpu
 
 LABEL maintainer="Marc Rußwurm <marc.russwurm@tum.de>"
 
diff --git a/S2parser.py b/S2parser.py
index 23d5ee1..53a5609 100644
--- a/S2parser.py
+++ b/S2parser.py
@@ -59,7 +59,7 @@ def write(self, filename, x10, x20, x60, doy, year, labels):
         sys.stdout.flush()
 
     def get_shapes(self, sample):
-        print "reading shape of data using the sample "+sample
+        print("reading shape of data using the sample "+sample)
         data = self.read_and_return(sample)
         return [tensor.shape for tensor in data]
 
@@ -92,12 +92,12 @@ def read(self,filenames):
         elif isinstance(filenames,tf.FIFOQueue):
             filename_queue = filenames
         else:
-            print "please insert either list or tf.FIFOQueue"
+            print("please insert either list or tf.FIFOQueue")
 
         reader = tf.TFRecordReader()
         f, serialized_example = reader.read(filename_queue)
 
-        print f
+        print(f)
 
         feature = tf.parse_single_example(serialized_example, features=self.feature_format)
 
@@ -153,10 +153,10 @@ def read_and_return(self,filename):
             return sess.run(feature_op)
 
 def test():
-    print "Running self test:"
-    print "temporary tfrecord file is written with random numbers"
-    print "tfrecord file is read back"
-    print "contents are compared"
+    print("Running self test:")
+    print("temporary tfrecord file is written with random numbers")
+    print("tfrecord file is read back")
+    print("contents are compared")
 
     filename="tmptile.tfrecord"
 
@@ -176,11 +176,11 @@ def test():
     x10_, x20_, x60_, doy_, year_, labels_ = read_and_return(filename)
 
     # test if wrote and read data is the same
-    print "TEST"
+    print("TEST")
     if np.all(x10_==x10) and np.all(x20_==x20) and np.all(x60_==x60) and np.all(labels_==labels) and np.all(doy_==doy) and np.all(year_==year):
-        print "PASSED"
+        print("PASSED")
     else:
-        print "NOT PASSED"
+        print("NOT PASSED")
 
 
     # remove file
@@ -195,4 +195,4 @@ def test():
     #x10, x20, x60, doy, year, labels = read_and_return("data/bavaria/1.tfrecord")
     parser = S2parser()
 
