add tsne (does not work with big data)

fix model loading with custom selu function

models/__init__.py

@@ -1,5 +1,6 @@
 import keras.backend as K
 
+from models.renes_networks import selu
 from . import flat_2, pauls_networks, renes_networks
 
 
@@ -24,11 +25,11 @@ def get_models_by_params(params: dict):
     dense_dim = params.get("dense_main")
     model_output = params.get("model_output", "both")
     # create models
-    if network_depth == "small":
+    if network_depth == "flat1":
         networks = pauls_networks
-    elif network_depth == "flat":
+    elif network_depth == "flat2":
         networks = flat_2
-    elif network_depth == "medium":
+    elif network_depth == "deep1":
         networks = renes_networks
     else:
         raise Exception("network not found")
@@ -49,6 +50,7 @@ def get_metrics():
         ("precision", precision),
         ("recall", recall),
         ("f1_score", f1_score),
+        ("selu", selu)
     ])
 
 

models/renes_networks.py

@@ -32,7 +32,7 @@ def get_embedding(embedding_size, input_length, filter_size, kernel_size, hidden
     y = Conv1D(filter_size, kernel_size=3, activation=selu)(y)
     y = Conv1D(filter_size, kernel_size=3, activation=selu)(y)
     y = GlobalAveragePooling1D()(y)
-    y = Dense(hidden_dims, activation="relu")(y)
+    y = Dense(hidden_dims, activation=selu)(y)
     return KerasModel(x, y)
 
 
@@ -53,7 +53,7 @@ def get_model(cnnDropout, flow_features, domain_features, window_size, domain_le
     y = GlobalMaxPooling1D()(y)
     y = Dropout(cnnDropout)(y)
     y = Dense(dense_dim, activation=selu)(y)
-    y = Dense(dense_dim // 2, activation=selu)(y)
+    y = Dense(dense_dim, activation=selu)(y)
     out_client = Dense(1, activation='sigmoid', name="client")(y)
     out_server = Dense(1, activation='sigmoid', name="server")(y)
 
@@ -67,6 +67,9 @@ def get_new_model(dropout, flow_features, domain_features, window_size, domain_l
     encoded = TimeDistributed(cnn, name="domain_cnn")(ipt_domains)
     merged = keras.layers.concatenate([encoded, ipt_flows], -1)
     y = Dense(dense_dim, activation=selu)(merged)
+    y = Dense(dense_dim,
+              activation="relu",
+              name="dense_server")(y)
     out_server = Dense(1, activation="sigmoid", name="server")(y)
     merged = keras.layers.concatenate([merged, y], -1)
     # CNN processing a small slides of flow windows
@@ -90,6 +93,7 @@ def get_new_model(dropout, flow_features, domain_features, window_size, domain_l
     # remove temporal dimension by global max pooling
     y = GlobalMaxPooling1D()(y)
     y = Dropout(dropout)(y)
+    y = Dense(dense_dim, activation=selu)(y)
     y = Dense(dense_dim,
               activation=selu,
               name="dense_client")(y)