rene/ma_cisco_malware
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

remove max pooling from models for better infromation flow

Browse Source
This commit is contained in:
René Knaebel 2017-07-28 17:25:08 +02:00
parent 18b60e1754
commit c1535b941b
1 changed files with 7 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
models/renes_networks.py
View File

@ -1,6 +1,7 @@
import keras import keras
from keras.engine import Input, Model 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, 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,)) x = y = Input(shape=(input_length,))
y = Embedding(input_dim=vocab_size, output_dim=embedding_size)(y) y = Embedding(input_dim=vocab_size, output_dim=embedding_size)(y)
y = Conv1D(filter_size, kernel_size=5, activation='relu')(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 = 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 = Conv1D(filter_size, kernel_size=3, activation='relu')(y)
y = GlobalMaxPooling1D()(y) y = GlobalAveragePooling1D()(y)
y = Dropout(drop_out)(y) # y = Dropout(drop_out)(y)
y = Dense(hidden_dims, activation="relu")(y) y = Dense(hidden_dims, activation="relu")(y)
return Model(x, y) return Model(x, y)