add soft parameter sharing network

models/__init__.py

@@ -46,7 +46,10 @@ def get_models_by_params(params: dict):
     long = networks.get_new_model2(0.25, flow_features, hidden_embedding, window_size, domain_length,
                                    filter_main, kernel_main, dense_dim, embedding_model, model_output)
 
-    return embedding_model, final, inter, long
+    soft = networks.get_new_soft(0.25, flow_features, hidden_embedding, window_size, domain_length,
+                                 filter_main, kernel_main, dense_dim, embedding_model, model_output)
+
+    return embedding_model, final, inter, long, soft
 
 
 def get_server_model_by_params(params: dict):

models/pauls_networks.py

@@ -135,3 +135,49 @@ def get_new_model2(dropout, flow_features, domain_features, window_size, domain_
     out_client = Dense(1, activation='sigmoid', name="client")(y)
     
     return Model(ipt_domains, ipt_flows, out_client, out_server)
+
+
+import keras.backend as K
+
+
+def get_new_soft(dropout, flow_features, domain_features, window_size, domain_length, cnn_dims, kernel_size,
+                 dense_dim, cnn, model_output="both") -> Model:
+    def dist_reg(distant_layer):
+        def dist_reg_h(weights):
+            print("REG FUNCTION")
+            print(weights)
+            print(distant_layer)
+            return 0.01 * K.sum(K.abs(weights - distant_layer))
+        
+        return dist_reg_h
+    
+    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, name="domain_cnn")(ipt_domains)
+    merged = keras.layers.concatenate([encoded, ipt_flows], -1)
+    y = conv_server = Conv1D(cnn_dims,
+                             kernel_size,
+                             activation='relu', name="conv_server")(merged)
+    # remove temporal dimension by global max pooling
+    y = GlobalMaxPooling1D()(y)
+    y = Dropout(dropout)(y)
+    y = dense_server = Dense(dense_dim,
+                             activation="relu",
+                             name="dense_server")(y)
+    out_server = Dense(1, activation="sigmoid", name="server")(y)
+    # CNN processing a small slides of flow windows
+    y = Conv1D(cnn_dims,
+               kernel_size,
+               activation='relu', name="conv_client")(merged)
+    # remove temporal dimension by global max pooling
+    y = GlobalMaxPooling1D()(y)
+    y = Dropout(dropout)(y)
+    y = Dense(dense_dim,
+              activation='relu',
+              name="dense_client")(y)
+    
+    out_client = Dense(1, activation='sigmoid', name="client")(y)
+    # model = KerasModel(inputs=(ipt_domains, ipt_flows), outputs=(out_client, out_server))
+    
+    
+    return Model(ipt_domains, ipt_flows, out_client, out_server)