diff --git a/models/renes_networks.py b/models/renes_networks.py
index db786f5..0d72c07 100644
--- a/models/renes_networks.py
+++ b/models/renes_networks.py
@@ -1,6 +1,7 @@
 import keras
 from keras.engine import Input, Model
-from keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense, Dropout, TimeDistributed, MaxPool1D
+from keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense, Dropout, TimeDistributed, MaxPool1D, \
+    GlobalAveragePooling1D
 
 
 def get_embedding(vocab_size, embedding_size, input_length,
@@ -8,12 +9,13 @@ def get_embedding(vocab_size, embedding_size, input_length,
     x = y = Input(shape=(input_length,))
     y = Embedding(input_dim=vocab_size, output_dim=embedding_size)(y)
     y = Conv1D(filter_size, kernel_size=5, activation='relu')(y)
-    y = MaxPool1D(pool_size=3, strides=1)(y)
+    # NOTE: max pooling destroys information flow for embedding
+    # y = MaxPool1D(pool_size=3, strides=1)(y)
     y = Conv1D(filter_size, kernel_size=3, activation='relu')(y)
-    y = MaxPool1D(pool_size=3, strides=1)(y)
+    # y = MaxPool1D(pool_size=3, strides=1)(y)
     y = Conv1D(filter_size, kernel_size=3, activation='relu')(y)
-    y = GlobalMaxPooling1D()(y)
-    y = Dropout(drop_out)(y)
+    y = GlobalAveragePooling1D()(y)
+    # y = Dropout(drop_out)(y)
     y = Dense(hidden_dims, activation="relu")(y)
     return Model(x, y)