diff --git a/checkpoints/b.hdf5 b/checkpoints/b.hdf5
index ef05303..0c24afd 100644
Binary files a/checkpoints/b.hdf5 and b/checkpoints/b.hdf5 differ
diff --git a/draw.py b/draw.py
index 0fe46a6..20a4d79 100644
--- a/draw.py
+++ b/draw.py
@@ -39,7 +39,7 @@ def draw(df, path=None):
     }
 
     for n in biome_numbers:
-        biomes[n] = MultiPoint(biomes[n]).buffer(1)
+        biomes[n] = MultiPoint(biomes[n]).buffer(0.5)
         # print(biomes[n])
         # legend = biome_names[n]
         if not hasattr(biomes[n], '__iter__'):
diff --git a/nn.py b/nn.py
index c10bab7..4f9f287 100644
--- a/nn.py
+++ b/nn.py
@@ -55,18 +55,22 @@ class Model():
         params = {
                 'kernel_initializer': 'lecun_uniform',
                 'bias_initializer': 'zeros',
+                # 'kernel_regularizer': keras.regularizers.l2(l=0.01)
         }
-        # dropout = keras.layersDropout(0.2, input_shape=[self.features])
-        self.model = keras.Sequential([
-            keras.layers.Dense(layers[0], activation=tf.nn.elu, input_shape=[self.features], **params)
+        dropout = [keras.layers.Dropout(0.1, input_shape=[self.features])]
+        self.model = keras.Sequential(dropout + [
+            keras.layers.Dense(layers[0], activation=tf.nn.elu, **params)
         ] + [
             keras.layers.Dense(n, activation=tf.nn.elu, **params) for n in layers[1:]
         ] + [
             keras.layers.Dense(self.output_size, activation=out_activation, **params)
         ])
 
-    def compile(self, loss='mse', metrics=['accuracy'], optimizer=tf.train.AdamOptimizer):
-        self.model.load_weights(self.path)
+    def compile(self, loss='mse', metrics=['accuracy'], optimizer=tf.train.AdamOptimizer, load_weights=True):
+        if load_weights:
+            self.model.load_weights(self.path)
+            print('loaded weights')
+
         optimizer = optimizer(self.learning_rate)
 
         self.model.compile(loss=loss,
@@ -81,12 +85,16 @@ class Model():
             verbose=1
         )
 
+    def evaluate_print(self):
+        loss, accuracy = self.evaluate()
+        print('Test evaluation: loss: {}, accuracy: {}'.format(loss, accuracy))
+
     def train(self):
         self.model.summary()
 
         checkpoint = keras.callbacks.ModelCheckpoint(self.path, monitor='acc', verbose=1, mode='max')
         tensorboard = keras.callbacks.TensorBoard(log_dir='./logs', update_freq='epoch')
-        # map_callback = keras.callbacks.LambdaCallback(on_epoch_end=self.map_callback)
+        map_callback = keras.callbacks.LambdaCallback(on_epoch_end=self.evaluate_print)
 
         self.model.fit(
             self.training,
@@ -101,11 +109,11 @@ class Model():
         return np.argmax(self.model.predict(a), axis=1)
 
 A = Model('a', epochs=2)
-B = Model('b', learning_rate=0.001, epochs=450)
+B = Model('b', learning_rate=0.001, epochs=20)
 
 def compile_b():
     B.prepare_dataset(df, dataframe_to_dataset_biomes)
-    B.create_model([32], tf.nn.softmax)
+    B.create_model([32, 32], tf.nn.softmax)
     B.compile(loss='sparse_categorical_crossentropy')
 
 def compile_a():
diff --git a/predict.py b/predict.py
index a2f2960..c620878 100644
--- a/predict.py
+++ b/predict.py
@@ -17,7 +17,7 @@ compile_b()
 for change in range(0, 1):
     print('TEMPERATURE MODIFICATION OF {}'.format(change))
 
-    inputs = ['latitude', 'longitude', 'elevation', 'distance_to_water']
+    inputs = ['elevation', 'distance_to_water', 'latitude']
 
     for season in SEASONS:
         inputs += [
@@ -25,8 +25,10 @@ for change in range(0, 1):
             'precip_{}_{}'.format(season, year)
         ]
 
+    print(inputs)
+
     # print(inputs)
-    frame = df[inputs]
+    frame = df[inputs + ['longitude']]
     # print(frame.head())
 
     for season in SEASONS:
@@ -38,7 +40,7 @@ for change in range(0, 1):
     for i, chunk in enumerate(chunker(frame, B.batch_size)):
         if chunk.shape[0] < B.batch_size:
             continue
-        input_data = normalize_ndarray(chunk.loc[:, chunk.columns != 'longitude'].values)
+        input_data = normalize_ndarray(chunk.loc[:, inputs].values)
         out = B.predict(input_data)
 
         f = pd.DataFrame({
@@ -48,5 +50,4 @@ for change in range(0, 1):
         }, columns=columns)
         new_data = new_data.append(f)
 
-    print(new_data)
     draw(new_data)