-    parser.tfrecord_to_pickle("1.tfrecord","1.pkl")
\ No newline at end of file
+    parser.tfrecord_to_pickle("1.tfrecord","1.pkl")
diff --git a/S2parser_africa.py b/S2parser_africa.py
new file mode 100644
index 0000000..4240e21
--- /dev/null
+++ b/S2parser_africa.py
@@ -0,0 +1,180 @@
+import tensorflow as tf
+import numpy as np
+import sys
+import os
+
+class S2parser():
+    """ defined the Sentinel 2 .tfrecord format """
+    def __init__(self):
+
+        self.feature_format= {
+            'x10/data': tf.FixedLenFeature([], tf.string),
+            'x10/shape': tf.FixedLenFeature([4], tf.int64),
+            'dates/doy': tf.FixedLenFeature([], tf.string),
+            'dates/year': tf.FixedLenFeature([], tf.string),
+            'dates/shape': tf.FixedLenFeature([1], tf.int64),
+            'labels/data': tf.FixedLenFeature([], tf.string),
+            'labels/shape': tf.FixedLenFeature([3], tf.int64)
+        }
+
+        return None
+
+    def write(self, filename, x10, doy, year, labels):
+        # https://stackoverflow.com/questions/39524323/tf-sequenceexample-with-multidimensional-arrays
+
+        writer = tf.python_io.TFRecordWriter(filename)
+
+        x10=x10.astype(np.int64)
+        doy=doy.astype(np.int64)
+        year=year.astype(np.int64)
+        labels=labels.astype(np.int64)
+
+        # Create a write feature
+        feature={
+            'x10/data' : tf.train.Feature(bytes_list=tf.train.BytesList(value=[x10.tobytes()])),
+            'x10/shape': tf.train.Feature(int64_list=tf.train.Int64List(value=x10.shape)),
+            'labels/data': tf.train.Feature(bytes_list=tf.train.BytesList(value=[labels.tobytes()])),
+            'labels/shape': tf.train.Feature(int64_list=tf.train.Int64List(value=labels.shape)),
+            'dates/doy': tf.train.Feature(bytes_list=tf.train.BytesList(value=[doy.tobytes()])),
+            'dates/year': tf.train.Feature(bytes_list=tf.train.BytesList(value=[year.tobytes()])),
+            'dates/shape': tf.train.Feature(int64_list=tf.train.Int64List(value=doy.shape))
+        }
+
+
+        example = tf.train.Example(features=tf.train.Features(feature=feature))
+
+        writer.write(example.SerializeToString())
+
+        writer.close()
+        sys.stdout.flush()
+
+    def get_shapes(self, sample):
+        print("reading shape of data using the sample "+sample)
+        data = self.read_and_return(sample)
+        return [tensor.shape for tensor in data]
+
+    def parse_example(self,serialized_example):
+        """
+        example proto can be obtained via
+        filename_queue = tf.train.string_input_producer(filenames, num_epochs=None)
+        or by passing this function in dataset.map(.)
+        """
+
+        feature = tf.parse_single_example(serialized_example, self.feature_format)
+        # decode and reshape x10
+        x10 = tf.reshape(tf.decode_raw(feature['x10/data'], tf.int64),tf.cast(feature['x10/shape'], tf.int32))
+
+        doy = tf.reshape(tf.decode_raw(feature['dates/doy'], tf.int64), tf.cast(feature['dates/shape'], tf.int32))
+        year = tf.reshape(tf.decode_raw(feature['dates/year'], tf.int64), tf.cast(feature['dates/shape'], tf.int32))
+
+        labels = tf.reshape(tf.decode_raw(feature['labels/data'], tf.int64), tf.cast(feature['labels/shape'], tf.int32))
+
+        return x10, doy, year, labels
+
+    def read(self,filenames):
+        """ depricated! """
+
+        if isinstance(filenames,list):
+            filename_queue = tf.train.string_input_producer(filenames, num_epochs=None)
+        elif isinstance(filenames,tf.FIFOQueue):
+            filename_queue = filenames
+        else:
+            print("please insert either list or tf.FIFOQueue")
+
+        reader = tf.TFRecordReader()
+        f, serialized_example = reader.read(filename_queue)
+
+        print(f)
+
+        feature = tf.parse_single_example(serialized_example, features=self.feature_format)
+
+        # decode and reshape x10
+        x10 = tf.reshape(tf.decode_raw(feature['x10/data'], tf.int64),tf.cast(feature['x10/shape'], tf.int32))
+
+        doy = tf.reshape(tf.decode_raw(feature['dates/doy'], tf.int64), tf.cast(feature['dates/shape'], tf.int32))
+        year = tf.reshape(tf.decode_raw(feature['dates/year'], tf.int64), tf.cast(feature['dates/shape'], tf.int32))
+
+        labels = tf.reshape(tf.decode_raw(feature['labels/data'], tf.int64), tf.cast(feature['labels/shape'], tf.int32))
+
+        return x10, doy, year, labels
+
+    def tfrecord_to_pickle(self,tfrecordname,picklename):
+        import cPickle as pickle
+
+        reader = tf.TFRecordReader()
+
+        # read serialized representation of *.tfrecord
+        filename_queue = tf.train.string_input_producer([tfrecordname], num_epochs=None)
+        filename_op, serialized_example = reader.read(filename_queue)
+        feature = self.parse_example(serialized_example)
+
+        with tf.Session() as sess:
+            sess.run([tf.global_variables_initializer(), tf.local_variables_initializer()])
+
+            coord = tf.train.Coordinator()
+            threads = tf.train.start_queue_runners(coord=coord)
+
+            feature = sess.run(feature)
+
+            coord.request_stop()
+            coord.join(threads)
+
+        pickle.dump(feature, open(picklename, "wb"), protocol=2)
+
+    def read_and_return(self,filename):
+        """ depricated! """
+
+        # get feature operation containing
+        feature_op = self.read([filename])
+
+        with tf.Session() as sess:
+
+            tf.global_variables_initializer()
+
+            coord = tf.train.Coordinator()
+            threads = tf.train.start_queue_runners(coord=coord)
+
+            return sess.run(feature_op)
+
+def test():
+    print("Running self test:")
+    print("temporary tfrecord file is written with random numbers")
+    print("tfrecord file is read back")
+    print("contents are compared")
+
+    filename="tmptile.tfrecord"
+
+    # create dummy dataset
+    x10 = (np.random.rand(6,48,48,6)*1e3).astype(np.int64)
+    labels = (np.random.rand(6,24,24)*1e3).astype(np.int64)
+    doy = (np.random.rand(6)*1e3).astype(np.int64)
+    year = (np.random.rand(6)*1e3).astype(np.int64)
+
+    # init parser
+    parser=S2parser()
+
+    parser.write(filename, x10, doy, year, labels)
+
+    x10_, doy_, year_, labels_ = read_and_return(filename)
+
+    # test if wrote and read data is the same
+    print("TEST")
+    if np.all(x10_==x10) and np.all(labels_==labels) and np.all(doy_==doy) and np.all(year_==year):
+        print("PASSED")
+    else:
+        print("NOT PASSED")
+
+
+    # remove file
+    os.remove(filename)
+
+    #return tf.reshape(x10, (1,48,48,6))
+    #return feature['x10shape']
+
+if __name__=='__main__':
+    #test()
+
+    #x10, x20, x60, doy, year, labels = read_and_return("data/bavaria/1.tfrecord")
+    parser = S2parser()
+
+    parser.tfrecord_to_pickle("1.tfrecord","1.pkl")
diff --git a/SimpleTrain.ipynb b/SimpleTrain.ipynb
index 1bcd1f4..80d872f 100644
--- a/SimpleTrain.ipynb
+++ b/SimpleTrain.ipynb
@@ -19,16 +19,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "from S2parser import S2parser\n",
+    "from S2parser_africa import S2parser\n",
     "import os\n",
     "import gzip\n",
     "import shutil\n",
     "import tensorflow as tf\n",
+    "import pickle\n",
+    "import h5py\n",
+    "\n",
+    "from scipy.misc import imresize\n",
     "\n",
     "parser=S2parser()"
    ]
@@ -37,15 +41,73 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 1. Create a fake random dataset"
+    "## 1. Create dataset"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hdf5_filepath = '/home/roserustowicz/croptype_data_local/data/ghana/data.hdf5'\n",
+    "grid_dir = '/home/roserustowicz/croptype_data_local/data/ghana'\n",
+    "num_classes = 4\n",
+    "numpix = 24\n",
+    "\n",
+    "use_s2 = True\n",
+    "country = 'ghana'\n",
+    "dataset = 'full'\n",
+    "\n",
+    "S2_BAND_MEANS = { 'ghana': np.array([2620.00, 2519.89, 2630.31, 2739.81, 3225.22, 3562.64, 3356.57, 3788.05, 2915.40, 2102.65]),\n",
+    "                  'southsudan': np.array([2119.15, 2061.95, 2127.71, 2277.60, 2784.21, 3088.40, 2939.33, 3308.03, 2597.14, 1834.81]),\n",
+    "                  'tanzania': np.array([2551.54, 2471.35, 2675.69, 2799.99, 3191.33, 3453.16, 3335.64, 3660.05, 3182.23, 2383.79])}\n",
+    "\n",
+    "S2_BAND_STDS = { 'ghana': np.array([2171.62, 2085.69, 2174.37, 2084.56, 2058.97, 2117.31, 1988.70, 2099.78, 1209.48, 918.19]),\n",
+    "                 'southsudan': np.array([2113.41, 2026.64, 2126.10, 2093.35, 2066.81, 2114.85, 2049.70, 2111.51, 1320.97, 1029.58]),\n",
+    "                 'tanzania': np.array([2290.97, 2204.75, 2282.90, 2214.60, 2182.51, 2226.10, 2116.62, 2210.47, 1428.33, 1135.21])}\n",
+    "\n",
+    "MIN_TIMESTAMPS = 25"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def write_field_sample(hdf5_filepath, filepath, country): \n",
+    "    grid_id = filepath.split('/')[-1].replace(\".tfrecord\", \"\")\n",
+    "    with h5py.File(hdf5_filepath, 'r') as data:\n",
+    "        s2 = None\n",
+    "        s2_doy = None\n",
+    "\n",
+    "        s2 = data['s2'][grid_id]\n",
+    "        s2_doy = data['s2_dates'][grid_id][()]\n",
+    "        s2, s2_doy, _ = sample_timeseries(s2, MIN_TIMESTAMPS, dates=s2_doy, sample_w_clouds=False)\n",
+    "\n",
+    "        year = (np.ones((len(s2_doy), )) * 2017).astype(int)\n",
+    "        \n",
+    "        grid = moveTimeToStart(s2)\n",
+    "        grid = np.transpose(grid, (0, 2, 3, 1))\n",
+    "        #grid = grid[:, :numpix, :numpix, :]      \n",
+    "        grid = np.resize(grid, new_shape=(grid.shape[0], numpix, numpix, grid.shape[3]))\n",
+    "        print('grid: ', grid.shape)\n",
+    "        \n",
+    "        label = data['labels'][grid_id][()]\n",
+    "        label[label > num_classes] = 0  \n",
+    "        label = np.resize(label, new_shape=(numpix, numpix))\n",
+    "        label = np.expand_dims(label, axis=0)\n",
+    "        print('label: ', label.shape)\n",
+    "        \n",
+    "    parser = S2parser()\n",
+    "    parser.write(filepath, grid, s2_doy, year, label)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def write_random_sample(filepath,nobs=46, pix10=24, bands10=4, pix20=12, bands20=6, pix60=6, bands60=3):\n",
@@ -67,58 +129,1464 @@
     "    # gzip .tfrecord to .tfrecord.gz\n",
     "    with open(infile, 'rb') as f_in:\n",
     "        with gzip.open(outfile, 'wb') as f_out:\n",
-    "            shutil.copyfileobj(f_in, f_out)\n",
+    "            shutil.copyfileobj(f_in, f_out)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def normalization(grid, country):\n",
+    "    \"\"\" Normalization based on values defined in constants.py\n",
+    "    Args: \n",
+    "      grid - (tensor) grid to be normalized\n",
+    "      satellite - (str) describes source that grid is from (\"s1\" or \"s2\")\n",
+    "\n",
+    "    Returns:\n",
+    "      grid - (tensor) a normalized version of the input grid\n",
+    "    \"\"\"\n",
+    "    num_bands = grid.shape[0]\n",
+    "    s2_band_means = S2_BAND_MEANS[country]\n",
+    "    s2_band_stds = S2_BAND_STDS[country]\n",
+    "    grid = (grid-s2_band_means[:num_bands].reshape(num_bands, 1, 1, 1))/s2_band_stds[:num_bands].reshape(num_bands, 1, 1, 1)\n",
+    "    return grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def moveTimeToStart(arr):\n",
+    "    return np.transpose(arr, [3, 0, 1, 2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sample_timeseries(img_stack, num_samples, dates=None, timestamps_first=False, sample_w_clouds=True):\n",
+    "    timestamps = img_stack.shape[3]\n",
+    "    scores = np.ones((timestamps,))\n",
+    "\n",
+    "    # Compute probabilities of scores with softmax\n",
+    "    probabilities = softmax(scores)\n",
+    "    samples = np.random.choice(timestamps, size=num_samples, replace=False, p=probabilities)\n",
+    "\n",
+    "    # Sort samples to maintain sequential ordering\n",
+    "    samples.sort()\n",
     "\n",
-    "    # remove unzipped .tfrecord\n",
-    "    #os.remove(infile)"
+    "    # Use sampled indices to sample image and cloud stacks\n",
+    "    sampled_img_stack = img_stack[:, :, :, samples]\n",
+    "\n",
+    "    # Samples dates\n",
+    "    sampled_dates = None\n",
+    "    if dates is not None:\n",
+    "        sampled_dates = dates[samples]\n",
+    "\n",
+    "    return sampled_img_stack, sampled_dates, None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def softmax(x):\n",
+    "    \"\"\"\n",
+    "    Computes softmax values for a vector x.\n",
+    "\n",
+    "    Args: \n",
+    "      x - (numpy array) a vector of real values\n",
+    "\n",
+    "    Returns: a vector of probabilities, of the same dimensions as x\n",
+    "    \"\"\"\n",
+    "    e_x = np.exp(x - np.max(x))\n",
+    "    return e_x / e_x.sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['tmp/0.tfrecord', 'tmp/1.tfrecord', 'tmp/2.tfrecord', 'tmp/3.tfrecord', 'tmp/4.tfrecord', 'tmp/5.tfrecord', 'tmp/6.tfrecord', 'tmp/7.tfrecord', 'tmp/8.tfrecord', 'tmp/9.tfrecord']\n",
-      "writing tmp/0.tfrecord\n",
-      "compressing tmp/0.tfrecord -> tmp/0.tfrecord.gz\n",
