add new network architecture - server label moves to the middle

models/__init__.py

@@ -32,7 +32,10 @@ def get_models_by_params(params: dict):
     predict_model = networks.get_model(dropout, flow_features, domain_features, window_size, domain_length,
                                        filter_main, kernel_main, dense_dim, embedding_model)
 
-    return embedding_model, predict_model
+    new_model = networks.get_new_model(dropout, flow_features, domain_features, window_size, domain_length,
+                                       filter_main, kernel_main, dense_dim, embedding_model)
+
+    return embedding_model, predict_model, new_model
 
 
 def get_metrics():

models/pauls_networks.py

@@ -30,8 +30,8 @@ def get_embedding(vocab_size, embedding_size, input_length,
     y = Embedding(input_dim=vocab_size, output_dim=embedding_size)(y)
     y = Conv1D(filters, kernel_size, activation='relu')(y)
     y = GlobalMaxPooling1D()(y)
-    y = Dense(hidden_dims)(y)
     y = Dropout(drop_out)(y)
+    y = Dense(hidden_dims)(y)
     y = Activation('relu')(y)
     return Model(x, y)
 
@@ -56,3 +56,27 @@ def get_model(cnnDropout, flow_features, domain_features, window_size, domain_le
     y2 = Dense(2, activation='softmax', name="server")(y)
 
     return Model(inputs=[ipt_domains, ipt_flows], outputs=(y1, y2))
+
+
+def get_new_model(dropout, flow_features, domain_features, window_size, domain_length, cnn_dims, kernel_size,
+                  dense_dim, cnn):
+    ipt_domains = Input(shape=(window_size, domain_length), name="ipt_domains")
+    ipt_flows = Input(shape=(window_size, flow_features), name="ipt_flows")
+    encoded = TimeDistributed(cnn)(ipt_domains)
+
+    y2 = Dense(2, activation="softmax", name="server")(encoded)
+    merged = keras.layers.concatenate([encoded, ipt_flows, y2], -1)
+
+    y = Conv1D(cnn_dims,
+               kernel_size,
+               activation='relu',
+               input_shape=(window_size, domain_features + flow_features))(merged)
+    # remove temporal dimension by global max pooling
+    y = GlobalMaxPooling1D()(y)
+    y = Dropout(dropout)(y)
+    y = Dense(dense_dim, activation='relu')(y)
+
+    y1 = Dense(2, activation='softmax', name="client")(y)
+    model = Model(inputs=[ipt_domains, ipt_flows], outputs=(y1, y2))
+
+    return model

models/renes_networks.py

@@ -42,3 +42,27 @@ def get_model(cnnDropout, flow_features, domain_features, window_size, domain_le
     y2 = Dense(2, activation='softmax', name="server")(y)
 
     return Model(inputs=[ipt_domains, ipt_flows], outputs=(y1, y2))
+
+
+def get_new_model(dropout, flow_features, domain_features, window_size, domain_length, cnn_dims, kernel_size,
+                  dense_dim, cnn):
+    ipt_domains = Input(shape=(window_size, domain_length), name="ipt_domains")
+    ipt_flows = Input(shape=(window_size, flow_features), name="ipt_flows")
+    encoded = TimeDistributed(cnn)(ipt_domains)
+
+    y2 = Dense(2, activation="softmax", name="server")(encoded)
+    merged = keras.layers.concatenate([encoded, ipt_flows, y2], -1)
+
+    y = Conv1D(cnn_dims,
+               kernel_size,
+               activation='relu',
+               input_shape=(window_size, domain_features + flow_features))(merged)
+    # remove temporal dimension by global max pooling
+    y = GlobalMaxPooling1D()(y)
+    y = Dropout(dropout)(y)
+    y = Dense(dense_dim, activation='relu')(y)
+
+    y1 = Dense(2, activation='softmax', name="client")(y)
+    model = Model(inputs=[ipt_domains, ipt_flows], outputs=(y1, y2))
+
+    return model