-      "writing tmp/1.tfrecord\n",
-      "compressing tmp/1.tfrecord -> tmp/1.tfrecord.gz\n",
-      "writing tmp/2.tfrecord\n",
-      "compressing tmp/2.tfrecord -> tmp/2.tfrecord.gz\n",
-      "writing tmp/3.tfrecord\n",
-      "compressing tmp/3.tfrecord -> tmp/3.tfrecord.gz\n",
-      "writing tmp/4.tfrecord\n",
-      "compressing tmp/4.tfrecord -> tmp/4.tfrecord.gz\n",
-      "writing tmp/5.tfrecord\n",
-      "compressing tmp/5.tfrecord -> tmp/5.tfrecord.gz\n",
-      "writing tmp/6.tfrecord\n",
-      "compressing tmp/6.tfrecord -> tmp/6.tfrecord.gz\n",
-      "writing tmp/7.tfrecord\n",
-      "compressing tmp/7.tfrecord -> tmp/7.tfrecord.gz\n",
-      "writing tmp/8.tfrecord\n",
-      "compressing tmp/8.tfrecord -> tmp/8.tfrecord.gz\n",
-      "writing tmp/9.tfrecord\n",
-      "compressing tmp/9.tfrecord -> tmp/9.tfrecord.gz\n"
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/018556.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/018556.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/018556.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/039008.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/039008.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/039008.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/034236.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/034236.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/034236.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/026306.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/026306.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/026306.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/026916.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/026916.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/026916.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/023035.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/023035.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/023035.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/013888.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/013888.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/013888.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/033594.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/033594.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/033594.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/002289.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/002289.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/002289.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001554.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001554.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001554.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/026759.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/026759.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/026759.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/044017.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/044017.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/044017.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/004423.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/004423.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/004423.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/034246.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/034246.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/034246.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/018123.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/018123.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/018123.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/044024.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/044024.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/044024.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/023494.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/023494.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/023494.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/008483.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/008483.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/008483.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/031840.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/031840.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/031840.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/036606.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/036606.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/036606.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/024787.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/024787.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/024787.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/025888.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/025888.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/025888.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/037452.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/037452.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/037452.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/009911.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/009911.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/009911.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/032524.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/032524.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/032524.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/013432.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/013432.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/013432.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/030810.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/030810.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/030810.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/027421.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/027421.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/027421.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001550.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001550.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001550.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/025909.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/025909.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/025909.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/005273.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/005273.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/005273.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/017361.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/017361.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/017361.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/014020.tfrecord\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/014020.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/014020.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/026790.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/026790.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/026790.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/039042.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/039042.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/039042.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/017613.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/017613.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/017613.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/031470.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/031470.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/031470.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001373.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001373.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001373.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/033814.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/033814.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/033814.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/037146.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/037146.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/037146.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
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+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/014481.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/014481.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/014481.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/043289.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/043289.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/043289.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/009293.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/009293.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/009293.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/027877.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/027877.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/027877.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/032751.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/032751.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/032751.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/002349.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/002349.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/002349.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/014426.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/014426.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/014426.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/032006.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/032006.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/032006.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001449.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001449.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001449.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/016233.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/016233.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/016233.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001216.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001216.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001216.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/022845.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/022845.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/022845.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/008488.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/008488.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/008488.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/014439.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/014439.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/014439.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/013933.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/013933.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/013933.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/019910.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/019910.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/019910.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001795.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001795.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001795.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/000452.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/000452.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/000452.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/000220.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/000220.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/000220.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/008547.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/008547.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/008547.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/019392.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/019392.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/019392.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/017904.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/017904.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/017904.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/036348.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/036348.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/036348.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/020351.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/020351.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/020351.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/008613.tfrecord\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/008613.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/008613.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001483.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001483.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001483.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/014021.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/014021.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/014021.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/020313.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/020313.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/020313.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/008493.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/008493.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/008493.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/029960.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/029960.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/029960.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/020360.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/020360.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/020360.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001446.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001446.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001446.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/013930.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/013930.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/013930.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/026341.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/026341.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/026341.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/016260.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/016260.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/016260.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/024276.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/024276.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/024276.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/018143.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/018143.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/018143.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001539.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001539.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001539.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/017395.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/017395.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/017395.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/017900.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/017900.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/017900.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/041071.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/041071.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/041071.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/027418.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/027418.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/027418.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/025885.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/025885.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/025885.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/027415.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/027415.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/027415.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/033590.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/033590.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/033590.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/032255.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/032255.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/032255.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/008548.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/008548.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/008548.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/008607.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/008607.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/008607.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/000380.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/000380.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/000380.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001351.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001351.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/001351.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/018576.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/018576.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/018576.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/025727.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/025727.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/025727.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/027316.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/027316.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/027316.tfrecord.gz\n",
+      "grid:  (25, 24, 24, 10)\n",
+      "label:  (1, 24, 24)\n",
+      "writing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/030063.tfrecord\n",
+      "compressing /home/roserustowicz/MTLCC-tf-fork/tmp_africa/030063.tfrecord -> /home/roserustowicz/MTLCC-tf-fork/tmp_africa/030063.tfrecord.gz\n"
      ]
     }
    ],
    "source": [
-    "# define directory to store the fake dataset\n",
-    "directory=\"tmp\"\n",
+    "# define directory to store the dataset\n",
+    "directory=\"/home/roserustowicz/MTLCC-tf-fork/tmp_africa\"\n",
     "if not os.path.exists(directory):\n",
     "    os.makedirs(directory)\n",
-    "\n",
-    "filepaths=[\"{}/{}.tfrecord\".format(directory,i) for i in range(10)]\n",
-    "zippedfilepaths=[\"{}/{}.tfrecord.gz\".format(directory,i) for i in range(10)]\n",
-    "print(filepaths)\n",
+    "    \n",
+    "for split in ['test']:\n",
+    "    grid_path = os.path.join(grid_dir, f\"{country}_{dataset}_{split}\")\n",
+    "    with open(grid_path, \"rb\") as f:\n",
+    "        grid_list = list(pickle.load(f))\n",
+    "        grid_list = grid_list\n",
+    "            \n",
+    "    filepaths=[\"{}/{}.tfrecord\".format(directory,i) for i in grid_list]\n",
+    "    zippedfilepaths=[\"{}/{}.tfrecord.gz\".format(directory,i) for i in grid_list]\n",
     "\n",
     "# create the dataset\n",
-    "for filepath,zippedfilepath in zip(filepaths,zippedfilepaths):\n",
-    "    write_random_sample(filepath)\n",
+    "for filepath, zippedfilepath in zip(filepaths, zippedfilepaths):\n",
+    "    write_field_sample(hdf5_filepath, filepath, country)\n",
     "    print(\"writing \"+filepath)\n",
     "    ziptfrecord(filepath,zippedfilepath)\n",
     "    print(\"compressing \"+filepath+\" -> \"+zippedfilepath)\n",
@@ -141,7 +1609,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -158,6 +1626,8 @@
     }
    ],
    "source": [
+    "import pdb\n",
+    "\n",
     "batchsize=2\n",
     "\n",
     "print(\"Reset all previously loaded graphs (in case this or the next cells have been executed twice)\")\n",
@@ -165,19 +1635,15 @@
     "\n",
     "print(\"creating dataset object\")\n",
     "dataset = tf.data.TFRecordDataset(zippedfilepaths, compression_type=\"GZIP\")\n",
-    "#dataset = tf.data.TFRecordDataset(filepaths)\n",
-    "\n",
     "\n",
     "def normalize(serialized_feature):\n",
     "    \"\"\" normalize stored integer values to floats approx. [0,1] \"\"\"\n",
-    "    x10, x20, x60, doy, year, labels = serialized_feature\n",
+    "    x10, doy, year, labels = serialized_feature\n",
     "    x10 = tf.scalar_mul(1e-4, tf.cast(x10, tf.float32))\n",
-    "    x20 = tf.scalar_mul(1e-4, tf.cast(x20, tf.float32))\n",
-    "    x60 = tf.scalar_mul(1e-4, tf.cast(x60, tf.float32))\n",
     "    doy = tf.cast(doy, tf.float32) / 365\n",
     "    year = tf.cast(year, tf.float32) - 2016\n",
-    "\n",
-    "    return x10, x20, x60, doy, year, labels\n",
+    "    \n",
+    "    return x10, doy, year, labels\n",
     "\n",
     "def mapping_function(serialized_feature):\n",
     "    # read data from .tfrecords\n",
@@ -206,25 +1672,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "1.10.0\n",
       "retrieving one sample as numpy array (just for fun)\n",
-      "x10.shape: (2, 46, 24, 24, 4)\n"
+      "x10.shape: (2, 25, 24, 24, 10)\n",
+      "doy.shape: (2, 25)\n",
+      "year.shape: (2, 25)\n",
+      "labels.shape: (2, 1, 24, 24)\n"
      ]
     },
     {
      "data": {
-      "image/png": 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+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5b08e5f850>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -232,12 +1704,16 @@
     "import matplotlib.pyplot as plt\n",
     "%matplotlib inline\n",
     "\n",
-    "\n",
+    "print(tf.__version__)\n",
+    "    \n",
     "with tf.Session() as sess:\n",
     "    sess.run(iterator.initializer)\n",
     "    print(\"retrieving one sample as numpy array (just for fun)\")\n",
-    "    x10, x20, x60, doy, year, labels = sess.run(iterator.get_next())\n",
+    "    x10, doy, year, labels = sess.run(iterator.get_next())\n",
     "    print(\"x10.shape: \" + str(x10.shape))\n",
+    "    print(\"doy.shape: \" + str(doy.shape))\n",
+    "    print(\"year.shape: \" + str(year.shape))\n",
+    "    print(\"labels.shape: \" + str(labels.shape))\n",
     "    plt.imshow(x10[0,0,:,:,0])\n",
     "    plt.title(\"x10\")"
    ]
@@ -251,25 +1727,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "loading network graph definition\n",
-      "initializing variables, tables and the data iterator\n",
-      "getting one string handle from the iterator that can be fed to the network\n",
-      "making some processing nodes accessible to python\n",
-      "performing one training step\n"
+      "loading network graph definition\n"
+     ]
+    },
+    {
+     "ename": "OSError",
+     "evalue": "File /home/roserustowicz/MTLCC-tf-fork/tmp_africa/convgru128/graph.meta does not exist.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-21-e57c56b56c96>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"loading network graph definition\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimport_meta_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"initializing variables, tables and the data iterator\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/croptype/lib/python3.6/site-packages/tensorflow/python/training/saver.py\u001b[0m in \u001b[0;36mimport_meta_graph\u001b[0;34m(meta_graph_or_file, clear_devices, import_scope, **kwargs)\u001b[0m\n\u001b[1;32m   1929\u001b[0m                        \"execution is enabled.\")\n\u001b[1;32m   1930\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeta_graph_or_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeta_graph_pb2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMetaGraphDef\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1931\u001b[0;31m     \u001b[0mmeta_graph_def\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeta_graph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_meta_graph_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeta_graph_or_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1932\u001b[0m   \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1933\u001b[0m     \u001b[0mmeta_graph_def\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeta_graph_or_file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/croptype/lib/python3.6/site-packages/tensorflow/python/framework/meta_graph.py\u001b[0m in \u001b[0;36mread_meta_graph_file\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m    631\u001b[0m   \u001b[0mmeta_graph_def\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeta_graph_pb2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMetaGraphDef\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    632\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfile_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile_exists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 633\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"File %s does not exist.\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    634\u001b[0m   \u001b[0;31m# First try to read it as a binary file.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    635\u001b[0m   \u001b[0mfile_content\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfile_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFileIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mOSError\u001b[0m: File /home/roserustowicz/MTLCC-tf-fork/tmp_africa/convgru128/graph.meta does not exist."
      ]
     }
    ],
    "source": [
     "# define the network to be loaded\n",
     "# if not yet created, make one with python modelzoo/seqencoder.py script, as described in the readme.md\n",
-    "graph=\"tmp/convgru128/graph.meta\"\n",
+    "graph=\"/home/roserustowicz/MTLCC-tf-fork/tmp_africa/convgru128/graph.meta\"\n",
     "\n",
     "with tf.Session() as sess:\n",
     "\n",
@@ -293,25 +1778,32 @@
     "    print(\"performing one training step\")\n",
     "    sess.run(train_op,feed_dict={iterator_handle_op: iterator_handle, is_train_op: True})"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python [conda env:croptype]",
    "language": "python",
-   "name": "python2"
+   "name": "conda-env-croptype-py"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
   }
  },
  "nbformat": 4,
diff --git a/modelzoo/seqencmodel.py b/modelzoo/seqencmodel.py
index 1e7e874..f969ec4 100644
--- a/modelzoo/seqencmodel.py
+++ b/modelzoo/seqencmodel.py
@@ -41,12 +41,10 @@
 
 ## expected data format ##
 tf.app.flags.DEFINE_string("expected_datatypes",
-                           "(tf.float32, tf.float32, tf.float32, tf.float32, tf.float32, tf.int64)", "expected datatypes")
+                           "(tf.float32, tf.float32, tf.float32, tf.int64)", "expected datatypes")
 tf.app.flags.DEFINE_integer("pix10m", 24, "number of 10m pixels")
-tf.app.flags.DEFINE_integer("num_bands_10m", 4, "number of bands in 10 meter resolution (4)")
-tf.app.flags.DEFINE_integer("num_bands_20m", 6, "number of bands in 20 meter resolution (6)")
-tf.app.flags.DEFINE_integer("num_bands_60m", 3, "number of bands in 20 meter resolution (3)")
-tf.app.flags.DEFINE_integer("num_classes", 17, "number of classes not counting unknown class -> e.g. 0:uk,1:a,2:b,3:c,4:d -> num_classes 4")
+tf.app.flags.DEFINE_integer("num_bands_10m", 10, "number of bands in 10 meter resolution (4)")
+tf.app.flags.DEFINE_integer("num_classes", 4, "number of classes not counting unknown class -> e.g. 0:uk,1:a,2:b,3:c,4:d -> num_classes 4")
 
 ## performance ##
 tf.app.flags.DEFINE_boolean("swap_memory", True, "Swap memory between GPU and CPU for recurrent layers")
@@ -86,6 +84,7 @@ def build_graph(self, modelpath=None):
 
         print("building inference...")
         self.logits = self.inference(input=(x, sequence_lengths))
+        print('tf logits: ', tf.shape(self.logits))
 
         # reduce label size to same shape like logits (important for downsampling)
         b,w,h,d = self.logits.get_shape()
@@ -102,6 +101,7 @@ def build_graph(self, modelpath=None):
 
         # keep rest of labels
         self.labels = labels[:,:,:,1:]
+        print('tf labels: ', tf.shape(self.labels))
 
 
         # mask out all classes labeled unknown (labelid=0)
@@ -165,11 +165,9 @@ def input(self):
                                                                output_types=eval(FLAGS.expected_datatypes))
 
         with tf.name_scope("raw"):
-            x10, x20, x60, doy, year, labels = iterator.get_next()
+            x10, doy, year, labels = iterator.get_next()
 
             self.x10 = tf.cast(x10, tf.float32, name="x10")
-            self.x20 = tf.cast(x20, tf.float32, name="x20")
-            self.x60 = tf.cast(x60, tf.float32, name="x60")
             self.doy = tf.cast(doy, tf.float32, name="doy")
             self.year = tf.cast(year, tf.float32,name="year")
             self.y = tf.cast(labels, tf.int32,name="y")
@@ -185,6 +183,7 @@ def resize(tensor, new_height, new_width):
             h = tf.shape(tensor)[2]
             w = tf.shape(tensor)[3]
             d = tf.shape(tensor)[4]
+            print('b: {}, t: {}, h: {}, w: {}, d: {}'.format(b, t, h, w, d))
 
             # stack batch on times to fit 4D requirement of resize_tensor
             stacked_tensor = tf.reshape(tensor, [b * t, h, w, d])
@@ -199,29 +198,28 @@ def expand3x(vector):
             return vector
 
         with tf.name_scope("reshaped"):
-
+            print(self.x10.shape)
             b = tf.shape(self.x10)[0]
             t = tf.shape(self.x10)[1]
             px = tf.shape(self.x10)[2]
 
             #b,t,w,h,d = self.x10.shape()
 
-            x20 = tf.identity(resize(self.x20, px, px),name="x20")
-            x60 = tf.identity(resize(self.x60, px, px),name="x60")
-
             tf.add_to_collection(ADVANCED_SUMMARY_COLLECTION_NAME,tf.identity(self.x10, name="x10"))
-            tf.add_to_collection(ADVANCED_SUMMARY_COLLECTION_NAME, x20)
-            tf.add_to_collection(ADVANCED_SUMMARY_COLLECTION_NAME, x60)
 
             # expand
             doymat = tf.multiply(expand3x(self.doy), tf.ones((b,t,px,px,1)),name="doy")
             yearmat = self.yearmat = tf.multiply(expand3x(self.year), tf.ones((b, t, px, px, 1)),name="year")
 
-            x = tf.concat((self.x10,x20,x60,doymat,yearmat),axis=-1,name="x")
+            print(tf.shape(doymat))
+            print(tf.shape(yearmat))
+
+            #x = tf.concat((self.x10,x20,x60,doymat,yearmat),axis=-1,name="x")
+            x = tf.concat((self.x10,doymat,yearmat),axis=-1,name="x")
             tf.add_to_collection(ADVANCED_SUMMARY_COLLECTION_NAME, x)
 
             # set depth of x for convolutions
-            depth = FLAGS.num_bands_10m + FLAGS.num_bands_20m + FLAGS.num_bands_60m + 2 # doy and year
+            depth = FLAGS.num_bands_10m + 2 # doy and year
 
             # dynamic shapes. Fill for debugging
 
diff --git a/scripts/tileids_from_splits.py b/scripts/tileids_from_splits.py
new file mode 100644
index 0000000..961a1bb
--- /dev/null
+++ b/scripts/tileids_from_splits.py
@@ -0,0 +1,33 @@
+"""
+Generate tileids (input to german-based model) from 
+data splits (input to our model)
+"""
+import os
+import pickle
+
+country = 'germany'
+setsize = 'full'
+splits = ['train', 'val', 'test']
+tile_dir = '/home/roserustowicz/MTLCC-all/germany/tileids/' + country
+
+if country in ['ghana', 'southsudan', 'tanzania']:
+    numgrids = 9
+elif country in ['germany']:
+    numgrids = 4
+
+for split in splits:
+    cur_file = '_'.join([country, setsize, split])
+    print('cur file: ', cur_file)   
+    with open(os.path.join(tile_dir, cur_file), "rb") as f:
+        inlist = list(pickle.load(f))
+        print(inlist)
+        outfname = os.path.join(tile_dir, cur_file + '.tileids')
+        with open(outfname, 'w') as outf:
+            for item in inlist:
+                item = item.zfill(6)
+                #print('item: ', item)
+                for append_i in range(numgrids):
+                    cur_item = item + str(append_i)
+                    outf.write("%s\n" % cur_item)
+
+
diff --git a/train.py b/train.py
index bf36f97..d68d687 100644
--- a/train.py
+++ b/train.py
@@ -3,6 +3,9 @@
 from Dataset import Dataset
 import argparse
 import datetime
+import pdb
+
+from tensorflow import ConfigProto
 
 MODEL_GRAPH_NAME = "graph.meta"
 TRAINING_IDS_IDENTIFIER = "train"
@@ -22,7 +25,9 @@
 
 
 def main(args):
-    if args.verbose: print "setting visible GPU {}".format(args.gpu)
+    tf.reset_default_graph()
+
+    if args.verbose: print("setting visible GPU {}".format(args.gpu))
     os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"  # see issue #152
     os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
 
@@ -50,9 +55,9 @@ def setupDatasets(args):
 
 
     datasets_dict=dict()
-    for section in ['2016','2017']:
+    for section in ['2016']:
         datasets_dict[section]=dict()
-        dataset = Dataset(datadir=args.datadir, verbose=True, temporal_samples=args.temporal_samples, section=section)
+        dataset = Dataset(datadir=args.datadir, verbose=True, temporal_samples=args.temporal_samples, section=section, country=args.country)
         for partition in [TRAINING_IDS_IDENTIFIER, TESTING_IDS_IDENTIFIER]:
             datasets_dict[section][partition] = dict()
             tfdataset, _, _, filenames = dataset.create_tf_dataset(partition,
@@ -66,7 +71,6 @@ def setupDatasets(args):
             datasets_dict[section][partition]["iterator"]=iterator
             datasets_dict[section][partition]["filenames"]=filenames
             #dataset_list.append({'sec':section,'id':identifier,'iterator':iterator,'filenames':filenames})
-
     return datasets_dict
 
 
@@ -91,7 +95,7 @@ def train(args, datasets):
 
     graph = os.path.join(args.modeldir, MODEL_GRAPH_NAME)
     if not graph_created_flag:
-        if args.verbose: print "importing graph from {}".format(graph)
+        if args.verbose: print("importing graph from {}".format(graph))
         dir(tf.contrib)  # see https://github.com/tensorflow/tensorflow/issues/10130
         _ = tf.train.import_meta_graph(graph)
 
@@ -119,7 +123,7 @@ def write_summaries(sess, datasets):
             for partition in datasets[dataset].keys():
                 handle = datasets[dataset][partition]["handle"]
                 writer = datasets[dataset][partition]["writer"]
-
+                #print('handle: ', handle.shape, handle)
                 ops = [tf.summary.merge_all(), cross_entropy_op, overall_accuracy_op]
                 sum, xe, oa = sess.run(ops, feed_dict={iterator_handle_op: handle, is_train_op: True})
                 writer.add_summary(sum, samples)
@@ -139,14 +143,15 @@ def write_summaries(sess, datasets):
 
     def save(saver, step, sess, checkpoint):
         saver.save(sess, checkpoint, global_step=step)
-        print "saving checkpoint step {}".format(step)
+        print("saving checkpoint step {}".format(step))
 
     saver = tf.train.Saver(max_to_keep=args.max_models_to_keep, keep_checkpoint_every_n_hours=args.save_every_n_hours,
                            save_relative_paths=True)
 
     checkpoint = os.path.join(args.modeldir, MODEL_CHECKPOINT_NAME)
 
-    config = tf.ConfigProto()
+    config = ConfigProto()
+    #config = tf.ConfigProto(use_per_session_threads=True)
     config.gpu_options.allow_growth = args.allow_growth
     with tf.Session(config=config) as sess:
         sess.run([tf.global_variables_initializer(), tf.local_variables_initializer(), tf.tables_initializer()])
@@ -165,47 +170,46 @@ def save(saver, step, sess, checkpoint):
                 writer = tf.summary.FileWriter(os.path.join(args.modeldir, summaryname), sess.graph)
                 datasets[dataset][partition]["writer"] = writer
 
-                print "initializing dataset {}, partition {}".format(dataset,partition)
+                print("initializing dataset {}, partition {}".format(dataset,partition))
                 sess.run([iterator.initializer])
 
         latest_ckpt = tf.train.latest_checkpoint(args.modeldir)
         if latest_ckpt is not None:
-            print "restoring from " + latest_ckpt
+            print("restoring from " + latest_ckpt)
             saver.restore(sess, latest_ckpt)
 
         step, samples = sess.run([global_step_op, samples_seen_op])
         current_epoch = samples / float(num_samples)
         while current_epoch <= args.epochs:
-            try:
-
-                for dataset in args.train_on:
-                    # normal training operation
-                    print "{} {} training step {}...".format(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),dataset, step)
+            #try:
 
-                    feed_dict = {iterator_handle_op: datasets[dataset]["train"]["handle"], is_train_op: True}
-                    if args.learning_rate is not None:
-                        feed_dict[learning_rate_op] = args.learning_rate
+            for dataset in args.train_on:
+                # normal training operation
+                print("{} {} training step {}...".format(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),dataset, step))
 
-                    sess.run(train_op, feed_dict=feed_dict)
+                feed_dict = {iterator_handle_op: datasets[dataset]["train"]["handle"], is_train_op: True}
+                if args.learning_rate is not None:
+                     feed_dict[learning_rate_op] = args.learning_rate
 
+                sess.run(train_op, feed_dict=feed_dict)
 
-                    # write summary
-                    if step % args.summary_frequency == 0:
-                        current_epoch = write_summaries(sess, datasets)
+                # write summary
+                if step % args.summary_frequency == 0:
+                    current_epoch = write_summaries(sess, datasets)
 
-                    # write checkpoint
-                    if step % args.save_frequency == 0:
-                        save(saver, step, sess, checkpoint)
-                        # print "saving to " + checkpoint
-                        # saver.save(sess, checkpoint, global_step=step)
+                # write checkpoint
+                if step % args.save_frequency == 0:
+                    save(saver, step, sess, checkpoint)
+                    # print "saving to " + checkpoint
+                    # saver.save(sess, checkpoint, global_step=step)
 
-                    step += 1  # keep local step counter
+                step += 1  # keep local step counter
 
 
 
-            except KeyboardInterrupt:
-                print "Training aborted at step {}".format(step)
-                break
+            #except KeyboardInterrupt:
+            #    print "Training aborted at step {}".format(step)
+            #    break
 
         # if loop ends or any caught exception
         write_summaries(sess, datasets)
@@ -221,7 +225,7 @@ def save(saver, step, sess, checkpoint):
                         help='directory containing the data (defaults to environment variable $datadir)')
     parser.add_argument('-g', '--gpu', type=str, default="0", help='GPU')
     parser.add_argument('-d','--train_on', type=str, default="2016",nargs='+', help='Dataset partition to train on. Datasets are defined as sections in dataset.ini in datadir')
-    parser.add_argument('-b', '--batchsize', type=int, default=16, help='batchsize')
+    parser.add_argument('-b', '--batchsize', type=int, default=2, help='batchsize')
     parser.add_argument('-v', '--verbose', action="store_true", help='verbosity')
     # parser.add_argument('-o', '--overwrite', action="store_true", help='overwrite graph. may lead to problems with checkpoints compatibility')
     parser.add_argument('-s', '--shuffle', type=bool, default=True, help="batch shuffling")
@@ -239,9 +243,10 @@ def save(saver, step, sess, checkpoint):
                         help="overwrite learning rate. Required placeholder named 'learning_rate' in model")
     parser.add_argument('--save_every_n_hours', type=int, default=1, help="save checkpoint every n hours")
     parser.add_argument('--queue_capacity', type=int, default=256, help="Capacity of queue")
-    parser.add_argument('--allow_growth', type=bool, default=False, help="Allow dynamic VRAM growth of TF")
+    parser.add_argument('--allow_growth', type=bool, default=True, help="Allow dynamic VRAM growth of TF")
     parser.add_argument('--limit_batches', type=int, default=-1,
                         help="artificially reduce number of batches to encourage overfitting (for debugging)")
+    parser.add_argument('--country', type=str, default=None, help="Country to use for data input")
 
     args = parser.parse_args()